UNLV Theses/Dissertations/Professional Papers/Capstones

5-1-2014

Evaluation of Conservation Voltage Reduction as a

tool for demand side management

Ali Dorrody

University of Nevada, Las Vegas, dorrodya@unlv.nevada.edu

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Repository Citation

Dorrody, Ali, "Evaluation of Conservation Voltage Reduction as a tool for demand side management" (2014). UNLV Theses/

Dissertations/Professional Papers/Capstones. Paper 2075.
EVALUATION OF CONSERVATION VOLTAGE REDUCTION AS A TOOL 

FOR DEMAND SIDE MANAGEMNET

By

Ali Dorrody

Bachelor of Science in Electrical Engineering

Tehran Azad University 

2000

A thesis submitted in partial fulfillment of the requirements for the 

Master of Science in Engineering - Electrical Engineering

Department of Electrical and Computer Engineering

Howard R. Hughes College of Engineering 

The Graduate College

University of Nevada, Las Vegas

May 2014
ii

THE GRADUATE COLLEGE

We recommend the thesis prepared under our supervision by

Ali Dorrody

entitled 

Evaluation of Conservation Voltage Reduction as a Tool for Demand 

Side Management

is approved in partial fulfillment of the requirements for the degree of

Master of Science in Electrical Engineering

Department of Electrical and Computer Engineering

Yahia Baghzouz, Ph.D., Committee Chair

Shahram Latifi, Ph.D., Committee Member

Ebrahim Saberinia, Ph.D., Committee Member

Hossein Tehrani, Ph.D., Graduate College Representative

Kathryn Hausbeck Korgan, Ph.D., Interim Dean of the Graduate College

May 2014
iii

Abstract

To ensure stability of the power grid, electricity supply and demand must 

remain in balance in real time. Traditionally utilities, call upon peaking power 

plants to increase power generation to meet the peak demand during the 

afternoons of the hot summer months. Demand-side management (DSM), which 

includes energy efficiency and demand response (DR), works from the other side 

of the equation – instead of adding more generation to the system, it pays energy 

users to reduce consumption as it is cheaper and easier to procure than 

traditional generation. As a consequence, demand-side management allows 

utility customers to act as a “virtual power plant”: by voluntarily lowering their 

demand for electricity, they help stabilize the grid, and they are paid for 

providing this important service. Utilities and grid operators treat demand 

response capacity as a dispatchable resource that is called upon only when 

needed.

This thesis reviews the different DMS methods, and then it evaluates one 

possible means of reducing demand by operating at the low end of the 

acceptable voltage supply a method known as Conservation Voltage Reduction 

(CVR). This method was used effectively in the past to conserve energy and it is 

still being applied in some regions. But as the load is becoming more and more 
iv

nonlinear and more energy efficient, the CVR is becoming a less effective tool for 

demand-side management.
v

Table of Contents

Abstract........................................................................................ iii

List of Tables..................................................................................... viii

List of Figures.................................................................................... ix

Chapter 1: Introduction.................................................................. 1

1.1. History of smart grids............................................................... 2

1.2. Smart grids and Efficiency.......................................................... 3

1.3. Smart grids and demand response support...................................... 5

1.3.1. Demand-Side Management............................................ 6

1.3.1.1. Demand-Side Management and Conservation Voltage

Reduction.......................................................... 8

Chapter 2: Demand Response (DR)....................................................10

2.1. History of demand response (DR)................................................ 11

2.2. Demand response definition........................................................ 12

2.3. Types of demand response...........................................................14

2.3.1. Emergency demand response............................................ 14

2.3.2. Economy demand response............................................. 14

2.3.3. Ancillary service demand response.................................... 14

2.3.4. Dispatchable demand response (DDR)............................... 15

2.3.5. Dynamic pricing (DP) response.........................................15

2.4. Demand response techniques....................................................... 17

2.5. Technologies for demand response............................................... 20
vi

2.5.1. Metering...................................................................... 20

2.5.2. Remote Communications.................................................21

2.5.2.1. Internet............................................................ 26

2.5.2.2. Power line carriers (PLC)..................................... 26

2.5.2.3. Telephone and cellular........................................ 27

2.5.2.4. Radio Frequency (RF) - Fixed network.................... 28

2.5.2.5. Radio Frequency (RF) - Mobile/ Drive by............... 29

2.5.3. Control Technologies...................................................... 29

2.6. Technology Applications............................................................30

2.6.1. System and transmission operators.................................... 30

2.6.2. Commercial and Industrial technologies............................. 33

2.6.3. Residential technologies.................................................. 35

Chapter 3: Conservation Voltage Reduction (CVR)............................ 37

3.1. CVR Definition......................................................................... 38

3.2. The effects of Conservation Voltage Reduction................................ 40

3.3. CVR’s Benefits.......................................................................... 42

3.4. Conservation Voltage Reduction’s Strategies.................................. 44

3.5. Effects of capacitors on CVR........................................................ 46

3.6. CVR Factor..............................................................................47

3.7. Modeling Principles...................................................................49

3.7.1. Determination of load type............................................... 50

3.7.2. Load Models................................................................ 52

3.7.2.1. Loads without thermal cycles................................ 52
vii

3.7.2.2. Loads with thermal cycles.................................... 60

3.7.3. Description of the Model................................................. 63

3.7.3.1. Energy Consumption of Constant-Resistance Devices 63

3.7.3.2. Energy Consumption of Constant-Energy Devices..... 64

3.7.3.3. Energy Consumption of Constant-Power Devices...... 65

3.7.3.4. Energy Consumption of Constant-Current Devices....65

3.7.4. A sample load modeling project........................................66

3.7.4.1. Test Preparation and Procedure............................. 69

3.7.4.1.1. Selection of feeders to be tested..................70

3.7.4.1.2. Selection of test recorder............................71

3.7.4.1.3. Type of tests to be conducted......................72

3.7.4.1.4. Transformer tap switching......................... 75

3.7.4.2. Load Modeling................................................... 75

3.7.4.2.1. Static load model..................................... 76

3.7.4.2.2. Dynamic load model............................... 81

3.8. Computer simulation for a sample project...................................... 84

Chapter4: Conclusion.................................................................... 92

Appendix-A: Power flow summary reports ....................................... 96

References................................................................................... 105

Vita............................................................................................ 110
viii

List of Tables

Table-2-1- US AMR Shipment Forecast.............................................. 24

Table-2-2- Characteristics of Supply Side Resources.............................. 31

Table-3-1- Results of CVR tests in terms of the CVR Factor (Relative energy 

savings per percent of voltage reduction).............................. 42

Table-3-2- End Use Load Classifications ............................................ 51

Table-3-3- Test concerns and preventive actions.................................. 70

Table-3-4- Feeders selected for testing................................................ 71

Table-3-5- Types of tests conducted................................................... 73

Table-3-6- Static load model parameters............................................ 78

Table-3-7- Modified static load model parameters............................... 80

Table-3-11- Buses voltage level of each scenario.................................. 88

Table-A1- Power flow summary reports (For Base scenario).................. 96

Table-A2- Power flow summary reports (For Scenario-1)...................... 99

Table-A3- Power flow summary reports (For Scenario-2)...................... 102
ix

List of Figures

Figure-2-1- Demand Side Management Categories................................ 16

Figure-2-2- Metering and Communication Topologies............................. 23

Figure-2-3- US AMR Shipment Forecast............................................. 24

Figure-2-4- Residential Automation for Real-time Pricing..................... 36

Figure-3-1- The Traditional ZIP Load Model....................................... 53

Figure-3-2- Voltage and Current waveforms for a sample refrigerator....... 55

Figure-3-3- Power and Energy Graphs for a sample refrigerator...............56

Figure-3-4- Power and Voltage Graphs for a sample Fan........................ 57

Figure-3-5- Power and Voltage Graphs for a sample Compact Fluorescent..58

Figure-3-6- Power and Voltage Graphs for a sample Fluorescent lamp (T5) 59

Figure-3-7- The ETP Model of a Residential HVAC System..................... 61

Figure-3-8- Power and Voltage Monitoring Points................................. 72

Figure-3-9- Planned Test Types......................................................... 74

Figure-3-10- Planned Voltage Disturbance by transformer tap changing.....75

Figure-3-11- Steady-State Equivalent circuit of induction motor............... 82

Figure-3-12- Voltage-drop one-line diagram (Base scenario).....................85

Figure-3-13- Voltage-drop one-line diagram (Scenario-1)......................... 86

Figure-3-14- Voltage-drop one-line diagram (Scenario-2)......................... 87

Figure-3-15- Voltage changes along the feeder (Base and first scenarios 

comparison).................................................................................. 89

Figure-3-16- Voltage changes along the feeder (Base and second scenarios 

comparison). .................................................................................90

Figure-3-17- Voltage changes along the feeder (All scenarios comparison).. 91
1

Chapter 1:

Introduction
2

1.1. History of smart grids

A smart grid is an updated electrical grid which uses analogue or digital 

information and communications technology to collect and act on information, 

such as information about the behaviors of suppliers and consumers, in an 

automated way to improve the efficiency, reliability, economics, and 

sustainability of the production and distribution of electricity. (U.S Department 

of Energy., 2012)

Smart grid policy in the United States is defined in 42 U.S.C. The first alternating 

current power grid system was installed in 1886. At that time, the grid was a 

centralized unidirectional system of electric power transmission, electricity 

distribution, and demand-driven control.

Between the 1970s and the 1990s, growing of demand led to a growing number

of power stations. In some areas, supply of electricity, mostly at peak times,

could not supply the demand, resulting in poor power quality including 

blackouts, power cuts, and brownouts. More and more, electricity was depended 

on by industry, heating, communication, lighting, and entertainment, and 

consumers demanded ever higher levels of reliability.

Towards the end of the 20th century, electricity demand patterns were 

established: domestic heating and air-conditioning led to daily peaks in demand 

that were met by an array of 'peaking power generators' that would only be 
3

turned on for short periods each day. The relatively low utilization of these 

peaking generators (commonly, gas turbines were used due to their relatively 

lower capital cost and faster start-up times), together with the necessary 

redundancy in the electricity grid, resulted in high costs to the electricity 

companies, which were passed on in the form of increased tariffs. In the 21st 

century, some developing countries like China, India and Brazil were seen as 

pioneers of smart grid deployment. (Nejad et al., 2013)

1.2. Smart Grids and Efficiency

Numerous contributions to the overall improvement of the efficiency of energy 

infrastructure is expected from the placement of smart grid technology, 

especially including demand-side management, for example turning off air 

conditioners during short-term effects electricity price. The overall effect is less 

redundancy in transmission and distribution lines, and greater utilization of 

generators, leading to lower power prices.

The total load connected to the power grid can vary significantly over time. 

Although the total load is the sum of many individual choices of the clients, the 

overall load is not a stable, slow varying, growth of the load if a popular 

television program starts and millions of televisions are turned on 

simultaneously. Traditionally, to respond to a rapid increase in power 
4

consumption, faster than the start-up time of a large generator, some reserved

generators are put on standby mode. A smart grid may warn all individual 

television sets, or another larger customer, to reduce the load temporarily (to 

allow time to start up a larger generator) or continuously (in the case of limited 

resources) (Sinitsyn, Kundu, & Backhaus, 2013). Using mathematical prediction 

algorithms, it is possible to predict how many standby generators need to be 

used, to reach a certain failure rate. In the traditional grid, the failure rate can 

only be reduced at the cost of more standby generators. In a smart grid, the load 

reduction by even a small portion of the clients may remove the problem.

