## New Jersey Energy Efficiency

Market Potential Assessment

Report Number 1401

Volume 2: Report

EnerNOC Utility Solutions Consulting 500 Ygnacio Valley Road

Suite 450 Walnut Creek, CA 94596

925.482.2000

www.enernoc.com Prepared for: Rutgers, T he State University of New Jersey

Presented on:

October 17 , 2012EnerNOC Utility Solutions Consulting iii

This report was prepared by

EnerNOC Utility Solutions Consulting 500 Ygnacio Valley Blvd., Suite 450

Walnut Creek , CA 945 96

I. Rohmund , Project Director

D. Costenaro , Project ManagerEnerNOC Utility Solutions Consulting v



## CONTENTS

1 INTRODUCTION

................................ ................................ ................................ .... 1

- 1
Background

................................ ................................ ................................ ................... 1

- 1

Objectives

................................ ................................ ................................ ..................... 1

- 1

Report Organization

................................ ................................ ................................ ....... 1

- 1

Definitions of Potential

................................ ................................ ................................ ... 1

- 1

Abbreviations and Acronyms

................................ ................................ ........................... 1

- 2

2 ANALYSIS APPROACH AN D DATA DEVELOPMENT

................................ ................ 2

- 1
Analysis Approach

................................ ................................ ................................ .......... 2

- 1

LoadMAP Model

................................ ................................ ................................ . 2

- 2

Market Characterization

................................ ................................ ...................... 2

- 3

Mark et Profiles

................................ ................................ ................................ ... 2

- 9

Baseline Forecast

................................ ................................ ............................. 2

- 10 Energy Efficiency Measure Analysis

................................ ................................ ... 2

- 10 Energy

- Efficiency Potential

................................ ................................ ............... 2

- 14

Alternative Avoided Cost Scenario

................................ ................................ ..... 2

- 14 Fuel Switching Analysis

................................ ................................ .................... 2

- 14

Conclusions and Recommendations

................................ ................................ ... 2

- 15
Data Development

................................ ................................ ................................ ....... 2

- 15

Data Sources

................................ ................................ ................................ ... 2

- 15 Data Application

................................ ................................ ............................... 2

- 17

3 MARKET CHARACTERIZAT ION AND MARKET PROFI LES

................................ ...... 3

- 1
Energy Use Summary

................................ ................................ ................................ ..... 3

- 1

Residential Sector

................................ ................................ ................................ .......... 3

- 2

Commercial Sector

................................ ................................ ................................ ......... 3

- 8

Industrial Sector

................................ ................................ ................................ .......... 3

- 15

4 BASELINE FORECAST

................................ ................................ ............................ 4

- 1

Residential Sector

................................ ................................ ................................ .......... 4

- 1

Commercial Sector

................................ ................................ ................................ ......... 4

- 5

Industrial Sector

................................ ................................ ................................ ............ 4

- 9

Baseline Forecast Summary

................................ ................................ .......................... 4

- 10

5 ENERGY EFFICIENCY ME ASURES

................................ ................................ .......... 5

- 1
List of Energy Efficiency Measures

................................ ................................ ................... 5

- 1

Results of the Economic Screen

................................ ................................ .................... 5

- 17

6 OVERALL ENERGY EFFIC IENCY POTENTIAL

................................ ......................... 6

- 1
Electric Energy Efficiency

– Overall Results

................................ ................................ ...... 6

- 3

Natural Gas Energy Efficiency

– Overall Results

................................ ............................... 6

- 5

Overview of Energy Efficiency Potential by Sector and Fuel

................................ ............... 6

- 6vi www.enernoc.com

7 ENERGY EFFICIENCY PO TENTIAL BY SECTOR

................................ ..................... 7

- 1
Residential Electricity Potential

................................ ................................ ........................ 7

- 1

Residential Electric Potential b y Market Segment

................................ .................. 7

- 2 Residential Electric Potential by End Use

................................ .............................. 7

- 3

Residential Natural Gas Potential

................................ ................................ ..................... 7

- 5

Residential Natural Gas Potential by Market Segment

................................ ........... 7

- 6 Residential Natural Gas Potential by End Use

................................ ....................... 7

- 7
Commercial Electricity Potential

................................ ................................ ...................... 7

- 9

Commercial Electric Potential by Market Segment

................................ .............. 7

- 11

Commercial Electric Potential by End Use

................................ .......................... 7

- 12

Commercial Natural Gas Potential

................................ ................................ ................. 7

- 14

Commercial Natural Gas Potential by Market Segment

................................ ........ 7

- 15 Commercial Natural Gas Potential by End Use

................................ .................... 7

- 17
Industrial Electricity Potential

................................ ................................ ........................ 7

- 18

Industrial Electric Potential by Market Segment

................................ .................. 7

- 19 Industrial Electric Potential by End Use

................................ .............................. 7

- 20

Industrial Nat ural Gas Potential

................................ ................................ ..................... 7

- 22

Industrial Natural Gas Potential by Market Segment

................................ ........... 7

- 24 Industrial Natural Gas Potential by End Use

................................ ....................... 7

- 24

8 ALTERNATE AVOIDED CO ST SCENARIO

................................ ............................... 8

- 1

9 FUEL SWITCHING ANALY SIS

................................ ................................ ................ 9

- 1

Technical Potential and Fuel Switching

................................ ................................ 9

- 1 Economic Potential and Fuel Switching

................................ ................................ 9

- 1 Achievable Potential and Fuel Switching

................................ .............................. 9

- 1

10 CONCLUSIONS AND RECO MMENDATIONS

................................ ......................... 10

- 1

General Recommendations

................................ ................................ ........................... 10

- 1

Residential Recommendations

................................ ................................ ....................... 10

- 1

Commercial and Industrial Recommendations

................................ ................................ 10

- 2

11 COMPARISON TO RECENT REGIONAL POTENTIAL S TUD IES

............................. 11

- 1

12 ENERGY EFFICIENCY PO TENTIAL BEYOND 2016

................................ ............... 12

- 1
Electric Energy Efficiency

– Overall Results

................................ ................................ .... 12

- 3

Natural Gas Energy Efficiency

– Overall Results

................................ ............................. 12

- 5

Overview of Energy Efficiency Potential by Sector and Fuel

................................ ............. 12

- 6EnerNOC Utility Solutions Consulting vii



## C ONTENTS LIST OF FIGURES

Figure 2

- 1 Overview of Analysis Approach

................................ ................................ .............. 2

- 2

Figure 2

- 2 LoadMAP Analysis Framework

................................ ................................ ................ 2

- 3

Figure 2

- 3 Approach for Measure Assessment

................................ ................................ ....... 2

- 11

Figure 3

- 1 Sector

- Level Electricity Use, 2010

................................ ................................ .......... 3

- 1

Figure 3

- 2 Sector

- Level Natural Gas Use, 2010

................................ ................................ ....... 3

- 2

Figure 3

- 3 Residential Market Segmentation by Housing Type

– Percent of Household s

............ 3

- 3

Figure 3

- 4 Residential Market Segmentation by Housing Type

– Percent of Energy Use

............. 3

- 3

Figure 3

- 5 Residential Electricity and Natural Gas Use by End Use (2010), All Homes

................ 3

- 5

Figure 3

- 6 Residential Electricity Intensity by End Use and Segment (kWh/household, 2010)

.... 3

- 6

Figure 3

- 7 Breakdown of Residential Electricity Use by End Use and Segment (2010)

............... 3

- 7

F igure 3

- 8 Residential Natural Gas Intensity by End Use and Segment (therm/hh, 2010)

.......... 3

- 7

Figure 3

- 9 Breakdown of Residential Natural Gas Use by End Use and Segment (2010)

............ 3

- 8

Figure 3

- 10 Commercial Market Segmentation by Building Type

– Percent of Energy Use

............ 3

- 9

Figure 3

- 11 Commercial Electricity and Natural Gas Use by End Use (2010), All Buildings

.......... 3

- 11

Figure 3

- 12 Commercial Electricity In tensity by End Use and Segment (kWh/sq ft, 2010)

.......... 3

- 12

Figure 3

- 13 Breakdown of Commercial Electricity Consumption by End Use & Segment (2010)

.. 3

- 13

Figure 3

- 14 Commercial Natural Gas Intensity by E nd Use and Segment (therms/sq ft, 2010)

... 3

- 13

Figure 3

- 15 Breakdown of Commercial Natural Gas Use by End Use and Segment (2010)

......... 3

- 14

Figure 3

- 16 Industrial Market Segmentation

– Percentage of Energ y Use

................................ . 3

- 15

Figure 3

- 17 Industrial Electricity and Natural Gas Use by End Use (2010), All Industries

........... 3

- 17

Figure 3

- 18 Industrial Electricity Use by End Use and Segment (GWh, 2010)

............................ 3

- 17

Figure 3

- 19 Breakdown of Industrial Electricity Use by End Use and Segment (2010)

................ 3

- 18

Figure 3

- 20 Industrial Natural Gas Use by End Use and Segment (million therms, 2010)

........... 3

- 19

F igure 3

- 21 Breakdown of Industrial Natural Gas Use by End Use and Segment (2010)

............ 3

- 19

Figure 4

- 1 Residential Electricity Baseline Forecast by End Use

................................ ................ 4

- 2

Figure 4

- 2 Residential Baseline Electricit y Use per Household by End Use

................................ . 4

- 2

Figure 4

- 3 Residential Natural Gas Baseline Forecast by End Use

................................ ............. 4

- 4

Figure 4

- 4 Residential Baseline Natural Gas Use per Household by End Use

.............................. 4

- 5

Figure 4

- 5 Commercial Electricity Baseline Forecast by End Use

................................ ............... 4

- 6

Figure 4

- 6 Commercial Natural Gas Baseline Forecast by End Use

................................ ............ 4

- 8

Figure 4

- 7 Industrial Electricity Baseline Forec ast by End Use

................................ .................. 4

- 9

Figure 4

- 8 Industrial Natural Gas Baseline Forecast by End Use

................................ ............. 4

- 10

Figure 4

- 9 Electricity Baseline Forecast Summary (GWh)

................................ ....................... 4

- 11

Figure 4

- 10 Natural Gas Baseline Forecast Summary (million therms)

................................ ...... 4

- 11

Figure 6

- 1 Summary of Combined Electric and Natural Gas Energy Savings

.............................. 6

- 2

Figure 6

- 2 Combined Electric and Natural Gas Potential Forecasts (mi llion BTU)

....................... 6

- 3

Figure 6

- 3 Summary of Electric Energy Savings

................................ ................................ ...... 6

- 4

Figure 6

- 4 Electric Potentials Forecasts (GWh)

................................ ................................ ........ 6

- 4

Figure 6

- 5 Summary of Natural Gas Energy Savings

................................ ................................ 6

- 5viii www.enernoc.com

Figure 6

- 6 Natural Gas Potential Forecasts (1000 therms)

................................ ....................... 6

- 6

Figure 6

- 7 Achievable Low Electric Potential by Sector (GWh)

................................ .................. 6

- 7

Figure 6

- 8 Achievable High Electric P otential by Sector (GWh)

................................ ................. 6

- 7

Figure 6

- 9 Achievable Low Natural Gas Potential by Sector (mmTherms)

................................ . 6

- 8

Figure 6

- 10 Achievable High Natural Gas Potential by Sector (mmTherms)

................................ . 6

- 9

Figure 7

- 1 Residential Electric Energy Efficiency Potential Savings

................................ ............ 7

- 2

Figure 7

- 2 Residential Electric Achievable Low Potential by End Use in 2016

............................. 7

- 5

Figure 7

- 3 Residential Natural Ga s Potential Savings

................................ ............................... 7

- 6

Figure 7

- 4 Residential Natural Gas Achievable Low Potential by End Use in 2016

...................... 7

- 9

Figure 7

- 5 Commercial Energy Efficiency Potential Savings

................................ .................... 7

- 10

Figure 7

- 6 Commercial Achievable Low Potential Electricity Savings by End Use in 2016

.......... 7

- 14

Figure 7

- 7 Commercial Natural Gas Potential Savings

................................ ............................ 7

- 15

Figure 7

- 8 Commercial Natural Gas Achievable Low Potential Savings by End Use in 2016

....... 7

- 18

Figure 7

- 9 Industrial Electric Potential Savings

................................ ................................ ...... 7

- 19

Figure 7

- 10 Industrial Achievable Low Electricity Potential Savings by End Use in 2016

............. 7

- 22

Figure 7

- 11 Industrial Natural Gas Potential Savings

................................ ............................... 7

- 23

Figure 7

- 12 Industrial Natural Gas Achievable Low Potential Savings by End Use in 2016

.......... 7

- 25

Figure 9

- 1 Space He ating

– Number of Fuel Switching Units, Achievable Low Case

................... 9

- 3

Figure 9

- 2 Space Heating

– Number of Fuel Switching Units, Achievable High Case

.................. 9

- 3

Figure 9

- 3 Water Heating

– Number of Fuel Switch ing Units, Achievable Low Case

................... 9

- 4

Figure 9

- 4 Water Heating

– Number of Fuel Switching Units, Achievable High Case

.................. 9

- 5

Figure 11

- 1 Comparison with recent regional potential studies

................................ ................ 11

- 2

Figure 12

- 1 Summary of Combined Electric and Natural Gas Energy Savings

............................ 12

- 2

Figure 12

- 2 Combined Electric and Natural Gas Potential Forecasts (million BTU)

..................... 12

- 3

Figure 12

- 3 Su mmary of Electric Energy Savings

................................ ................................ .... 12

- 4

Figure 12

- 4 Electric Potentials Forecasts (GWh)

................................ ................................ ...... 12

- 4

Figure 12

- 5 Summary of Natural Gas Energy Savings

................................ .............................. 12

- 5

Figure 12

- 6 Natural Gas Potential Forecasts (1000 therms)

................................ ..................... 12

- 6

Figure 12

- 7 Achievable Low Electric Potential by Sector (GWh)

................................ ................ 12

- 7

Figure 12

- 8 Achievable High Electric Potential by Sector (GWh)

................................ ............... 12

- 7

Figure 12

- 9 Achievable Low Natural Gas Potential by Sector (million Therms)

........................... 12

- 9

Figure 12

- 10 Achievable High Natural Gas Potential by Sector (million Therms)

.......................... 12

- 9EnerNOC Utility Solutions Consulting ix



## LIST OF TABLES

Table 1

- 1 Explanation of Abbreviations and Acronyms

................................ ............................ 1

- 3

Table 2

- 1 Overview of Segmentation Scheme for Potentials Modeling

................................ ..... 2

- 4

Table 2

- 2 Residential Electric End Uses and Technol ogies

................................ ...................... 2

- 5

Table 2

- 3 Residential Natural Gas End Uses and Technologies

................................ ................ 2

- 5

Table 2

- 4 Commercial Electric End Uses and Technologies

................................ ..................... 2

- 7

Table 2

- 5 Commercial Natural Gas End Uses and Technologies

................................ ............... 2

- 8

Table 2

- 6 Industrial Electric End Uses and Technologies

................................ ......................... 2

- 9

Table 2

- 7 Industrial Natural Gas End Uses and Technologies

................................ .................. 2

- 9

Table 2

- 8 Sa mple Equipment Measures for Central Air Conditioning

– Single Family Home

..... 2

- 12

Table 2

- 9 Sample Non

- Equipment Measures

– Single Family Home, Existing

......................... 2

- 13

Table 2

- 10 Economic Screen Results for Se lected Residential Equipment Measures

.................. 2

- 14

Table 2

- 11 Data Applied for the Market Profiles

................................ ................................ ..... 2

- 18

Table 2

- 12 Data Needs for the Baseline Forecast and Potentials Estimation in LoadMAP

........... 2

- 19

Table 2

- 13 Residential Electric Equipment Standards Applicable to New Jersey

........................ 2

- 20

Table 2

- 14 Commercial Electric Equipment Standards Applicable to New Jersey

...................... 2

- 21

Table 2

- 15 Re sidential Gas Appliance Standards Applicable to New Jersey

.............................. 2

- 22

Table 2

- 16 Commercial Gas Appliance Standards Applicable to New Jersey

............................. 2

- 22

Table 2

- 17 Data Needs for the Measure Characteristics in LoadMAP

................................ ....... 2

- 23

Table 3

- 1 Residential Sector Energy Usage and Intensity by Segment Type, 2010

................... 3

- 2

Table 3

- 2 Electric Market Profile for the Residential Sector

................................ ..................... 3

- 4

Table 3

- 3 Natural Gas Market Profile for the Residential Sector

................................ ............... 3

- 5

Table 3

- 4 Residential Electricity Use by End Use and Segment (kWh/HH/year, 2010)

............... 3

- 6

Table 3

- 5 Residential Natural Gas Use by End Use and Segment (therm/HH/year, 2010)

......... 3

- 8

Table 3

- 6 Commercial Market Segmentation by Building Type, Base Year 2010

....................... 3

- 9

Table 3

- 7 Commercial Sector Composite Electric Market Profile, 2010

................................ ... 3

- 10

Table 3

- 8 Commercial Sector Composite Natural Gas Market Profile, 2010

............................. 3

- 11

Table 3

- 9 Commercial Electricity Intensity by End Use and Segment (kWh/sq ft, 2010)

.......... 3

- 12

Table 3

- 10 Commercial Natural Gas Intensity by End Use and Segment (therms/sq ft, 2010)

... 3

- 14

Table 3

- 11 Industrial Market Segmentation by Industry Type, Base Year 2010

........................ 3

- 15

Table 3

- 12 Industrial Sector C omposite Electric Market Profile, 2010

................................ ...... 3

- 16

Table 3

- 13 Industrial Sector Composite Natural Gas Market Profile, 2010

................................ 3

- 16

Table 3

- 14 Industrial Electricity Use by End Use and Segment (GWh, 2010)

............................ 3

- 18

Table 3

- 15 Industrial Natural Gas Use by End Use and Segment (million therms, 2010)

........... 3

- 19

Table 4

- 1 Residential Electricity Consumption by End Use (GWh)

................................ ............ 4

- 1

Table 4

- 2 Residential Electricity Baseline Forecast by End Use and Technology (GWh)

............. 4

- 3

Table 4

- 3 Residential Natural Gas Consumption by End Use (million therms)

........................... 4

- 4

Table 4

- 4 Residential Natural Gas Bas eline Forecast by End Use and Tech (million therms)

...... 4

- 5

Table 4

- 5 Commercial Electricity Consumption by End Use (GWh)

................................ .......... 4

- 6

Table 4

- 6 Commercial Baseline Electricity Forecast by End Use and Technolo gy (GWh)

............ 4

- 7x www.enernoc.com

Table 4

- 7 Commercial Natural Gas Consumption by End Use (million therms)

.......................... 4

- 8

Table 4

- 8 Commercial Baseline Electricity Forecast by End Use and Technology (GWh)

............ 4

- 8

Table 4

- 9 Industrial Electricity Consumption by End Use (GWh)

................................ .............. 4

- 9

Table 4

- 10 Industrial Natural Gas Consumption by End Use (million therms)

........................... 4

- 10

Table 4

- 11 Electricity Baseline Forecast Summary (GWh)

................................ ....................... 4

- 10

Table 4

- 12 Natural Gas Baseline Forecast Summary (million therms)

................................ ...... 4

- 11

Table 5

- 1 Summary of Residential Equipment Measures

................................ ......................... 5

- 2

Table 5

- 2 Summary o f Residential Non

- Equipment Measures

................................ .................. 5

- 5

Table 5

- 3 Summary of Commercial Equipment Measures

................................ ........................ 5

- 6

Table 5

- 4 Summary of Commercial Non

- Equipment Measures

................................ .............. 5

- 10

Table 5

- 5 Summary of Industrial Equipment Measures

................................ ......................... 5

- 12

Table 5

- 6 Summary of Industrial Non

- Equipment Measures

................................ .................. 5

- 15

Table 5

- 7 Number of Measures Evaluated

................................ ................................ ........... 5

- 17

Table 6

- 1 Summar y of Combined Electric and Natural Gas Energy Efficiency Potential

............. 6

- 2

Table 6

- 2 Summary of Electric Energy Efficiency Potential

................................ ...................... 6

- 3

Table 6

- 3 Summary of Natural Gas Energy Efficiency Potential

................................ ............... 6

- 5

Table 6

- 4 Electric Achievable Potential by Sector (GWh)

................................ ......................... 6

- 6

Table 6

- 5 Natural Gas Achievable Potential by Sector (million therms)

................................ .... 6

- 8

Table 7

- 1 Electricity Energy E fficiency Potential for the Residential Sector

............................... 7

- 2

Table 7

- 2 Residential Electric Potential by Market Segment, 2016

................................ ........... 7

- 3

Table 7

- 3 Res. Electric Achievable Low Potential by End Use & Market Segment , 2016 (GWh)

.. 7

- 3

Table 7

- 4 Residential Electric Savings by End Use and Potential Type (GWh)

........................... 7

- 3

Table 7

- 5 Natural Gas Energy Efficiency Potential for the Residential Sector

............................ 7

- 6

Table 7

- 6 Residential Natural Gas Potential by Market Segment, 2016 (1000 therms)

.............. 7

- 7

Table 7

- 7 Res Achievable Potential Low by End Use & Market Segment, 2016 (1000 therms)

... 7

- 7

Table 7

- 8 Residential Natural Gas Savings by End Use and Potential Type (million therms)

...... 7

- 7

Table 7

- 9 Electricity Efficiency Potential for the Commercial Sector

................................ ....... 7

- 10

Table 7

- 10 Commercial Electric Potent ial by Market Segment, 2016

................................ ........ 7

- 11

Table 7

- 11 Com Electric Achievable Low Potential by End Use & Market Segment, 2016 (GWh) 7

- 12

Table 7

- 12 Commercial Potential by End Use and Potential Type (GWh)

................................ . 7

- 13

Table 7

- 13 Natural Gas Efficiency Potential for the Commercial Sector

................................ .... 7

- 15

Table 7

- 14 Commercial Natural Gas Potential by Market Segment, 2016

................................ . 7

- 16

Table 7

- 15 Com mercial Natural Gas Achievable High Potential by End Use & Market Segment,

2016 (million therms)

................................ ................................ .......................... 7

- 16

Table 7

- 16 Commercial Natural Gas Potential by End Use and Potential Type (million therms)

.. 7

- 17

Table 7

- 1 7 Electric Efficiency Potential for the Industrial Sector

................................ .............. 7

- 19

Table 7

- 18 Industrial Electric Potential by Market Segment, 2016

................................ ........... 7

- 20

Table 7

- 19 Industrial Electric Achievable Potential Low by End Use & Market Segment, 2016

... 7

- 20

Table 7

- 20 Industrial Electric Potential by End Use and Potential Type (GWh)

......................... 7

- 21

Table 7

- 21 Natural Gas Efficiency Potential for the Industrial Sector

................................ ....... 7

- 23

Table 7

- 22 Industrial Natural Gas Potential by Market Segment, 2016

................................ .... 7

- 24

Table 7

- 23 Industrial Natural Gas Achievable Potential Low by End Use & Market Segment, 2016

(million therms)

................................ ................................ ................................ .. 7

- 24

Table 7

- 24 Industrial Natural Gas Potential by End Use and Potential Type (thousand therms)

. 7

- 25

Table 8

- 1 Alternate Avoided Cost Scenario, Count of Measures Passing Economic Screen

......... 8

- 1EnerNOC Utility Solu tions Consulting xi

Table 8

- 2 Alternate Avoided Cost Scenario, 2016 Achievable Low Potential (GWh savings)

....... 8

- 1

Table 8

- 3 Alternate Avoided Cost Scenario, 2016 Achievable Low Potential (million therm savings)

................................ ................................ ................................ ........................... 8

- 2

Table 9

- 1 Converting to kBTU for Switching Comparison

................................ ........................ 9

- 1

Table 9

- 2 Switching Options and Directionality

................................ ................................ ...... 9

- 2

Table 9

- 3 Space Heating

– Number of Fuel Switching Units

................................ .................... 9

- 4

Table 9

- 4 Water Heating

– Number of Fuel Switching Units

................................ .................... 9

- 5

Table 11

- 1 Comparison to other studies (average annual savings vs. baseline forecast)

........... 11

- 1

Table 12

- 1 Summary of Combined Elec tric and Natural Gas Energy Efficiency Potential

........... 12

- 2

Table 12

- 2 Summary of Electric Energy Efficiency Potential

................................ .................... 12

- 3

Table 12

- 3 Summary of Natural Gas Energy Efficiency Potential

................................ ............. 12

- 5

Table 12

- 4 Electric Achievable Potential by Sector (GWh)

................................ ....................... 12

- 6

Table 12

- 5 Natural Gas Achievable Potential by Sector (million therms)

................................ .. 12

- 8EnerNOC Utility Solutions Consulting 1

- 1



## INTRODUCTION

Background

Th e New Jersey Board of Public Utilities (BPU or the Board) is commencing a proceeding to examine the appropriate funding level for energy efficiency (EE) programs over the next four years, 2013 through 2016. Pursuant to the 1999, Electric Discount and Energ y Competition Act, this will be the BPU’s fourth comprehensive resource analysis (CRA).

