# Presentation: 125342

## ACE06San Antonio, 2006 

- Steven Carlson, P.E.
- CDH Energy Corp.
- Evansville, WI
- www.cdhenergy.com

**ACE06****San Antonio, 2006**** **

**Development of a****Utility Energy Index**

## Background

- Support Benchmarking Efforts
- Water
- Wastewater
- Funding Partnership
- AwwaRF
- CEC
- NYSERDA
- Contractors
- CDH Energy
- Stearns & Wheeler
- Consultants from ORNL
- EPA Metric Development

## Project Goals

- What?
- Produce Industry-wide energy performance metrics for:
- Water Utilities
- Wastewater Utilities
- Why?
- Comparison to Peers
- Comparison to Ideal
- Identify savings potential
- Prioritize where to look for improvements
- How?
- Mimic EPA Energy Star for Buildings Ratings
- Building characteristics
- Operating Characteristics

## Larger Picture:Benchmarking - History

- Business:  Total Quality Management
***"Benchmarking - a continuous, systematic process for evaluating the products, services, and work processes of organizations that are recognized as representing best practices for the purpose of organizational improvement."***
- Michael J. Spendolini, ***The Benchmarking Book***, 1992
- Identify actions to improve performance
- Identify issues (metrics)
- Collect Internal data (baseline)
- Collect External data (comparison framework)
- Analysis
- Implement change
- Monitor Impact

**Notes:**
Our effort is on the first three items – How the metric is used is for later.

## Energy Benchmarking

- Energy Management Tool
- How am I doing?
- Relative to previous performance
- Relative to other plants in the system
- Relative to national average
- Relative to a standard (“Best Practices”)

**Notes:**
Are you a “fat” or “thin” energy user? 
How do you know unless you have a sense of the range of possibilities?

## Single Parameter Comparisons

**Notes:**
The standard simple one-parameter metric.
Are you efficient if you are low?  Or are there other factors that put you  there? (e.g. lower effluent quality requirements, better quality influent, etc)

## Define Performance:A Meaningful Metric

- Rich dataset for comparison
- Compare to what?
- Data source?
- Comparison method?
- Normalize for unmanaged characteristics
- Flow
- Treatment level
- Individual processes

**Notes:**
MPG might be good metric for thse looking at a Prius
0-60 times might be a better metric for those looking at a Porsche

## Metrics

- Devised based on type of available data
- Often normalized to flow
- Energy Cost ($/MG)
- Energy Use (kWh/MG)
- Source / Site ?
- Electricity / Gas ?
- Related to...
- Water source, Quality, Distribution topography, etc
- Desire to include multiple factors
***f ***(operating conditions, plant loading, process, etc)

**Notes:**
We are pursuing a metric with multiple parameters.

## Information Model

**Information Model**

- Available energy use data?
- Factors impacting energy use?
- Exogenous vs Endogenous factors?

- Electricity & Gas

- Topography

- Pressure

- Volume

- Water Quality

- Water Source

**Water Utility**

**Notes:**
External factors vs internal factors
Consider all factors that might influence energy use at initial stage
Multiple utility bills gives additional categories – but are they consistent across all utilities?

## Population

- Literature Review
- Population size
- 4,000 utilities serve pop > 10,000
- Source water distinction
- Treatment levels (definitions, use frequency)
- Existing energy data
- Surface 1,400 kWh/MG (85% pumping)
- Ground 1,800 kWh/MG (nearly all pumping)
- Little information on characterizing distribution
- Total main length

**Notes:**
EPA surveys
Electric utility research
AWWA surveys (Water/Stats)

## Population – Treatment

**Notes:**
Example of Treatment objectives – know relative frequency before-hand
Similar data on processes

## Sample / Response

- Source
- EPA Safe Drinking Water Information System
- Criteria
- Population > 10,000
- Representation
- 85% of national flow
- 3,611 Water utilities
- Process
- Pilot / survey refinement
- 1,723 three page surveys mailed
- SDWIS address and contact info incomplete
- Results
- 217 responses (13%)
- Additional NY surveys from State effort

**Notes:**
Difficult to get addresses to all utilities – security?

## Survey

- 97 parameters queried
- Water Source
- Average/design/maximum flow
- Turbidity
- Pumping HP / well depth
- Treatment Objectives
- Process Methods
- Clarification, filtration, etc
- Residuals handling
- Distribution System
- Population & area served
- Main length
- Pump HP
- Elevation range
- Pressure / zones