To reduce demand during the high cost peak usage periods, communications 

and metering technologies inform smart devices in the home and business when 

energy demand is high and track how much electricity is used and when it is 

used. It also gives utility companies the ability to reduce consumption by 

communicating to devices directly in order to prevent system overloads. 

Examples would be a utility reducing the usage of a group of electric vehicle 

charging stations or shifting temperature set points of air conditioners in a city

(Sinitsyn et al., 2013). To encourage them to cut back use and perform what is 

called peak curtailment or peak leveling, prices of electricity are increased during 

high demand periods, and decreased during low demand periods. It is assumed

that consumers and businesses will tend to consume less during high demand 
5

periods if it is possible for consumers and consumer devices to be aware of the 

high price for using electricity at peak periods. This could mean making tradeoffs such as cycling on/off air conditioners or running dishes at 9 pm instead of 5 

pm. When businesses and consumers see a direct economic advantage of using 

energy at off-peak times, they can include energy cost of operation into their 

consumer device and building construction decisions and hence become more 

energy efficient.

According to supporters of smart grid plans, this will reduce the amount of 

spinning reserve that electric utilities have to keep on stand-by, as the load curve 

will level itself through a combination of "invisible hand" free-market capitalism 

and central control of a large number of devices by power management services 

that pay consumers a portion of the peak power saved by turning their device 

off.

1.3. Smart grids and Demand response support

Demand response support allows generators and loads to cooperate in an 

automated fashion in real time, coordinating demand to flatten spikes. Removing

the portion of demand that occurs in these spikes removes the cost of adding 

reserve generators, and extends the life of equipment, and allows users to reduce
6

their energy bills by telling low priority devices to use energy only when it is 

cheapest. (ACT, )

Currently, power grid systems have varying degrees of communication within 

control systems for their high value properties, such as in generating plants, 

transmission lines, substations and major energy users. In general information 

runs one way, from the users and the loads they control back to the utilities. The 

utilities attempt to meet the demand and succeed or fail to varying degrees 

(brownout, rolling blackout, uncontrolled blackout). The total amount of power 

demand by the users can have a very wide probability distribution which 

requires spare generating plants in standby mode to respond to the quickly

changing power usage. This one-way flow of information is expensive; the last 

10% of generating capacity may be required as little as 1% of the time, and 

brownouts and outages can be costly to consumers.

Latency of the data flow is a major concern, with some early smart meter 

architectures allowing actually as long as 24 hours delay in receiving the data, 

stopping any possible reaction by either supplying or demanding devices.

1.3.1. Demand-side management

Energy demand management, also known as demand side management (DSM), 

is the adjustment of consumer demand for energy through different methods 
7

such as financial encouragements and education. Usually, the goal of demand 

side management is to encourage the consumer to use less energy during peak 

hours, or to move the time of energy use to off-peak times such as nighttime and 

weekends. (Office of Energy., 2010). Peak demand management does not 

necessarily decrease total energy consumption, but could be expected to reduce 

the need for investments in networks and/or power plants for meeting peak 

demands. An example is the use of energy storage units to store energy during 

off-peak hours and discharge them during peak hours. (Chiu et al., 2012)

There are different types of demand-side management:

Energy Efficiency: Using less power to perform the same tasks.

Demand Response: Any reactive or preventative method to reduce, flatten or 

shift peak demand. Demand Response includes all intended changes to 

consumption patterns of electricity of end user customers that are intended to 

alter the timing, level of instantaneous demand, or the total electricity 

consumption. Demand Response refers to a wide range of actions which can be 

taken at the customer side of the electricity meter in response to particular 

conditions within the electricity system (such as peak period network congestion 

or high prices) (Torriti, Hassan, & Leach, 2010). Chapter 2 presents concept of 

demand response and its relatives.
8

Dynamic Demand: Advance or delay appliance operating cycles by a few 

seconds to increase the Diversity factor of a set of loads. The concept is that by 

monitoring the power factor of the power grid, as well as their own control 

parameters, individual, intermittent loads would switch on or off at optimal 

moments to balance the overall system load with generation, reducing critical 

power mismatches. As this switching would only advance or delay the appliance 

operating cycle by a few seconds, it would be unnoticeable to the end user 

(Schweppe, 1982)

1.3.1.1. Demand-side management and Conservation voltage reduction 

Most U.S. utilities practice some form of system voltage reduction as part of 

System Emergency Procedures for control of short term demand related 

emergencies. They use single step or multiple step reduction of distribution 

system voltage such as 2.5 % or 3%, 5% and 8% voltage reduction. The system

used to do this includes communications to automatic voltage regulating relays 

on LTC Power Transformers and Step-Voltage Regulators. Through a dry contact 

closure to the tap-changer control relay, its regulating center band voltage is 

reduced to a lower level by boosting the sensed voltage level of the voltage 

regulating relay. This causes an overall reduction in the Distribution System 
9

Automatic Regulating Voltage Schedule. Various specific schemes are in practice 

due to a variety of Voltage Regulating Relays in service.
10

Chapter 2:

Demand Response (DR)
11

2.1. History of Demand Response(DR)

For many years, utilities called large industrial and commercial customers to 

reduce load, working their phones to find good corporate citizens willing to turn 

off non-essential lighting, motors or other equipment. To validate this 

relationship, some utilities offered “interruptible” electric rates that exchanged 

regularly cheaper power for the right to demand infrequent usage reductions. 

Except in very extreme cases, power flow was never cut off; instead, customers 

might be penalized if they did not reduce usage to the level promised to receive 

the lower rate. Because utilities infrequently made such calls, interruptible rates 

gradually became the “normal” price for industrial power. To minimize their 

electric bills, many large institutions also signed such agreements, even when 

they did not have an easy way to cut their load.

In the 1990s, however, several factors soured this relationship. For some reasons, 

the addition of new power plants failed to keep pace with load growth. As 

regional grids originally created for reliability transformed into interstate 

wholesale power markets, power sales between utilities and private generators 

were no longer regulated at the state level. This resulted in commodity 

speculators that increase on price instability.

Sudden price spikes appeared in 1998 during a very hot Midwest summer, but it 

was the 2000-01 California power disaster that concentrated national attention on 
12

the problem. When called upon to reduce load, many “interruptible” customers 

failed to do so just when power prices were going very high. To regain their 

losses, utilities tried to penalize those customers. Many of them declined to pay, 

stating they had been assured that such calls would never come. They tried to 

escape their pricing agreements, but utilities and regulators would not allow

them to escape. A better way was needed to secure load reductions on very short 

notice.

2.2. Demand Response Definition

Changes in electric usage by end-use customers from their normal consumption 

patterns in response to changes in the price of electricity over time, or to 

incentive payments designed to induce lower electricity use at times of high 

wholesale market prices or when system reliability is jeopardized (Balijepalli, 

Pradhan, Khaparde, & Shereef, 2011).

Demand response (DR) is normally used to refer to mechanisms used to 

encourage consumers to reduce demand, thus reducing the peak demand for 

electricity. Since electrical generation and transmission systems are commonly

sized to correspond to peak demand (plus margin for forecasting error and 

unexpected events), lowering peak demand reduces overall plant and capital 

cost requirements. Depending on the configuration of generation capacity, 
13

however, demand response may also be used to increase demand (load) at times 

of high production and low demand. Some systems may by this means

encourage energy storage to arbitrage between periods of low and high demand 

(or low and high prices).

 In electricity grids, demand response (DR) is similar to dynamic demand 

mechanisms to manage customer consumption of electricity in response to 

supply conditions, for example, having electricity customers reduce their 

consumption at critical times or in response to market prices. The difference is 

that demand response mechanisms respond to obvious requests to shut off, but

dynamic demand devices passively shut off when stress in the grid is sensed.

(Albadi & El-Saadany, 2008)

Any program which communicates with the end customer regarding price 

changes in the market and/or their own energy use and encourages them to 

reduce or shift their consumption (demand) of energy is called demand response. 

The active participation of the end-customers is a response to factors such as 

incentive pricing, new tariffs schemes, education and feedback. Feedback can be 

categorized as direct and indirect feedback. Indirect feedback is provided at set 

times by a third party. Informative bills with graphical representations of the

consumer’s historical consumption data would be one example of indirect 

feedback. Direct feedback is accessible to the consumer at any time and comes 
14

directly to their through, for example, an in-house display, computer site or text 

message.

2.3. Types of demand response

Three types of demand response are: emergency demand response, economic 

demand response and ancillary services demand response. Each will be:

2.3.1. Emergency demand response:

Emergency demand response is engaged to avoid involuntary service 

interruptions during times of supply shortage.

2.3.2. Economy demand response:

Economic demand response is engaged to allow electricity customers to limit

their consumption when the productive or convenience of consuming that 

electricity is worth less to them than paying for the electricity.

2.3.3. Ancillary service demand response:

Ancillary services demand response consists of a number of specialty services 

that are needed to ensure the secure operation of the transmission grid and 

which have traditionally been provided by generators.

Also, “Demand Response” can be categorized in other aspects like: Dispatchable

and Non-Dispatchable.
15

2.3.4. Dispatchable Demand Response (DDR):

“Dispatchable demand response” refers to planned changes in consumption that 

the customer agrees to make in response to direction from someone other than 

the customer. It includes direct load control of customer appliances such as those 

for air conditioning and water heating, directed reductions...and a variety of 

wholesale programs offered by RTOs/ISOs that compensate participants who 

reduce demand when directed for either reliability or economic reasons.

This direction to reduce load can be in response to acceptance of the consumer’s 

bid to sell its demand reduction at a price in an organized market (a wholesale 

price - responsive demand response) or to a retail provider. (Federal Energy 

Regulatory Commission, 2010)

2.3.5. Dynamic Pricing (DP) response:

“Non-Dispatchable demand response” refers to programs and products in which 

the customer decides whether and when to reduce consumption based on a retail 

rate design that change over time. This is sometimes called retail price -

responsive demand and includes dynamic pricing programs that charge higher 

prices during high-demand hours and lower prices at other times. (Federal 

Energy Regulatory Commission, 2010)

Different types of demand-side management are illustrated in figure-2-1.
16

Figure-2-1: Demand-Side Management Categories (NERC 2011)

Vehicle for DSM 

Dispatchability

Decision

Demand Reducing Measures Demand-Side Management

Demand 

Response

Energy 

Efficiency

Dispatchable Non-Dispatchable

Reliability Economic Time-Sensitive Pricing

Capacity Reserves EnergyVoluntary

Regulation EnergyPrice

Time-of-Use (TOU)

Critical Peak 

Pricing (CPP)

Real-Time Pricing 

(RTP)

System Peak 

Response 

Transmission 

Tariff (4CP 

Response)

Demand 

Bidding & 

Buy-Back

Emergency

Spinning 

Reserves

Direct 

Control Load 

Management

Interruptible

Load

Critical Peak 

Pricing (CPP)

with control

Load as a 

Capacity 
17

2.4. Demand Response Techniques

There are many demand response techniques available to utility companies. 

They may be active or passive, direct or indirect. Active techniques allow the 

consumer to take a hands-on role in determining the programs they will 

participate through and to what extent. The consumer can choose in or out at any 

time, sometimes even during an event. Energy cutoff solutions, time-of-use rate 

plans and utility-controlled thermostats are the examples of active DR. In passive 

demand response, the consumer has little to no control. The utility applies the 

solution without informing or consulting the customer, and does not let choosein or choose-out capability. Examples of passive demand management 

techniques include rolling blackouts and voltage reduction.

Demand response techniques can also be direct or indirect. A direct method

results in measurable, quantifiable outcomes, and directly affects demand; 

examples might include air conditioner and pool pump cutoff controllers. For 

direct demand side management, it is possible to relate the action to the reaction. 