The BPU has contracted with EnerNOC to conduct a market assessment and energy efficiency potential study to assess the potential statewide impacts from energy efficien cy resources for this time period

. Toward this end, EnerNOC conducted a detailed, bottom

- up assessment of the New Jersey

market to deliver forecasts of both electric and natural gas energy use, as well as forecasts of the electric and natural gas energy s avings achievable through energy efficiency measures . The potential study addresses the residential, commercial, and industrial sectors.



## Objectives

The study addresses energy efficiency potential and inform s the CRA process in the following

ways :

 Determin es markets to address with EE programs

 Finds the potential for energy savings for the 2013

– 2016 period

 Provides high

- level recommendations regarding programs



## Report Organization

This report is presented in 3 volumes as outlined below

. This document is Vol ume 2 : Report

.

 Volume 1, Executive Summary

 Volume 2

, Report

 Volume 3

, Appendices



## Definitions of Potential

In this study, we estimate the potential for energy efficiency savings. The savings estimates represent gross savings 1 developed into three types of potential: technical potential, economic potential, and achievable potential. Technical and economic potential are both theoretical limits to efficiency savings. Achievable potential embodies a set of assumptions about the decisions consumers make regardi ng the efficiency of the equipment they purchase, the maintenance activities they undertake, the controls they use for energy

- consuming equipment, and the elements of building construction. Because estimating achievable potential involves the inherent

unce rtainty of predicting human behaviors and responses to market conditions , we developed low and high achievable potential as boundaries for a likely range . The various levels are

described below.

1 Savings in “gross” terms instead of “net” terms means that the baseline forecast does not include nat urally occurring efficiency. In other words, the baseline assumes that energy efficiency levels remain fixed as they are today. This rule holds true except i n cases where enactment of future codes and standards were on the books before January 2012, e.g. t he effects of the EISA 2007 lighting efficiency standard. 

### CHAPTER 1Introduction

1

- 2 www.enernoc.com  Technical potential is defined as the theoretical upper limit of energy efficiency potential. It assumes that customers adopt all feasible measures regardless of their cost. At the time of existing equipment failure, customers replace their equipment with the most efficient option available. In new construction, cus tomers and developers also choose the most efficient equipment option. Examples of measures that make up technical potential for electricity in

the residential sector include:

o Ductless mini

- split air conditioners with variable refrigerant flow

o Ground sour ce (or geothermal) heat pumps

o LED lighting

Technical potential also assumes the adoption of every other available measure, where applicable. For example, it includes installation of high

- efficiency windows in all new construction opportunities and air co nditioner maintenance in all existing buildings with central and room air conditioning. The se retrofit measures are phased in over a number of years, which is longer for higher

- cost and complex measures.

 Economic potential represents the adoption of all c ost

- effective energy efficiency measures. In this analysis, the cost effectiveness is measured by the total resource cost (TRC) test, which compares lifetime energy and capacity benefits to the incremental cost of

the measure. If the benefits outweigh the costs (that is, if the TRC ratio is greater than 1.0), a given measure is considered in the economic potential

. Customers are then assumed to purchase the most cost

- effective option applicable to them at any decision juncture

.

 Achievable High potential est imates customer adoption of economic measures when delivered through efficiency programs under ideal market , implementation, and customer preference conditions

. Information channels are assumed to be established and efficient for marketing, educating consu mers , and coordinating with trade allies and delivery partners

. Achievable High potential establishes a maximum t arget for the EE savings that an

administrator can hope to achieve through its EE programs and involves incentives that represent a substantial portion of the incremental cost combined with high administrative and marketing costs.

 Achievable Low potential reflects expected program participation given significant barriers
to customer acceptance, non

- ideal implementation conditions, and limited pr ogram budgets. This represents a lower bound on achievable potential.



## Abbreviations and Acronyms

Throughout the report we use several abbreviations and acronyms. Table 1

- 1 shows the abbreviation or acronym, along with an explanati on.Introduction

EnerNOC Utility Solutions Consulting 1

- 3 Table 1

- 1 Explanation of Abbreviations and Acronyms

Acronym Explanation

ACS American Community Survey

AEO Annual Energy Outlook forecast developed annual by the Energy

Information Administration of the DOE

A HAM Association of Home Appliance Manufacturers

B/C Ratio Benefit to c ost r atio

BEST EnerNOC’s Building Energy Simulation Tool

CEEEP Rutgers University’s Cen ter for Energy, Economic and Environmental
Policy

CAC Central air conditioning

C&I Commercial and i ndustrial

CFL Compact f luorescent l amp

DEEM EnerNOC’s Database of Energy Efficiency Measures

DEER State of California Database for Energy

- Efficient Reso urces

DSM Demand side management

DR Demand r esponse

EE Energy e fficiency

EIA Energy Information Administration

EISA Energy Efficiency and Security Act of 2007

EPACT Energy Policy Act of 2005

EPRI Electric Power Research Institute

EUEA Efficient Use of Energy Act

EUI Energy

- use i ndex

HH Household

HID High i ntensity d ischarge lamps

HPWH Heat p ump w ater h eater

LED Light e mitting d iode lamp

LoadMAP EnerNOC’s Load Management Analysis and Planning TM tool

NJCEP New Jersey Clean Energy Program

NJOCE New Jersey Office of Clean Energy

NWPCC Northwest Power and Conservation Council

RTU Roof top unit

Sq. ft. Square feet

TRC Total r esource c ost

UEC Unit e nergy c onsumptionEnerNOC Utility Solutions Consulting 2

- 1



## ANALYSIS APPROACH AN D DATA DEVELOPMENT

This section describe s the analysis approach taken for the study and the data sources used to develop the potential estimates.



## Analysis Approach

To perform the energy efficiency analysis, EnerNOC used a bottom

- up analysis approach as

shown in Figure 2

- 1 . Th is involved the following steps.

1. Held a meeting with the client project team to refine the objectives of the project in detail

. This resulted in a work plan for the study.

2. Performed a market characterization to describe sector

- level electricity and natural gas use for the residential , commercial, and industrial sectors for the base year

, 2010. This included using existing information contained in prior New Jersey studies, in EnerNOC’s own databases and tools, and in other secondary data sources such as the American Community Survey (ACS) and the Energy Information Administration ( EIA )

.

3. Developed a baseline electricity and natural gas forecast by sector, segment, and end use for

20 13 through 2024. Results presented in this volume focus on the upcoming four

- ye ar implementation cycle of 2013 through 2016. Results beyond 2016 are available in the Appendices and are given at a high level in Chapter 12 of this document.

4. Identified several hundred measures and estimated their effects in four levels of energy

- efficie ncy potential: Technical, Economic, Achievable High, and Achievable Low

.

5. Reviewed the current programs offered in New Jersey in light of the study findings to make strategic program recommendations for achieving savings.

The se steps are described in furth er detail throughout the remainder of this chapter. 

### CHAPTER 2Analysis Approach and Data Development

2

- 2 www.enernoc.com Figure 2

- 1 Overview of Analysis Approach



### LoadMAP Model

We used EnerNOC ’s Load Management Analysis and Planning too l (LoadMAP TM ) version 3.0 to develop both the baseline forecast and the estimates of energy efficiency potential. EnerNOC developed LoadMAP in 2007 and has enhanced it over time, using it for the EPRI National Potential Study and numerous utility

- specific forecasting and potential studies. Built in Excel, the LoadMAP framework (se e Figure 2

- 2 ) is both accessible and transparent and has the following key features.

 Embodies the basic principles of rigorous end

- use models (such as EPR I’s REEPS and COMMEND) but in a more simplified, accessible form.

 Includes stock

- accounting algorithms that treat older, less efficient appliance/equipment stock separately from newer, more efficient equipment. Equipment is replaced according to

the measu re life and appliance vintage distributions defined by the user.

 Balances the competing needs of simplicity and robustness by incorporating important modeling details related to equipment saturations, efficiencies, vintage, and the like, where

market data are available, and treats end uses separately to account for varying importance and availability of data resources.

 Isolates new construction from existing equipment and buildings and treats purchase decisions for new construction and existing buildings s eparately.Analysis Approach and Data De velopment

EnerNOC Utility Solutions Consulting 2

- 3  Uses a simple logic for appliance and equipment decisions. Other models available for this purpose embody complex decision choice algorithms or diffusion assumptions, and the model parameters tend to be difficult to estimate or observe and some times produce anomalous results that require calibration or even overriding. The LoadMAP approach allows the user to drive the appliance and equipment choices year by year directly in the model. This flexible approach allows users to import the results fro m diffusion models or to input individual assumptions. The framework also facilitates sensitivity analysis.

 Includes appliance and equipment models customized by end use. For example, the logic for lighting is distinct from refrigerators and freezers.

 Ca n accommodate various levels of segmentation. Analysis can be performed at the sector level (e.g., total residential) or for customized segments within sectors (e.g., housing type or income level).

Consistent with the segmentation scheme and the market pro files we describe below, the LoadMAP model provides forecasts of baseline energy use by sector, segment, end use

, and technology for existing and new buildings. It also provides forecasts of total energy use and

energy

- efficiency savings associated with th e four types of potential. 2

Figure 2

- 2 LoadMAP Analysis Framework



### Market Characterization

In order to estimate the savings potential from energy

- efficient measures, it is necessary to understand how much energy is used today and what equipment is currently being used. This characterization begins with a segmentation of New Jersey’s energy footprint to quantify energy use by sector, segment, fuel, end

- use application, and the current set of technologies used. We incorporate information from the secondary research sources to advise the market

characterization.

2 The model computes energy and peak

- demand forecasts for each type of potential for each end use as an intermediate calculation.

Annual

- energy and peak

- demand savings are calculated as the differen ce between the value in the baseline forecast and the value in the potential forecast (e.g., the technical potential forecast).Analysis Approach and Data Development

2

- 4 www.enernoc.com Segmentation for Modeling Purposes

The market assessment first defined the market segments (building types, end uses and other dimensions) that are relevant in New Jersey . The segmentation scheme for this project is presented in Table 2

- 1

.

Table 2

- 1 Overview of Segmentation Scheme for Potentials Modeling

Market

Dimension Segmentation Variable Dimension Examples

1 Sector Residential, commercial, industrial

2 Building type Residential (housing type, incom e level, ownership)
Commercial (Office , Restaurant, Retail, etc.)
Industrial ( Chemicals & Pharmaceuticals, Paper
Products, Food Products

, and Other industrial)

3 Vintage Existing and new construction

4 Fuel Electricity, natural gas

5 End uses Cooling, lighting, water heat, motors, etc. (as
appropriate by sector)

6 Appliances/end uses and

technologies Technologies such as lamp type, air conditioning
equipment, motors by application , etc.

7 Equipment efficiency levels for

new purchases Baseline and hi gher

- efficiency options as appropriate
for each technology

Following this scheme, the residential sector was segmented as described below, starting with customer segments by building type:

 Single family (including duplexes)

 Limited income single family ( including duplexes)

 Multi family renters

 Limited income multi family renters

 Multi family owners

 Limited income multi family owners

For the purposes of this study, Limited Income refers to homes having income below $35,000 per year, or approximately twice the federal poverty level. 3

In addition to segmentation by housing type, we identified the set of end uses and technologies that are appropriate for New Jersey . These are shown in Table 2

- 2 and Table 2

- 3

.

3 This definition aligns with categories in available data sources from the US Department of Energy . It is also similar to the

q ualification criteria for households to participate in the New Jersey Comfort Partners program, which are currently set at 225% of the federal poverty level, and change based on household size. See : http://www.njcleanenergy.com/residential/programs/comfort

- partners/comfort

- partnersAnalysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 5 Table 2

- 2 Residential Electric End Uses and Technologies

End Use Technology
Cooling Central Air Conditioning (CAC)
Cooling Room Air Conditioning (RAC)
Cooling/Heating Air

- Source Heat Pump Cooling/Heating Geothermal Heat Pump

Space Heating Electric Resistance

Space Heating Electric Furnace

Water Heating Water Heater <= 55 gal

Water Heating Water Heater > 55 gal

I nterior Lighting Screw

- in Lamps
Interior Lighting Linear Fluorescent Lamps
Exterior Lighting Screw

- in Lamps
Appliances Clothes Washer
Appliances Clothes Dryer
Appliances Dishwasher
Appliances Refrigerator
Appliances Freezer
Appliances Second Ref rigerator
Appliances Stove
Appliances Microwaves

Electronics Personal Computers

Electronics Monitor

Electronics Laptops

Electronics TV s

Electronics Printer/Fax/Copier

Electronics Set

- top Boxes/DVR

Electronics Devices and Gadgets

Miscellaneous Po ol Pump

Miscellaneous Pool Heater

Miscellaneous Hot Tub / Spa

Miscellaneous Well Pump

Miscellaneous Furnace Fan

Miscellaneous Miscellaneous

Table 2

- 3 Residential Natural Gas End Uses and Technologies

End Use Technology

Space Heating Furnace

Space Heating Boiler

Space Heating Other Heating

Water Heating Water Heater <= 55 gal

Water Heating Water Heater > 55 gal
Appliances Clothes Dryer
Appliances Stove/Oven

Miscellaneous Pool Heater

Miscellaneous Hot Tub / Spa

Miscellaneous MiscellaneousAnalysis Approach and Data Development

2

- 6 www.enernoc.com For the commercial sector, it is useful to analyze the segments based on the unique characteristics of the building typ e. For this study, we used the following building types

.

 Small Office

— all types of offices, including medical/dental offices

 Large Office

— all types of offices, including large government facilities

 Restaurant

— fast

- food, sit

- down and cafeteria

- style restau rants

 Retail

— retail establishments such as small boutiques, and large box retailers

 Grocery

— convenience stores, small markets, and supermarkets

 College

— colleges, universities and technical colleges

 School

— primary and secondary schools

 Health

— hospitals and nursing homes

 Lodging

— motels, hotels, resorts and small inns

 Warehouse

— storage facilities, refrigerated and unrefrigerated

 Miscellaneous

— all remaining building types, such as police stations, parking garages, public assembly, amusement parks, etc.

In addit ion to segmentation by building type, we identified the set of end uses and technologies that are appropriate for New Jersey

. Table 2

- 4 and Table 2

- 5 list the end uses and technologies used in this study.Analysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 7 Table 2

- 4 Commercial Electric End Uses and Technologies

End Use Technology
Cooling Air

- Cooled Chiller
Cooling Water

- Cooled Chiller
Cooling Roof top AC
Cooling Other Cooling
Cooling/Heating Air

- Source Heat Pump Cooling/Heating Geothermal Heat Pump

Heating Electric Room Heat

Heating Electric Furnace

Ventilation Ventilation

Water Heating Water Heater
Interior Ligh ting Screw

- in
Interior Lighting High

- Bay Fixtures
Interior Lighting Linear Fluorescent
Exterior Lighting Screw

- in
Exterior Lighting HID
Exterior Lighting Linear Fluorescent
Refrigeration Walk

- in Refrigerator
Refrigeration Reach

- in Refrigerator

Refr igeration Glass Door Display
Refrigeration Open Display Case
Refrigeration Icemaker
Refrigeration Vending Machine
Food Preparation Oven
Food Preparation Fryer
Food Preparation Dishwasher
Food Preparation Hot Food Container
Office Equipment Desktop Computer
Office Equipment Laptop
Office Equipment Server
Office Equipment Monitor
Office Equipment Printer/Copier/Fax

Office Equipment POS Terminal

Miscellaneous Non

- HVAC Motors

Miscellaneous Pool Pump

Miscellaneous Pool Heater

Miscellaneous Misce llaneousAnalysis Approach and Data Development

2

- 8 www.enernoc.com Table 2

- 5 Commercial Natural Gas End Uses and Technologies

End Use Technology

Heating Furnace

Heating Boile r Heating Other Heating

Water Heating Water Heater
Food Preparation Oven
Food Preparation Fryer
Food Preparation Broiler
Food Preparation Griddle
Food Preparation Range
Food Preparation Steamer

Miscellaneous Pool Heater

Miscellaneous Miscellaneou s

For the industrial sector, the study isolated the top few industries in New Jersey by energy consumption, which accounted for 57% of the total 2010 industrial electricity sales and 65% of natural gas sales . The remaining group of industrial customers i s considered in aggregate as “other industrial.” While the commercial sector has a relatively small set of building types that have relatively uniform characteristics, the sheer number of unique industry types makes it infeasible to perform a deep dive int o all but the largest ones. This results in larger “other” or “miscellaneous” segment than that which exists in the commercial sector. Nonetheless, these “other” industries typically have energy use characteristics that are similar enough to perform an

acc urate potential assessment.

The resulting segmentation is as follows:

 Chemical and Pharmaceutical Products (analyzed together due to similar allocation of energy within end uses and processes)

 Paper Products

 Food Products

 Other Industrial

In addition to segmentation by industry, we identified the set of end uses and technologies that are appropriate for New Jersey . These are shown in Table 2

- 6 and Table 2

- 7

.Analysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 9 Table 2

- 6 Industrial Electric End Uses and Technologies

End Use Technology
Cooling Air

- Cooled Chiller
Cooling Water

- Cooled Chiller
Cooling Roof top AC
Cooling Ot her Cooling
Cooling/Heating Air

- Source Heat Pump Cooling/Heating Geothermal Heat Pump

Heating Electric Room Heat

Heating Electric Furnace

Ventilation Ventilation

Water Heating Water Heater
Interior Lighting Screw

- in
Interior Lighting High

- Bay Fixtu res Interior Lighting Linear Fluorescent
Exterior Lighting Screw

- in
Exterior Lighting HID
Exterior Lighting Linear Fluorescent

Motors Pumps

Motors Fans & Blowers

Motors Compressed Air

Motors Material Handling

Motors Material Processing

Motors Oth er Motors

Process Process Heating

Process Process Cooling and Refrigeration

Process Electro

- Chemical Processes

Process Other Process

Miscellaneous Miscellaneous

Table 2

- 7 Industrial Natural Gas End Uses and Technologies

End Use Technology

Heating Furnace

Heating Boiler

Heating Other Heating

Process Process Heating

Process Process Boiler

Process Process Cooling

Process Other Process

Miscellaneous Miscellaneous

With the segmentation scheme defined, we then performed a high

- level market characterization of electricity and natural gas sales in the base year to allocate sales to each customer segment.

We used var ious data sources to identify the annual sales in each customer segment, as well as

the market size for each segment . This information provided control totals at a sector level for calibrating the LoadMAP model to known data for the base

- year

.



### Market Pro files

The next step was to develop market profiles for each sector, customer segment, end use

, and technology. A market profile includes the following elements:Analysis Approach and Data Development

2

- 10 www.enernoc.com  Market size is a representation of the number of customers in the segment. For the

residential sector, it is number of households. In the commercial sector, it is floor space measured in square feet. For the industrial sector, it is number of employees.

 Saturations define the fractio n of homes and square feet with the various technologies. (e.g., h omes with electric space heating, commercial floor space with gas water heating ).

 UEC (unit energy consumption) or EUI (energy

- use index) describes the amount of

energy consumed in 2010 by a specific technology in buildings that have the technology. For

e lectricity, UECs are expressed in kWh/household for the residential sector, and EUIs are expressed in kWh/square foot or kWh/employee for the commercial and industrial sector s, respectively

.

 Intensity for the residential sector represents the average ener gy use for the technology

across all homes in 2010 . It is computed as the product of the saturation and the UEC and is defined as kWh/household for electricity. For the commercial and industrial sector s , intensity, computed as the product of the saturation and the EUI, represents the average use for the technology across all floor space or all employees in 2010

.

 Usage is the annual energy use by a technology/end use in the segment. It is the product of the market size and intensity and is quantified in GWh for electricity and MMTherms for natural gas

. The market assessment results and the market profiles are presented in Chapter 3.



### Baseline Forecast

The next step was to develop the baseline forecas t of annual electricity and natural gas us age for 2010 throu gh 2016 by customer segment and end use without new utility programs or naturally occurring efficiency. The end

- use forecast does include the relatively certain impacts of codes and standards that will unfold over the study timeframe. All such mandates tha t were
defined as of January 2012 are included in the baseline. The baseline forecast is the foundation for the analysis of savings from future EE efforts as well as the metric against which potential savings are measured.

Inputs to the baseline forecast i nclude:

 Current economic growth forecasts (i.e., customer growth, income growth )

 Electricity and natural gas price forecasts

 Trends in fuel shares and equipment saturations

 Existing and approved changes to building codes and equipment standards

We present the results of the baseline forecast development in Chapter 4.



### Energy Efficiency Measure Analysis

This section describes the framework used to assess the savings, costs, and other attributes of

energy

- efficiency measures. These characteristics form the ba sis for measure

- level cost

effectiveness analyses as well as for determining measure

- level savings. For all measures,

EnerNOC assembled information to reflect equipment performance, incremental costs, and equipment lifetimes. We used this information, alon g with New Jersey’s avoided costs data , in the economic screen to determine economically feasible measures. Figure 2

- 3 outlines the framework for measure analysis.Analysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 11 Figure 2

- 3 Approach for Measure Assessment

The framework for assessing savings, costs, and other attributes of energy efficiency measures involves identifying the list of energy efficiency measures to include in the analysis, deter mining their applicability to each market sector and segment, fully characterizing each measure, and performing cost

- effectiveness screening.

We compiled a robust list of energy efficiency measures for each customer sector, drawing upon the New Jersey pro gram experience and protocols, EnerNOC’s own measure databases and building simulation models, and secondary sources . This universal list of EE measures covers all major types of end

- use equipment, as well as devices and actions to reduce energy consumptio n. If considered today, some of these measures would not pass the economic screens initially, but may pass in future years as a result of lower projected equipment costs or higher avoided costs.

The selected measures are categorized into two types accordin g to the LoadMAP taxonomy: equipment measures and non

- equipment measures

.

 Equipment measures are efficient energy

- consuming pieces of equipment that save energy by providing the same service with a lower energy requirement than a standard unit. An example is an ENERGY STAR refrigerator that replaces a standard efficiency refrigerator. For equipment measures, many efficiency levels may be available for a given technology, ranging from the baseline unit (often determined by code or standard) up to the most e fficient product commercially available. For instance, in the case of central air conditioners, this list begins with the current federal standard SEER 13 unit and spans a broad spectrum up to a maximum efficiency of a SEER 21 unit.

 Non

- equipment measures save energy by reducing the need for delivered energy, but do not involve replacement or purchase of major end

- use equipment (such as a refrigerator or air conditioner). An example would be a programmable thermostat that is pre

- set to run heating and cooli ng systems only when people are home. Non

- equipment measures canAnalysis Approach and Data Development

2

- 12 www.enernoc.com apply to more than one end use. For instance, addition of wall insulation will affect the energy use of both space heating and cooling. Non

- e quipment m easures typically fall into

one of the f ollowing categories:

 Building shell (windows, insulation, roofing material)

 Equipment controls (thermostat, energy management system)

 Equipment maintenance (cleaning filters, changing setpoints)

 Whole

- building design (building orientation, passive solar l ighting)

 Lighting retrofits (included as a non

- equipment measure because retrofits are performed prior to the equipment’s normal end of life)

 Displacement measures (ceiling fan to reduce use of central air conditioners)

 Commissioning and retrocommissioning

We developed a preliminary list of EE measures , which was distributed to the BPU and CEEEP for

review. The list was finalized after incorporating comments, and can be found in Chapter 5 of this report

. Once we assembled the list of EE measures, the projec t team assessed their energy

- saving

characteristics. For each measure we also characterized incremental cost, service life, and other performance factors. Following the measure characterization, we performed an economic screening of each measure, which ser ves as the basis for developing the economic and

achievable potential.

Representative Measure Data Inputs
To provide an example of the measure data, Table 2

- 8 and T able 2

- 9 pre sent sample s of the detailed data inputs behind both equipment and non

- equipment measures, respectively, for the case of residential CAC in single

- family homes. Table 2

- 8 displays the various efficiency levels available as equipment measures , as well as the corresponding useful life, energy usage, and cost estimates. The columns labeled On Market and Off Market reflect equipment availability due to codes and standards or the entry of new products to the market.

Table 2

- 8 Sample Equipment Measures for Central Air Conditioning

– Single Family Home

Efficiency Level Useful Life Equipment

Cost Energy

Usage(kWh/yr ) On

Market Off

Market

SEER 13 15 $1,640 2,483 2010 n/a

SEER 14 (ENERGY STAR) 15 $ 1,766 2,278 2010 n/a

SEER 15 (CEE Tier 2) 15 $ 1,892 2,202 2010 n/a

SEER 16 (CEE Tier 3) 15 $ 2,018 2,137 2010 n/a

SEER 17 ( Ductless Mini

- split ) 15 $ 3,863 2,083 2010 n/a

SEER 21 15 $3,784 1,686 2010 n/a

T able 2

- 9 lists some of the non

- equipment measures applicable to CAC in an existing single

family home. All measures are evaluated for cost effectiveness based on the lifetime benefits

relative to the cost of the measure. The total savings and costs are calculated for each year of

the study and depend on the base year saturation of the measure, the applicability 4 of the measure, and the savings as a percentage of the relevant energy end uses.