## Survey

- Energy
- Electricity (kWh, kW, cost)
- Source
- Treatment
- Distribution
- Natural Gas
- Other
- On-site Generation
- Engine-driven pumps
- Misc.
- Floor area of buildings (administration at plant)
- Operational review of utility bills
- Extraordinary events
- General notes

## Analysis

- Minimal Filtering
- Total kWh > 2,000 kWh/year
- Total kWh < 5,000 kWh/MG
- Flow, pump HP and main length were reported
- 137 observations remain
- Dependent Variable
- Cost
- Operators interest
- Easily combines fuel types
- Price variation
- Source Energy
- Policy interest
- Fuel mix impacts
- Log Transformation

**Notes:**
Likely to stay with source energy, but looked at cost as it is the important parameter for operators.

## Analysis – Single Parameter

**Analysis – Single Parameter**

- Single parameter model
- ln(Source kBtu) = 15.49 + 0.988 x ln(Flow MGD)
- R2 = 0.76

**Notes:**
Logarithmic transformation need to span huge range in data.
Do other parameters explain variation from mean trend line?

## Multi-Parameter Model

**Multi-Parameter Model**

- Improved from 0.76

- Flow (total & purchased)
- Pumping Horsepower (total, raw, distribution)
- Main Length

**Notes:**
Most of the energy use variation is explained by flow, but adding  these parameters improves model fit.  
These are exogenous terms, some process terms are significant, but can be chosen independently (endogenous):
 - Distribution unaccounted for %
 - Pressure filtrations

## Analysis – Critique Model

**Notes:**
Cumulative distribution of prediction with actual data over plotted.  Show the range of variation from the model at any given prediction.

## Grading on a Curve

**Grading on a Curve**

- Parameters define peer group curve.
- Enter source energy to find score.

## 1 - Calculate Source Energy Use

**Notes:**
Convert site annual energy use to source energy, total and apply log transformation.

## 2 – Predict Model Energy Use       and Resulting Score

- Apply utility parameters to model
- Adjust model to sample mean
- Determine benchmark score

**Notes:**
Use model to determine average plant energy use.  Determine adjustment factor between model average plant and sample average plant.  Apply adjustment factor and determine score from sample model distribution.

## 3 - Calculate Target Energy Use

- Determine energy use for other scores
- Apply energy source fraction (site or sample) to restate in site energy units
- Determine relative change to reach target

**Notes:**
Use distribution to determine how much improvement is possible by relating target scores back to site energy.

## Additional Data Characteristics

- Raw / Treatment / Distribution kWh isolation finds more related parameters
- n = 93 have two or more categories
- n = 35 have all three categories

- Raw
- Flow
- Raw HP
- Purchased flow
- Raw NTU
- Other energy
- Pressure filter

- Distribution
- Flow
- Distrib. HP
- Distrib. main length

- Treatment
- Flow
- Distrib. Main
- Well depth
- Raw NTU
- Raw HP
- Distrib. Pressure
- Distrib. Storage
- Treat Oxidation
- Treat Recarbonation
- Treat Aeration
- Residual Sand
- Residual Gravity
- Pressure Filter

**Notes:**
Energy data available at model level – three categories. Could develop benchmark for source, treatment and distribution, if consistent data are available.

## Energy Use by Category

- Categories don’t appear different
- Difficult to consistently segregate Source/Treatment/Distribution?

## How to Use the Information?

**Notes:**
Knowing where you are is only useful if you are trying to get somewhere else.

## Moving Toward Best Practice

- How is it defined?
- Target Score / Rating (relative performance)
- System performance (rules of thumb)
- Process level energy data & metrics
- Energy Model (absolute standard)
- How is it achieved?
- Look at system details
- Design characteristics (changeable?)
- Operational parameters (changeable?)
- Management actions (changeable?)
- Implementation & Feedback

**Notes:**
How one might use the metrics

**Using the Metrics**

- The metric isn’t the destination,
- 					Just the mile marker...
- 					A hint that potential 					improvements exists.
- Still need to figure out where to go
- Apply expertise
- Investigate systems
- Devise changes
- Assess performance

**Notes:**
A philosophical note – hopefully we have more choices than the last sign (at a fork in the road in a Wisconsin State park)

## Summary 

- Project Goals
- Collect Data (surveys)
- Define Comparison Framework
- Create Metrics
- Water / Wastewater
- Demonstrate Use (case studies)
- Metric only gives the score
- Use information in Applying Benchmarking process
- Investigate why
- Motivate action
- Confirm project impact
- Manage energy use