Indirect demand response includes influencing someone or something to behave 

differently, e.g., showing a user the amount of energy currently being consumed 

and provide an incentive to turn off lights or appliances by showing how such a 

shift in behavior could positively affect the bill at the end of the month. Examples 

of indirect demand response techniques include real-time information displays, 
18

or distribution of real-time requests to limit energy use through instant 

messaging or social networking sites. With the indirect methods, it is difficult to 

directly relate the action to the response. (Office of Energy., 2010)

Technologies are available, and more are under development, to automate the 

process of demand response. Such technologies detect the need for load 

shedding, communicate the demand to participating users, automate load 

shedding, and verify compliance with demand-response programs. Grid-Wise 

and Energy-Web are two major federal initiatives in the United States to develop 

these technologies. 

Some utilities are considering and testing automated systems connected to 

industrial, commercial and residential users that can reduce consumption at 

times of peak demand, essentially delaying draw marginally. Although the 

amount of demand delayed may be small, the consequences for the grid 

(including financial) may be considerable, since system stability planning often 

involves building capacity for extreme peak demand events, plus a margin of 

safety in reserve. Such events may only happen a few times per year.

The process may involve turning down or off certain appliances or sinks (and, 

when demand is unexpectedly low, potentially increasing usage). For example, 

heating may be turned down or air conditioning or refrigeration may be turned 

up (turning up to a higher temperature uses less electricity), delaying slightly the 
19

draw until a peak in usage has passed. (Sinitsyn et al., 2013). In the city of 

Toronto, certain residential users can participate in a program (Peak saver AC) 

whereby the system operator can automatically control hot water heaters or air 

conditioning during peak demand; the grid benefits by delaying peak demand 

(allowing peaking plants time to cycle up or avoiding peak events), and the 

participant benefits by delaying consumption until after peak demand periods, 

when pricing should be lower. Although this is an experimental program, at 

scale these solutions have the potential to reduce peak demand considerably. The 

success of such programs depends on the development of appropriate 

technology, a suitable pricing system for electricity, and the cost of the 

underlying technology.

Other methods for implementing demand response approach the issue of tact

reducing duty cycles rather than implementing thermostat setbacks. These can be 

implemented using customized building automation systems programming, or 

through swarm-logic methods coordinating multiple loads in a facility.

It was recently announced that electric refrigerators will be sold in the UK fitted 

with a frequency sensing device which will delay or advance the cooling cycle 

based on monitoring grid frequency.
20

2.5. Technologies for Demand Response

2.5.1. Metering

The technology available from today’s metering devices will play a key role in 

enabling many of the benefits of increased demand response. The traditional role 

of electricity meters has been to determine how much electricity consumers’ use 

over a long time interval; in the case of demand response, a measurement is 

required to determine how much electricity use has been avoided or displaced 

over much shorter time intervals.

Traditionally, most small customers have been provided with a basic 

accumulation meter that provides a single consumption figure for the period 

between meter readings. The liberalization of markets has seen the value of 

electricity captured in wholesale markets according to timed intervals, reflecting 

the true cost of marginal production according to such externalities as primary 

fuel cost, weather, and time of day. These timed intervals at the wholesale level 

represent the smallest unit of timed electricity that could be used for tariff or 

billing purposes. Support of tariffs which reflect this real-time price component, 

whether through time-of-use, real-time pricing or critical-peak-pricing tariffs, has 

placed increased demands on the metering device beyond its traditional energy

billing function.
21

Minimum core functional requirements to enable the metering device to 

accommodate basic forms of price-response tariff require additional 

accumulating registers to record timed periods of consumption, such as peak and 

off-peak usage. In addition, a timing capability is required to determine start and 

stop times for the timed periods. This switching capability may be provided by 

an internal clock device or by an external source via Radio (RF), Ripple Control 

(PLC) or Time-switch (clock).

Meters which support functionality beyond traditional accumulation of 

consumption are often referred to as Advanced Meters or Interval Meters. The 

highest resolution of consumption recording provided by Advanced Meters is 

so-called interval recording, whereby data is recorded to the timed interval, 

typically in 15, 30 or 60 minute periods. Consumption recorded period-by-period 

provides a complete daily profile per consumer. Once this data has been 

recorded, in addition to providing a level of data resolution able to support 

complex price response tariffs, the profile can also be used to determine changes 

to, or movements away from, the baseline consumption, often used to verify 

performance of restriction programs. (Willis, 2010)

2.5.2. Remote Communications

Networks which support metering infrastructure are categorized as being either 

one-way or two-way in design. There are various design topologies delivering a 
22

range of functionalities, costs and benefits. Figure-2-2 below provides an 

indication of the different technical solutions implemented and available in the 

current market.

The design solution chosen will be governed by the application requirements 

and the meter reading environment. Geographic areas of high population 

density are typically required for fixed network infrastructure and for topologies 

which require data routers and concentrators. All the topologies shown below

are able to support both one-way and two-way communications. One-way 

systems are often referred to as those that enable outgoing readings to be 

collected, although they can also indicate a one-way solution involving signaling 

a price or rate change to the end customer. Full two-way communications refer to 

those systems that are able to both receive and send data. (Willis, 2010)
23

Figure-2-2: Metering and communication Topologies (Willis, 2010)

Automatic Meter Reading (AMR) has been the term traditionally used to 

represent metering solutions which incorporate a communications solution and 

whose prime function is to replace manual meter reading. The acronym itself, 

which remains widely used within Organization for Economic Cooperation and 

Development (OECD) markets, suggests that the real benefits of communications 

are directed to the meter reading function – principally for utility cost saving. 

Automatic Meter Reading technologies have achieved little penetration in most 

OECD liberalized markets, but have seen significant recent growth in the United 

Radio Tx/Rx

Radio Tx/Rx

PLC Modem

Telephone 

modem

Meter device

Meter device

Meter device

Meter device

Communications and metering 

equipment may be integrated

Mobile 

RF

Central data 

collection

Telephony 

switch

PLC 

concentrator

Fixed RF
24

States market as shown in Table-2-1. This is for the most part accounted for by 

the fact that the economic justification for Automatic Meter Reading investments 

was based upon obviating the need to manually read meters, and in the United 

States utilities read meters more frequently76 than in many other OECD markets.

Table-2-1: US AMR Shipments Forecast (Frost et al., 2003)

Year 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Optimistic 

Outlook

1757 1146 2320 2311 2375 3064 3308 3330 3319 3494 3703 3934

Current

Outlook

1757 1146 2320 2311 1884 2130 1947 1988 2080 2124 2182 2248

Figure-2-3: US AMR Shipment Forecast (Frost et al., 2003)

0

1000

2000

3000

4000

5000

1998 2000 2002 2004 2006 2008 2010 2012

1000's of units

Optimistic Outlook

Current Outlook
25

The detailed study of the United States Automatic Meter Reading market 

illustrated above considers an optimistic outlook for growth of traditional 

Automatic Meter Reading contingent on:

- Federal tax incentives for technology investment will be included in a 

Federal energy bill.

- Utilities achieve expected improvements in their balance sheets.

- The technical success of pilot test either underway or planned within 

forecast period.

- Benefit analysis of pilot test either underway or planned within forecast 

period;

- SMD (Standard Market Design) is implemented by (Federal Energy 

Regulatory Commission (FERC).

- Higher wholesale electric prices.

Communication links and Automatic Meter Reading technologies provide the 

capability to automate and add value to traditional utility metering functions 

such as meter reading, field operations, billing and customer services. These 

same metering systems can provide customers with both the capability to 

interrogate and read meter information on demand, and to receive up-to-date 

energy pricing. More significantly, interval metering combined with integrated 
26

communications enables fully flexible information architecture capable of 

delivering a full range of demand response options. (Ellens, Berry, & West, 2012)

A brief summary of the usual communications technologies is presented below:

2.5.2.1. Internet

The growth and availability of the Internet as a technology will probably have 

the greatest impact on the commercial viability of demand response 

technologies. Systems are now available which enable the connection of 

consumers and aggregators with Independent System Operators and with 

retailers. The common language of the Internet and its ability to remotely serve 

applications will enable software suppliers to distribute control, measurement 

and settlements software directly through the consumers’ browser.

2.5.2.2. Power Line Carrier (PLC)

Power Line Carrier systems used for utility applications are typically low 

bandwidth devices capable of utilizing existing in-home wiring networks, 

providing two-way communications of metering and related control data. Whilst 

the signaling technique has the advantage of not requiring the installation of a 

local area network (LAN), the wide area network (WAN) signaling between the 

host utility and the consumer does require access to the network infrastructure. 

Most metering commercial Power Line Carrier metering systems operate without 

using the high and medium voltage network for Wide Area Network Power Line 
27

Carrier communications. Instead they use data concentrators connected to utility 

systems by means of remote telephony or existing utility communications 

infrastructure.

As well as providing remote two-way communications, the system will support 

in-home communications, and appliance control and monitoring. The meters 

also provide functionality for remote meter reading and are capable of 

supporting full time-of-use tariffs.

2.5.2.3.Telephone and Cellular

Telephony services have long been used as a means of remote collection of 

metering data, making use of existing infrastructure and commercial and 

communication technology. The growth of telephony and cellular metering 

applications is linked to the commercial viability of network access provided by 

the local telephone operator. Some OECD telephony markets have developed 

specific tariffs for use by metering applications, which enable low-cost calling 

during defined timing windows.

There has been a recent growth in remote telephony applications including both 

cellular data calling and digital messaging. These systems typically have low 

bandwidths and relatively high costs, although they have the advantage of not 

requiring additional infrastructure investment beyond the metering end-point.
28

Telephony systems are traditionally one-way, whereby the reading agency will 

dial up the end device to collect metering data. Depending on the line-sharing 

arrangements of the local service provider, there are advanced metering devices 

that have the capability to dial out (initiate the call), functionality which can be 

used both to deliver meter readings and potentially to provide a confirmation of 

load reduction actions.

2.5.2.4. Radio Frequency (RF) – Fixed Network

The costs per point, or node, of fixed radio systems are dependent on a number 

of factors: the number of customers connected to the system; the geographic 

density of the customers; the topology of the area (which affects the propagation 

of the radio waves) and the location of meters (indoors vs. outdoors). 

Furthermore, fixed-radio networks are more economically-efficient when all (or 

almost all) the customers in a particular area are served by the same 

infrastructure. These radio systems are typically owned and operated by thirdparty vendors, which sell the service to utilities on a dollar-per-meter-month 

basis. The costs vary from about $1 to $5/meter-month, depending on the 

frequency of meter reading and the amount of data transferred. (Lefebvre et al., 

2008)
29

2.5.2.5. Radio Frequency (RF) – Mobile / Drive-By

Drive-by metering systems use low-power radio signals to transmit meter data to 

walk-by or drive-by receivers. Such systems are used to reduce operational costs 

associated with meter reading. Radio modules can be either retrofitted to existing 

meters or installed into traditional electricity meters at manufacture.

2.5.3. Control Technologies

The function of the control equipment is to effect the necessary change in 

electricity consumption in response to a demand signal. In this role, control 

equipment is used to switch on and off the relevant electrical load to execute an 

agreed load reduction or to provide an automated consumer response to a predetermined price threshold. The major differences between the various control 

products relate to the speed or notice required for switching, and whether the 

process control is manual or automatic.

In the case of rapid response, the control technology is usually fully automatic. 

For example, for frequency control, load is switched off when an automatic 

frequency relay detects a change in frequency of a fixed amount. Control to 

switch the process back on may also be automatic, or it may be manual, activated 

upon delivery of notification that the process can be restored. Advanced process 

control technologies may also be required to determine the optimal schedule of 
30

loads to deliver the agreed load reduction and still give satisfactory performance 

of the process.