4 The applicability factors take into account whether the measure is applicable to a particular building type and whether it is fe asible to install the measure. For instance, attic fans are not applicable to homes where there is insufficient space in the attic or t here is no attic

at all.Analysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 13 T able 2

- 9 Sample Non

- Equipment Measures

– Single Family Home, Existing

End Use Measure Saturation

in 2010 5 Applica

bili ty Lifetime

(yrs) Measure

Installed

Cost Energy

Savings (%)

Cooling Central AC

- Maintenance 37 % 100% 4 $125 9.9 %

Cooling Repair and Sealing

–

Ducting 1 6 % 50% 18 $ 500 8 .8%

Cooling Insulation

- Ceiling 16% 72 % 20 $ 1000 1 5 .5%

Cooling Windows

– Install R eflective

Film 20 % 45% 10 $895 26.2 %

Cooling Windows

- ENERGY STAR 47 % 90% 2 5 $ 7500 7.2 %

Cooling Thermostat

-

Clock/Programmable 54 % 56% 12 $73 7.3 %

Screening Measures for Cost

- Effectiveness
Only measures that are cost

- effective are included in econo mic and achievable potential

. Therefore

, for each individual measure , LoadMAP performs an economic screen. This study uses the TRC test that compares the lifetime energy benefits ( and peak demand for electricity ) of each applicable measure with its increme ntal installed cost , including material and labor . There is no program administration cost considered in this analysis, and therefore, no specific program delivery methods or mechanism s are assumed. The lifetime benefits are calculated by multiplying

the a nnual energy and demand savings for each measure by all appropriate avoided costs for each year, and discounting the dollar savings to the present value equivalent. The analysis uses each measure’s values for savings, costs

, and lifetimes that were develop ed as part of the measure characterization process described above.

The LoadMAP model performs this screening dynamically, taking into account changing savings and cost data over time. Thus, some measures pass the economic screen for some

— but not all

— of the years in the forecast.

It is important to note the following about the economic screen:

 The economic evaluation of every measure in the screen is conducted relative to a baseline condition. For instance, in order to determine the kilowatt

- hour (kW h) savings potential of a measure, kWh consumption with the measure applied must be compared to the kWh consumption of a baseline condition.

 The economic screening was conducted only for measures that are applicable to each building type and vintage; thus if a measure is deemed to be irrelevant to a particular building type and vintage, it is excluded from the respective economic screen.

Table 2

- 10 shows the results of the economic screen for CAC and select other measures

. Through out the time frame shown, the most cost

- effective CAC option is SEER 16

. For pool heaters, the heat pump unit becomes cost effective in 2015 due to changing market conditions and avoided costs, and eclipses the electric resistance unit as the highest effic iency economic unit. For refrigerators, however, the AHAM federal efficiency standards cause existing Energy Star units to become unavailable in 2014. Units compliant with AHAM 2014 thus become the new minimum efficiency baseline and are therefore assigned a benefit

- to

- cost ( B/C) ratio of 1. Since there is not a more efficient, cost

- effective unit available, they become the economic unit by

default. If the measure passes the screen (has a B/C ratio greater t han or equal to 1 ), the

measure is included in eco nomic potential. Otherwise, it is screened out for that year. If multiple

5 Note that saturation levels reflected for the base year change over time as more measures are adopted.Analysis Approach and Data Development

2

- 14 www.enernoc.com equipment measures have B/C ratios greater than or equal to 1.0, the most efficient technology is selected by the economic screen.

Table 2

- 10 Economic Screen Results for Selected Residential Equipment Measures

Technology 2013 2014 2015 2016

Central AC SEER 16 SEER 16 SEER 16 SEER 16

Pool Heater El ectric

Resistance Electric

Resistance Heat Pump

(COP = 5.0) Heat Pump

(COP = 5.0)

Refrigerator Energy Star AHAM (2014) AHAM (2014) AHAM (2014)



### Energy

- Efficiency Potential

The approach we used for this study adheres to the approaches and conventions outli ned in the National Action Plan for Energy

- Efficiency (NAPEE) Guide for Conducting Potential Studies (November 2007). 6 The NAPEE Guide represents the most credible and comprehensive industry practice for specifying energy

- efficiency potential. As described in Chapter 1, four types of potentials were developed as part of this effort : Technical potential, Economic potential, Achievable High potential and Achievable Low potential

The calculation of Technical and Economic potential is a straightforward algorith m. To develop

estimates for A chievable potential , we develop market adoption rates for each measure that specify the percentage of customers that will select the highest

– efficiency economic option. The Achievable High adoption rates are based on the ramp r ates from the Northwest Power & Conservation Council’s Sixth Plan as a starting point . The NWPCC has been running programs in the Pacific Northwest for many years, and the portfolio of programs reflects a similar profile of market maturity. The ramp rates are then adjusted with actual New Jersey program history and information from program evaluations. The Achievable Low adoption rates start with the Achievable High rates and decrement them by 40% to 75% based on where measures lie in the

time horizon of th e study or whether they are already familiar inclusions in existing programs. Finally, reasonableness checks are applied by comparing the adoption rates to those from other relevant potential studies and market research.

T he overall energy efficiency pote ntial results are available in Chapter 6, and the results by sector are given in Chapter 7.



### Alternative Avoided Cost Scenario

To observe the sensitivity of study results to changes in forecast market prices of electricity and natural gas, we ran a scenari o in which the avoided costs for each fuel in each year of the study were increased by 20%. The models were then re

- r u n with all other variables held constant

. The results of this review are presented in Chapter 8

.



### Fuel Switching Analysis

Typically, energy

- efficiency potential studies may not explore the effects of switching technologies between system types and fuel types. In this context, where both electricity and natural gas are being studied on a statewide basis, we investigate these effects, including the switch from fuel oil furnaces to natural gas furnaces, which is particularly relevant to the New Jersey market.

The equipment stock accounting inside the LoadMAP modeling tool supports fuel switching, and this analysis has been incorporated for the r esidential sector. The switching forecast is integrated with the traditional potential analysis, and operates on the same principles to calculate technical, economic, and achievable cases in turn. Several important adjustments are made to account for

6 National Action Plan for Energy Efficiency (2007). National Action Plan for Energy Efficiency Vision for 2025: Developing a Framework for Change

. www.epa.gov/eeactionplan

.Analysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 15 compa rison of technical potential on a same

- fuel, BTU basis; to account for increased switching costs; and to account for the increased complexity of the market decision and corresponding decreased customer adoption rates. These factors and the results are disc ussed and presented in Chapter 9

.



### Conclusions and Recommendations

In this final step, we review the potential estimates by measure, customer segment

, and sector

to develop a set of conclusions and recommendations to guide program efforts toward optimal

pur suit of energy efficiency savings. The results of this review are presented in Chapter 10

.



## Data Development

This section details the data sources used in this study, followed by a discussion of how these sources were applied . In general, data were adapted to local conditions, for example, by using local sources for measure data and local weather for building simulations.



### Data Sources

The data sources are organized into the following categories:

 New Jersey data

 EnerNOC’s databases and analysis tools

 Other s econdary data and reports

New Jersey Data
Our highest priority data sources for this study were those that were specific to New Jersey.

 Residential Energy Consumption Survey (RECS). In the most recent RECS survey conducted by the U.S. DOE, New Jersey was one of a handful of states singled out for a larger sample size and State

- level estimates. We used these data extensively to develop residential market profiles as described below. http://w ww.eia.gov/consumption/residential/data/2009/

 American Community Survey: The US Census American Community Survey is an ongoing survey that provides data every year on household characteristics . Data for New Jersey were available for this study. http://www.census.gov/acs/www/

 EIA

- 826 data and EIA

- 176 forms from New Jersey utilities: These data sets were

used to determine the state

- wide current electricity and natural gas use for the study. http://www.eia.gov/electricity/sales_revenue_price/ and http://www.eia.gov/dnav/ng/ng_cons_sum_dcu_SNJ_a.htm

 New Jersey Clean Energy Library : Th e NJOCE website provides a comprehensive library of past research reports, program results, and evaluation studies , which were used to inform program recommendations and provide updated information on measure adoption and penetration. http://www.njcleanenergy.com/library

 New Jersey Economic Projections: Projections of key economic indicators were obtained from the Rutgers Economic Advisory Service (R / ECON). http://policy.rutgers.edu/cupr/recon/ Employment forecasts were obtained from the US Bureau of Labor Statistics. http://bls.gov/ Forecasts of avoided costs were obtained from a 2012 analysis performed by the New Jersey

Ra te Counsel.

 New Jersey Weather Data: Weather from NOAA’s National Climatic Data Center for Hightstown , NJ was used as the basis for building simulations

.An alysis Approach and Data Development

2

- 16 www.enernoc.com EnerNOC Databases, Analysis Tools, and Reports

EnerNOC maintains several databases and modeling tools that we use for forecasting and potential studies.

 EnerNOC Energy Market Profiles: For more than 10 years, EnerNOC staff have maintained profiles of end

- use consumption for the residential, commercial, and industrial sectors. These profiles include market size, fuel shares, unit consumption estimates, and annual energy use by fuel (electricity and natural gas), customer segment and end use for 10 regions in the U.S. The Energy Information Administration surveys (RECS, CBECS and MECS)

as well as state

- level statistics and local customer research provide the foundation for these regional profiles.

 Building Energy Simulation Tool (BEST)

. EnerNOC’s BEST is a derivative of the DOE 2.2 building simulation model, used to estimate base

- year UECs and EUIs, as well a s measure savings for the HVAC

- related measures.

 EnerNOC’s EnergyShape TM : This database of load shapes includes the following:

Residential

– electric load shapes for 10 regions, 3 housing types, 13 end uses ; Commercial

– electric load shapes for 9 regions, 54 building types, 10 end uses ; Industrial

– e lectric load shapes, whole facility only, 19 2

- digit SIC codes, as well as various 3

- digit and 4

- digit SIC

codes

 EnerNOC’s Database of Energy Efficiency Measures (DEEM) : EnerNOC maintains an

extensive database of measure data for our studies. Our database draws upon reliable sources including the California Database for Energy Efficient Resources (DEER), the EIA Technology Forecast Updates

– Residential and Commercial Building Technologies

– Reference Case, RS Means cost data, and Grainger Catalog Cost data

.

 Recent s tudies

. EnerNOC has conducted numerous studies of EE potential in the last five years. We checke d our input assumptions and analysis results against the results from these other studies, which inclu de AmerenUE, Los Angeles Department of Water and Power, Consolidated Edison of New York, Avista Utilities, the State of New Mexico, Tennessee Valley Authority, and Seattle City Light. In addition, we used the information about impacts of building codes and appliance standards from a recent report for the Institute for Energy Efficiency. 7

Other Secondary Data and Reports
Finally, a variety of secondary data sources and reports were used for this study. The main

sources are identified below.

 New Jersey and r egional data from past E nerNOC projects: EnerNOC referenced data from our project with MISO, as well as regional data from similar studies for Con Edison of NY, PECO Energy, and the Connecticut Energy Efficiency Board (CEEB).

 California Statewide Surveys

. The Residential Appliance Saturation Survey (RASS) and the Commercial End Use Survey (CEUS) are comprehensive market research studies conducted by the California Energy Commission. These databases provide a wealth of information on appliance use in homes a nd businesses. RASS is based on information from almost 25,000 homes and CEUS is based on information from a stratified random sample of almost 3,000 businesses in California.

 Annual Energy Outlook . The Annual Energy Outlook (AEO), conducted each year by the U.S. Energy Information Administration (EIA), presents yearly projections and analysis of energy topics. For this study, we used data from the 2011 AEO.

7 “Assessment of Electrici ty Savings in the U.S. Achievable through New Appliance/Equipment Efficiency Standards and Building Efficiency Codes (2010

– 2025).” Global Energy Partners, LLC for the Institute for Electric Efficiency, May 2011. http://www.edisonfoundation.net/iee/repor ts/IEE_CodesandStandardsAssessment_2010

- 2025_UPDATE.pdfAnalysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 17  Electric Power Research Institute

– Assessment of Achievable Potential from Energy Efficiency and Demand Response Programs in the U.S. , also known as the EPRI National Potential Study (2009). In 2009, EPRI hired EnerNOC to conduct an assessment of the national potential for energy efficiency, with estimates derived for the four DOE regions (including t he Mid Atlantic region that includes New Jersey ).

 EPRI End

- Use Models (REEPS and COMMEND) . These models provide the elas ticities we apply to electricity prices, household income, home size and heating and cooling.

 Database for Energy Efficient Resources (D EER). The California Energy Commission and California Public Utilities Commission (CPUC) sponsor this database, which is designed to provide well

- documented estimates of energy and peak demand savings values, measure costs, and effective useful life (EUL) for the state of California

. We used the DEER database to cross check the measure savings we developed using BEST and DEEM.

 Northwest Power and Conservation Council Sixth Plan workbooks. To develop its Power Plan, the Council maintains workbooks with detai led information about measures.

 Other relevant regional sources: These include reports from the Consortium for Energy Efficiency, the Northeast Energy Efficiency Partnership

, the EPA, and the American Council for an Energy

- Efficient Economy.



### Data Applicat ion

We now discuss how the data sources described above were used for each step of the study.

Data Application for Market Characterization
To construct the high

- level market characterization of electricity use and households/floor space

for the residential , commercial, and industrial sectors, we applied the following data sources:

 RECS 2009 and the American Community Survey to allocate residential customers by housing
type

 EIA , AEO 2011 and our Energy Market Profiles Database to allocate sales and square f ootage by building type for the commercial sector

 EIA data on energy use by industry type, Bureau of Labor Statistics and AEO 2011 data to allocate sales and employment for the industrial sector

Data Application for Market Profiles
The specific data eleme nts for the market profiles, together with the key data sources, are

shown in Table 2

- 11

. To develop the market profiles for each segment, we used the following approach:

1. Developed control totals for each segment. These include m arket size, segment

- level annual electricity use, and annual intensity.

2. Used RECS 2009 and the American Housing Survey to incorporate information on existing appliance saturations, appliance and equipment characteristics, and building characteristics

.

3. In corporated secondary data sources to supplement and corroborate the data from items 1

and 2 above.

4. Compared and cross

- checked with regional data obtained as part of the EPRI National Potential Study and with the Energy Market Profiles Database

.

5. Ensured cal ibration to control totals for annual electricity and natural gas sales in each sector
and segment.

6. Worked with BPU and CEEEP staff to vet the data against their knowledge and experience.Analysis Approach and Data Development

2

- 18 www.enernoc.com Table 2

- 11 Data

Applied for the Market Profiles

Model Inputs Description Key Sources

Market size Base

- year residential dwellings
commercial floor space, and industrial
employment  EIA Fo rm 826 and 176 data

 American Community Survey

 Energy Market Profiles

 AEO

Annual intensity Residential: Annual energy use (kWh/household)
Commercial: Annual energy use (kWh/sq ft)
Industrial: Annual energy use (kWh/employee)  Energy Market Profiles

 AEO

 Previous studies

Appliance/equipment

saturations Fraction of dwellings with an appliance/technology

Percentage of C&I floor space/employment with equipment/technology  RECS 2009

 Energy Market Profiles

UEC/EUI for each

end

- use technology UEC: Annual elect ricity use for a
technology in dwellings that have the
technology

EUI: Annual electricity use per square foot/employee for a technology in
floor space that has the technology  HVAC uses: BEST simulations using
prototypes developed for NJ

 Engineering analy sis

 DEEM

 Previous EnerNOC studies

 California RASS and CEUS

Appliance/equipment
vintage distribution Age distribution for each technology  RECS 2009

 Previous EnerNOC studies

Efficiency options for
each technology List of available efficiency options and

a nnual energy use for each technology  DEEM

 DEER

 NWPCC workbooks

 Annual Energy Outlook

 Previous studies

Peak factors Share of technology energy use that
occurs during the peak hour  EnergyShape database

Data Application for Baseline Forecast

Table 2

- 12 summarizes the LoadMAP model inputs required for the baseline forecast. These inputs are required for each segment within each sector, as well as for new construction and existing dwellings/buildings.Analysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 19 Table 2

- 12 Data Needs for the Baseline Forecast and Potentials Estimation in LoadMAP

Model Inputs Description Key Sources

Customer growth forecasts F orecasts of new construction in

residential and C&I sectors  AEO 2011 growth forecast

 R / ECON

 US BLS

Equipment purchase shares for

baseline forecast For each equipment/technology,
purchase shares for each
efficiency level; specified
separately for existing
equipment replacement and new

construction  Shipments data from AEO

 AEO 2011 regional forecast

assumptions 8

 Appliance/efficiency standards
analysis

 NJOCE program results and
evaluation reports

Electricity and natural gas prices Forecast of average energy and
capacity avoided costs and retail

prices  CEEEP July 2012 analysis

 AEO 2011

Utilization model parameters Price elasticities, elasticities for
other variables (income,
weather)  EPRI’s REEPS and COMMEND

models

 AEO 2011

 NOAA data for normal cooling

& he ating degree days for
High t stown, NJ

The avoided cost forecasts implemented in the models, sourced from a 2012 CEEEP analysis, are available in Appendix G. The discount rate used for NPV analysis is a nominal rate of 8%, the

same rate used in the 2012 C EEEP analysis and the previous EE potential studies done for the State of New Jersey. When adjusted for an average inflation rate of 2.92%, the discount rate is

4.94% in real terms.

W e also implemented assumptions for known future equipment standards as o f January, 2012,

as shown in the tables below.

8 We developed b aseline purchase decisions using the Energy Information Agency’s Annual Energy Outlook report (20 11 ), which utilizes the National Energy Modeling System (NEMS) to produce a self

- consis tent supply and demand economic model. We calibrated

e quipment purchase options to match manufacturer shipment data for recent years and then held values constant for the study period. This removes any effects of naturally occurring conservation or effects of future DSM programs that may be embedded in the AEO

forecasts.EnerNOC Utility Solutions Consulting 2

- 20

Table 2

- 13 Residential Electric Equipment Standards Applicable to New J erseyAnalysis Approach and Data Development

EnerNOC Utility Solutions Consulting 2

- 21 Table 2

- 14 Commercial Electric Equipment Standards Applicable to New JerseyAnalysis Approach and Data Development

2

- 22 www.enernoc.com Table 2

- 15 Residential Gas Appliance Standards Applicable to New Jersey

Table 2

- 16 Commercial Gas Appliance Standards Applicable to New JerseyEnerNOC Utility Solutions Consulting 2

- 23

Energy Efficiency Measure Data Applicati on

Table 2

- 17 details the data sources used for measure characterization.

Table 2

- 17 Data Ne

eds for the Measure Characteristics in LoadMAP

Model Inputs Description Key Sources

Energy Impacts The annual reduction in consumption attributable
to each specific measure. Savings were developed
as a percentage of the energy end use that the

measure aff ects.  BEST

 DEEM

 DEER

 NWPCC workbooks

 Other secondary sources

Peak Demand Impacts Savings during the peak demand periods are
specified for each electric measure. These impacts
relate to the energy savings and depend on the

extent to which each measure is c oincident with
the system peak.  BEST

 EnergyShape

Costs Equipment Measures: Includes the full cost of
purchasing and installing the equipment on a per

household, per

- square

- foot , or per employee basis

for the residential , commercial, and industrial

sectors , respectively

. Non

- equipment measures: Existing buildings

– full

installed cost. New Construction

- the costs may be
either the full cost of the measure, or as
appropriate, it may be the incremental cost of
upgrading from a standard level to a higher

effi ciency level.  DEEM

 DEER

 NWPCC workbooks

 RS Means

 Other secondary sources

Measure Lifetimes Estimates derived from the technical data and
secondary data sources that support the measure
demand and energy savings analysis

.  DEEM

 DEER

 NWPCC workbooks

 Other s econdary sources

Applicability Estimate of the percentage of either dwellings in
the residential sector or square feet /employment
in the C&I sectors where the measure is applicable
and where it is technically feasible to implement

.  DEEM

 DEER

 NWPCC workboo ks

 Other secondary sources

On Market and Off
Market Availability Expressed as years for equipment measures to
reflect when the equipment technology is available
or no longer available in the market

.  EnerNOC appliance

standards and building
codes analysis

Data Application for Cost

- effectiveness Screening
To perform the cost

- effectiveness screening, a number of economic assumptions were needed

. All cost and benefit values were analyzed as real 2010 dollars. A discount rate of 8% in nominal terms was used, a s developed in the CEEEP 2008 cost

- effectiveness analysis. This is equivalent to a 4.93% discount rate in real terms when adjusting for 2.92% inflation. 9 As mentioned above,

avoided costs were obtained from an analysis performed by the New Jersey Rate Coun sel.

E lectric delivery losses of 7.6% were obtained from an EIA 10

- year New Jersey average 10

, whereas natural gas delivery losses of 1.4% are based on an EIA engineering estimate, as the gas distribution system is not tracked and monitored as rigorously as the electric system. 11

9 Inflation adjuster of 2.92% based on the average annual growth forecast in US Consumer Price Index from the 2012 Annual Energ y Outlook for 2010

- 2035.

10 http://www.eia.gov/electricity/sta te/newjersey

11 http://www.eia.gov/pub/itg/ghgp9.htmAnalysis Approach and Data Development

2

- 24 www.enernoc.com Achievable Potential Estimation

To estimate achievable potentials, three sets of parameters were required to account for the decision making behavior of humans in the efficiency marketplace

.

 Adoption curves for non

- equipment measur es

. Equipment measures are installed when existing units fail. Non

- equipment measures do not have this natural periodicity, so r ather than installing all available non

- equipment measures in the first year of the forecast (instantaneous potential), they are phased in according to adopti on schedules that vary

based on cost and complexity. The adoption rates used in this analysis take several factors

into account to determine how quickly the market can absorb these measures. Typically,

measures that cause disr uption to the building, such as wall insulation in existing buildings, receive longer adoption curves

, while those with drop

- in installations, such as programmable thermostats in new buildings, receive shorter ones. High capital cost measures will also

rec eive longer adoption curves than ones with low capital cost. These adoption rates are used within LoadMAP to generate the T echnical and E conomic potentials. In general, the rates align with the diffusion of similar equipment measures.

 Achievable High adopt ion rates . These factors are applied to Economic potential to

estimate the upper bound: Achievable High. These estimate customer adoption of economic measures when delivered through efficiency programs under ideal market, implementation, and customer prefe rence conditions. Information channels are assumed to be established and efficient for marketing, educating consumers, and coordinating with trade allies and delivery partners. The Achievable High adoption rates are based on the ramp rates from the

Northwe st Power & Conservation Council’s Sixth Plan as a starting point . The NWPCC has been running programs in the Pacific Northwest for many years, and the portfolio of programs reflects a similar profile of market maturity. The ramp rates are then adjusted wit h actual New Jersey program history and information from program evaluations. Achievable High potential establishes a maximum t arget for the EE savings that an administrator can hope to achieve through its EE programs and involves incentives that represent a substantial portion of the incremental cost combined with high administrative and marketing costs.

 Achievable Low adoption rates . These factors are applied to Achievable High potential to calculate Achievable Low potential

, decrementing them by a range of 40% to 75% based on where measures lie in the time horizon of the study or whether they are already familiar inclusions in existing programs. These rates reflect e xpected program participation given significant barriers to customer acceptance, non

- idea l implementation conditions, and limited

program budgets. This r epresents a lower bound on achievable potential.

Achievable Low and Achievable High adoption rates are presented in Appendix E

.EnerNOC Utility Solutions Consulting 3

- 1



### CHAPTER 3



## MARKET CHARACTERIZAT ION AND MARKET PROFI LES

In this section, we describe how customers in New Jersey use electricity and natural gas in the base year of the study, 2010. It begins with a high

- level summary of energy use by sector and

then delves into each sector in detail.



## Energy Use Summary

Total electrici ty use for the residential, commercial and industrial sectors for New Jersey in 2010

was 78,859 GWh. 12 As shown in Figure 3

- 1 , the largest sector is commercial , accounting for 51 %,

or 40,123 GWh. The remaining use i s split between the residential and industrial sectors, at 30,307 GWh and 8,429 GWh respectively.

Figure 3

- 1 Sector

- Level Electricity Use, 2010

Total natural gas use for all sectors in 2010 was 4,619 million therms

. 13 As shown in Figure 3

- 2

, the largest sector is residential , accounting for 49 %, or 2,255 million therms . The remaining use is split between the commercial and industrial sectors, a t 1,867 million therms and 497 million

therms respectively.

12 Energy given “at

- the

- meter,” i.e. does not include line losses.

13 Energy given “at

- the

- meter,” i.e. does not include line losses.Market Characterization and Market Profiles

3

- 2 www.enernoc.com Figure 3

- 2 Sector

- Level Natural Gas Use, 2010



## Residential Sector

The total number of households, electric sales, and natural gas sales for the State of New Jersey were obtained for the year 2010 from the US DOE Energy Information Administration website. In 2010, there were 3.45 million households in New Jersey. They used 30.3 TWh of electricity 14 and

2.25 bill ion therms of natural gas. 15 We allocated these totals into the six residential segments based on survey data from the DOE’s latest Residential Energy Consumption Survey (RECS 2009), which has an oversample for New Jersey. The values are shown in Table 3

- 1 below, and referred to throughout the study as the control totals to which all energy usage is calibrate d in the base year of the study.