Consumer control technologies represent the point of intervention between the 

end user and the demand response service provider. For larger industrial and 

commercial customers demand response event notification may be provided 

utilizing existing pathways such as email, cellular telephone and paging devices.

(Goldman, Kintner-Meyer, & Heffner, 2002)

2.6. Technology Applications

2.6.1. System and Transmission Operators

Transmission or systems operators at either the local retail level or the national 

transmission level are required to perform complex load scheduling and 

dispatch functions to ensure the reliability and security of supply. In traditional 

electricity markets this process has been developed almost exclusively with 

supply-side resources, with a consequent focus on generation performance 

characteristics as illustrated in Table 2-2. (Willis, 2010)

Direct consumer access to network markets for ancillary or congestion relief 

services is often effectively prevented by service requirements that have been 

designed for traditional supply side resources: a requirement for scaled loads, 

with system-operator-specified minimum contract positions often starting at 
31

around 5 MW of service capacity, and further standards set for such parameters 

as response time, ramp rates and minimum/maximum load cycles(Willis, 2010).

Table-2-2: Characteristics of Supply Side Resources (Willis, 2010)

Reliability Resources Traditional Supply Side Resources

Fast Reserve Partially loaded thermal plant

Hydro Electric (Inc. Pumped Storage)

Peaking Plant

Renewables Plants

Standing Reserve Stand-by Generation (Uneconomical base load plant)

Peaking Plant

Voltage/Frequency 

Response

Synchronous Generators / Compensators

Capacitors and Inductors

Transformers (Tap Changers and Voltage Boosters)

Dispatchable generator metering and communications equipment consistent 

with the resources shown in the table above will be specified to supply the 

Independent System Operator with output data in the order of seconds. This 

requirement is consistent with the security of supply objectives of the system 

operator, since the loss of one large generator must be compensated for 
32

immediately. It follows that since virtually all loads are small compared to 

generators, the statistical averaging across loads greatly reduces the need to 

closely monitor any one individual load.

To establish demand resources on an equal footing with supply side resources at 

the control desk of network operators would require high degrees of process –

and technology-integration. In the United Kingdom, the National Grid Company 

typically issues in excess of 500 instructions a day to market participants to 

balance supply and demand on a second by- second basis. Network operators 

often use technical standards and dispatch control technologies and software, 

which have been developed and refined over the course many years of market 

operation.

In this situation, only very large individual consumers will have the required 

resources to perform the necessary technology assessments and investments to 

enable direct participation in Independent System Operator markets. It therefore 

follows that the effective integration of smaller demand resources in the delivery 

of network operation services will only become feasible when an intermediary 

has a scale of aggregated response to justify such investments.

Whilst the emergence of such aggregators is feasible, it is becoming clear that 

there may not be enough “critical mass” representation from the demand-side to 

support and sustain such initiatives, at least without stronger incentives for such 
33

representation. Network operators may be willing to adapt and modify their 

control and technology requirements; however, greater demand will be required 

from consumers to drive such change. Regulators and public bodies should give 

consideration to the formation of research programs to consider the potential for 

increased demand side participation, and particularly to consider the potential to

adapt Independent System Operator systems to specific demand-side practices 

and technical approaches.(Willis, 2010)

2.6.2. Commercial and Industrial Technologies

Advanced metering is often a mandatory requirement for large commercial and 

industrial loads to enable, or make eligible, a load to participate in a demand 

response program. In part this is due to the need for accuracy in the amount and 

value of net demand reductions.

Direct load programs are usually those that need some form of signal sent from 

either the market operator or an intermediary to the demand resource, to request 

a demand side load reduction. The loads concerned will vary considerably by 

OECD markets, where heating and cooling load requirements especially vary 

according to local climate and economic conditions. Detailed analysis of specific 

load types is a critical step in selecting technology according to the potential for 

demand reduction.
34

For larger commercial and industrial customers, utility interfaces are often 

centered on traditional Buildings Energy Management Systems (BEMS) and 

more and more on internet-served energy management applications. BEMS 

technologies are implemented to provide in-building and process efficiencies and 

as such are synergistic with the objectives of both system-led and market-led 

response programs. Furthermore, as the potential for load control emerged as a 

market resource, a new market emerged for instantaneous power monitoring 

and recording, with low cost devices for on-site and load management being 

introduced. Under load participation programs, the requirements for accurate 

settlement and performance measurement has increased the accuracy and 

security requirements of these monitoring and measurement devices in order to

match on-site control with billed or contracted data.

The emergence of the internet in OECD markets during the 1990s provided a 

significant spur to automation technologies, providing a stimulus to lighting, 

heating, cooling and motive power equipment manufacturers. Increasing 

standardization of information exchanges, using XML and Microsoft Net 

standards, has provided increased opportunities for technology developers to 

connect consumers’ loads to commercial and industrial BEMS and with utility 

control and billing systems. (Willis, 2010)
35

2.6.3. Residential Technologies

Because of the relatively small amounts of energy and capacity used at 

residential loads, installed technology is required to serve the consumer in a nonintrusive manner. For this reason technologies usually serve two primary 

applications:

- Delivery of real-time pricing/time-of-use functionality.

- Control of heating/cooling/lighting and other application loads 

(swimming pools, irrigation systems, etc.).

Neither application requires automatic control, although residential demand 

response programs on offer today are able to deliver this if required.

The Advanced Energy Management program architecture (Figure-2-4) includes

of a Main gate System local area network which is used to receive pricing signals, 

to alert the consumer and to automate control of end uses according to preprogrammed customer preferences. The wide area network includes of a 

switched telephone uplink, to retrieve billing information, and a Very High 

Frequency (VHF) paging link to transmit pricing signals to the consumer and the 

LAN.(Willis, 2010)
36

Figure-2-4: Residential Automation for Real-Time Pricing (Willis, 2010)

Customer 

Interface Unit

Electric Water 

Heater

Pool Pump

HVAC 

equipment

Telephone line

Main-gate 

communications 

gateway

WAN Infrastructure 

VHF Paging signal

PLC Network

PLC Network

PLC Network

Power 

Company

Existing 

thermostat wires

Super Stat 

(An AEM 

programmable 

thermostat)
37

Chapter 3:

Conservation Voltage Reduction

(CVR)
38

3.1. CVR Definition

CVR (also known as Voltage Optimization), is one of the ways for Energy 

Conservation through Dynamic Voltage Management.

According to the US Department of Energy, because of inefficiencies in the 

electrical grid, up to 67% of energy used to create electricity is wasted by the time 

the consumer uses the electricity. Conservation Voltage Regulation (CVR) or 

Voltage Optimization (VO) is a technique for improving the efficiency of the 

electrical grid by optimizing voltage on the feeder lines that run from substations 

to homes and businesses. Utilities have been experimenting with CVR for over 30 

years. Although it was partially implemented in California in 1977, CVR has not 

been widely implemented because of high costs and technical limitations. In the 

past, broad implementation of CVR would require extensive additions to the 

utility infrastructure, adding many substations and shortening feeder lines. New 

technologies enable CVR to be implemented easily and economically.

CVR is not a new energy conservation measure; tests have been performed as 

early as in 1973. However, recent developments in the field of smart grids have 

drawn new attention to CVR, now also called voltage optimization.

Integrated Volt-Var Control (also known as Volt-Var management), which is an 

important feature of the future grid, enables the application of CVR. 

Traditionally, the substation voltage is set to the maximum allowed voltage in 
39

order to guarantee a minimum voltage level to customers at the end of the 

distribution lines. In future energy networks, it will be possible to control the 

voltage level along the distribution lines in order to supply all customers with 

approximately the same voltage, such that low-voltage substations can operate 

close to the minimum voltage level, without exposing customers at the end of the 

lines to under-voltage conditions.

In the US, regulations require that voltage be made available to consumers at 

120V +/- 5% – which yields a range of 126V to 114V (European Standards: 230V 

+/- 10%). On any feeder line, especially those are over three miles length, voltage 

on the line gradually decreases as the cumulative load (number of customers) on 

the line increases. This is called “line drop.” Because of line drop, power must be 

transmitted at a high enough voltage that the last customer on the end of the line 

gets at least 114V. So, power is often transmitted from the substation at 126V. US 

homes receive an average of 122.5V, with approximately 90% of homes and 

businesses receiving more voltage than they need.

The objective of CVR is to have the customer's utilization voltage at the lowest 

level consistent with proper operation of equipment, within nameplate ratings of 

utilization equipment and within levels set by regulatory agencies and standards 

setting organizations. American National Standards Institute (ANSI) Standard 
40

C84.1 sets the range for voltages at the distribution transformer secondary 

terminals at 120 volts +/- 5% or between 114 volts and 126 volts.

3.2. The Effects of Conservation Voltage Reduction (Ellens et al., 2012)

Assuming that household devices require no reactive power, the effect of CVR 

on energy consumption can be explained as follows. By Joule's law, the power P, 

voltage V and current I in a resistive circuit satisfy P = VI. It follows from Ohm's

Law V = I R that lowering the voltage level reduces the power when the load 

consists of pure resistors with constant resistance R, because in that case we have 

P = V2/ R. In reality, this is true if loads are constant-resistance (hot water system, 

fridge, oven, incandescent lighting, pool pump, etc.), but not if loads are 

constant-power (computer, TV, etc.). If we have constant-power loads, dropping 

the voltage will mean that the current has to increase, which leads to higher 

energy loss in the lines, according to the power loss formula PLines = I

2RLines. 

Also, many constant-resistance devices (hot-water system, fridge, oven, etc.) 

have a feedback loop - mostly measuring the temperature - that extends the 

operating time, leading to constant energy consumption (the energy 

consumption U satisfies U=PT, where T is the duration of the power 

consumption). Some lighting technologies (compact fluorescent lighting) keep 

the current constant. The power consumption of these devices decreases linearly 
41

with the voltage according to P=VI. The savings are therefore smaller than for 

constant-resistance devices, for which the power consumption is quadratic in V. 

Because CVR effectiveness depends on the type of device, we categorize

electrical devices into four load categories:

1) Constant-resistance loads without a feedback loop, which we call constantresistance loads, reduce the energy consumption for both the loads and the lines;

2) Constant-resistance loads with a feedback loop, which we call constant-energy 

loads, have a constant energy consumption (for time-scales that are longer than 

the duration of the feedback loop);

3) Constant-power loads increase the energy consumption, because of increasing 

line losses due to increased current draw caused by the lowered voltage.

4) Constant-current loads reduce the energy consumption for the loads.

Table-3-1 lists some experimental results in the US. The list shows that CVR 

savings for residential areas are usually 0.6% to 0.8% for each percent of voltage 

reduction, the savings being higher for commercial areas and lower for industrial 

areas. The average savings across the three market sectors are approximately 

0.6%.The experimentally determined CVR factors (relative savings per 1 % 

voltage reduction) differ significantly from utility to utility. Therefore most 

utilities feel that another utility's voltage regulation results are not necessarily 

transferable to their own service territory. Although the need for models 
42

assessing the factors that determine CVR effectiveness is clear, no such models 

have been found in the literature. 

Table-3-1: Results of CVR tests in terms of the CVR factor (Relative energy 

saving per percent of voltage reduction) (Ellens et al., 2012)

Year Source Residential Commercial Industrial Overall

1973

(Preiss & 

Warnock, 1978)

0.61 % 0.89 % 0.35 % 0.62 %

1977

(Kirshner & 

Giorsetto, 1984)

0.76 % 0.99 % 0.41 %

1979

(Warnock & 

Kirkpatrick, 1986)

0.73% 0.84 % 0.49 % 0.71 %

1989

(Kennedy & 

Fletcher, 1991)

0.62 %

3.3. CVR’s Benefits

CVR lowers the voltage at which electrical power is delivered and yields on 

average, a 1% energy savings for each 1% in voltage reduction down to 114V. 