Table 3

- 1 Residential Sector Energy Usage and Intensity by Segment Type, 2010

Segment No. of

Household s Electricity

Us e (GWh) Electricity

Avg Use (kWh /hh ) Natural Gas

Use (million

th e rm s ) Natural Gas

Avg Use ( therms /hh )

Single Family 1,682,988 17,946 10,663 1,286 764

Single Family LI 579,087 4,540 7,840 346 598

Multi Family Rent 500,082 3,785 7,570 307 614

Multi Family Rent LI 429,995 2,464 5,731 206 478

Multi Family Own 222,199 1,380 6,212 92 415

Multi F amily Own LI 40,489 191 4,729 18 437

Total 3,454,840 30,307 8,772 2,255 653

14 U.S. DOE Energy Information Administration, See Table 2, http://www.eia.gov/electricity/sales_revenue_price/

15 U.S. DOE Energy Information Administration, http://205.254.135.7/dnav/ng/ng_cons _sum_dcu_nus_a.htmMarket Characterization and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 3 Figure 3

- 3 and Figure 3

- 4 sh ow the size of each of the segments as a percentage of customers and percentage of residential sector energy use

. Figure 3

- 3 Residential Market Segmentation by Housing Type

– Percent of Households

Figure 3

- 4 Residential Market Segmentation by Housing Type

– Percent of Energy Use

As we describe in the previous chapter, the market profiles provide the foundation upon which we develop the baselin e forecast. The market profile for the residential sector as a whole is presented in Table 3

- 2 and Table 3

- 3 . The residential market profiles for each housing segment are presented in Appendix A

.Market Characterization and Market Profiles

3

- 4 www.enernoc.com Table 3

- 2 Electric Market Profile for the Residential SectorMarket Characterization and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 5 Table 3

- 3 Natural Gas Market Profile for the Residential Sector

Figure 3

- 5 shows the distribution of electricity and natural gas energy consumption by end use for all homes. Three main electricity end uses

— cooling, lighting, and appliances

— account for

over 60% of total us e. The remaining energy is allocated to space heating, water heating,

electronics (computers, televisions, video game consoles, etc.) a nd miscellaneous. The

mis cellaneous category includes furnace fans, pool pumps, and other “plug” loads (hair dryers, power tools, coffee makers, etc.). Within the appliance category, 58% of electricity is used by refrigerators and freezers.

Natural gas usa ge is dominated by space heating (69%) and water heating (19%), with small amounts in appliances for cooking or clothes drying, as well as miscellaneous uses such as pool

heaters.

Figure 3

- 5 Residential Electricity and Natural Gas Use by End Use (201 0 ), All Homes

New Jersey has high saturations of natural gas heating equipment , resulting in an average annual electricity intensity that is relatively low at 8,772 kWh pe r household. Meanwhile, the natural gas intensity is 653 therms per household 16

. Figure 3

- 6 and Table 3

- 4 present the electricity intensities by end

- use and housing type, as well

as all homes on average. Figure 3

- 7 shows the same data as a percentage of total energy use.

16 This is average natural gas use across all homes, including both those with natural gas and those without.Market Characterization and Market Profiles

3

- 6 www.enernoc.com Figure 3

- 6 Residential El ectricity Intensity by End Use and Segment (kWh/household, 201 0 )

Table 3

- 4 Residential Electricity Use by End Use and Segment ( kWh/HH/year, 201

0 )

End Use Single

Family Single

Family LI Multi

Family

Rent Multi

Family

Rent LI Multi

Family

Own Multi

Family

Own LI All

Homes

Cooling 2,286 1,467 1,454 1,020 1,456 716 1,799

Space Heating 408 352 597 94 373 85 381

Water Heati ng 367 94 350 684 255 479 352

Interior Lighting 1,965 1,778 1,732 1,224 926 866 1,728

Exterior Lighting 273 260 238 232 176 164 253

Appliances 2,497 1,968 1,571 1,405 1,517 1,335 2,062

Electronics 1,503 1,056 1,093 696 1,047 772 1,230

Miscellaneous 1, 363 865 536 375 463 313 967

Total 10,663 7,840 7,570 5,731 6,212 4,729 8,772Market Characterization and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 7 Figure 3

- 7 Breakdown of Residential Electricity Use by End Use and Segment (2010)

Figure 3

- 8 and Table 3

- 5 p resent the natural gas intensities by end

- use and housing type, as well as all homes on average. Figure 3

- 9 shows the same data as a percentage of total ene rgy use.

Figure 3

- 8 Residential Natural Gas Intensity by End Use and Segment ( therm /household,

201 0 )Market Characterization and Market Profiles

3

- 8 www.enernoc.com Table 3

- 5 Residential Natural Gas Use by End Use and Segment ( therm/HH/year, 201 0 )

End Use Single

Family Single

Family LI Multi

Family

Rent Multi

Family

Rent LI Multi

Family

Own Multi

Family

Own LI All

Homes

Space Heating 516 373 458 364 284 332 448

Water Heating 149 137 105 72 81 63 126

Appliances 62 69 36 29 40 30 54

Miscellaneous 36 20 15 14 10 13 26

Total 764 598 614 478 415 437 653

Figure 3

- 9 Breakdown

of Residential Natural Gas Use by End Use and Segment (2010)



## Commercial Sector

The total electric energy consumed by commercial customers in New Jersey in 2010 was 40,123

GWh 17 and the tota l natural gas energy consumed was 1,867 million therms. 18 We used

EnerNOC’s database of Energy Market Profiles for the Mid

- Atlantic region to allocate this energy usage to the various building types according to regional average square footage and energy

in tensity data

. The values are shown in Table 3

- 6 below, and referred to throughout the study as the control totals to which all energy usage is calibrate d in the base year of the study.

17 U.S. DOE Energy Information Administration, See Table 2, h ttp://www.eia.gov/electricity/sales_revenue_price/

18 U.S. DOE Energy Information Administration, http://205.254.135.7/dnav/ng/ng_cons_sum_dcu_nus_a.htmMarket Characterization and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 9 Table 3

- 6 Commercial Market Segmentation by Building Type, Base Year 2010

Segment Floor Space
(million sq. ft.) Electricity

2010 Use (GWh) Natural Gas

2010 Use (mi llion therms)

Small Office 384 4,470 196

Large Office 433 6,054 152

Restaurant 52 1,971 103

Retail 551 7,985 318

Grocery 97 4,421 61

College 209 2,188 109

School 325 2,011 149

Health 198 4,084 302

Lodging 100 1,113 64

Warehouse 365 2,640 102

Mis cellaneous 407 3,187 311

Total 3,121 40,123 1,867

Figure 3

- 10 shows the size of each of the building

- types as a percentage of commercial sector energy sales.

Figure 3

- 10 Commercial Market Segmentation by Building Type

– Percent of Energy Use

Table 3

- 7 shows the market profile for electricity of the commercial sector as a whole, representing a composite of all the building types . Overall, about 55 % of commercial floor s pace is cooled. Only a bout 13 % of commercial floor space is heated using electric equipment, either some form of resistance heating or heat pumps. Linear fluorescent lighting and screw

- in lamps

are th e largest energy

- consuming technologies in the commercial sector, followed by ventilation and roof top AC units

.

Table 3

- 8 shows the natural gas market profile for the commercial sector as a whol e. Boilers are the largest natural gas

- consuming technology, followed by water heaters, and furnaces.

Market profiles for each building type are presented in Appendix A

.Market Characterization and Market Profiles

3

- 10 www.enernoc.com Table 3

- 7 Commercial Sector Composite Electric Market Profile, 2010Market Characterization and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 11 Table 3

- 8 Commercial Sector Comp osite Natural Gas Market Profile, 2010

Figure 3

- 11 shows the distribution of electricity and natural gas energy consumption by end use for all commercial building s . Electric usage is dominated by lighting, with interior and exte rior varieties accounting for over one third of consumption. After lighting, the largest end uses are cooling, refrigeration, ventilation, and office equipment. The remaining end uses comprise 7% or less of total usage: miscellaneous, space heating, water heating, and food preparation

. Natural gas usage is dominated by space heating (66%) and water heating (22%), with a small amount in food preparation and miscellaneous.

Figure 3

- 11 Commercial Electricity and Natural Gas Use by End Use (201 0 ), All Buildings

Figure 3

- 12 and Table 3

- 9 present the electricity intensity in kWh per square f oot by end use and building type. Figure 3

- 13 shows the same data as a percentage of total energy use for e ach
segment.M arket Characterization and Market Profiles

3

- 12 www.enernoc.com Figure 3

- 12 Commercial Electricity Intensity by End Use and Segment (kWh/sq ft , 2010)

Table 3

- 9 Commercial Electricity Inte nsity by End Use and Segment ( kWh/sq ft

, 201 0 )

End Use Small Office Large Office Restaurant Retail Grocery College

Cooling 2.20 2.00 5.31 2.52 4.84 1.84

Heating 0.60 0.64 0.66 0.84 0.97 0.77

Ventilation 0.81 1.94 2.65 1.33 2.16 1.21

Water Heating 0.27 0.29 2.03 0.46 0.95 0.56

Interior Lighting 3.49 3.77 7.03 5.99 8.25 3.49

Exterior Lighting 0.91 0.49 2.30 0.79 1.22 0.68

Refrigeration 0.03 0.15 10.13 0.91 24.82 0.12

Food Preparation 0.18 0.20 5.67 0.32 1.04 0.14

Office Equipment 2.21 3.58 0.72 0.49 0.38 1.02

Misc 0.94 0.90 1.41 0.84 0.92 0.64

Total 11.63 13.98 37.92 14.48 45.55 10.48

End Use School Health Lodging Warehouse Misc. All Buildings

Cooling 0.65 5.32 2.14 0.48 1.16 2.04

Heating 0.17 1.23 0.88 0.28 0.20 0.58

Ventilation 0.57 3.23 1.08 0.32 0.56 1.20

Water Heating 0.19 0.30 0.74 0.13 0.40 0.39

Interior Lighting 2.92 4.86 3.97 3.25 3.07 4.14

Exterior Lighting 0.48 0.46 0.57 0.73 1.02 0.76

Refrigeration 0.32 0.60 0.54 0.48 0.19 1.30

Food Preparation 0.17 0.96 0.24 0.01 0.09 0.34

Offi ce Equipment 0.46 0.86 0.13 0.94 0.37 1.21

Misc 0.24 2.83 0.88 0.62 0.79 0.89

Total 6.18 20.63 11.16 7.24 7.83 12.85Market Characterization and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 13 Figure 3

- 13 Breakdown of Commercial Electricity Consumption by End Use and Segment ( 201 0)

Figure 3

- 14 and Table 3

- 10 present the natural gas intensity in therms per square foot by end use and building type. Figure 3

- 15 shows the same data as a percentage of total energy use for each segment.

Figure 3

- 14 Commercial Natural Gas Intensity by End Use and Segment ( therms /sq ft , 201 0 )Market Characterization and Market Profiles

3

- 14 www.enernoc.com Table 3

- 10 Commercial Natural Gas Intensity by End Use and Segment (therms/sq ft , 201 0 )

End Use Small

Office Large

Office Restaurant Retail Grocery College

Heating 0.425 0.262 0.378 0.439 0.422 0.367

Water Heating 0.081 0.067 0.506 0.086 0.143 0.134

Food Preparation 0.002 0.019 1.082 0.046 0.059 0.013

Misc 0.004 0.002 0.008 0.00 6 0.001 0.010

Total 0.511 0.351 1.973 0.576 0.625 0.524

End Use School Health Lodging Warehouse Misc. All
Buildings

Heating 0.295 0.727 0.218 0.259 0.537 0.394

Water Heating 0.108 0.542 0.352 0.019 0.149 0.135

Food Preparation 0.055 0.244 0.065 0.001 0.033 0.059

Misc 0.001 0.012 0.008 0.001 0.046 0.010

Total 0.458 1.524 0.643 0.281 0.765 0.598

Figure 3

- 15 Breakdown of Commercial Natural Gas Use by End Use and Se gment (2010)Market Characterization and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 15 

## Industrial Sector

The total electric energy consumed by industrial customers in New Jersey in 2010 was 8,429

GWh 19 and the total natural gas energy consumed was 497 million therms. 20 To allocate this energy usage to the various industries, w e used energy per employee data from our internal Energy Market Profiles for the Mid

- Atlantic region in combination with employee data from the

Bureau of Labor Statistics

. The resulting allocations are shown in Table 3

- 11 and refe rred to throughout the study as the control totals to which all energy usage is calibrate d in the base year of the study.

Table 3

- 11 Industrial Market Segmentation by Industry Type, Base Year 2010

Segment Employees Electricity 2010
Use (GWh) Natural Gas 2010

Use (million
therms)

Chemical and Pharmaceutical 88,800 2,830 180

Paper 11,500 1,166 76

Food 30,000 783 68

Misc ellaneous 126,900 3,651 173

Total 257,200 8,429 497

Figure 3

- 16 shows the size of each of the segments as a percentage of industrial sector energy

sales.

Figure 3

- 16 Industrial Market Segmentation

– Percentage of Energy Use

As with the residential and commercial sectors, the industrial market profiles characterize electricity use in terms of end use and technology for the base year 20 10

. Table 3

- 12 and Table

3

- 13 show the composite market profiles for the industrial sector.

19 U.S. DOE Energy Information Ad ministration, See Table 2, http://www.eia.gov/electricity/sales_revenue_price/

20 U.S. DOE Energy Information Administration, http://205.254.135.7/dnav/ng/ng_cons_sum_dcu_nus_a.htmMarket Characterization and Market Profiles

3

- 16 www.enernoc.com Table 3

- 12 Industrial Sector Composite Electric Market Profile, 2010

Table 3

- 13 Industrial Sector Composite Natural Gas Market Profile, 2010

Figure 3

- 17 shows the distribution of electricity and natural gas energy consumption by end use

for all industrial customers

. Motors are clearly the largest overall end use for the ind u strial sector, accounting for 45 % of energy use. Note that this end use includes a wide range of industrial equipment, such as air compressors and refrigeration compressors, pumps, conveyor motors, and fans

. The process end use account s for 25 % of energy use , which includes heating,Market Characterizatio n and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 17 cooling, refrigeration, and electro

- chemical processes. Lighting is the next highest, followed by cooling, space heating, ventilation , and miscellaneous.

Natural gas usage is dominated by process usage at 72%, most particularl y process heating. Space heating (22%) and miscellaneous (6%) comprise the remainder of the sector’s energy

usage.

Figure 3

- 17 Industrial Electricity and Natural Gas U se by End Use (201

0), All Industries

Figure 3

- 18 and Table 3

- 14 present the electric consumption by end

- use and industry type. Figure 3

- 19 shows the same data as a percentage o f total energy use for each segment.

Figure 3

- 18 Industrial Electricity Use by End Use and Segment ( GWh , 201 0 )Market Characterization and Market Profiles

3

- 18 www.enernoc.com Table 3

- 14 Industrial Electricity Use by End Use and Segment ( GWh, 201

0 )

End Use Chemical &

Pharmaceutical Paper Food Misc. All Industries

Combined

Cooling 90 26 32 429 577

Heating 53 15 19 250 337

Interior Lighting 124 53 59 613 848

Exterior Lighting 12 5 6 58 80

Motors 1,559 839 316 1,063 3,778

Process 845 182 294 758 2,079

Misc. 88 27 36 192 343

Totals 2,830 1,166 783 3,651 8,429

Figure 3

- 19 Breakdown

of Industrial Electricity Use by End Use and Segment (2010)

Figure 3

- 20 and Table 3

- 15 present the natural gas consumption by end

- use and industry type. Figure 3

- 21 shows the same data as a percentage of total energy use.Market Characterization and Market Profiles

EnerNOC Utility Solutions Consulting 3

- 19 Figure 3

- 20 Industrial Natural Gas Use by End Use and Segment ( million therms

, 201 0 )

Table 3

- 15 Industrial Natural Gas Use by End Use and Segment ( million therms, 201

0 )

End Use Chemical &

Pharmaceutical Paper Food Misc. All Industries

Combined

Heating 4.6 2.8 3.5 98.5 109.4

Process 167.7 65.5 61.2 64.2 358.6

Misc. 8.1 7.1 3.2 10.2 28.6

Totals 180.3 75.5 67.9 172.8 496.6

Figure 3

- 21 Breakdown of Industrial Natural Gas Use by End Use and Segment (2010)EnerNOC Utility Solutions Consulting 4

- 1



### CHAPTER 4



## BASELINE FORECAST

Prior to developing estimates of energy

- efficiency potential, a baseline end

- use forecast was

d eveloped to quantify what the consumption is likely to be in the future in absence of new efficiency programs and naturally occurring efficiency. The baseline forecast serves as the metric against which energy efficiency potentials are measured. This chapt er presents the baseline forecast for electricity and gas for each sector.



## Residential Sector

T he baseline forecast incorporates assumptions about economic growth, electricity prices, and appliance/equipment standards and building codes that are already m andated as described in Chapter 2

.

Table 4

- 1 and Figure 4

- 1 present the baseline forecast for electricity at the end

- use level for the

residential sector as a whole. Overall, residential use decreases sl ightly from 30,307 GWh in 2010

to 29, 502 GWh in 2016, a decrease of 2. 7 %, or an average reduction of 0.4 % per year. This reflects the impact of the EISA lighting standard, additional appliance standards adopted in 2011, and modest customer growth. Figure 4

- 2 presents the forecast of use per household. Most noticeable is that lighting use decreases significantly throughout the time period as the lighting efficiency standard s from EISA come into effect.

Table 4

- 1 Residential Electricity Consumption by End Use (GWh)

End Use 2010 2013 2014 2015 2016 %Change Avg.

Growth

Rate

Cooling 6,215 6,210 6,250 6 ,300 6,372 3% 0.4%

Heating 1,316 1,364 1,385 1,407 1,432 9% 1.4%

Water Heating 1,217 1,198 1,202 1,204 1,207

- 1%

- 0.1%

Interior Lighting 5,970 6,245 5,576 5,183 4,923

- 18%

- 3.2%

Exterior Lighting 875 800 675 603 555

- 37%

- 7.6%

Appliances 7,123 6,350 6 ,158 5,996 5,872

- 18%

- 3.2%

Electronics 4,160 4,539 4,688 4,837 5,016 21% 3.1%

Miscellaneous 3,431 3,737 3,859 3,986 4,126 20% 3.1%

Total 30,307 30,442 29,793 29,515 29,502

- 2.7%

- 0.4%Baseline Forecast

4

- 2 www.enernoc.com Figure 4

- 1 Residential Electricity Baseline Forecast by End Use

Figure 4

- 2 Residential Baseline Electricity Use per Household by End Use

Table 4

- 2 shows the end

- use forecast at the technology level. Specific observations include:

1. The primary reason for the reduction in the baseline forecast beginning in 2012 is the federal lighting standards. The standa rd phases general service incandescent lamps out of the

market over a three

- year period , causing a decline in interior screw

- in lighting use by 3 7.6 % over the forecast period.Baseline Forecast

EnerNOC Utility Solutions Consulting 4

- 3 2. Appliance energy use decrease s markedly, reflecting efficiency gains from stand ards

.

3. Growth in use in electronics is substantial and reflects an increase in the saturation of electronics and the trend toward higher

- powered computers.

4. Growth in miscellaneous use is also substantial. This use includes various plug loads not

elsewhere classified (e.g., hair dryers, power tools, coffee makers, etc.). This end use has grown consistently in the past and we incorporate future growth assumptions that are consistent with the Annual Energy Outlook.

Table 4

- 2 Residential Electricity Baseline Forecast by End Use and Technology (GWh)

End Use Technology 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Cooling Central AC 4,764 4,765 4,804 4,851 4,916 3.2% 0.5%

Room AC 1,407 1,400 1,401 1,404 1,409 0.2% 0.0%

Air

- Source Heat Pump 44 45 45 46 46 4.3% 0.7%

Geothermal Heat Pump

- - - - - 0.0% 0.0%

Heating Electric Room Heat 739 768 781 793 808 9.3% 1.5%

Elec tric Furnace 447 465 472 480 488 9.1% 1.5%

Air

- Source Heat Pump 130 131 133 134 136 4.6% 0.7%

Geothermal Heat Pump

- - - - - 0.0% 0.0%

Water Heating Water Heater <=55 gal 1,142 1,123 1,128 1,131 1,136

- 0.5%

- 0.1%

Water Heater > 55 gal 75 74 75 73 71

- 5.8%

- 1.0%

Interior Lighting Screw

- in 3,627 3,525 2,878 2,509 2,265

- 37.6%

- 7.9%

Linear Fluorescent 453 475 484 493 503 10.8% 1.7%

Specialty 1,889 2,245 2,214 2,181 2,156 14.1% 2.2%

Ext

. Lighting Screw

- in 875 800 675 603 555

- 36.5%

- 7.6%

Appliance s Clothes Washer 296 250 240 228 218

- 26.3%

- 5.1%

Clothes Dryer 940 835 816 800 789

- 16.1%

- 2.9%

Dishwasher 986 854 829 807 790

- 19.9%

- 3.7%

Refrigerator 2,688 2,372 2,273 2,189 2,119

- 21.2%

- 4.0%

Freezer 443 389 374 362 354

- 20.2%

- 3.8%

Second R efrigerator 966 826 792 766 747

- 22.7%

- 4.3%

Stove 366 380 385 391 397 8.3% 1.3%

Microwave 436 445 449 453 458 5.1% 0.8%

Electronics Personal Computers 733 803 826 843 870 18.7% 2.9%

Monitor 146 156 161 166 172 17.9% 2.7%

Laptops 283 309 315 324 3 34 18.1% 2.8%

TVs 1,585 1,715 1,768 1,822 1,882 18.7% 2.9%

Printer/Fax/Copier 157 154 154 154 153

- 2.5%

- 0.4%

Set

- top Boxes/DVR 904 1,012 1,059 1,106 1,164 28.8% 4.2%

Devices and Gadgets 352 390 406 422 440 25.1% 3.7%

Miscellaneous Pool Pump 591 6 16 627 638 651 10.1% 1.6%

Pool Heater 103 103 104 105 106 3.3% 0.5%

Hot Tub / Spa 137 143 145 148 151 10.1% 1.6%

Well Pump 202 211 215 218 222 10.1% 1.6%

Furnace Fan 739 771 784 798 813 10.0% 1.6%

Miscellaneous 1,660 1,893 1,984 2,079 2,183 31.5% 4.6%

Total 30,307 30,369 29,731 29,459 29,448

- 2.8%

- 0.5%Baseline Forecast

4

- 4 www.enernoc.com Table 4

- 3 and Figure 4

- 3 present the residential sector baseline forecast for natural gas at the

end use level. Natural gas use increases from 2,255 million therms in 2010 to 2,352 million therms in 2016, an overall increase of 4.3 % and an average growth of 0.7 % per year. Figure 4

- 4

shows that n atural gas usage grows sl ightly , reflecting lower fuel prices.

Table 4

- 3 Residential Natural Gas Consumption by End Use (million therms )

End Use 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Heating 1,546 1,592 1,607 1,619 1,634 6% 0.9%

Water Heating 435 429 430 429 429

- 1%

- 0.2%

Appliances 185 179 178 178 178

- 4%

- 0.7%

Miscellaneous 89 99 103 107 111 25% 3.8%

Total 2,255 2,300 2,319 2,333 2,352 4.3% 0.7%

Figure 4

- 3 Residential Natural Gas Baseline Forecast by End UseBaseline Forecast

EnerNOC Utility Solutions Consulting 4

- 5 Figure 4

- 4 Residential Baseline Natural Gas Use per Household by End Use

Table 4

- 4 shows the end use forecast for natural gas at the technology level. Gas water heating has a high saturation relative to other areas around the country, but grows only modestly over

the fo recast period.

Table 4

- 4 Residential Natural Gas Baseline Forecast by End Use and Technology (million

therms )

End Use T echnology 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Heating Furnace 859 880 886 891 897 4.5% 0.7%

Boiler 673 696 705 712 721 7.1% 1.1%

Other Heating 15 16 16 16 17 11.5% 1.8%

Water Heating Water Heater <=55 gal 401 396 397 397 397

- 1.1%

- 0.2 %

Water Heater > 55 gal 33 33 33 32 32

- 4.2%

- 0.7%

Appliances Clothes Dryer 50 40 38 37 35

- 28.7%

- 5.6%

Stove 136 138 140 141 142 5.0% 0.8%

Miscellaneous Pool Heater 26 28 28 29 29 12.3% 1.9%

Hot Tub / Spa 10 10 10 11 11 12.3% 1.9%

Miscellaneous 53 62 65 68 71 34.1% 4.9%

Total 2,255 2,300 2,319 2,333 2,352 4.3% 0.7%



## Commercial Sector

Electricity us e in the commercial sector shows a decline of 11% overall during the forecast horizon, an average of 1.9% per year. Usage starts at 40,123 GWh in 201 0, and decreases to 35,797 GWh in 2016. Table 4

- 5 and Figure 4

- 5 present the electricity baseline forecast at the

end

- use level for the commercial sector as a whole. Usage is generally declining due to a sluggish economic recovery and the phasing in of codes and standards. The EISA 2007 lighting

standards and EPACT 2005 refrigeration standards are also showing significant effects.Baseline Forecast

4

- 6 www.enernoc.com Table 4

- 5 Commercial Electricity Consumption by End Use (GWh)

End Use 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Cooling 6,353 6,212 6,185 6,088 6,082

- 4%

- 0.7%

Heating 1,826 1,798 1,800 1,786 1,798

- 2%

- 0.3%

Ventilation 3,760 3,665 3,634 3,506 3,463

- 8%

- 1.4%

Water Heating 1,207 1,179 1,178 1,170 1,185

- 2%

- 0.3%

Interior Lighting 12,915 11,719 10,981 10,190 9,948

- 23%

- 4.3%

Exterior Lighting 2,359 2,2 43 2,181 2,064 2,020

- 14%

- 2.6%

Refrigeration 4,067 3,847 3,666 3,346 3,178

- 22%

- 4.1%

Food Preparation 1,066 1,049 1,054 1,043 1,063 0%

- 0.1%

Office Equipment 3,782 3,728 3,736 3,672 3,749

- 1%

- 0.1%

Miscellaneous 2,788 2,842 2,939 3,099 3,310 19% 2.9%

Total 40,123 38,281 37,354 35,964 35,797

- 11%

- 1.9%

Figure 4

- 5 Commercial Electricity Baseline Forecast by End Use

Table 4

- 6 presents th e commercial sector electricity forecast by technology. Interior screw

- in lighting and refrigeration decrease significantly over the forecast period as a result of efficiency

standard s

.Baseline Forecast

EnerNOC Utility Solutions Consulting 4

- 7 Table 4

- 6 Commercial Baseline Electricity Forecast by End Use and Technology (GWh)

End Use Technology 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Cooling Air

- Cooled Chiller 598 588 589 585 592

- 1.1%

- 0.2%

Water

- Cooled Chiller 520 512 513 511 517

- 0.6%

- 0.1%

Roof top AC 3,295 3,204 3,173 3,099 3,068

- 6.9%

- 1.2%

Air

- Source Heat Pump 593 578 573 563 559

- 5.8%

- 1.0%

Geothermal Heat Pump 99 98 99 99 101 2.3% 0.4%

Other Cooling 1 ,247 1,232 1,237 1,231 1,245

- 0.1% 0.0%

Heating Air

- Source Heat Pump 755 740 738 731 731

- 3.2%

- 0.5%

Geothermal Heat Pump 126 123 124 123 124

- 1.1%

- 0.2%

Electric Room Heat 90 89 90 89 90

- 0.2% 0.0%

Electric Furnace 854 845 848 843 853

- 0.2% 0.0%

V entilation Ventilation 3,760 3,665 3,634 3,506 3,463

- 7.9%

- 1.4%

Water Heating Water Heater 1,207 1,179 1,178 1,170 1,185

- 1.9%

- 0.3%

Interior
Lighting Screw

- in 5,280 4,366 3,769 3,263 3,063

- 42.0%

- 9.1%

High

- Bay Fixtures 1,868 1,660 1,504 1,270 1,189

- 36.4%

- 7.5%

Linear Fluorescent 5,767 5,693 5,708 5,656 5,697

- 1.2%

- 0.2%

Exterior Lighting Screw

- in 576 545 533 507 480

- 16.8%

- 3.1%

HID 1,633 1,547 1,494 1,397 1,364

- 16.5%

- 3.0%

Linear Fluorescent 149 151 155 161 176 18.1% 2.8%

Refrigeration Wal k

- in Refrigerator 1,172 1,037 938 787 699

- 40.4%

- 8.6%

Reach

- in Refrigerator 127 114 105 91 83

- 34.3%

- 7.0%

Glass Door Display 1,545 1,509 1,444 1,329 1,265

- 18.1%

- 3.3%

Open Display Case 697 689 690 677 681

- 2.4%

- 0.4%

Icemaker 263 247 245 244 249

- 5.0%

- 0.9%

Vending Machine 263 251 244 218 200

- 23.8%

- 4.5%

Food

Preparation Oven 227 228 233 240 253 11.4% 1.8%

Fryer 302 303 309 318 333 10.2% 1.6%

Dishwasher 428 416 412 396 393

- 8.3%

- 1.4%

Hot Food Container 108 103 99 90 84

- 22.6%

- 4.3%

Of fice

Equipment Desktop Computer 1,927 1,919 1,927 1,888 1,911

- 0.8%

- 0.1%

Laptop 291 292 296 295 304 4.8% 0.8%

Server 881 858 858 844 876

- 0.6%

- 0.1%

Monitor 357 348 348 347 358 0.2% 0.0%

Printer/Copier/Fax 261 252 250 243 244

- 6.6%

- 1.1%

POS Ter minal 65 59 57 55 56

- 12.7%

- 2.3%

Miscellaneous Non

- HVAC Motors 166 163 163 161 164

- 1.2%

- 0.2%

Pool Pump 0 0 0 0 1 2.5% 0.4%

Pool Heater 0 0 0 0 0 2.1% 0.4%

Miscellaneous 2,622 2,679 2,776 2,937 3,145 20.0% 3.0%

Total 40,123 38,281 37,354 35,964 3 5,797

- 10.8%

- 1.9%

Table 4

- 7 and Figure 4

- 6 show the baseline forecast for natural gas, which is expected to decline by 6 % between 2010 and 2016. The sluggish economy and more efficient equipment in new construction and normal equipment replacement cycles account for this decline.Baseline Fo recast

4

- 8 www.enernoc.com Table 4

- 7 Commercial Natural Gas Consu mption by End Use (million therms )

End Use 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Heating 1,230.7 1,171 1,155 1,150 1,153

- 6%

- 1.1%

Water Heating 421 386 383 382 385

- 9%

- 1.5%

Food Preparation 185 184 184 185 187 1% 0.1%

Miscellaneous 30 31 31 31 31 4% 0.6%

Total 1,867 1,771 1,753 1,748 1,756

- 6%

- 1.0%

Figure 4

- 6 Commercial Natural Gas Baseline Forecast by End Use

Table 4

- 8 shows the commercial baseline gas forecast at the technology level.

Table 4

- 8 Commercial Baseline Electricity Forecast by End Use and Technology (GWh)

End Use Technology 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Heating Furnace 231 229 228 227 229

- 1.1%

- 0.2%

Boiler 834 814 798 769 766

- 8.1%

- 1.4%

Other Heating 166 163 162 159 158

- 4.4%

- 0.8%

Water Heating Water Heater 421 401 391 383 385

- 8.6%

- 1.5%

Food Preparation Oven 22 22 22 22 23 1.0% 0.2%

Fryer 33 32 32 32 33 1.1% 0.2%

Broiler 33 32 33 33 33 2.6% 0.4%

Griddle 33 32 32 33 33 2.0% 0.3%

Range 33 32 32 33 33 2.5% 0.4%

Steamer 33 32 32 31 31

- 4 .8%

- 0.8%

Miscellaneous Pool Heater 2 2 2 2 2 0.2% 0.0%

Miscellaneous 28 28 28 28 29 3.9% 0.6%

Total 1,867 1,821 1,794 1,753 1,756

- 6.0%

- 1.0%Baseline Forecast

EnerNOC Utility Solutions Consulting 4

- 9 

## Industrial Sector

Table 4

- 9 and Figure 4

- 7 present the electricity baseline forecast at the end

- use level for the

industrial sector as a whole. Overall, industrial annual electricity use decreases from 8,429 GWh in 2010 to 7,732 GWh in 2016, an overall decrease of 8%, or 1.4% per year. This is largely

driven b y the economy and sluggish projections for recovery

.

Table 4

- 9 Industrial Electricity Consumption by End Use (GWh)

End Use 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Cooling 577 565 561 566 551

- 4%

- 0.7%

Heating 337 340 340 345 338 0% 0.1%

Ventilation 387 372 365 364 352

- 9%

- 1.6%

Interior Lighting 848 595 559 549 530

- 38%

- 7.8%

Exterior Lighting 80 61 58 58 5 6

- 30%

- 5.8%

Motors 3,778 3,536 3,588 3,626 3,531

- 7%

- 1.1%

Process 2,079 1,978 1,999 2,026 1,967

- 5%

- 0.9%

Miscellaneous 343 377 387 402 407 18% 2.8%

Total 8,429 7,822 7,858 7,937 7,732

- 8%

- 1.4%

Figure 4

- 7 Industrial Electricity Baseline Forecast by End Use

Table 4

- 10 and Figure 4

- 8 show a similar story for the industrial natural gas baseline forecast.

The ove rall decrease from 2010 to 2016 is 3%, with an average decline of 0.5% per year.Baseline Forecast

4

- 10 www.enernoc.com Table 4

- 10 Industrial Natural Gas Co nsumption by End Use (million therms)

End Use 2010 2013 2014 2015 2016 %
Change Avg.

Growth

Rate

Heating 109,437 111,708 110,834 112,106 108,831

- 1%

- 0.1%

Process 358,634 348,987 347,894 346,215 343,679

- 4%

- 0.7%

Miscellaneous 28,576 28,391 28,437 28,61 5 28,388

- 1%

- 0.1%

Total 496,647 489,086 487,165 486,936 480,898

- 3%

- 0.5%

Figure 4

- 8 Industrial Natural Gas Baseline Forecast by End Use



## Baseline Forecast Summary

Table 4

- 11 and Figure 4

- 9 provide a summary of the baseline forecast for electricity by sector for

the State of New Jersey. Overall, the forecast shows a slight to moderate decline in electricity

use , du e to a challenging macroeconomic environment and codes and standards.

Table 4

- 11 Electricity Baseline Forecast Summar y (GWh)

Sector 2010 2013 2014 2015 2016 % Change Avg.

Growth

Rate

Residential 30,307 30,442 29,793 29,515 29,502

- 2.7%

- 0.4%

Commercial 40,123 36,511 35,964 35,699 35,797

- 10.8%

- 1.9%

Industrial 8,429 7,822 7,858 7,937 7,732

- 8.3%

- 1.4%

Total 78,859 74 ,776 73,615 73,151 73,031

- 7.4%

- 1.3%Baseline Forecast

EnerNOC Utility Solutions Consulting 4

- 11 Figure 4

- 9 Electricity Baseline Forecast Summary (GWh)

Table 4

- 12 and Figure 4

- 10 provide a summary of the natural gas baseline forecast by sector for New Jersey. Overall, the forecast is increasing slightly across all sectors.

Table 4

- 12 Natural Gas Baseline Forecast Summary (million therms )

Sector 2010 2013 2014 2015 2016 % Change Avg.

Growth

Rate

Residential 2,255 2,300 2,319 2,333 2,352 4.3% 0.7%

Commercial 1,867 1,771 1,753 1,748 1,756

- 6.0%

- 1.0%

Industrial 497 489 487 487 481

- 3.2%

- 0.5%

Total 4,619 4,560 4,559 4,568 4,589

- 0.7%

- 0.1%

Figure 4

- 10 Natural Gas Baseline Forecast Summary (million ther ms )EnerNOC Utility Solutions Consulting 5

- 1



### CHAPTER 5



## ENERGY EFFICIENCY ME ASURES

List of Energy Efficiency Measures

The first step of the energy efficiency measure analysis is to identify the list of all relevant energy efficiency measures that should be considered for the New Jersey potentia l assessment.

For this study, EnerNOC prepared a preliminary list of measures for BPU and CEEEP staff to review. After incorporating feedback, we populated the full databases for the three sectors.

Sources for the measure assumptions were drawn from past New Jersey program experience,
EnerNOC’s building simulation tool ( BEST ), EnerNOC’s measure database (DEEM), DEER, NWPCC workbooks, other secondary sources, and other data from EnerNOC’s previous studies and program work.

 Residential Measures . The residen tial measures span all end uses and vary significantly in the manner in which they impact energy consumption. Table 5

- 1 shows the residential equipment measure options and the segments for which they were modeled. Table 5

- 2

shows the residential non

- equipment measure options. All residential measures considered for this study are described in Appendix B.

 Commercial Measures

. Table 5

- 3 and Table 5

- 4 present a summary of the commercial equipment and non

- equipment measures, respectively. The measures shown were modeled for nearly all of the commercial building types, both new and existing, with on ly a few exceptions. For instance, hotel guestroom controls were only modeled for the lodging sector. All commercial measures considered for this study are described in Appendix C.

 Industrial Measures

. Table 5

- 5 and Table 5

- 6 present a summary of the industrial equipment and non

- equipment measures, respectively. All industrial measures considered for this study are described in Appendix D

.Energy Efficiency Measures

5

- 2 www.enernoc.com Table 5

- 1 Summary of Residential Equipment MeasuresEnergy Efficiency Measures

EnerNOC Utility Solutions Consulting 5

- 3 Table 5

- 1 Summary of Residen tial Equipment Measures (cont.)Energy Efficiency Measures

5

- 4 www.enernoc.com Table 5

- 1 Summary of Residential Equipment Measures (cont.)Energy Efficiency Measures

EnerNOC Utility Solutions Consulting 5

- 5 Table 5

- 2 Summary of Residential Non

- Equipment MeasuresEnergy Efficiency Measures

5

- 6 www.enernoc.com Table 5

- 3 Summary of Commercial Equipment MeasuresEnergy Efficiency Measures

EnerNOC Utility Solutions Consulting 5

- 7 Table 5

- 3 Summary of Commercial Equipment Measures (continued)Energ y Efficiency Measures

5

- 8 www.enernoc.com Table 5

- 3 Summary of Commercial Equipment Measures (continued)Energy Efficiency Measures

EnerNOC Utility Solutions Consulting 5

- 9 Table 5

- 3 Summary of Commercial Equipment Measures (continued)Energy Efficiency Measures

5

- 10 www.enernoc.com Table 5

- 4 Summary of Commerci al Non

- Equipment MeasuresEnergy Efficiency Measures

EnerNOC Utility Solutions Consulting 5

- 11 Table 5

- 4 Summary of Commerci al Non

- Equipment Measures (continued)Energy Efficiency Measures

5

- 12 www.enernoc.com Table 5

- 5 Summary of Industrial Equipment MeasuresEnergy Efficiency Measures

EnerNOC Utility Solutions Consulting 5

- 13 Table 5

- 5 Summary of Industrial Equipment Measures (continued)Energy Efficiency Measures

5

- 14 www.enernoc.com Table 5

- 5 Summary of Industrial Equipment Measures (continued)Energy Efficiency Measures

EnerNOC Utility Solutions Consulting 5

- 15 Table 5

- 6 Summary of Industrial Non

- Equipment MeasuresEnergy Efficiency Measures

5

- 16 www.enernoc.com Table 5

- 6 Summary of Industrial Non

- Equipment Measures (continued)Energy Efficiency Measures

EnerNOC Utility Solutions Consulting 5

- 17 

## Results of the Economic Screen

Table 5

- 7 summarizes the number of equipment and non

- equipment measures evaluated for each segment within each sector.

Table 5

- 7 Number of Measures Evaluated

Residential Commercial Industrial Total Number

of Measures

Equipment Measures Evaluated 165 179 125 469

Non

- Equipment Measures Evaluated 4 9 94 87 229

Total Measures Evaluated 21 4 273 212 69 9

Appendix B gives results for the economic screening process by segment, vintage, end use and measure for the residential sector. Appendi ces C and D shows the equivalent information for the

commercial a nd industrial sectors , respectively

.EnerNOC Utility Solutions Consulting 6

- 1



### CHAPTER 6



## OVERALL ENERGY EFFICIENCY PO TENTIAL

This chapter presents the overall results of the energy

- efficiency analysis for the State of New Jersey

. Key findings related to potentials are summarized below.

 Achieva ble Low potential forms a lower point on the range of achievable potential

. Across all sectors, this metric is 5, 678 BTU in 2013 and increases to 29,925 BTU by 2016 . This
represents 0.4 % of the baseline forecast in 201 3 and 2. 3 % in 2016

.

 Achievable High p otential forms the upper bound on the range of achievable potential

. It

is 11, 799 BTU in 201 3 , which represents 0.9 % of the baseline forecast. By 2016 , the cumulative savings are 5 7,144 BTU

, 4.4 % of the baseline forecast, for an annual average of just over 1% per year.

 Economic potential

, which reflects the savings when all cost

- effective measures are taken,

is 45,966 BTU in 2013 . This represents 3.5 % of the baseline energy forecast. By 20 16

, economic potential reaches 1 27,588 BTU

, 9.9 % of the baseline ene rgy forecast

.

 Technical potential

, which reflects the adoption of all energy efficiency measures regardless of cost

- effectiveness, is a theoretical upper bound on savings. In 2013 , energy savings are 6 2,293 BTU , or 4.8 % of the baseline energy forecast. By 2016 , technical potential reaches 1 77,410 BTU

, 13.7 % of the baseline energy forecast

.

Table 6

- 1 and Figure 6

- 1 summarize the energy

- efficiency savings for the different levels of potential relative to t he baseline forecast. Figure 6

- 2 displays the energy

- efficiency forecasts. T o combine t he electric and natural gas energy efficiency potential s, kWhs and therms are both converted to a common unit, BTUs, in order to facilitate com parison. Table 9

- 1 in the fuel switching analysis chapter shows the conversion factors.

Because of the fuel switching analysis, we observe some interesting trends in the data

. The technical potential case causes significant migrat ion toward the electric technologies of geothermal heat pumps and heat pump water heaters

. Due to their high capital costs and low natural gas prices , however, the economic potential case saw significant migration in the opposite direction

- from electric s pace and water heating to high efficiency natural gas units

. When viewed from the limited perspective of the residential electric analysis, for instance, this caused the technical potential case to use more energy than the economic case

. When viewed

from t he total BTU content, however, this makes perfect sense

. Because the geothermal heat pump is the most efficient heating technology in all cases, many units were switched and were added for technical potential, so the electric load actually grew while the t otal system was becoming more efficient

. Meanwhile, the natural gas load dropped off considerably because of a corresponding l oss of units

. In the economic scenario, however, the tables were turned

. The most efficient economic unit is the natural gas furna ce; so many electric units were switching to natural gas

. The number of consumers that actually adopted these measures, due to their complexity and added switching costs , is so minimal that effects to the achievable potential cases

are virtually negligible at this time

. Chapter 9 contains more details on fuel switching

.Overall Energy Efficiency Potential

6

- 2 www.enernoc.com Table 6

- 1 Summary of Combined Electric and Natural Gas Energy Efficiency Potential

2013 2014 2015 2016

Baseline Forecast (million BTU) 1,308,205 1,294,830 1,290,496 1,291,174

Cumulative Savings (million BTU)

Achievable Low Potential 5,678 14,223 22,181 29,925

Achievable High Potential 11,799 28,6 80 43,780 57,144

Economic Potential 45,966 82,457 110,745 127,588

Technical Potential 62,293 109,714 148,625 177,410

Energy Savings (% of Baseline)

Achievable Low Potential 0.4% 1.1% 1.7% 2.3%

Achievable High Potential 0.9% 2.2% 3.4% 4.4%

Economi c Potential 3.5% 6.4% 8.6% 9.9%

Technical Potential 4.8% 8.5% 11.5% 13.7%

Figure 6

- 1 Summary of Combined Electric and Natural Gas Energy SavingsOverall Energy Efficiency Potential

EnerNOC Utility Solutions Consulting 6

- 3 Figure 6

- 2 Combined Electric and Natural Gas Potential Forecasts ( million BTU

)



## Electric Energy Efficiency

– Overall Results

Table 6

- 2 and Figure 6

- 3 summarize the electric energy

- efficiency savings for the different levels of potential relative to the baseline forecast. Figure 6

- 4 displays the electric energy

- efficiency

forecasts.

Table 6

- 2 Summary of Electric Energy Efficiency Potential

2013 2014 2015 2016

Baseline Forecast (GWh) 74,776 73,615 73,151 73,031

Cumulative Sa vings (GWh)

Achievable Low Potential 446 1,125 1,718 2,255

Achievable High Potential 918 2,251 3,368 4,277

Economic Potential 3,418 6,255 8,316 9,369

Technical Potential 3,695 6,638 8,779 9,870

Energy Savings (% of Baseline)

Achievable Low Po tential 0.6% 1.5% 2.3% 3.1%

Achievable High Potential 1.2% 3.1% 4.6% 5.9%

Economic Potential 4.6% 8.5% 11.4% 12.8%

Technical Potential 4.9% 9.0% 12.0% 13.5%Overall Energy Efficiency Potential

6

- 4 www.enernoc.com Figure 6

- 3 Summary of Electric Energy Savings

Figure 6

- 4 Electric Potentials Forecasts ( GWh )Overall Energy Efficiency Potential

EnerNOC Utility Solutions Consulting 6

- 5 

## Natural Gas Energy Efficiency

– Overall Results

Table 6

- 3 and Figure 6

- 5 summarize the natural gas energy

- efficiency savings for the different levels of potential relative to the baseline forecast. Figure 6

- 6 displays the natural gas energy

efficiency forecasts.

Table 6

- 3 Summary of Natural Gas Energy Efficiency Potential

2013 2014 2015 2016

Baseline Forecast (1,000 therms) 4,560,186 4,558,750 4,568,218 4,588,711

Cumulative Savings (1,000 therms)

Achievable Low Potential 5,906 14,004 26,066 42,208

Achievable High Potential 13,420 30,291 54,007 83,980

Economic Potential 70,161 111,704 159,709 208,193

Technica l Potential 201,774 340,611 485,779 649,293

Energy Savings (% of Baseline)

Achievable Low Potential 0.1% 0.3% 0.6% 0.9%

Achievable High Potential 0.3% 0.7% 1.2% 1.8%

Economic Potential 1.5% 2.5% 3.5% 4.5%

Technical Potential 4.4% 7.5% 10.6% 14.1%

Figure 6

- 5 Summary of Natural Gas Energy SavingsOverall Energy Efficiency Potential

6

- 6 www.enernoc.com Figure 6

- 6 Natural Gas Pote ntial Forecasts (1000 therms)



## Overview of Energy Efficiency Potential by Sector and Fuel

Table 6

- 4

, Figure 6

- 7 , and Figure 6

- 8 summarize the range of electric achievable potent ial b y
sector. The commercial sector accounts for the largest portion of the savings, followed by residential. The industrial sector contributes a relatively small amount of potential.

Table 6

- 4 Electric Achievable Potential by Sector (GWh)

2013 2014 2015 2016

Achievable Low Savings (GWh)

Residential 173 493 751 926

Commercial 251 585 889 1,217

Industrial 22 47 78 11 3

Total 446 1,125 1,718 2,255

Achievable High Savings (GWh)

Residential 352 994 1,488 1,769

Commercial 519 1,164 1,729 2,296

Industrial 46 93 150 212

Total 918 2,251 3,368 4,277Overall Energy Efficiency Potential

EnerNOC Utility Solutions Consulting 6

- 7 Figure 6

- 7 Achievable Low Electric Potential by Sector (GWh)

Figure 6

- 8 Achievable High Electric Potential by Sector (GWh)Overall Energy Efficiency Potential

6

- 8 www.enernoc.com Table 6

- 5

, Figure 6

- 9 and Figure 6

- 10 present the range of natural gas a chievable potential by sector. Again, like the electric analysis, t he commercial sector accounts for the largest portion of

the savings, followed by the residential and then the industrial sectors

.

Table 6

- 5 Natural Gas Achievable Potential by Se ctor (million therms)

2013 2014 2015 2016

Achievable Low Savings (million therms)

Residential 0.6 2.3 4.8 6.9

Commercial 5.2 11.4 20.6 34.3

Industrial 0.1 0.3 0.6 1.0

Total 5.9 14.0 26.1 42.2

Achievable High Savings (million therms)

Residenti al 1.1 4.6 9.3 12.8

Commercial 12.0 25.1 43.5 69.2

Industrial 0.3 0.7 1.3 2.0

Total 13.4 30.3 54.0 84.0

Figure 6

- 9 Achievable Low Natural Gas Potential by Sect or (mmTherms)Overall Energy Efficiency Potential

EnerNOC Utility Solutions Consulting 6

- 9 Figure 6

- 10 Achievable High Natural Gas Potential by Sector (mmTherms)

Year by year savings for electric energy, electric peak demand, and natural gas energy are available in Appendix F. Details for each sector are presented in Chapter 7.EnerNOC Utility Solutions Consulting 7

- 1



### CHAPTER 7



## ENERGY EFFICIENCY PO TENTIAL BY SECTOR

This chapter presents the results of the energy efficiency analysis at the sector level . First, the

residential pot ential is presented, followed by the commercial, and lastly, industrial. Within each sector, electric results are presented first and natural gas results second

.