Electrical equipment, including air conditioning, refrigeration, appliances and 

lighting is designed to operate most efficiently at 114V. If power is delivered at a 

voltage higher than 114V, energy is wasted. Higher necessary voltage also 
43

shortens the useful life of many types of equipment, since the extra energy is 

degenerate as heat.

Delivering voltages at the optimal levels reduces consumption, improves service 

quality and extends the life of equipment. Utilities and consumers save energy 

and lower operating costs by reducing the need to generate additional energy at 

power plants.

CVR also helps to lower greenhouse gas emissions; CVR is expected to have 

considerable environmental benefits, because the reduction of the energy 

consumption will lead to fewer CO2 emissions associated with energy 

production.

Besides the obvious advantages of energy conservation, there are other benefits 

of CVR and of AVC based CVR. One advantage to utility customers is that 

incandescent lamps and electric hot water heater elements will have a longer life. 

It is not uncommon to have incandescent lamps and hot water heater elements 

fail well before their rated lifetime hours.

Electrical energy is saved through reduced distribution system losses due to the 

lower voltage.

Consumers benefit through lower energy bills, and quicker response to power 

outage. 
44

Utilities benefit through lower losses, longer transformer life and increased 

knowledge of their system’s current condition. Operating costs are reduced 

during outages due to a better understanding of the fault location. The

information provided can form the basis of a predictive maintenance system.

Utilities may control the amount of conservation and demand by quickly 

adjusting set points from the Master station. (T. Wilson, 2002)

3.4. Conservation Voltage Reduction Strategies

Historically CVR has been implemented using two different strategies (De 

Steese, Merrick, & Kennedy, 1990). The first method is Line Drop Compensation 

(LDC) and the second is Voltage Spread Reduction (VSR). In LDC a distribution 

feeder is modeled and the Load Tap Changer (LTC) controls or voltage regulator 

controls are set so that the end of line voltage remains at 114 volts while the 

source voltage will be adjusted as the load varies. With VSR the voltage limits are 

narrowed from the +/-5% range to something less, typically +/-2.5% using the 

regulator or LTC controls. This usually requires enhancements to the distribution 

lines such as load balancing, reconductoring, and addition of capacitors. 

Utility engineers tend to be conservative and because of changes from light load 

hours (LLH) to heavy load hours (HLH), the LDC settings or voltage bandwidth 

settings on distribution LTCs or regulators are often made very conservatively to 
45

assure that the end of the line voltage never droops below a preset value. In 

addition, daily changes in temperature, day-of-the-week, etc. can lead to load 

changes that reduce the effectiveness of CVR settings.

In fact, in many utilities the distribution LTC or regulator controls are set so that 

the nearest customer never has voltages above 126 volts and the end of line 

voltage drops and rises as loads vary. The reasons for this are many. Lack of 

engineering resources to perform the required studies or prepare the distribution 

line models design the line upgrades are often a major reason. Another is the lack 

of capital resources or the inability to justify the capital and expenses needed to 

upgrade distribution.

A third strategy is to regulate the voltage at the customer's meter and the utility 

does not need to try to set controls for CVR. There is at least one company that is 

manufacturing a device that is installed at the customers meter and will regulate 

the voltage at the lower level within the customer's premises. A drawback to this 

strategy is that it depends on the customers to install equipment on their site and 

to pay the capital costs. Additionally, it does not have the benefit of less 

distribution transformer iron loss (iron loss in transformers is a function of 

transformer voltage) nor does it provide quite as much energy savings for line 

losses.
46

A fourth strategy is to use an Adaptive Voltage Control (AVC) system to 

implement CVR. This strategy makes use of new automatic control and 

communications technologies that were unavailable at the time of earlier CVR 

efforts. (T. Wilson, 2002)

3.5. Effects of Capacitors on CVR

Capacitors reduce the kVAR demand on the system, and since the total kVA 

demand consists of a real and reactive component, the capacitors provide the 

needed kVAR to the loads rather than the reactive power coming from the source 

and flowing through the distribution lines. Load amps are a function of the total 

kVA, and reducing the kVAR reduces the kVA, thus reducing the amount of 

current and line losses. Losses are function of current squared, so that is another 

important benefit of using capacitors to deliver kVAR to loads rather than from 

the generation source.

In addition to reducing the amount of line losses, capacitors help to increase line 

voltage delivered to the end-use consumer. Obviously this can cause an adverse 

effect during periods of full CVR implementation since capacitors raise line 

voltage. The voltage drop through the line is a function of the voltage drop 

through the line resistance as well as the drop through the line reactance,
47

V =I. R. Cosø + I. X. Sinø. So reducing the line current will reduce the voltage 

drop, or conversely stated will increase the voltage utilized at the loads.

Care should be taken when deciding how many capacitors should be turned on 

or off during times of running CVR on the system.

3.6. CVR Factor

The CVR factor is a measure of how effective voltage reduction is from an energy 

and demand standpoint. It is simply the ratio between the percent demand (Or 

energy) and voltage:

(3-1) CVR Factor = %demand reduction

%voltage reduction

It is the term commonly used to refer to the ratio between voltage reduction and 

energy load consumption for a particular part of a distribution system (load, 

feeder, substation, or utility).

The CVR factor is a measure of how effectively voltage reductions can be 

converted to calculate energy savings or reactive power savings. Mathematically, 

CVR factor can be calculated as shown in equations (3-2) and (3-3): (Singh, 

Tuffner, Fuller, & Schneider, 2011)

(3-2) CVR fACTORReal = 

%kWh Savings

%Voltage Reduction

(3-3) CVR fACTORReactive = 

%kVAR Savings

%Voltage Reduction
48

CVR factor is also occasionally expressed as the change in kilowatts or kVAR 

divided by the change in volts to show a reduction in peak demand.

Factors vary widely from substation to substation, feeder to feeder, and 

especially load to load. Contributions to the overall factor for a utility include 

consumers' load mix, transformer and conductor characteristics, and voltage

control schemes as moderated by voltage regulators, line drop compensators, 

and switched capacitor banks. Because of the large number of components 

involved, CVR factors for feeders and substations typically are measured 

experimentally, not theoretically generated.

Progress is being made in calculating CVR factors theoretically, with an eye 

toward predicting control scheme performance before installation. Load 

behavior is a large contributor to feeder CVR factor. Many load modeling studies 

have been completed; a good recent study is the 2010 report by the Pacific 

Northwest National Laboratory (PNNL), which evaluated CVR on a national 

level and built models that divided loads into two primary classes: those with 

and without thermal cycles. In the first category, lighting loads, for example, will 

consume energy as a function of voltage when on. In the second category, loads 

with thermal cycles, such as a hot water heater, will vary their duty cycles 

depending on the supply voltage. Moreover, inside each of these classes, loads' 

response can be described by their ratio of constant power, impedance and 
49

current characteristics—ZIP models. ZIP models can be constructed from 

experimental results on load behavior under changing voltage conditions.

3.7. Modeling Principles

To efficiently model CVR, as well as most distribution level behaviors, it is 

necessary to perform time series simulations. Examining the peak load behavior 

and concluding behavior for the rest of the year is not sufficient. 

Furthermore, standard power flow solutions are insufficient for analyzing the 

effects of CVR. Many loads within distribution systems cannot be defined as 

simple constant impedance, constant current and constant power loads (ZIP). 

Many are thermostatically controlled, provide constant mechanical power, or 

draw a constant amount of energy over different time periods. To properly 

understand the effects of voltage reduction on the distribution system, such 

loads must be properly modeled. Additionally, standard distribution solvers 

ignore the effects of residential transformers (typically split-phase or centertapped) and the cabling that connects the consumer to the transformer. While 

ignoring these components may be acceptable for traditional capacity planning 

studies, when studying the effects of voltage reduction, they must be included. 

(Willis, 2010)
50

3.7.1. Determination of Load Type

Load information in the original feeder models was limited. The original models 

limited a small amount of information on commercial loads and no information 

on residential loads. Loads were defined as static spot loads, where blocks of 

individual commercial and residential loads were summed to a single peak spot 

load on the primary system (no secondary voltage loads were defined). To more 

accurately classify the loads, Google Earth images of the feeders were located 

and the physical dimensions of the feeder overlaid. The loads provided by the 

original model were then manually classified by the type of building found at 

that location, and were categorized into nine different load types via visual 

inspection. These were classified as Residential 1-6, Commercial 1-2, and 

Industrial. Brief descriptions are provided in Table 3-2. Each load classification 

describes the properties of the load in that area. (Willis, 2010)
51

Table-3-2: End Use Load Classifications (Willis, 2010)

Load Class Description

Residential 1 Pre-1980<2000 square feet

Residential 2 Pos-1980<2000 square feet

Residential 3 Pre-1980>2000 square feet

Residential 4 Pos-1980>2000 square feet

Residential 5 Mobile Homes

Residential 6 Apartment Complex

Commercial 1 >35KVA

Commercial 2 <35KVA

Industrial All Industrial

By defining each building as older or newer, and larger or smaller, approximate 

physical properties for those homes could be assumed. These were then used to 

define multiple building models at each load location, depending upon the type 

of building that was found through observation in Google Earth©. Defining 

these properties gives insight into the benefits of voltage reduction not only at a 

single given load level, but as a function of seasonal and daily variations in load. 

Once again, while a particular building model at that location does not 

accurately represent a specific building in reality, the aggregate of the 
52

distribution of the buildings should approximate the response of all of the real 

buildings. Within each building, appliance loads were also modeled, as will be

seen in the following sections. (Willis, 2010)

3.7.2. Load Models

Once each of the points of interconnection were categorized in compliance with 

Table-3-2, it was necessary to completely represent the load. Due to the 

complexity of end use load behavior, load models can be divided into two 

separate classes: those without thermal cycles and those with thermal cycles. 

Loads without thermal cycles consume energy in a time-invariant manner, with 

the exception of voltage variations. Specifically, there is no control feedback loop. 

As an example, a light bulb will consume energy when turned on, as a function 

of voltage, in a fixed manner. In contrast, a load with a thermal cycle, such as a 

hot water heater, will have a varying duty cycle dependent on the supply 

voltage. For example, if the supply voltage is lowered, the hot water heater will 

draw less instantaneous power, but it must remain on for a longer time to heat 

the same amount of water. (Willis, 2010)

3.7.2.1. Loads without Thermal Cycles

The traditional method for modeling a load without a thermal cycle is to use a 

ZIP model. The ZIP model is a load which is composed of time-invariant 
53

constant impedance (Z), constant current (I), and constant power (P) elements. 

Figure-3-1 shows the circuit representation of the ZIP model.

Figure-3-1: The Traditional ZIP Load Model

The total real power consumed by a ZIP load at a given voltage is shown in 

equations (3-4) and (3-5) gives the reactive power consumption. The values of the 

constants within equations (3-4) and (3-5) are limited by the constraint of 

equation (3-6).