## Residential Electricity Potential

Table 7

- 1 presents estimates for the four types of potential for the residential electricity sector

. Figur e 7

- 1 depicts the se potential energy savings estimates graphically.

 Achievable Low potential projects 173 GWh of energy savings in 2013

, 0.6 % of the

baselin e forecast. This increases to 9 26 GWh, 3.1 % of the baseline forecast, in 2016.

 Achievable High potential i s 35 2 GWh in 2013 , which represents 1.2 % of the baseline forecast. By 2016 , the cumulative energy savings are 1, 769 GWh, 6. 0 % of the baseline

forecast

.

 Economic potential, which refle cts a theoretical limit to savings whe n all cost

- effective measures are taken, is 1,3 29 GWh in 2013 . This represents 4.4 % of the baseline energy forecast. By 2016 , economic potential reaches 4, 234 GWh, 14. 4 % o f the baseline energy forecast.

 Technical potential

, which reflects the adoption of all energy efficiency measures regardless of cost, is a theoretical upper bound on savings. In 2013 , energy savings are 1, 271 GWh, or 4. 2 % of the baseline energy forecast. By 2016 , techn ical potential reaches 3,809 GWh, 1 2.9 % o f the baseline energy forecast.

The reason that electric technical potential savings are less than economic potential savings has to do with the fuel switching analysis that was applied to the residential sector. As discussed in the previous section, c ertain electric technologies were the most technically efficient option, while their corresponding natural gas technologies were the most economically efficient option.

This caused customers to switch en masse to elec tric units in the technical potential case, while

customers switched en mass to natural gas units in the economic potential case. When considering electric and natural gas on a total energy basis, the technical potential uses less energy than the economic potential, as one would expect. This is explained in more detail in Chapter 9. It should be noted that the effects of the fuel switching units are minimal in the achievable potential cases, as the adoption rates are so low that they put the number of

state wide transactions in the hundreds and low thousands.Energy Efficiency Potential By Sector

7

- 2 www.enernoc.com Table 7

- 1 Electricity Energy Efficiency Potential for the Reside ntial Sector

2013 2014 2015 2016

Baseline Forecast (GWh) 30,442 29,793 29,515 29,502

Energy Savings (GWh)

Achievable Low Potential 173 493 751 926

Achievable High Potential 352 994 1,488 1,769

Economic Potential 1,329 2,935 3,987 4,234

Technical Potential 1,271 2,831 3,759 3,809

Energy Savings (% of Baseline)

Achievable Low Potential 0.6% 1.7% 2.5% 3.1%

Achievable High Potential 1.2% 3.3% 5.0% 6.0%

Economic Potential 4.4% 9.9% 13.5% 14.4%

Technical Potential 4.2% 9.5% 12.7% 12.9%

Figur e 7

- 1 Residential Electric Energy Efficiency Potential Savings



### Residential Electric Potential by Market Segment

Single

- family homes in New Jersey account for the majo rity of this sector’s total sales in the base year and throughout the forecast. Similarly, single

- family homes account for the largest share of potential savings by segment, as displayed in Table 7

- 2 , which shows results for 2016

.

Tab le 7

- 3 shows the Achievable Low savings by end use and market segment in 2016 . The segments are similar in terms of the distribution of savings opportunities by end use, but there are a few notable differences. Single

- family homes have more exterior lighting and so have more savings potential for this end use. Similarly, single

- family homes are more likely to have swimming pools and therefore have more potential for savings in pool pumps (captured in the miscellaneous end use) . Multi

- family homes have a relatively larger opportunity in home electronics and air conditioning compared to single

- family homes, reflecting an older appliance

stock.Energy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 3 Table 7

- 2 Residential Electric Potential by Market Segment, 2016

Single
Family Single

Family LI Multi

Family

Rent Multi

Family

Rent LI Multi

Family

Own Multi

Family

Own LI

Baseline Forecast (GWh) 17,555 4,391 3,665 2,351 1,355 185

Energy Savings (GWh)

Achievable Low Potential 531 159 119 76 36 6

Achievable High Potential 1,013 304 227 147 68 11

Economic Potential 2,395 678 601 375 170 17

Technical Potential 2,187 598 552 305 152 16

Energy Sav ings as % of Baseline

Achievable Low Potential 3.0% 3.6% 3.2% 3.3% 2.6% 3.0%

Achievable High Potential 5.8% 6.9% 6.2% 6.2% 5.0% 5.7%

Economic Potential 13.6% 15.4% 16.4% 15.9% 12.5% 9.0%

Technical Potential 12.5% 13.6% 15.1% 13.0% 11.2% 8.7%

Tab le 7

- 3 Residential Electric Achievable Low Potential by End Use and Market Segment, 2016 (GWh)

End Use Single
Family Si ngle

Family LI Multi

Family Rent Multi

Family Rent

LI Multi

Family Own Multi

Family Own

LI

Cooling 39.8 10.2 10.7 8.5 3.5 0.3

Heating 3.1 0.9 1.1 0.2 0.3 0.0

Water Heating 3.9 0.3 0.9 1.5 0.3 0.0

Interior Lighting 353.5 113.5 81.8 49.7 21.6 3.7

Exteri or Lighting 41.9 13.9 10.3 8.6 3.8 0.6

Appliances 0.8 0.2 0.1 0.1 0.1 0.0

Electronics 84.5 19.7 13.7 8.0 6.0 0.8

Miscellaneous 3.3 0.3 0.0

- 0.0

-

Total 530.9 159.0 118.7 76.5 35.5 5.5



### Residential Electric Potential by End Use

Table 7

- 4 provides estimates of savings for each end use and type of potential. The most significant savings opportunities come from the lighting end use. Negative savings in space heating and water heating for the technical potential case reflects the a ddition of geothermal heat pump and heat pump water heating units. This is due to the fuel switching analysis, as discussed in more detail in Chapter 9.

Table 7

- 4 Residential Electric Savings by End Use and Potential Type (GWh)

End Use Case 2013 2014 2015 2016

Cooling Achievable Low Potential 19.7 38.1 55.4 73.1Energy Efficiency Potential By Sector

7

- 4 www.enernoc.com

Achievable High Potential 38.1 71.1 100.4 129.2

Econom ic Potential 132.5 192.4 254.7 311.5

Technical Potential 245.6 376.4 511.1 651.7

Heating Achievable Low Potential 0.8 1.9 3.4 5.6

Achievable High Potential 1.5 3.6 6.3 10.0

Economic Potential 93.6 181.0 264.2 344.6

Technical Potential

- 315.6

- 630. 5

- 940.5

- 1,280.9

Water Heating Achievable Low Potential 1.0 2.5 4.4 7.0

Achievable High Potential 2.2 5.1 8.6 13.2

Economic Potential 69.2 131.7 186.2 251.6

Technical Potential 75.8 140.9 198.0 266.9

Interior Lighting Achievable Low Potential 105. 8 342.9 523.0 623.7

Achievable High Potential 216.4 691.6 1,040.9 1,201.0

Economic Potential 728.5 1,873.6 2,521.5 2,442.2

Technical Potential 810.4 2,103.8 2,840.4 2,797.4

Exterior Lighting Achievable Low Potential 13.8 46.1 69.6 79.1

Achievable High Potential 32.8 106.2 157.2 170.0

Economic Potential 84.5 210.1 276.0 253.4

Technical Potential 105.9 264.4 347.5 330.2

Appliances Achievable Low Potential 18.7 29.2 34.0 36.2

Achievable High Potential 36.4 54.5 61.3 63.2

Economic Potential 95 .9 97.2 102.0 104.9

Technical Potential 166.5 228.1 288.4 350.1

Electronics Achievable Potential Low 12.6 32.0 59.3 97.8

Achievable Potential High 24.2 60.2 108.9 175.3

Economic Potential 120.8 241.1 363.0 496.4

Technical Potential 124.1 245.5 368 .9 504.4

Miscellaneous Achievable Low Potential 0.2 0.6 1.9 3.5

Achievable High Potential 0.6 1.4 4.3 7.6

Economic Potential 4.4 8.5 19.2 29.4

Technical Potential 46.9 89.1 128.8 169.2

Total Achievable Low Potential 172.6 493.2 751.0 926.1

Achiev able High Potential 352.1 993.6 1,487.9 1,769.5

Economic Potential 1,329.4 2,935.5 3,986.9 4,234.0

Technical Potential 1,259.5 2,817.7 3,742.6 3,789.0

Figure 7

- 2 focuses on the residential a chievable potential in 2016 . Light ing equipment replacement accounts for the highest portion of the savings in the near term as a result of the efficiency gap between CFL lamps and advanced incandescent lamps , even those that will meet

the EISA 2007 standard. Electronics, cooling, and appl iances also contribute significantly to the savings. Detailed measure information is available in A ppendix B

. The key measures comprising the potential are listed below:Energy Efficienc y Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 5  Lighting: mostly CFL lamps and specialty bulbs

 Electronics (reduce standby wattage, t elevisions, set top boxes, PCs)

 Second refrigerator/ freezer removal

 HVAC: Removal of second room AC unit, efficient air conditioners, ducting repair/sealing, insulation, home energy management system and programmable thermostats

 Behavioral feedback tools

Figure 7

- 2 Residential Electric Achievable Low Potential by End Use in 2016



## Residential Natural Gas Potential

Table 7

- 5 presents estimat es for the four types of potential for natural gas usage in the

residential sector

. Figure 7

- 3 depicts the se potential energy savings estimates graphically.

 Achievable Low potential projects 0.6 million therms (mmTherms) of energ y savings in

2013

, 0.0 % of the baseline forecast. This increases to 6.9 mmTherms

, 0.3 % of the baseline forecast, in 2016.

 Achievable High potential i s 1.1 mmTherms in 2013 , which represents 0.0 % of the baseline forecast. By 2016 , the cumulative energy savi ngs are 12.8 mmTherms

, 0.5 % of the

baseline forecast.

 Economic potential, which refle cts a theoretical limit to savings whe n all cost

- effective measures are taken, is 1.7 mmTherms in 2013 . This represents 0.1 % of the baseline energy forecast. By 2016 , econ omic potential reaches 28.2 mmTherms

, 1.2 % o f the baseline energy

forecast.

 Technical potential

, which reflects the adoption of all energy efficiency measures regardless of cost

, is a theoretical upper bound on savings. In 2013 , energy savings are 1 05

mmTh erms , or 4. 6 % of the baseline energy forecast. By 2016 , technical potential reaches

394 .1 mmTherms

, 16. 8 % o f the baseline energy forecast.Energy Efficiency Potential By Sector

7

- 6 www.enernoc.com The reason that natural gas technical potential savings are so high relative to economic potential savings has to d o with the fuel switching analysis that was applied to the residential sector. Certain electric technologies were the most technically efficient option, while their corresponding natural gas technologies were the most economically efficient option. This ca used customers to switch en masse to electric units in the technical potential case, while customers switched en

mass to natural gas units in the economic potential case. When considering electric and natural gas on a total energy basis, the technical pote ntial uses less energy than the economic potential, as one would expect. This is explained in more detail in Chapter 9.

Table 7

- 5 Natural Gas Energy Efficiency Potential for the Residential Sector

2013 2014 2015 2016

Energy Forecasts (million therms) 2,299.6 2,318.6 2,332.9 2,351.6

Cumulative Energy Savings (million therms)

Achievable Low Potential 0.6 2.3 4.8 6. 9

Achievable High Potential 1.1 4.6 9.3 12.8

Economic Potential 1.7 12.7 24.0 28.2

Technical Potential 105.3 200.2 293.3 394.1

Energy Savings (% of Baseline Forecast)

Achievable Low Potential 0.0% 0.1% 0.2% 0.3%

Achievable High Potential 0.0% 0.2 % 0.4% 0.5%

Economic Potential 0.1% 0.5% 1.0% 1.2%

Technical Potential 4.6% 8.6% 12.6% 16.8%

Figure 7

- 3 Residential Natural Gas Potential Savings



### Residential Natu ral Gas Potential by Market Segment

Single

- family homes in New Jersey account for the majority of this sector’s total sales in the base year and throughout the forecast. Similarly, single

- family homes account for the largest share of potential savings by s egment, as displayed in Table 7

- 6 , which shows results for 2016

. The fuel switching analysis causes an influx of natural gas water heaters in the economic potential, whichEnergy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 7 actually increases the load so much in the Single Family L ow Income sector that it pushes the savings negative. It should be noted that the effects of the fuel switching units are minimal in the achievable potential cases, as the adoption rates are so low that they put the number of

statewide transactions in the hundreds and low thousands.

Table 7

- 7 shows the savings by end use and market segment in 2016

. Heating and water heating comprise the lion share of savings.

Table 7

- 6 Residential Natural Gas Potential by Market Segment, 2016 (thousand therms)

Single
Family Single

Family LI Multi

Family

Rent Multi

Family

Rent LI Multi

Family

Own Multi

Family

Own LI

Baseline Forecast (thousand therms) 1,334,761 358,074 324,636 218,574 96,860 18,739

Energy Savings (thousand therms)

Achievable Low Potential 4,185 1,116 830 534 216 40

Achievable High Potential 7,756 2,073 1,543 991 392 73

Economic Pote ntial 11,984

- 1,522 9,628 3,379 3,218 1,499

Technical Potential 232,232 60,617 49,533 35,027 13,268 3,439

Energy Savings as % of Baseline

Achievable Low Potential 0.3% 0.3% 0.3% 0.2% 0.2% 0.2%

Achievable High Potential 0.6% 0.6% 0.5% 0.5% 0.4% 0. 4%

Economic Potential 0.9%

- 0.4% 3.0% 1.5% 3.3% 8.0%

Technical Potential 17.4% 16.9% 15.3% 16.0% 13.7% 18.4%

Table 7

- 7 Residential Achievable Potential Low by End Use and Market Segment, 2016 (thousand therms)

End Use Single

Family Single

Family LI Multi

Family Rent Multi

Family Rent

LI Multi

Family Own Multi

Family Own

LI

Heating 2,763 664 595 409 126 23

Appliances 1 ,368 447 236 126 90 16

Miscellaneous 0 0 0 0 0 0

Water Heating 54 5 0 0 0 0

Total 4,185 1,116 830 534 216 40



### Residential Natural Gas Potential by End Use

Table 7

- 8 provides estimates of savings for each end use and type of po tential. Negative savings in water heating for the economic potential case reflects the conversion of electric and fuel oil water heaters to high efficiency condensing natural gas fired units. This is due to the fuel switching analysis, as discussed in mor e detail in Chapter 9.

Table 7

- 8 Residential Natural Gas Savings by End Use and Potential Type (million therms)

End Use Case 2013 2014 2015 2016

Heating Achievable Low Potential 0.4 1.4 2.9 4.6

Achievable High Potential 0.8 2.6 5.4 8.3

Economic Potential 5.5 15.1 25.8 33.4

Technical Potential 97.0 186.0 274.2 369.2Energy Efficiency Potential By Sector

7

- 8 www.enernoc.com Appliances Achievable Low Potential 0.1 0.9 1.8 2. 3

Achievable High Potential 0.3 1.9 3.7 4.4

Economic Potential

- 3.8

- 2.6

- 2.1

- 5.7

Technical Potential 6.3 10.6 14.0 18.2

Miscellaneous Achievable Low Potential 0.0 0.0 0.0 0.0

Achievable High Potential 0.0 0.0 0.0 0.0

Economic Potential 0.0 0.0 0.0 0.0

Technical Potential 0.6 1.3 1.9 2.5

Water Heating Achievable Low Potential 0.0 0.0 0.0 0.1

Achievable High Potential 0.0 0.0 0.1 0.1

Economic Potential 0.0 0.2 0.3 0.5

Technical Potential 0.8 1.5 2.2 2.8

Total Achievable Low Potential 0. 6 2.3 4.8 6.9

Achievable High Potential 1.1 4.6 9.3 12.8

Economic Potential 1.7 12.7 24.0 28.2

Technical Potential 104.7 199.4 292.2 392.7

Figure 7

- 4 focuses on the range of residential achievable potential in 2016

. As exp ected, space heating and water heating savings are the largest opportunities. Detailed measure information is

available in A ppendix B

. The key measures comprising the potential are listed below:

 Efficient furnaces & boilers, boiler hot water reset ,ductin g repair/sealing, insulation, home energy management system & programmable thermostats

 Efficient water heaters, low

- flow showerheads, faucet aerators, and water heater tank

blankets

 Behavioral feedback tools.Energy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 9 Figure 7

- 4 Residential Natural Gas Achievable Low Potential by End Use in 2016



## Commercial Electricity Potential

The baseline forecast for the commercial sector only grows slightly, which reflects the sluggish

ne ar

- term economy and forthcoming codes and standards. Nevertheless, the opportunity for

energy

- efficiency savings is still significant for the commercial sector. Table 7

- 9 present s estimates for the four types of potential for the residential electricity sector. Figure 7

- 5 depicts

the se potential energy savings estimates graphically.

 Achievable Low potential projects 25 1 GWh of energy savings in 2013

, 0.7 % of the

baseline forecast. This increases to 1,2 17 G Wh, 3.4 % of the baseline forecast, in 2016.

 Achievable High potential i s 519 GWh in 2013 , which represents 1.4 % of the baseline forecast. By 2016 , the cumulative energy savings are 2, 296 GWh, 6.4 % of the baseline

forecast.

 Economic potential, which refle ct s a theoretical limit to savings whe n all cost

- effective measures are taken, is 1,8 83 GWh in 2013 . This represents 5.2 % of the baseline energy forecast. By 2016 , economic potential reaches 4,6 70 GWh, 13. 0 % o f the baseline energy

forecast.

 Technical potenti al

, which reflects the adoption of all energy efficiency measures regardless of cost, is a theoretical upper bound on savings. In 2013 , energy savings are 2, 215 GWh, or 6. 1 % of the baseline energy forecast. By 2016 , technical potential reaches 5,5 4 1 GWh, 1 5. 5 % o f the baseline energy forecast.Energy Efficiency Potential By Sector

7

- 10 www.enernoc.com Table 7

- 9 Electricity Efficiency Potential for the Commercial Sector

2013 2014 2015 2016

Baseline Forecast (GWh) 36,511 35,964 35,699 35,797

Savings (GWh)

Achievable Low Potential 251 585 889 1,217

Achievable High Potential 519 1,164 1,729 2,296

Economic Potential 1,883 3,030 3,943 4,670

Technical Potential 

### 2,215 3,500 4,5 98 5,541

Savings (% of Baseline)

Achievable Low Potential 0.7% 1.6% 2.5% 3.4%

Achievable High Potential 1.4% 3.2% 4.8% 6.4%

Economic Potential 5.2% 8.4% 11.0% 13.0%

Technical Potential 6.0% 9.7% 12.9% 15.5%

Figure 7

- 5 Commercial Energy Efficiency Potential SavingsEnergy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 11 

### Commercial Electric Potential by Market Segment

Table 7

- 10 shows potential estimates by building type segment in 2016

. Retail has the largest

achievable

- low savings potential in 2016, followed by grocery, large office, and small office.

Table 7

- 10

. Table 7

- 11 summarizes achievable potential for each segment and end use.

Table 7

- 10 Commercial Electric Potential by Market Segment, 2016

Small

Office Large

Office Restaura

nt Retail Grocer y College

Baseline Forecast 4,094 5,581 1,616 6,811 3,683 2,082

Energy Savings (1,000 MWh)

Achievable Low Potential 130 149 48 263 151 67

Achievable High Potential 249 295 91 489 285 126

Economic Potential 493 612 191 1,026 544 260

Technical Potentia l 590 728 236 1,206 691 299

Energy Savings (% of Baseline)

Achievable Low Potential 3.2% 2.7% 3.0% 3.9% 4.1% 3.2%

Achievable High Potential 6.1% 5.3% 5.6% 7.2% 7.7% 6.0%

Economic Potential 12.0% 11.0% 11.8% 15.1% 14.8% 12.5%

Technical Potential 14.4% 13.0% 14.6% 17.7% 18.8% 14.3%

School Health Lodging Warehouse Misc. Total

Baseline Forecast 1,814 4,126 915 2,197 2,877 35,797

Energy Savings (1,000 MWh)

Achievable Low Potential 72 111 30 97 99 1,217

Achievable High Potential 131 211 56 179 184 2, 296

Economic Potential 252 444 110 355 384 4,670

Technical Potential 304 510 135 389 456 5,541

Energy Savings (% of Baseline)

Achievable Low Potential 3.9% 2.7% 3.3% 4.4% 3.5% 3.4%

Achievable High Potential 7.2% 5.1% 6.1% 8.2% 6.4% 6.4%

Economic Pot ential 13.9% 10.8% 12.1% 16.2% 13.3% 13.0%

Technical Potential 16.7% 12.4% 14.7% 17.7% 15.9% 15.5%Energy Efficiency Potential By Sector

7

- 12 www.enernoc.com Table 7

- 11 Comm

ercial Electric Achievable Low Potential by End Use and Market Segment,

2016 (GWh)

Segment Cooling Space
Heating Ventil

ation Water

Heat Int.

Lighting Ext.

Lighting Food

Prep Refriger

ation Office

Equip Misc Total

Small Office 16.5 3.7 4.6 3.3 50.4 24.8 0.0 0.8 26.1 0.0 130.3

Large Office 16.9 4.0 22.4 3.6 49.2 15.2 0.0 1.9 36.1 0.0 149.3

Restaurant 4.3 0.5 4.3 4.5 11.0 9.4 0.6 11.9 1.3 0.0 47.8

Retail 23.2 9.7 25.4 9.7 132.7 41.1 1.3 10.7 9.0 0.0 262.8

Grocery 9.4 1.9 6.9 3.5 28.2 8.3 1.5 90.4 1.0 0.0 1 50.9

College 8.1 2.1 5.9 3.7 29.6 10.6 0.1 0.7 6.2 0.0 67.0

School 3.4 0.6 1.6 1.9 44.5 12.0 0.3 2.3 5.1 0.0 71.6

Health 7.0 1.1 2.5 2.5 29.4 6.6 0.1 2.4 4.8 0.0 56.5

Lodging 3.2 2.2 1.4 1.8 32.1 7.4 0.1 4.0 0.6 0.0 52.9

Warehouse 8.2 1.6 2.5 2.3 37.1 25.4 0.0 1.5 13.1 0.0 91.9

Miscellaneous 33.3 4.7 13.7 5.1 47.5 23.8 0.2 2.5 4.6 0.0 135.5

Total 133.6 32.3 91.3 41.7 491.7 184.6 4.3 129.1 107.8 0.1 1217



### Commercial Electric Potential by End Use

Table 7

- 12 presents the comme rcial sector savings by end use and potential type. The end uses with the highest techn ical and economic potential are lighting, cooling, ventilation, and refrigeration.

Figure 7

- 6 focus es on achievable potential savings by end us e. Not surprisingly, interior lighting delivers the highest achievable savings throughout the study period

. In 2016, exterior lighting is second, and refrigeration is third. Regarding refrigeration, it is interesting to point out a relatively new control a nd sensing technology that vendors such as “e Cube ” are using to regulate the system energy. The technology consists of a solid, waxy food simulant that is fitted around a thermostat sensor that would otherwise measure air temperature. The refrigeration c ontrols therefore attempt to regulate the temperature of food, which changes more slowly and gradually than air, thereby reducing the frequency of refrigeration on/off cycles. Refrigeration energy savings are then followed in descending order by cooling, v entilation, office equipment, and

small amounts of the other end uses.

Detailed measure information is available in the A ppendix C . The key measures comprising the potential are listed below:

 Lighting

– CFLs, LED lamps, linear fluorescent, daylighting co ntrols, occupancy sensors , and HID lamps for exterior lighting

 Energy management systems & programmable thermostats

 Ventilation

– variable speed control

 Refrigeration

– efficient equip ment , control systems, decommissioning

 Efficient office equip ment

– computers, serversEnergy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 13 Table 7

- 12 Commercial Potential by End Use and Potential Type (GWh)

End Use Case 2013 2014 2015 2 016

Cooling Achievable Low Potential 23 46 82 134
Achievable High Potential 51 100 168 262

Economic Potential 257 342 449 585

Technical Potential 320 444 611 810

Heating Achievable Low Potential 6 11 20 32
Achievable High Potential 14 25 42 66

Economic Potential 76 97 125 161

Technical Potential 91 118 156 204

Ventilation Achievable Low Potential 12 28 53 91
Achievable High Potential 29 65 117 192

Economic Potential 211 354 505 668

Technical Potential 239 392 555 733

Water Heating Achi evable Low Potential 5 14 26 42
Achievable High Potential 12 30 55 87

Economic Potential 65 120 176 234

Technical Potential 80 140 201 266

Interior Lighting Achievable Low Potential 126 295 399 492
Achievable High Potential 244 553 732 875

Econo mic Potential 752 1,278 1,565 1,700

Technical Potential 839 1,396 1,725 1,919

Exterior Lighting Achievable Low Potential 30 79 133 185
Achievable High Potential 59 148 243 324

Economic Potential 156 294 403 465

Technical Potential 167 309 424 494

Refrigeration Achievable Low Potential 32 60 90 126
Achievable High Potential 70 123 176 237

Economic Potential 260 307 360 422

Technical Potential 343 424 513 611

Food Preparation Achievable Low Potential 1 3 5 7
Achievable High Potential 2 6 10 16

Economic Potential 10 20 30 41

Technical Potential 10 20 30 41

Office Equipment Achievable Low Potential 16 49 82 108
Achievable High Potential 38 113 185 236

Economic Potential 96 219 329 393

Technical Potential 125 258 382 461

Miscellaneo us Achievable Low Potential 0 0 0 0
Achievable High Potential 0 0 0 0

Economic Potential 0 1 1 1

Technical Potential 0 1 1 1

Total Achievable Low Potential 251 585 889 1,217
Achievable High Potential 519 1,164 1,729 2,296

Economic Potential 1,88 3 3,030 3,943 4,670

Technical Potential 2,215 3,500 4,59 8 5,54 1Energy Efficiency Potential By Sector

7

- 14 www.enernoc.com Figure 7

- 6 Commercial Achievable Low Potential Electricity Savings by End Use in 2016



## Commercial Natural Gas Potential

Table 7

- 13 and Figure 7

- 7 present the savings associated with each level of potential. The highlights from the potentials are described below.