(3-4): Pa=

Va2

Vg2*Sg*Z%*Cos(Zθ)+VaVn

*Sg*I%* Cos(Iθ)+ Sg*P%* Cos(Pθ)

(3-5): Qa=

Va2

Vg2*Sg*Z%*Sin(Zθ)+VaVn

*Sg*I%* Sin(Iθ)+ Sg*P%* Sin(Pθ)

(3-6): 1= Z%+ I% + P%

Where:

Pi: Real power consumption of the ith load

ZIP Model

Z I P
54

Qi: Reactive power consumption of the ith load

Va: Actual terminal voltage

Vg: Nominal terminal voltage

Sg: Apparent power consumption at nominal voltage

Z%: Fraction of load that is constant impedance

I%: Fraction of load that is constant current

P%: Fraction of load that is constant power

Zθ: Phase angle of the constant impedance component

Iθ: Phase angle of the constant current component

Pθ: Phase angle of the constant power component

In equations (3-4) and (3-5), there are six constants that define the voltage 

dependent behavior of the ZIP load: Z%, I%, P%,Zθ, Iθ andPθ. Because CVR 

changes the voltage of a feeder, it is critical to understand how typical end use 

loads will respond to changes in voltage. Specifically, what the six constants that

accurately reflect various end use loads are. For loads such as a heating element, 

it is clear that the load is 100% Z, but for more complicated loads such as a 

Liquid Crystal Display (LCD) or Compact Fluorescent Light (CFL), the proper 

ratios are not as obvious.(Willis, 2010)
55

Some practical experiments which are done in UNLV power lab are shown in

figure 3-2 to figure 3-6. 

Figure-3-2: Voltage and Current waveforms for a sample refrigerator
56

Figure-3-3: Power and Energy Graphs for a sample refrigerator

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

3:24:30 PM3:28:30 PM3:32:30 PM3:36:30 PM3:40:30 PM3:44:30 PM3:48:30 PM3:52:30 PM3:56:30 PM4:00:30 PM4:04:30 PM4:08:30 PM4:12:30 PM4:16:30 PM4:20:30 PM4:24:30 PM4:28:30 PM4:32:30 PM4:36:30 PM4:40:30 PM4:44:30 PM4:48:30 PM

Power (W) and Enrgy (Wh)

Time (hh:mm:ss)

Power @ 120 V Energy @ 120 V

Power @ 114 V Energy @ 114 V
57

Figure-3-4: Power and Voltage Graphs for a sample Fan

0

20

40

60

80

100

120

140

16

111621263136414651566166717681869196

101106111116121126131136141146

Voltage (V) and Power (W)

Time (sec)

Voltage power
58

Figure-3-5: Power and Voltage Graphs for a sample Compact Fluorescent

0

2

4

6

8

10

12

14

16

0

20

40

60

80

100

120

140

1

112131415161718191

101111121131141151161171181191201211221231241251261271

Voltage

Wattage

Time(Sec)

Voltage power
59

Figure-3-6: Power and Voltage Graphs for a sample Fluorescent lamp (T5)

0

20

40

60

80

100

120

140

0

20

40

60

80

100

120

140

1

23456789

111133155177199221243265287309331353375397419441463485

Power(Wattage)

Voltage(V)

Time(Sec)

Voltage Power
60

3.7.2.2. Loads with Thermal Cycles

Whether a load has a thermal cycle or not, it must have the voltage dependent 

energy consumption of a ZIP loads. If the load does have a thermal cycle, there is 

the added complexity of an additional control loop, which determines when the 

load is energized, and for how long. One of the largest load types that have a 

thermal cycle are Heating, Ventilation, and Air Conditioning (HVAC) systems. 

An equivalent thermal parameter (ETP) model is used to approximate the 

response of the electrical demand of the HVAC system as a function of solar 

input, temperature, humidity, voltage, and thermostatic set points. (Sonderegger, 

1978; N. Wilson, Wagner, & Colborne, 1985)The thermal parameters of the 

building are the mass of the building, which defines how much stored thermal 

energy is in the building, and the envelope, which defines how quickly the 

energy moves from inside to outside the building and can loosely be described as 

the insulation quality. These parameters are determined by the actual physical 

properties of the building, and include such things as floor area, ceiling height, 

aspect ratio, window types, air exchange rate, etc. Additionally, HVAC 

properties such as heating and cooling set points, heat type (gas, electric, or heat 

pump), fan power, motor losses, etc. can be defined. Figure-3-3 is a diagram of 

the ETP model for a residential HVAC system.
61

Figure-3-7: The ETP model of a residential HVAC system (Willis, 2010)

Where,

Cair: Air heat capacity

Cmass: Mass heat capacity

UUegv: The gain/heat loss coefficient between air and outside

UUmass: The gain/heat loss coefficient between air and mass

Tsut: Air temperature outside the house

Qaaa

Qhv v

Caaa Cm mm

T ss

HVAC

Total heat

SUN

Too Taaa Tm mm

Internal gains

Qsss

Qg g

Qm

UUe UUm
62

Taaa: Air temperature inside the house

Tmass: Mass temperature inside the house

Tset: Temperature set points of HVAC system

Qaaa: Heat rate to house air

Qgaags: Heat rate from appliance waste heat

Qhvav: Heat rate from HVAC

Qmass: Heat rate to house mass

Qsssaa: Heat rate from solar gains

Equations (3-7) and (3-8) are the two ordinary differential equations (ODEs) 

which describe the heat flows shown in Figure-3-7. These equations are used to 

determine the thermal behavior of the house in response to the three heat sources 

and the user defined thermostatic set points. The solution to equations (3-7) and 

(3-8) represents the thermal behavior of the house and forms the basis for 

determining the electrical power consumption of the HVAC system.

(3-7) dTair

dt

= 

1

Cair

[Tmass U Umass - Taaa(UUegv + UUmass) + Qaaa + Tsut UUegv]

(3-8) dTmass

dt

= 

1

Cmass

[U Umass(Taaa+Tmass) +Qmass ]

Equations (3-7) and (3-8) can also be represented by a single second order 

differential equation as shown in Equation (3-9).
63

(3-9) a

d2Tair

dt2 + bdTairdt

+ cTaaa=d

Where:

a = 

CmassCair

U Amass

b = Cmass(UAenv+ U Amass)

UAmass

+ Cair

c = UUegv

d = Qmass+ Qaaa+ UUegvTsut

3.7.3. Description of the Model

The model takes the part of the electrical network that is affected by CVR. It is

part of the network which is from the low voltage substations to the consumers. 

The energy consumption for a given voltage level is calculated as the sum of the 

energy consumption of the devices. For all devices, the circuit looks like a load in 

series with a resistor representing the line resistance, together connected to the 

source voltage, with equal source voltage Vsourceand line resistance Rlines for all 

devices. The energy consumption of a device consists of energy consumed by the 

load and line losses. 

3.7.3.1.Energy Consumption of Constant-Resistance Devices

The load resistance of constant-resistance devices is derived by applying the

Formula (3-10) to the power consumption data:
64

(3-10) Rload = 

Vsource2

P∗

load

Where we have assumed that the nameplate power ( P∗

load ) is measured at a 

voltage of Vsource. For constant-resistance devices the actual power consumption

P� load at 120 V is somewhat lower than the nameplate power, because the actual 

voltage over the load is slightly less than 120 V (as the line resistance is nonzero). It is

(3-11) P� load = I

2 Rload = 

Vsource2

Rtot

 Rload = 

Vsource2

(Rload+Rlines)

Rload

Similarly we have for the line loss and the total power consumption:

(3-12) P lines = 

Vsource2

(Rload+Rlines)

 Rlines

(3-13) P tot = 

Vsource2

(Rload+Rlines)

The energy consumption of the load, the lines and the total energy consumption 

of the device are now easily calculated by multiplication by the operating time. 

For the standard voltage of 120 V, the calculated energy consumption of the load 

will be equal to the yearly energy consumption given in the data. (Ellens et al., 

2012)

3.7.3.2. Energy Consumption of Constant-Energy Devices

The load consumption and line losses for constant-energy devices are easier to 

calculate.
65

For every voltage level the load consumption is equal to the yearly energy 

consumption in the data. The line loss can be calculated by:

(3-14) Ulines = 

Uload Rlines

Rload

 

The load resistance is derived by the nameplate power in the same way as for 

constant-resistance devices. (Ellens et al., 2012)

3.7.3.3. Energy Consumption of Constant-Power Devices

As for constant-energy devices, for constant-power devices the energy consumed 

by the load is equal to the yearly energy consumption data, regardless of the 

voltage level. The power consumed by the lines is:

(3-15) P lines = I

2 Rlines = 

(Vsource − �Vsource2−4P loadRlines) )2

4Rlines

Where, P load, is the nameplate power of the device. The line energy consumption 

is a product of the line power consumption and the operating time, which is 

simply the yearly energy consumption divided by the power consumption.

(Ellens et al., 2012)

3.7.3.4. Energy Consumption of Constant-Current Devices

For constant-current devices, the current through the load and lines and the

Power consumptions by the lines are constant:

(3-16) I = P∗

load

Vsource

 and P lines = I

2 Rlines

The total and load power consumption are given by
66

(3-17) P tot = Vsource I and P load = P tot - P lines

The operating time of a constant-current device is calculated as follows:

(3-18) T = U∗

load

120 I− I2Rlines

With U∗

load (the given yearly power consumption), which is used to find the 

energy consumption for the load, lines and the total energy consumption.(Ellens 

et al., 2012)

3.7.4. A sample load modeling project

It is a well-known fact that stable operation of a power system depends on the 

ability to continuously match the electrical output of generating units to the 

electrical load on the system. Hence, load representation is an important element 

in power system dynamic performance (W. Price et al., 1993; W. Price et al., 

1995). Traditional load modeling concerns that dealt with the impact PF load 

characteristics on angular stability have been expanded to other system dynamic 

studies. For instance, it is found that voltage dependency of loads affect both the 

voltage secure operating region and rate of voltage collapse. While significant 

progress has been made in this field over the past two decades, a recent survey 

shows that nearly half of the electric utility companies are dissatisfied with their 

present load models and are pursuing further work.

Load representation is a complicated task because a typical load bus is composed 

of a large number of devices such as compressors, lamps, and electronic loads. 
67

The exact composition is difficult to estimate since it changes continuously. Even 

if the exact composition is known, it is impossible to represent each individual 

component involved and there are uncertainties regarding to the characteristics 

of individual load components. Furthermore, the common practice is to 

represent the composite load as seen from the power substation including 

feeders, compensation devices and distribution transformers. Consequently, 

considerable simplification is necessary when deriving load models.

In the past, loads have been modeled as constant impedance, constant current, or 

constant power. These models, however, do not take into account the load mix, 

nor the short term dynamic response and the long-term effect encountered in 

other loads. A solution to the load mix problem is to use a combination of the 

three models above; namely the so-called ZIP model representing a weighted 

sum of constant impedance (Z), constant current (I) and constant power (P). 

These weights are derived using either the components-based approach or the 

measurement-based approach. The component-based approach involves 

building up a load model from detailed information on its class mix, and load 

components, and their characteristics in the form required for dynamic studies. 

LOADSYN program (W. W. Price et al., 1988) offers an automated way for 

developing static and dynamic models based on the above methodology.
68

In the measurement-based approach, load models are derived from staged tests 

or actual system transients (W. W. Price et al., 1988; Sabir & Lee, 1982) Ways to 

produce a staged voltage disturbance include changing substation transformer 

taps, switching station capacitors, and switching off a transformer from a parallel 

set. Switched feeder capacitors and voltage regulators are blocked to obtain 

meaningful results during the tests. The measured real and reactive powers are 

then fitted to polynomial expression of voltage.

This section presents the results of a load modeling where the measurement or 

field-test method was used to provide adequate models for local pure residential 

loads, combined residential/commercial, commercial/light industrial loads, 

industrial loads and pure commercial loads including hotel/casino loads which 

are unique to the State of Nevada. The voltage disturbance is caused by quickly 

tapping down (up) the transformer taps till the minimum (maximum) voltage 

limits at the feeder end are reached. One of the objectives is to find out if a small 

(5%) voltage change is sufficient for the load parameter estimation, and to derive 

these parameters if such a disturbance is sufficient.

First, a test plan and fall back procedure developed specifically for this project 

summarized. This is followed by the analysis of the load response of the two 

feeders tested at each of the following substations: Warm Spring Substation, and 

Quail Substation (All Tests were implemented in 1999). Static load parameters in 
69

the form of ZIP models are then derived from the recorded data using curve 

fitting techniques. The generic dynamic load model is also considered by 

examining the load recovery from the staged disturbance. As to the fast load 

dynamics, the slow mechanical tap changers (4-5 seconds per step and one 

second to change steps) did not permit the capture of such characteristics. 

However, noticeable load recovery in reactive power is noted in half of feeders 

tested even for such small voltage increments. The dynamic response is best 

derived from sudden a large voltage disturbance.

3.7.4.1. Test Preparation and procedure

Three of the important factors that were of concern during the testing period are 

listed in table-3-3 along with the corresponding preventive actions, the test 

preparation addressed several issues, some of which include a) selection of the 

feeders to be tested, b) selection of test equipment, c) types of tests to be 

conducted, d) transformers tap switching schedule, e) test schedule, f) detailed 

test procedure and Fall-back. Each of these subjects is briefly discussed below.
70

Table-3-3: Test Concerns and Preventive Actions

Concern Method of Prevention

Fluctuation of the high voltage supply 

feeding the substation, and the natural 

fluctuation of the load. These variations 

will interfere with the load response to 

staged disturbance, thus the collected 

data will be limited use.

Pre-disturbance monitoring of load and 

voltage at selected feeders was planned 

few days before the testing date to 

determine the best time windows near 

peak load with minimal fluctuations.

Automatic switching of shunt 

capacitors and voltage regulators along 

the feeders. These switching will also 

affect both the real and reactive power 

responses.

Switched capacitors locations along 

each feeder were identified for locking 

off prior to testing. Due to their 

relatively short length, none of the 

tested feeders had voltage regulators. 

Potential effects on sensitive loads and 

customer service. The planned voltage 

disturbances will affect the quality of 

voltage supply to all customers served 

by the substation under testing, which 

in turn might cause some electronic 

loads such as variable-frequency 

derives to trip.

Critical customers, including a fruit 

juice processing plant, were identified 

and a plan for switching them to other 

substation prior to testing was drafted. 

Customer service concerns led to 

limiting voltage disturbance within the 

limits specified by operating standards, 

i.e., between 114V and 126V (on 120V 

base) at the feeder end.

3.7.4.1.1. Selection of feeders to be tested

It is desired to test a variety of feeders serving pure residential loads, pure 

commercial loads, pure hotel/casino loads, combined residential/commercial 

loads, and combined commercial/light industrial loads, and pure industrial 

loads. Potential candidate feeders corresponding to each of the above load class 

and load mix are identified and those selected for testing are shown in table-3-4. 

The initial plan was to include the BMI plant at the pure industrial load, but 
71

complication were faced due to the fact that this industrial plant is supplied by 

Western Ares Power Administration (WAPA).

Table-3-4: Feeders Selected for Testing

Substation Feeder # Ref.# Type of Load

Warm Springs 1203

1208

WS1203

WS1208

Mall (Commercial)

Hotel/Casino

Haven 

Haven

1202

1203

H1202

H1203

Residential

Commercial/Light Industrial

Quail

Quail

1210

1213

Q1210

Q1213

Residential/Commercial

Pump Load (Industrial Motor)

3.7.4.1.2. Selection of Test Recorder

The instrument should be capable of recording and storing all currents, voltages, 

system frequency and active and reactive powers for a sufficient length of time. 

Its sampling rate should be fast enough to accurately compute the RMS current 

and voltage at each cycle. The leading manufacturers of such instruments are 

Dranetz/BMI and Reliable Power Meters (RPM). The latter was chosen due to its 

higher data storage capability and its ability to capture all events without 

specifying a threshold. The recorder has a very fast sampling rate of 8 kHz. The 

recorder is to be installed at feeder main breaker point in the substation as shown 

in figure-3-8. The figure also shows the voltage monitor at the feeder end to be 

read by a crew member during staged voltage disturbances. Since two feeders 

have been selected for testing at each substation, test procedures have been set 
72

up such that testing will be conducted in parallel to conserve testing time and 

maximize crew utilization. Consequently, two test instruments have been leased 

so that two feeders can be tested simultaneously.

Figure-3-8: Power and Voltage Monitoring Points

3.7.4.1.3. Types of Tests to be Conducted

First, the pre-disturbance monitoring of load and voltage at selected feeders is to 

be conducted a day or two prior to the testing date in order to determine the best 

time window near peak load with minimal fluctuations. During the test day, 

load response to the following tests is to be recorded: transformer tap changing 

with feeder capacitors OFF, capacitors switching, and transformer tap changing 

Feeder

Voltage Monitor

Test Recorder

Caps Load

Power System

LTC

LTC

T

T

M

M
73

with capacitors ON. These tests are summarized in table-3-5 and are illustrated 

by schematics in figure-3-9 below.

Switching OFF on of the parallel transformers to cause a sudden voltage 

disturbance was considered initially. Before the switching, the two parallel 

transformers should be set to different tap positions. When the transformer that 

gives higher secondary open circuit voltage is switched off, the secondary 

voltage drops to the value determined by the remaining transformer; thus a 

sudden voltage drop is created. This complex method of causing voltage 

disturbance was not experimented as it is known to lead to circulating currents 

that may trip proactive devices and cause a power outage.

Table-3-5: Types of Tests Conducted

Test Description

A

B

C

D

Base Case

Transformer Tap Switching with Feeder Capacitors OFF

Feeder Capacitor Switching

Transformer Tap Switching with Feeder Capacitors ON
74

Figure-3-9: Planned Test Types: (A) Base Case, (B) LTC Operation without 

Capacitors, (C) Capacitor Switching, (D) LTC Operation with Capacitors.

T

A. Pre-Test (Base Case)

T M

T M

B. LTC Operation w/o Caps.

C. Capacitors Switching

T M

D. LTC Operation with Caps
75

3.7.4.1.4. Transformer Tap Switching

All the 3 substations selected for the testing are supplied by two 138/12.47 kV 

station transformers operated in parallel, each is equipped with 32 taps. Sudden 

voltage changes on the secondary side are induced by simultaneous and rapid 

tap movement on both transformers to avoid circulating currents. A 5-minute 

voltage dip is initially caused, and it is followed by a voltage by a rise to the 

maximum value allowed under normal operating conditions. The voltage is then 

restored to its initial value 5 minutes later. The voltage 

Figure-3-10: Planned Voltage Disturbance by Transformer Tap Changing

3.7.4.2. Load Modeling

Load models are classified into two broad categories: static models and dynamic 

models. A static load model expresses the characteristics of the load at any 

V min

V nom

V max

Recording Time Window 

5 min 5 min 5 min 5 min

....

... ....

....
76

instant of time as an algebraic function of the bus voltage magnitude and 

frequency (the active and reactive powers are considered separately). The 

dynamic load, on the other hand, represents the load components that respond 

to disturbances dynamically, i.e., responses to disturbances do not occur 

instantly but require some time. Depending on the nature of the disturbances 

and the purpose of the study, the dynamics with a certain range of response 

times become more important. 

3.7.4.2.1. Static Load Model

Traditionally, the voltage dependency of load characteristics has been 

represented by the exponential model

(3-19) P = P0(

V

V0

)α

(3-20) Q = Q0(

V

V0

)β

Where, the subscript “0” identifies the values of the respective variables at the 

initial operating condition. For composite system loads, the exponents α and β 

typically vary between 0.5 -1.8 and 1.5 -6.0, respectively (Kundur, Balu, & Lauby, 

1994)

An alternative model which is widely being used in industry (e.g., including 

EPRI/ETMS, GE/PSLF Dynamics, and WSCC stability programs) to represent the 

voltage dependency of the load is the polynomial model:

(3-21) P = a0 + a1V + a2V2

(3-22) Q = b0 + b1V + b2V2
77

Where the powers represent the total MW and MVAR, and the voltage 

represents that phase voltage in kV. The above model is often referred to as the 

ZIP model as it is composed of constant impedance (Z), constant current (I) and 

constant power (P) components corresponding to the third, second and first 

elements in Eqns. (3-21)-(3-22), respectively . this model is adopted since it is of 

interest to NPC, and its parameters from recorded data is to match the P-V and 

Q-V data during voltage change to these second-order polynomial expressions 

using lest-squared error technique. The mathematical steps for this method are 

listed next.

The problem of approximating as set of recorded data (va, pa) , i= 1,2,...,n with a 

second-order polynomial P(V) = a0 + a1V + a2V2 using least-squares 

procedure requires choosing the constants a0, a1, a2 that minimize the following 

expression:

(3-23) E = � (pa

g

1 − P(va))2

In order for E to be minimized, it is necessary that

(3-24) a0 ∑ va g 0

1 + a1 ∑ va g 11 + a2 ∑ va g 21 = ∑ pava g 01

(3-25) a0 ∑ va g 1

1 + a1 ∑ va g 21 + a2 ∑ va g 31 = ∑ pava g 11

(3-26) a0 ∑ va g 2

1 + a1 ∑ va g 31 + a2 ∑ va g 41 = ∑ pava g 21

The above conditions result in specific values of a0, a1 and a2 which satisfy the 

following set of linear equations:

(3-28) x0a0 + x1a1 + x2a2 = y1
78

(3-29) x1a0 + x2a1 + x3a2 = y2

(3-30) x2a0 + x3a1 + x4a2 = y3

Where,

(3-31) x0 = n, x1 = ∑ va, g

1 x2 = ∑ va g 2

1 , x3 = ∑ va g 31 , x4 = ∑ va g 41

(3-32) y1 = ∑ pa, g

1 y2 = ∑ pava g

1 , y3 = ∑ pava g 21

The solution of the above equation is as follows:

(3-33) a0 = 1

d (y1(x2x4 − x3

2) − x1 ∗ (y2x4 − y3x3) + x2(y2x3 − y3x2))

(3-34) a1 = 1

d (x0(y2x4 − y3x3) − y1 ∗ (x1x4 − x2x3) + x2(x1y3 − y2x2))

(3-35) a2 = 1

d (x0(x2y3 − y2x3) − x1 ∗ (x1y3 − y2x2) + y1(x1x3 − x2

2))

Where,

(3-36) d = x0(x2x4 − x3

2) − x1 ∗ (x1x4 − x2x3) + x2(x1x3 − x22)

The reactive power parameters (b0, b1, b2 ) are found in a similar manner by 

replacing the P-V data set with that of the Q-V data. 

The resulting parameters are listed in Table-3-6 for each feeder tested, except for 

Q1213 which did not have the expected pump load on the test day.

Table-3-6: Static load model parameters

Feeders a0 a1 a2 b0 b1 b2 

WS 1203 10.95 -1.75 0.15 15.22 -4.5 0.36

WS 1208 69.8 -19.6 1.44 78.3 -21.48 1.52

H 1202 23.33 -4.80 0.37 36.66 -10.85 0.86

H 1203 7.84 -0.46 0.06 18.58 -5.65 0.50

Q 1210 6.23 -0.48 0.05 16.82 -5.20 0.44
79

The significant deviations of the load on WS 1208 are due to the random 

momentary changes indicated earlier. To take this into account, a static-random 

load model is proposed. It is basically a sum of the derived static components 

and the random components: (ps+pa), (Qs+Qa), where the random components 

(pa, Qa) are calculated by subtracting the static components above from the 

measured values at each voltage level. These random components can only be 

expressed analytically by probability functions. Their histograms are found to 

resemble normal probability density function with a standard deviation σ = 0.2. 