 Achievable Low potential projects 5 mil lion therms (mmTherms) of energy savings in

2013

, 0.3 % of the baseline forecast. This increases to 34 mmTherms

, 2.0 % of the baseline forecast, in 2016.

 Achievable High potential i s 12 mmTherms in 2013 , which represents 0.7 % of the baseline forecast. By 201 6 , the cumulative energy savings are 69 mmTherms

, 3.9 % of the

baseline forecast.

 Economic potential, which refle cts a theoretical limit to savings whe n all cost

- effective measures are taken, is 67 mmTherms in 2013 . This represents 3. 8 % of the baseline ener gy forecast. By 2016 , economic potential reaches 175 mmTherms

, 9.9 % o f the baseline energy

forecast.

 Technical potential

, which reflects the adoption of all energy efficiency measures regardless of cost

, is a theoretical upper bound on savings. In 2013 , en ergy savings are 87

mmTherms , or 4.9 % of the baseline energy forecast. By 2016 , technical potential reaches

2 26 mmTherms

, 12.9 % o f the baseline energy forecast.Energy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 15 Table 7

- 13 Natural Gas Efficiency Potential for the Commercial Sector

2013 2014 2015 2016

Energy Forecasts (mmTherm) 1,771 1,753 1,748 1,756

Cumulative Energy Savings (mmTherm)

Achievable Low Potential 5 .2 11.4 20.6 34.3

Achievable High Potential 12.0 25.1 43.5 69.2

Economic Potential 66.6 96.3 131.5 174.6

Technical Potential 86.5 125.9 171.2 225.8

Energy Savings (% of Baseline Forecast)

Achievable Low Potential 0.3% 0.7% 1.2% 2.0%

Achievable High Potential 0.7% 1.4% 2.5% 3.9%

Economic Potential 3.8% 5.5% 7.5% 9.9%

Technical Potential 4.9% 7.2% 9.8% 12.9%

Figure 7

- 7 Commercial Natural Gas Po tential Savings



### Commercial Natural Gas Potential by Market Segment

Table 7

- 14 below shows natural gas potential estimates by segment in 2016

. Table 7

- 15

summarizes the achievable potential for each segm ent by end use

.Energy Efficiency Potential By Sector

7

- 16 www.enernoc.com Table 7

- 14 Commercial Natural Gas Potential by Market Segment, 2016

Small Office Large Office Re staurant Retail Grocery College

Baseline Forecast 176,002 137,427 99,587 305,804 57,370 102,268

Energy Savings (thousand therms)

Achievable Low Potential 3,553 2,448 1,557 6,380 1,257 2,234

Achievable High Potential 7,225 4,981 3,259 12,967 2,565 4,443

Economic Potential 18,677 11,930 8,495 33,366 6,406 10,105

Technical Potential 26,360 18,767 9,216 39,950 7,251 13,481

Energy Savings (% of Baseline)

Achievable Low Potential 2.0% 1.8% 1.6% 2.1% 2.2% 2.2%

Achievable High Potential 4.1% 3.6% 3.3% 4.2% 4.5% 4.3%

Economic Potential 10.6% 8.7% 8.5% 10.9% 11.2% 9.9%

Technical Potential 15.0% 13.7% 9.3% 13.1% 12.6% 13.2%

School Health Lodging Warehouse Misc. Total

Baseline Forecast 140,064 285,802 58,486 96,759 296,601 1,756,171

Energy Savings (thousa nd therms)

Achievable Low Potential 1,588 5,425 778 1,804 7,258 34,281

Achievable High Potential 3,256 10,847 1,569 3,674 14,379 69,165

Economic Potential 8,737 26,464 4,118 10,620 35,641 174,559

Technical Potential 16,001 32,263 5,963 13,160 43,427 22 5,838

Energy Savings (% of Baseline)

Achievable Low Potential 1.1% 1.9% 1.3% 1.9% 2.4% 2.0%

Achievable High Potential 2.3% 3.8% 2.7% 3.8% 4.8% 3.9%

Economic Potential 6.2% 9.3% 7.0% 11.0% 12.0% 9.9%

Technical Potential 11.4% 11.3% 10.2% 13.6% 14.6% 12 .9%

Table 7

- 15 Commercial Natural Gas Achievable High Potential by End Use and Market

Segment, 2016 (million therms)

Segment Space Heating Water Heat Food Prep Misc. Total

Small Office 6.4 0.8 0.0 6.2 13.4

Large Office 4.2 0.6 0.2 4.2 9.1

Restaurant 0.7 1.1 1.5 1.4 4.7

Retail 10.6 1.7 0.7 11.0 24.0

Grocery 1.9 0.5 0.1 2.3 4.8

College 3.6 0.8 0.1 3.9 8.3

School 1.9 0.9 0.4 2.2 5.4

Health 6.7 2.8 1.3 8.5 19.3

Lodging 0.4 1.0 0.2 1.1 2.6

Warehouse 3.6 0.1 0.0 3.3 7.0

Misc. 12.8 1.2 0.4 12.9 27.2

Total 52.8 11.4 4.9 56.9 126.1Energy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 17 

### Commercial Natural Gas Potential by End Use

Table 7

- 16 pres ents the commercial sector savings by end use and potential type. The end uses with the highest technical and economic potential are heating, water heating, and food preparation. This study shows no savings available in the miscellaneous end use due to its uncertain composition.

Table 7

- 16 Commercial Natural Gas Potential by End Use and Potential Type (million therms)

End Use Case 2013 2014 2015 2016

Heating Achievable Low Potential 4.0 8.8 15.8 26.3

Achievable High Potential 9.3 19.2 33.1 52.8

Economic Potential 50.7 71.7 97.3 129.5

Technical Potential 68.0 97.6 131.7 173.8

Water Heating Achievable Low Potential 0 .9 1.9 3.5 5.7

Achievable High Potential 1.9 4.1 7.3 11.4

Economic Potential 12.3 17.7 23.9 31.1

Technical Potential 15.0 21.4 29.1 38.1

Food Preparation Achievable Low Potential 0.3 0.7 1.4 2.2

Achievable High Potential 0.7 1.7 3.1 4.9

Economic Potential 3.5 6.9 10.3 13.9

Technical Potential 3.5 6.9 10.3 13.9

Miscellaneous Achievable Low Potential 0.0 0.0 0.0 0.0

Achievable High Potential 0.0 0.0 0.0 0.0

Economic Potential 0.0 0.0 0.0 0.1

Technical Potential 0.0 0.1 0.1 0.1

Total Achie vable Low Potential 5.2 11.4 20.6 34.3

Achievable High Potential 12.0 25.1 43.5 69.2

Economic Potential 66.6 96.3 131.5 174.6

Technical Potential 86.5 125.9 171.2 225.8

Figure 7

- 8 below shows achievable potential savings b y end use. As expected

, heating provides the largest share of the savings, with water heating and food preparation each successively

smaller. Detailed measure information is available in A ppendix C

. The key measures comprising the potential are listed belo w:

 Energy management systems, programmable thermostats, HVAC occupancy sensors

 Efficient boilers, boiler maintenance, steam trap repair and hot water reset

 Efficient water heaters

 Efficient food preparation equipment for the restaurant segment

 Insulation and high efficiency windowsEnergy Efficiency Potential By Sector

7

- 18 www.enernoc.com Figure 7

- 8 Commercial Natural Gas Achievable Low Potential Savings by End Use in 2016



## Industrial Electricity Potential

The industrial se ctor in New Jersey only accounts for 11% of total energy consumption, but there are prime efficiency opportunities nonetheless. Table 7

- 17 and Figure 7

- 9 present the savings for the various types of poten tial considered in this study

.

 Achievable Low potential projects 22 GWh of energy savings in 201 3 and 113 GWh in

20 16 . This corresponds to 0.3 % of the baseline forecast in 201 3 and 1.5 % in 20 16

.

 Achievable High potential is 46 GWh in 201 3 , which represent s 0.6 % of the baseline forecast. By 20 16 , the cumulative energy savings are 212 GWh, 2 .7% of the baseline

forecast.

 Economic potential

, which reflects the savings when all cost

- effective measures are taken,

is 205 GWh in 201 3 . This represents 2.6 % of the baseline energy forecast. By 2016

, economic potential reaches 464 GWh, 6.0 % of the baseline energy forecast.

 Technical potential

, which reflects the adoption of all energy efficiency measures regardless of cost, is a theoretical upper bound on savings. In 201 3 , energy savings are 222 GWh, or 2.8 % of the baseline energy forecast. By 20 16 , technical potential reaches 518 GWh, 6.7 % of the baseline energy forecast.Energy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 19 Table 7

- 17 Electric Efficiency Potential for the Industrial Sector

2013 2014 2015 2016

Energy Forecasts (GWh) 7,822 7,858 7,937 7,732

Cumulative Energy Savings (GWh)

Achievable Low Potential 22 47 78 11 3

Achievable High Potential 46 93 150 212

Economic Potential 205 290 386 464

Technical Potential 222 316 426 518

Energy Savings (% of Baseline Forecast)

Achievable Low Potential 0.3% 0.6% 1.0% 1.5%

Achievable High Potential 0.6% 1.2% 1.9% 2.7%

Ec onomic Potential 2.6% 3.7% 4.9% 6.0%

Technical Potential 2.8% 4.0% 5.4% 6.7%

Figure 7

- 9 Industrial Electric Potential Savings



### Industrial Electric Potential by Mark et Segment

Table 7

- 18 shows electric energy efficiency potential for the four industrial segments in 2016.

Table 7

- 19 shows the Achievable Low savings by end use and market segment in 2016

.Energy Efficiency Potential By Sector

7

- 20 www.enernoc.com Table 7

- 18 Industrial Electric Potential by Market Segment, 2016

Chemical and

Pharmaceutical Paper Food Other

Industrial Total

Baseline Forecast (GWh) 2,475 1,074 720 3,464 7,732

Energy Savings (GWh)

Achievable Low Potential 31.5 15.5 9.8 55.9 112.8

Achievable High Potential 58.8 28.7 18.2 106.0 211.7

Economic Potential 119.7 56.8 38.3 249.3 464.1

Technical Potential 128 .1 58.7 43.3 287.6 517.6

Energy Savings as % of Baseline

Achievable Low Potential 1.3% 1.4% 1.4% 1.6% 1.5%

Achievable High Potential 2.4% 2.7% 2.5% 3.1% 2.7%

Economic Potential 4.8% 5.3% 5.3% 7.2% 6.0%

Technical Potential 5.2% 5.5% 6.0% 8.3% 6.7%

Table 7

- 19 Industrial Electric Achievable Potential Low by End Use and Market Segment, 2016

End Use Chemical and

Phar maceutical Paper Food Other

Industrial Total

Cooling 2.09 0.66 0.79 11.58 15.12

Heating 0.57 0.18 0.21 3.09 4.05

Ventilation 0.74 0.23 0.28 3.84 5.09

Interior Lighting 2.46 1.14 1.25 13.95 18.79

Exterior Lighting 0.26 0.12 0.13 1.43 1.94

Motors 22.69 12.81 5.22 19.27 59.99

Process 2.71 0.39 1.93 2.76 7.79

Miscellaneous

- - - - -

Grand Total 31.52 15.52 9.81 55.92 112.78



### Industrial Electric Potential by End Use

Table 7

- 20 provides estimates of savings for each end use an d type of potential. Not surprisingly, the largest savings opportunities are found in motors and drives.Energy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 21 Table 7

- 20 Industrial Electric Potential by End Use and Potential Type (GWh)

End Use Potential 2013 2014 2015 2016

Cooling Achievable Low Potential 2.5 5.4 9.7 15.1

Achievable High Potential 5.7 11.4 19.8 29.6

Economic Potential 29.8 39.9 54.0 67.3

Technical P otential 38.1 53.2 74.0 92.4

Heating Achievable Low Potential 0.7 1.4 2.5 4.1

Achievable High Potential 1.7 3.2 5.5 8.4

Economic Potential 9.2 11.8 15.2 18.8

Technical Potential 12.2 16.4 22.7 28.8

Ventilation Achievable Low Potential 0.7 1.8 3.4 5 .1

Achievable High Potential 1.7 4.1 7.6 11.1

Economic Potential 16.9 32.8 49.8 63.2

Technical Potential 18.2 34.3 51.4 65.1

Interior Lighting Achievable Low Potential 3.8 8.5 14.1 18.8

Achievable High Potential 7.6 16.4 26.3 33.8

Economic Poten tial 34.8 55.0 71.8 74.5

Technical Potential 37.9 59.0 77.1 81.6

Exterior Lighting Achievable Low Potential 0.5 1.1 1.7 1.9

Achievable High Potential 1.0 2.1 3.1 3.5

Economic Potential 2.8 4.4 5.6 5.4

Technical Potential 2.9 4.6 5.8 5.7

Motors Ac hievable Low Potential 13.1 26.3 41.8 60.0

Achievable High Potential 26.9 51.9 79.9 111.2

Economic Potential 101.9 131.8 168.9 206.4

Technical Potential 97.7 125.9 160.6 197.4

Process Achievable Low Potential 0.9 2.2 4.4 7.8

Achievable High Potent ial 1.9 4.2 8.2 14.0

Economic Potential 9.7 14.3 20.7 28.2

Technical Potential 12.4 18.3 26.7 36.4

Miscellaneous Achievable Low Potential

- - - -

Achievable High Potential

- - - -

Economic Potential

- - - -

Technical Potential

- - - -

Total Ach ievable Low Potential 22.3 46.7 77.6 112.8

Achievable High Potential 46.5 93.3 150.4 211.7

Economic Potential 205.1 290.0 385.9 464.0

Technical Potential 219.5 311.8 418.3 507.4

Figure 7

- 10 illustrates the achievable poten tial savings by electric end use in 2016 for the industrial sector. The largest shares of savings opportunities are in the motors and machine

drive s. P otential savings for straight equipment change

- outs are diminishing due to the National

Electrical Manufa cturer’s Association (NEMA) standards, which now make premium efficiency motors the baseline efficiency level. As a result, potential savings are incrementally small toEnergy Efficiency Potential By Sector

7

- 22 www.enernoc.com upgrade to even more efficient levels. Many of the savings opportunities in this end us e come from controls, timers, and variable speed drives, which improve system efficiencies where motors

are utilized.

Beyond the replacement of motors, there are large opportunities for savings in cooling, lighting, process, ventilation, and finally spac e heating. Detailed meas ure information is available in

A ppendix D . The key measures comprising the potential are listed below:

 Motors

– drives and controls

 Process

– timers and controls

 Application optimization and control

– fans, pumps, compressed air

 Efficient high bay lighting

 Efficient ventilation systems

 Energy management systems & programmable thermostats

Figure 7

- 10 Industrial Achievable Low Electricity Potent ial Savings by End Use in 2016



## Industrial Natural Gas Potential

Table 7

- 21 and Figure 7

- 11 present the savings for the various types of potential considered in this study for the industrial sector

.

 Ac hievable Low potential projects 0.1 million therms (mmTherms) of energy savings in

2013

, 0.0 % of the baseline forecast. This increases to 1.0 mmTherms

, 0.2 % of the baseline forecast, in 2016.

 Achievable High potential i s 0.3 mmTherms in 2013 , which represe nts 0.1 % of the baseline forecast. By 2016 , the cumulative energy savings are 2.0 mmTherms

, 0.4 % of the

baseline forecast.Energy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 23  Economic potential, which refle cts a theoretical limit to savings whe n all cost

- effective measures are taken, is 1.9 mmTherms in 2013 . This represents 0.4 % of the baseline energy forecast. By 2016 , economic potential reaches 5.4 mmTherms

, 1.1 % o f the baseline energy

forecast.

 Technical potential

, which reflects the adoption of all energy efficiency measures regardless of cost

, is a theo retical upper bound on savings. In 2013 , energy savings are 10.0

mmTherms , or 2.0 % of the baseline energy forecast. By 2016 , technical potential reaches

29.3 mmTherms

, 6.1 % o f the baseline energy forecast.

Table 7

- 21 Natural Gas Efficiency Potential for the Industrial Sector

2013 2014 2015 2016

Energy Forecasts (million therms) 489 487 487 481

Cumulative Energy Saving s (million therms)

Achievable Low Potential 0.1 0.3 0.6 1.0

Achievable High Potential 0.3 0.7 1.3 2.0

Economic Potential 1.9 2.7 4.1 5.4

Technical Potential 10.0 14.5 21.3 29.3

Energy Savings (% of Baseline Forecast)

Achievable Low Potential 0.0% 0.1 % 0.1% 0.2%

Achievable High Potential 0.1% 0.1% 0.3% 0.4%

Economic Potential 0.4% 0.5% 0.8% 1.1%

Technical Potential 2.0% 3.0% 4.4% 6.1%

Figure 7

- 11 Industrial Nat ural Gas Potential SavingsEnergy Eff iciency Potential By Sector

7

- 24 www.enernoc.com 

### Industrial Natural Gas Potential by Market Segment

Table 7

- 22 shows electric energy efficiency potential for the four industrial segments in 2016.

Table 7

- 23 shows the Achiev able Low savings by end use and market segment in 2016. A large portion of the savings comes from space heating improvements in the Other Industrial category. The largest industrial segments typically dedicate very little of their energy to space condition ing, so the smaller businesses that are grouped into the Other Industrial category will have more by comparison.

Table 7

- 22 Industrial Natural Gas Potential by Market Segment, 2016

Chemical and

Pharmaceutical Paper Food Other

Industrial Total

Baseline Forecast (thousand therms ) 165,316 73,008 66,053 176,521 480,898

Energy Savings ( thousand therms)

Achievable Low Potential 88.4 51.7 50.0 815.5 1,005.5

Achievable High Potential 163.0 96.6 94.5 1,632.7 1,986.8

Economic Potential 494.7 311.7 279.5 4,361.4 5,447.2

Technical Potential 9,926.5 4,413.5 3,865.6 11,132.8 29,338.5

Energy Savings as % of Baseline

A chievable Low Potential 0.1% 0.1% 0.1% 0.5% 0.2%

Achievable High Potential 0.1% 0.1% 0.1% 0.9% 0.4%

Economic Potential 0.3% 0.4% 0.4% 2.5% 1.1%

Technical Potential 6.0% 6.0% 5.9% 6.3% 6.1%

Table 7

- 23 Industrial Natural Gas Achievable Potential Low by End Use and Market Segment, 2016 (million therms)

End Use Chemical and

Pharmaceutic

al Paper Food Other

Industrial Total

Heating 32 22 27 795 876

Process 56 30 23 20 130

Miscellaneous 25 16 21 609 671

Total 114 68 71 1,425 1,677



### Industrial Natural Gas Potential by End Use

Table 7

- 24 provides estimates of savings for each end use and type of po tential. Since natural gas is chiefly used for heating, the number of end uses is more limited than the electricity analysis.Energy Efficiency Potential By Sector

EnerNOC Utility Solutions Consulting 7

- 25 Table 7

- 24 Industrial Natural Gas Potential by End Use and Potential Type (thousand therms)

End Use Potential 2013 2014 2015 2016

Heating Achievable Low Potential 140.4 291.7 558.9 875.9

Achievable High Potential 324.3 640.4 1,164.9 1,759.3

E conomic Potential 1,887.1 2,613.3 3,722.7 4,670.3

Technical Potential 3,340.6 4,733.3 6,780.9 8,793.7

Process Achievable Low Potential

- 6.8 58.6 129.6

Achievable High Potential

- 12.6 105.1 227.5

Economic Potential

- 63.0 409.7 777.0

Technical Po tential 6,486.8 9,539.7 14,068.8 19,827.2

Miscellaneous Achievable Low Potential

- - - -

Achievable High Potential

- - - -

Economic Potential

- - - -

Technical Potential

- - - -

Total Achievable Low Potential 140.4 298.5 617.5 1,005.5

Achievable High Potential 324.3 653.0 1,269.9 1,986.8

Economic Potential 1,887.1 2,676.4 4,132.4 5,447.2

Technical Potential 9,827.5 14,273.0 20,849.7 28,621.0

Figure 7

- 12 illustrates the achievable potential savings by natural gas end use in 2016 for the

industrial sector. Space heating and process heating are the only opportunities to speak of.

Detailed measure information is available in A ppendix D

. The key measures comprising the potential are listed below:

 Energy management system s & programmable thermostats

 Efficient boilers & furnaces

 Insulation

Figure 7

- 12 Industrial Natural Gas Achievable Low Potential Savings by End Use in 2016EnerNOC Utility Solutions Consulting 8

- 1



### CHAPTER 8



## ALTERNATE AVOIDED CO ST SCENARIO

To observe the sensitivity of study results to changes in forecast market prices of electricity and natural gas, we ran a scenario in which the avoided costs for each fuel in each year of the study were increased by 20%. T he models were then re

- r u n with all other variables held constant.

The total number of measures passing the economic screen (with TRC benefit

- to

- cost ratio greater or equal to 1.0) i ncreased from 6,343 to 6,578. 21 This is an increase of only 3.7%,

because only a fraction of measures are close to the marginal value of 1.0. Details can be seen

below in Table 8

- 1

.

Table 8

- 1 Alternate Avoided Cost Scenario, Count of Measures Passing Economic Screen

Sector Count of All Measures with B/C ratio > 1.0, All Fuels

Reference Case:

Avoided Costs from New
Jersey Rate Counsel Alternate Case:
20% Higher Avoided

Costs Delta

Residential 995 1,064 6.9%

Commercial 3,842 3,975 3.5%

Industrial 1,506 1,539 2.2%

Total 6,343 6,578 3.7%

With the newly passing measures migrating from technical potential into economic and achievable potential , the savings are increase d by a small amount. The electric achievable low potential in 2016, for instance, only increased from 3.1% of the baseline forecast to 3.2%

. This is a change of 66 GWh or 2.9% of total savings, as shown in Table 8

- 2

Table 8

- 2 Alternate Avoided Cost Scenario, 2016 Achievable Low Potential (GWh savings)

Sector Reference Case: Avoided

Costs from New Jersey

Rate Counsel Alternate Case: 20%
Higher Avoided Costs Delta

Residential 936 944 0.9%

Commercial 1,214 1,265 4.2%

Industrial 112 119 6.3%

T otal 2,262 2,328 2.9%

The effects of the change are more pronounced for n atural gas measures , as the savin gs potential in the reference case is already quite low. The natural gas achievable low potential in

2016 increased from 1.0% to 1.2%. This is a change of 2.6 million therms or 12.3% of total savings in the reference case, as shown in Table 8

- 3

.

21 The number of measures cited here includes all permutations of technologies and measures, as well as their inclusion in the various building segments. It should be noted that this is distinct f rom the measure count given in Chapter 5, which for simplicity of reporting does not consider the dimension of building segment.Alternate Avoided Cost Scenario

8

- 2 www.enernoc.com Table 8

- 3 Alternate Avoided Cost Scenario, 2016 Achievable Low Potential (million therm savings)

Sector Reference Case:

Avoided Costs from New
Jersey Rate Counsel Alternate Case: 20%
Higher Avoided Costs Delta

Residential 8.4 9.2 8.6%

Commercial 38.9 41.6 7.0%

Industrial 1.3 3.9 194.1%

T otal 48.6 54.6 12.3%EnerNOC Utility Solutions Consulting 9

- 1



### CHAPTER 9



## FUEL SWITCHING ANALY SIS

Energy eff iciency potential studies have not traditionally explored the effects of switching technologies between system types and fuel types

. In this context, where both electricity and natural gas are being studied on a statewide basis , one may wish to investigate the economic potential of switching from room air conditioning to central air conditioning technology

. Or an example that is very relevant in the New Jersey market ; will consumers switch from fuel oil furnaces to natural gas furnaces, and if so

, how many?

The equipment stock accounting inside the LoadMAP modeling tool has the ability to support such a switching analysis, and has been activated and incorporated for the residential sector in all the results given above

. The switching forecast is integrated w ith the traditional potential analysis, and operates on the same principles to calculate technical, economic, and achievable

cases in turn

. There are several important distinctions to the switching case, however, that

should be discussed

.



### Technical Potent ial and Fuel Switching

First, the technical potential must compare all eligible technologies on an apples

- to

- apples basis

. Therefore, kWh, therms, gallons of fuel oil, or any other fuel source must be converted to BTUs so the most efficient option can be s elected on a same

- fuel basis

. The conversion factors used in the study are shown in Table 9

- 1 below

. It is important to note our assumption in this study that electric energy is considered at the system level rather than at the me ter

. To convert directly

from a kWh to kBTU, the factor is 3.414

. This was factored up by 3.34 to account for the energy expended at the generator and lost during transmission

.

Table 9

- 1 Converting to kBTU for S witching C

omparison

Fuel Value Units

Electricity 11.4 kBTU per kWh

Natural Gas 100.0 kBTU per Therm

Fuel Oil 138.7 kBTU per Gallon



### Economic Potential and Fuel Switching

The next consideration is that of economics

. The lifetime benefits from energy saving are again compared to the incremental measure cost in the same way as the core potential analysis described above. The switching analysis, however, include s an additional switching cost between $280 and $462 depending on the technology, extra labor required, and additional parts or components required

. If we revisit the example of switching from room AC to c entral AC, one would need to install ductwork, pour a concrete pad outside the home, and so on in order to enable the technology

.



### Achievable Potential and Fuel Switching

Finally, all economic decisions are decremented by the market adoption rates to arrive at achievable potential in the same manner as the core analysis

. After this, the adoption is penalized by an additional factor to account for the fact that people very rarely choose fuel switching due to its inherently higher complexity

. This factor, which we term “propensity,” has

been set at 5% in this analysis for all years and all measures.Fuel Switching Analysis

9

- 2 www.enernoc.com The technologies made available for the switching analysis are shown below in Table 9

- 2 , along with information regarding the directionality that they can be moved if the energy, economic, and adoption data align appropriately

. For example, the analysis assumes that a customer may wish to switch away from a natural gas boiler to a geothermal heat pump, but never the opposite.

Table 9

- 2 Switching O

ptions and D

irectionality

Fuel Technology Switchable Directionality

Electric Central AC TRUE Both Ways

Electric Room AC TRUE From Only

Electric Electric Room H eat TRUE From Only

Electric Electric Furnace TRUE From Only

Electric Air

- Source Heat Pump TRUE To Only

Electric Geothermal Heat Pump TRUE To Only

Electric Water Heater <=55 gal TRUE Both Ways

Electric Water Heater > 55 gal TRUE Both Ways

Natural Gas Furnace TRUE Both Ways

Natural Gas Boiler TRUE From Only

Natural Gas Water Heater <=55 gal TRUE Both Ways

Natural Gas Water Heater > 55 gal TRUE Both Ways

Fuel Oil Furnace TRUE From Only

Fuel Oil Boiler TRUE From Only

Fuel Oil Water Heater <=55 gal T RUE From Only

Fuel Oil Water Heater > 55 gal TRUE From Only

The fuel switching analysis results for the achievable potential cases show that the market will likely support the migration of 3,177 to 5,713 space heating systems between 2013 and 2016

. Thes e will virtually all occur in the form of converting natural gas boilers, electric furnaces, and fuel oil systems to natural gas furnaces

. During the same time period, the analysis also predicts that 1,118 to 2,434 water heaters will be switched, primaril y from electric to gas water heaters, although some segments and niches have load profiles and economics that dictate switching in the opposite direction

. The model does not predict any air conditioning or cooling technology switches.

These results can be viewed pictorially or in tables on the following pages

. Figure 9

- 1 and Figure

9

- 2 show the achievable low and high potential cases for space heating, with all data in the positive y

- axis representing a uni t that has switched into a category

. Each graph is symmetrical, therefore, with a corresponding unit along the negative y

- axis that has been switched away from
a category

. Figure 9

- 3 and Figure 9

- 4 show t he same information for water heating technologies

. The unit counts are available in Table 9

- 3 and Table 9

- 4

.Fuel Switching Analysis

EnerNOC Utility Solutions Consulting 9

- 3 Figure 9

- 1 Space Heating

– Number of Fuel Switching Units, Achievable Low Case

Figure 9

- 2 Space Heating

– Number of Fuel Switching Units, Achievable High CaseFuel Switching Analysis

9

- 4 www.enernoc.com Table 9

- 3 Space Heating

– Number of Fuel Switching Units

Switching

Direction Technology Achievable Low Achievable High

2013 2014 2015 2016 2013 2014 2015 2016

Outgoing Electric Furnace (out)

- 19

- 28

- 37

- 20

- 37

- 52

- 65

- 34

Electric Room Heat (out)

- 32

- 47

- 60

- 73

- 62

- 87

- 108

- 125

Gas Boiler (out)

- 301

- 452

- 603

- 755

- 579

- 838

- 1077

- 1301

Gas Furnace (out) 0 0 0 0 0 0

- 1

- 1

Fuel Oil Boiler (out)

- 82

- 124

- 166

- 208

- 158

- 230

- 296

- 359

Fuel Oil Furnace (out)

- 23

- 35

- 47

- 64

- 44

- 64

- 84

- 110

Incoming Air

- Source Heat Pump (in) 0 0 0 0 0 1 1 1

Gas Furnace (in) 458 686 913 1

, 119 881 1

, 271 1

, 630 1

, 929

Figure 9

- 3 Water Heating

– Number of Fuel Switching Units, Achievable Low CaseFuel Switching Analysis

EnerNOC Utility Solutions Consulting 9

- 5 Figure 9

- 4 Water Heating

– Number of Fuel Switching Units, Achievable High Case

Table 9

- 4 Water Heating

– Number of Fuel Switching Un its

Switching

Direction Technology Achievable Low Achievable High

2013 2014 2015 2016 2013 2014 2015 2016

Outgoing Elec tric WH (out)

- 108

- 155

- 202

- 306

- 258

- 353

- 439

- 638

Natural Gas WH (out)

- 5

- 7

- 8

- 108

- 12

- 15

- 18

- 226

Fuel Oil WH (out)

- 30

- 44

- 59

- 85

- 71

- 101

- 129

- 176

Incoming Elec tric WH (in) 5 7 8 108 12 15 18 226

Natural Gas WH (in) 138 200 261 391 329 454 568 814

Fuel Oil WH (in) 0 0 0 0 0 0 0 0EnerNOC Utility Solutions Consulting 10

- 1



### CHAPTER 10



## CONCLUSIONS AND RECOMMENDATIONS

The resul ts of this study reveal that s ign ificant energy efficiency opportunities exist in New Jersey , despite appliance and efficiency standards and a challenging macroeconomic

environment

. Our analysis has shown that New Jersey can realize an achievable range of

reductions from 3.1% to 5.9 % of t he forecasted 2016 electric load, as well as 1.0 % to 1.9% of the forecasted 2016 natural gas load, by implementing the measures presented in this report.

New Jersey’s energy

- efficiency programs have a strong legacy, ample market momentum, and a good platfo rm to deliver joint electric and natural gas programs on a uniform, statewide basis.

The suite of programs is being evaluated for the next four

- year planning cycle, and based on this study, EnerNOC provides the following recommendations for the programs.



## G eneral Recommendations

 Capitalize on joint electric and natural gas programs : Since the New Jersey Office of Clean Energy manages the statewide energy efficiency portfolio in all New Jersey electric and natural gas utility territories

, there is a good opp ortunity to create cross

- cutting programs with uniform marketing messages for combined electric and natural gas savings opportunities for customers. This should allow for cost savings and administrati ve savings

.

 Increase focus on business programs: Our stu dy shows that the majority of energy efficiency p otential exists in the commercial and industrial sectors. Historically, 60

- 70% of

the New Jersey EE budgets have been allocated to residential programs. The NJOCE should

c onsider increasing program efforts i n the C&I sectors, not only to harvest larger EE savings, but to increase business competitiveness and decrease operating costs. Additionally, these sectors offer larger projects, which can be bundled more easily with creative financing and

incentives.

 Col laboration among stakeholder s : The discourse and information sharing between NJOCE, CEEEPS, stakeholders, implementers, and EnerNOC on this study has been efficient

and transparent. Continuing this trend to cultivate a mutual understanding of continuous

im provement and development is of paramount importance to the future success of programs. We recommend a continued interaction among these parties with regular touch points and workshops. Possible workshop topics are: technical resource manual with deemed

me asure databases; evaluation, measurement, and verification protocols; emerging technologies; innovative program strategies; periodic reviews of program results; and sharing success stories from individual programs or customers.



## Residential Recommendations

 Focus on lighting : The largest share of achievable energy efficiency potential in the

residential sector continues to come from CFLs . This is in spite of the forthcoming EISA standards that will reduce their per

- unit savings compared to the new baseline. Also, New Jersey should focus strong attention on specialty lamps, as the y are not addressed in the

EISA standard.

 Appliance recycling programs show considerable promise : Recycling programs for

a ppliance s such as second refrigerators and room air conditi oners show a considerable amount of potential, and should be considered for the New Jersey market. Consider ways to combine this offering with trade allies or market partners who are delivering other appliance related programs.Conclusions and Recommendation s

10

- 2 www.enernoc.com  Develop w hole

- house opportu nities : Measures such as duct sealing, insulation, energy management systems, and programmable thermostats offer opportunities for a jointly delivered electric and natural gas program for whole

- house savings.

 Investigate behavioral feedback programs : Be havioral change programs have traditionally not been formal components of energy efficiency portfolios, largely due to

difficulties in measuring and attributing savings. However, there is mounting evidence that these programs do produce measurable savings and p rogram administrators around the country are beginning to aim personalized messaging, energy reports, and other media at customers in an effort to reduce their energy usage. This study includes fairly conservative assumptions regarding savings, penetr at ion rates, and program rollouts for a “behavioral

feedback tools” measure for the residential sector. The measure passed the cost effectiveness screen and contributed meaningful savings to the portfolio. While the impacts are relatively small compared to other measures (< 1% of total energy savings), this is a new and emerging opportunity that may undergo meaningful change in the coming years. For New Jersey, this would be an interesting opportunity to keep in mind, as it is a program

that lends itself we ll to the combined statewide electric and natural gas delivery framework.

 Consider social media avenues for targeted program delivery : As internet social media paradigms become the norm in today’s wired society, companies like Groupon,

Amazon L ocal D eals, and Living Social have assembled a nationwide network of businesses

into a well

- oiled, rebate

- issuing machine. NJOCE should consider if there are opportunities to link their energy efficiency trade ally network to one of these companies to facilitate the target marketing, processing, and delivery of rebates. These vendors have sophisticated tracking systems and databases that may facilitate EM&V reporting on the back end as well

.



## Commercial and Industrial Recommendations

 Aggressively pursue lighting savin gs : The commercial sector in particular has significant savings potential in lighting, both interior, exterior, screw

- in, and high bay. Savings are also

available through occupancy sensors, timers, and energy management systems. NJOCE should strongly pursu e lighting savings to accelerate the phase out of T12 fluorescent lighting. In particular, program efforts can help intercept building operators before they make purchase and stocking decisions that could lead to the hoarding of T12 lamps.

 Create customiz ed, multi

- year plans for large, complex customers : For large customers, strategic energy management (SEM) 22 initiatives can deliver savings over longer time horizons. This means a larger tracking and time commitment, but many jurisdictions are finding this to be a more effective method than a “one and done” installation and rebate approach. These relationships involve personalized plans, identification of metrics, goal

setting, technical assistance, and attention from dedicated account executives or energy

c oaches. This is similar to the current “Energy Savings Improvement Plan” program, and administrators should consider expanding or refining it to cater to New Jersey’s largest C&I

customers.

 Focus industrial program efforts on motor controls and system opt imizations : The savings for the industrial sector are all about control and optimization of motors and processes. Low

- cost retrofits can often have significant energy impacts with minimal disruption of (and often times improvement of) business processes.

 Target niches with segment specific programs : There are specific segments that offer considerable savings potential, but will not typically be reached by standard rebate programs

and generic business programs . Consider whether it makes sense to initiate a specifically targeted program such as one for food preparation equipment in restaurants or refrigeration equipment in grocery stores.

22 Sometimes called Continuous Energy Improvement (CEI).EnerNOC Utility Solutions Consulting 11

- 1



### CHAPTER 11



## COMPARISON TO RECENT REGIONAL POTENTIAL STUDIES

We compared the average annual potential savings for all sect ors from our study with two recent regional studies to see how they aligned

. In general, the results that we produced are on a reasonable par with these studies

. The two studies compared were:

 Delaware’s Energy Efficiency Potential and Program Scenarios t o Meet Its Energy Efficiency Resource Standard. A s tudy by Center for Energy and Environmental Policy, University of Delaware , available at: http://www.ceep. udel.edu/energy/publications/2011_DNREC_EERS_CEEP_Report_May_2011. pdf

Our Achievable High case most closely corresponds to the case in this study that assumes the most aggressive participation rates in the country (i.e. V ermont) , and so is compared to

it

. We compare our Achievable Low case with their case that assumes the average participation rates from the “ 14 leading states ” that have the highest participation rates in

the nation. This study covers electricity and natural gas.

 Electric Energy Efficienc y Potential for Pennsylvania

. A study by GDS Associates and Nexant, May 10, 2012 , available at: http://www.puc.state.pa.us/electric/pdf/Act129/Act129

- PA_ Market_Potential_Study051012.pdf

Our Achievable High Case corresponds to their high case, in which they assume high adoption rates and an incentive amount that is 100% of the incremental cost of measures

. We compare our Achievable Low Case to their case with incentives of 30% to 60% of

incremental cost. This study only covers electricity.

The three studies show an annual Achievable High potential for electric savings in the range of 1.1% to 1.9%, while the Achievable Low is in the range of 0.7% to 0.8% p er year.

For natural gas, the two studies have an annual Achievable High potential between 0.5% and 0.9%, whereas the Achievable Low is in the range of 0.3% to 0.5% per year. Please see the

Table 11

- 1 and Figure 11

- 1 for the data.

Table 11

- 1 Comparison to other studies (average annual savings vs. baseline forecast)

Achievable High Achievable Low

Electricity

N ew Jersey

– ENOC (2013

- 2016) 1.5% 0.8%

D elaware

– U of DL (2011

- 2015) 1.9% 0.8%

Pennsylvania

– GDS (2013

- 2016) 1.1% 0.7%

Natural Gas

N ew Jersey

– ENOC (2013

- 2016) 0.5% 0.3%

D elaware

– U of DL (20 11

- 2015) 0.9% 0.5%

Pennsylvania

– GDS (2013

- 2016) N/A N/AComparison to Recent Regional Potential Studies

11

- 2 www. enernoc.com Figure 11

- 1 Comparison with recent regional potential studiesEnerNOC Utility Solutions Consulting 12

- 1



### CHAPTER 12



## ENERGY EFFICIENCY PO TENTIAL BEY OND 2016

New Jersey’s next program planning cycle for the years 2013 to 2016 was the primary focus of this study and report. However, because there is a small incremental effort to include additional years in our modeling framework, we have extended the methodologies described above to produce estimates of energy efficiency potential through the year 2024. This will provide insight into longer term planning for the next two planning cycles: 2017 to 2020 and 2021 to 2024.

We present overall and sector

- lev el results of the energy

- efficiency analysis for the State of New Jersey. Key findings related to potentials are summarized below.

 Achievable Low potential forms a lower point on the range of achievable potential. Across all sectors, this metric is 29,925 BTU in 2016 and increases to 160 ,55 2 BTU by 20 24. This represents 2.3 % of the baseline forecast in 201 6 and 11.4 % in 2024

.

 Achievable High potential forms the upper bound on the range of achievable potential. It

is 57

, 144 BTU in 2016, which represents 4.4 % o f the baseline forecast. By 2024 , the cumulative savings are 225

, 579 BTU, 16. 0 % of the baseline forecast.

 Economic potential

, which reflects the savings when all cost

- effective measures are taken,

is 127

, 588 BTU in 201 6 . This represents 9.9 % of the b as eline energy forecast. By 2024

, economic potential reaches 326,00 6 BTU, 23. 1 % of the baseline energy forecast.

 Technical potential

, which reflects the adoption of all energy efficiency measures regardless of cost

- effectiveness, is a theoretical upper boun d on savings. In 201 6 , energy savings are 177

, 390 BTU, or 13.7 % of the baseline energy forecast. By 20 24 , technical potential reaches 4 71, 288 BTU, % of the baseline energy forecast.

Table 12

- 1 and Figure 12

- 1 summarize the energy

- efficiency savings for the different levels of potential relative to the baseline forecast. Figure 12

- 2 displays the energy

- efficiency forecasts.

To combin e t he electric and natural gas energy efficiency potential s, kWhs and therms are both converted to a common unit, BTUs, in order to facilitate comparison. Table 9

- 1 in the fuel switching analysis chapter shows the conversion facto rs.Energy Efficiency Potential BEYOND 2016

12

- 2 www. enernoc.com Table 12

- 1 Summary of Combined Electric and Natural Gas Energy Efficiency Potential

2013 2016 2020 2024

Baseli ne Forecast (million BTU) 1,308,205 1,291,174 1,344,903 1,412,982
Cumulative Savings (million BTU)

Achievable Potential Low 5,678 29,925 87,381 160,552
Achievable Potential High 11,799 57,144 140,787 225,579

Economic Potential 45,966 127,588 224,56 0 326,006

Technical Potential 62,433 177,390 324,214 471,288
Energy Savings (% of Baseline)

Achievable Potential Low 0.4% 2.3% 6.5% 11.4%

Achievable Potential High 0.9% 4.4% 10.5% 16.0%

Economic Potential 3.5% 9.9% 16.7% 23.1%

Technical Potential 4.8% 13.7% 24.1% 33.4%

Figure 12

- 1 Summary of Combined Electric and Natural Gas Energy SavingsEnerNOC Utility Solutions Consulting 12

- 3 Figure 12

- 2 Combined Electric and Natural Gas Potential Forecasts (million BTU)



## Electric Energy Efficiency

– Overall Results

Table 12

- 2 and Figure 12

- 3 summarize the electric ener gy

- efficiency savings for the different levels of potential relative to the baseline forecast. Figure 12

- 4 displays the electric energy

efficiency forecasts.

Table 12

- 2 Summary of Electric Energy Efficiency Potential

2013 2016 2020 2024

Baseline Forecast (GWh) 74,776 73,031 76,603 81,041

Cumulative Savings (GWh)

Achievable Potential Low 446 2,255 6,200 10,945

Achievable Potential High 918 4,277 9,891 15,219

Economic Potential 3,418 9,369 15,536 21,932

Technical Potential 3,708 9,868 16,276 23,126

Energy Savings (% of Baseline)

Achievable Potential Low 0.6% 3.1% 8.1% 13.5%

Achiev able Potential High 1.2% 5.9% 12.9% 18.8%

Economic Potential 4.6% 12.8% 20.3% 27.1%

Technical Potential 5.0% 13.5% 21.2% 28.5%Energy Efficiency Potential BEYOND 2016

12

- 4 www. enernoc.com Figure 12

- 3 Summary of Electric Ener gy Savings

Figure 12

- 4 Electric Potentials Forecasts (GWh)EnerNOC Utility Solutions Consulting 12

- 5



## Natural Gas Energy Efficiency

– Overall Results

Table 12

- 3 and Figure 12

- 5 s ummarize the natural gas energy

- efficiency savings for the different levels of potential relative to the baseline forecast. Figure 12

- 6 displays the natural gas energy

efficiency forecasts.

Table 12

- 3 Summary of Natural Gas Energy Efficiency Potential

2013 2016 2020 2024

Baseline Forecast (1,000 therms) 4,560,186 4,588 ,711 4,718,937 4,893,965

Cumulative Savings (1,000 therms)

Achievable Potential Low 5,906 42,208 167,194 358,224

Achievable Potential High 13,420 83,980 280,684 521,336

Economic Potential 70,161 208,193 475,061 760,595

Technical Potential 201,774 649,293 1,387,187 2,077,276

Energy Savings (% of Baseline)

Achievable Potential Low 0.1% 0.9% 3.5% 7.3%

Achievable Potential High 0.3% 1.8% 5.9% 10.7%

Economic Potential 1.5% 4.5% 10.1% 15.5%

Technical Potential 4.4% 14.1% 29.4% 42.4%

Figure 12

- 5 Summary of Natural Gas Energy SavingsEnergy Efficiency Potential BEYOND 2016

12

- 6 www. enernoc.com Figure 12

- 6 Natural Gas Potential For ecasts (1000 therms)



## Overview of Energy Efficiency Potential by Sector and Fuel

Table 12

- 4

, Figure 12

- 7 , and Figure 12

- 8 summarize the range of electric achievable potential b y
sector. The commercial sector accounts for the largest portion of the savings, followed by

residential. The i ndustrial sector contributes a relatively small amount of potential.

Table 12

- 4 Electric Achievable Potential by Sector (GWh)

2013 2016 2020 2024

Achievable Low Savings (GWh)

Residential 173 926 2,159 2,919

Commercial 251 1,217 3,676 7,309

Industrial 22 113 365 716

Total 446 2,255 6,200 10,945

Achievable High Savings (GWh)

Residential 352 1,769 3,497 4,174

Commercial 519 2,296 5,802 10,034

Industrial 46 212 592 1,011

Total 918 4,277 9,891 15,219EnerNOC Utility Solutions Consulting 12

- 7 Figure 12

- 7 Achievable Low Electric Potential by Sector (GWh)

Figure 12

- 8 Achievable High Electric Potential by Sector (GW h)Energy Efficiency Potential BEYOND 2016

12

- 8 www. enernoc.com Table 12

- 5

, Figure 12

- 9 , and Figure 12

- 10 present the range of natural gas a chievable potential by sector. Again, like the electric analysis, t he commercial sector account s for the largest portion of the savings, followed by the residential and then the industrial sectors

.

Table 12

- 5 Natu

ral Gas Achievable Potential by Sector (million therms)

2013 2016 2020 2024

Achievable Low Savings (million therms)

Residential 0.6 6.9 32.0 83.2

Commercial 5.2 34.3 131.2 266.8

Industrial 0.1 1.0 4.0 8.2

Total 5.9 42.2 167.2 358.2

Achievable High Savings (million therms)

Residential 1.1 12.8 50.6 115.9

Commercial 12.0 69.2 223.3 392.9

Industrial 0.3 2.0 6.8 12.5

Total 13.4 84.0 280.7 521.3EnerNOC Utility Solutions Consulting 12

- 9 Figure 12

- 9 Achievable Low Natural Gas Potential by Sector (million Therms)

Figure 12

- 10 Achievable High Natural Gas Potential by Sector (m illion Therms)Energy Efficiency Potential BEYOND 2016

12

- 10 www. enernoc.comEnerNOC Utility Solutions Consulting 500 Ygnacio Valley Road

, Suite 450 Walnut Creek, CA 94596 P: 925.482.2000

F: 925.284.3147 About EnerNOC Utility Solutions Consulting

EnerNOC Utility Solutions Consulting is part of EnerNOC Utility Solutions group, which

provides a comprehensive suite of demand

- side management (DSM) services to

util ities and grid operators worldwide. Hundreds of utilities have leveraged our

technology, our people, and our proven processes to make their energy efficiency

(EE) and demand response (DR) initiatives a success. Utilities trust EnerNOC to work

with them at every stage of the DSM program lifecycle

– assessing market potential,

designing effective programs, implementing those programs, and measuring program

results.

EnerNOC Utility Solutions delivers value to our utility clients through two separate

practice areas

– Program Implementation and EnerNOC Utility Solutions Consulting.

• Our Program Implementation team leverages EnerNOC ’s deep “behind

- the

- meter
expertise” and world

- class technology platform to help utilities create and

manage DR and EE programs that d eliver reliable and cost

- effective energy

savings

. We focus exclusively on the commercial and industrial (C&I) customer

segments, with a track record of successful partnerships that spans more than a

decade. Through a focus on high quality, measurable savi ngs, EnerNOC has

successfully delivered hundreds of thousands of MWh of energy efficiency for

our utility clients, and we have thousands of MW of demand response capacity

under management.

• The EnerNOC Utility Solutions Consulting team provides expertise an d analysis

to support a broad range of utility DSM activities, including: potential

assessments; end

- use forecasts; integrated resource planning; EE, DR, and

smart grid pilot and program design and administration; load research;

technology assessments and demonstrations; evaluation, measurement and

verification; and regulatory support.

The EnerNOC Utility Solutions Consulting team has decades of combined experience

in the utility DSM industry. The staff is comprised of professional electrical,

mechanical, chemical, civil, industrial, and environmental engineers as well as

economists, business planners, project managers, market researchers, load research

professionals, and statisticians. Utilities view our experts as trusted advisors, and we

work together co llaboratively to make any DSM initiative a success

.