Mathematically, these functions are: 

(3-37) S(Sa) = exp(− Sr

2

2σ2), Sa =pa, Qa

The P-V and Q-V Correlation model is sometimes expressed in terms the initial 

quantities (p0,Q0,V0) of the load and feeder voltage before the disturbance, i.e.,

(3-38) P = p0(a′

0 + a′1 � V

V0

� + a′

2(� V

V0

�

2

),

(3-39) Q = Q0(b′

0 + b′1 � V

V0

� + b′

2(� V

V0

�

2

),

The values of the new load parameters (with primes) are calculated from the old 

ones (without primes) as follows:

(3-40) α′

0 = α0 /S0

(3-41) α′

1 = α1 /S0

(3-42) α′

2 = α2 /S0 
80

Where (α0,α′

0,S0)= (aa,a′a,P0) for active power, and (ba,b′a,Q0) for reactive power. 

The resulting modified parameters are listed in Table-3-7.

 Table-3-7: Modified static load model parameters

Feeders V0 P0 α′

0 α′1 α′2 Q0 b′0 b′1 b′2

WS1203 7.55 6.58 1.66 -2.00 1.30 2.12 7.18 -16.02 9.68

WS1208 7.55 3.80 18.37 -38.94 21.6 2.75 28.47 -58.97 31.50

H1202 7.48 7.92 2.94 -4.53 2.61 3.98 9.21 -20.39 12.10

H1203 7.48 7.87 0.91 -4.37 0.42 4.27 4.34 -9.89 6.55

Q1210 7.52 5.75 1.08 -0.68 0.49 2.85 5.90 -13.72 8.73

The frequency dependency of load characteristics is usually represented by 

multiplying the polynomial model by a factor as follows:

(3-43) P = (P0 + P1V + P2V2)(1 + KPΔf)

(3-44) Q = (Q0 + Q1V + Q2V2)(1 + KqΔf)

Where Δf is the frequency deviation from the nominal value (i.e.,60 HZ). The 

parameter KP ranges from 0 to 3 and Kq ranges from -2 to 0. Load-frequency 

characteristics require an isolated system, a request that is rarely acceptable by 

system operation/dispatch. Furthermore, to obtain valid data, care must be taken 

to separate the effects of voltage changes and frequency changes.
81

Finally, it is important to mention that static models above are not realistic at low 

voltages and may lead to computational problems. This is why stability 

programs usually switch to constant impedance model when the bus voltage 

falls below a specified value.

3.7.4.2.2. Dynamic Load Model

Dynamic models of composite loads are complicated due to the fact that there 

are a number of phenomena in power system dynamic performance analysis 

spanning from a fraction of a second to several minutes. There are also different 

load types with diverse response times and characteristics. Currently, 

representation of loads in system dynamic studies include the static ZIP model 

and the dynamic load model of induction motors, since their dynamic 

contribution is most significant as the consume over half of the power generated. 

The motor dynamics characteristics have been studies extensively and their 

dynamics are quite noticeable during the first few seconds, right after the 

disturbance, after which the motor basically behaves with its steady-state 

characteristics. There are basically four levels of details of the induction motor 

model, and the adequate model to use depends on the type PF dynamic study to 

be conducted.

In long-term dynamic studies, the induction motor can be represented by its 

steady-state circuit since its steady response to modest amplitudes of voltage 
82

changes is reached very quickly (within seconds). The induction motor 

equivalent circuit is shown in figure-3-11, and its electrical torque calculated by

(3-45) Te = 3 p

2

Rr

Sws

Ia

2

Where p is the number of motor poles, S is the rotor slip, is the synchronous 

angular frequency (= 2πf), Ra is the rotor resistance referred to the stator side, 

and Ia is the rotor current which can easily be expressed in terms of the stator 

supply voltage and the motor circuit parameters.

Figure3-11: Steady-State Equivalent Circuit of Induction Motor

In extended mid-term dynamic stability, the rotor acceleration equation is 

necessary, i.e., the motor is modeled by a first-order differential equation:

(3-46) dwr

dt = 1

2H (Te − Tm)

Where Te is a function of the rotor speed (obtained by replacing the slip S =

ws − wa in the equation above).

+

_

Vs Xm

X Rs

Xr s

Rr

S
83

In short-term and mid-term stability studies, the stator flux dynamics are often 

neglected and only the rotor flux dynamics in addition to the mechanical 

equation above are considered. This model is derived in the d-q reference frame 

(Ertem & Baghzouz, 1989; W. Price et al., 1993)and the resulting differential 

equations are:

(3-47) dvd⁄dt = −(vd + (Xs − Xs

′

)iqs)⁄T0 + vqdθa⁄dt

(3-48) dvq⁄dt = −(vq − (Xs − Xs

′

)ids⁄T0 + vddθa⁄dt

(3-49) dθa⁄dt = ws−wr

ws

The tests conducted are considered for long-term stability or voltage stability 

studies since one transformer tap change requires an average of 4-5 sec. the fast 

dynamic response of induction motors requires fast and relatively large voltage 

changes (e.g., transformer or large capacitor switching). The intent was to 

examine whether the small and slow LTC operation will result is noticeable 

dynamic on Quail 1213. Unfortunately this pump load was disconnected during 

the testing period.

For voltage stability analysis, the load-voltage response characteristics can be 

represented by a generic dynamic load model where the load model parameters 

are obtained from field measurements showing load recovery. Three parameters 

are sufficient to describe such a response: the transient characteristics parameter 

(defined as the instantaneous demand change of load under a sudden voltage 

change), the steady state characteristic parameter defines the steady-state load 
84

demand as a function of steady-state supply voltage), and the time constant 

characterizes the time span needed for a load changes while lowering the voltage 

in necessary.

3.8. Computer Simulation for a sample project

Base Scenario: For computer simulating a sample project and evaluate the CVR 

on it by “Easy Power” software, has been considered a 10 mile long feeder that 

serve a 4MVA (85% Power Factor) uniformly distributed load from a 69kv/13.2kv 

15MVA substation transformer with 8% impedance. The uniform load can be 

lumped at 20 uniformly spaced nodes (each supplying 0.2MVA).The voltage at 

the substation is kept constant at 1 PU by means of a LTC. The feeder consists of 

ACC/266.8 kcmil with a GMD of 4’ and an operating temperature of 25deg.

Scenario-1: In this scenario, loads increased by 10% more. After load flow 

analysis, results show that in some part of the grid, there is a voltage drop over 

the 5% allowable. So, in next scenario, some solution applied to solve extra 

existence voltage drop.

Scenario-2: In this scenario, two sets of capacitors which each are two sets of 

0.15kVAR added to system and also tap changer of transformer increased to 4%.

The voltage drop issues were solved along the grid. Finally, voltage behavior of 

each scenario was compared together which are shown by some graphs.
 85

4.247 MVA

0.973 pf

 Voltage drops One-Line diagrams for all scenarios are illustrated in Figure-3-12 to

 Figure-3-14.

BUS-22

1.000 UTIL-1

BUS-22

68.669 kV 

(0.121%)

13.200 kV

TX-1

(0.508%) (0.465%) (0.422%) (0.380%)

BUS-3

BUS-1 BUS-2 BUS-4 BUS-5 BUS-6

(0.550%)

L-1 L-2 L-3 L-4 L-5

(-0.337%)

(-0.167%)

(-0.209%) (-0.252%) (-0.294%)

BUS-3_A

BUS-2_A

BUS-4_A BUS-5_A BUS-6_A

L-1_A L-2_A L-3_A L-4_A L-5_A

(0.124%)

(0.082%) (0.040%) (0.245%) (0.203%)

BUS-3_B

BUS-2_B

BUS-4_B BUS-5_B BUS-6_B

L-1_B L-2_B L-3_B L-4_B L-5_B

(0.162%)

(0.003%) (-0.038%) (-0.080%) (-0.121%)

BUS-3_C

 BUS-2_C

BUS-4_C BUS-5_C BUS-6_C

L-1_C L-2_C L-3_C L-4_C L-5_C

Figure-3-12: Voltage drops one-line diagram (Base scenario)
 86

4.758 MVA

0.960 pf

BUS-22

1.000

UTIL-1

BUS-22

68.669 kV 

(0.319%)

13.200 kV

TX-1

(0.600%) (0.553%) (0.506%) (0.458%)

BUS-3

BUS-1 BUS-2 BUS-4 BUS-5 BUS-6

(0.647%)

L-1 L-2 L-3 L-4 L-5

(-0.411%)

(-0.222%)

(-0.269%) (-0.316%) (-0.364%)

BUS-3_A

BUS-2_A BUS-4_A BUS-5_A BUS-6_A

L-1_A L-2_A L-3_A L-4_A L-5_A

(0.175%)

(0.128%) (0.081%) (0.278%) (0.232%)

BUS-3_B

BUS-2_B BUS-4_B BUS-5_B BUS-6_B

L-1_B L-2_B L-3_B L-4_B L-5_B

(0.186%)

(-0.002%) (-0.048%) (-0.094%) (-0.140%)

BUS-3_C

BUS-2_C BUS-4_C BUS-5_C BUS-6_C

L-1_C L-2_C L-3_C L-4_C L-5_C

Figure-3-13: Voltage drops one-line diagram (Scenario-1)
 87

4.717 MVA

0.967 pf

BUS-22

1.000

UTIL-1

BUS-22

70.825 kV 

(-0.541%)

13.728 kV

TX-1

(0.572%) (0.525%) (0.478%) (0.432%)

BUS-3

BUS-1 BUS-2 BUS-4 BUS-5 BUS-6

(0.619%)

L-1

0.259 MVA

0.850 pf

L-2

0.259 MVA

0.850 pf

L-3

0.259 MVA

0.850 pf

L-4

0.259 MVA

0.850 pf

L-5

0.259 MVA 

(-0.

03.

85%0 )

pf

(-0.197%)

(-0.244%) (-0.291%) (-0.338%)

BUS-3_A

BUS-2_A BUS-4_A BUS-5_A BUS-6_A

L-1_A

0.259 MVA 

(0.1

05.815

%0) pf

L-2_A

0.259 MVA

0.850 pf

L-3_A

0.259 MVA

0.850 pf

L-4_A

0.259 MVA

0.850 pf

L-5_A

0.259 MVA

0.850 pf

(0.104%) (0.058%) (0.259%) (0.213%)

BUS-3_B

BUS-2_B BUS-4_B BUS-5_B BUS-6_B

L-1_B

0.259 MVA

L-2_B

0.259 MVA

L-3_B

1.525 MVA 0.259 MVA

L-4_B

0.259 MVA

L-5_B

0.259 MVA

0.850 pf 0.850 pf 0.000 pf 0.850 pf 0.850 pf 0.850 pf (0.168%)

(-0.016%) (-0.061%) (-0.077%) (-0.122%)

BUS-3_C

BUS-2_C BUS-4_C BUS-5_C BUS-6_C

0.181 MVA

0.000 pf

L-1_C

0.259 MVA

0.850 pf

L-2_C

0.259 MVA

0.850 pf 0.182 MVA

0.000 pf

L-3_C

0.259 MVA

0.850 pf

L-4_C

0.259 MVA

0.850 pf

L-5_C

0.259 MVA

0.850 pf

Figure-3-14: Voltage drops one-line diagram (Scenario-2)
88

Table-3-11: Buses Voltage level of each Scenario

Bus Base Scenario(VPU) Scenario-1(VPU) Scenario-2(VPU)

22 1 1 1

1 0.999 0.997 1.005

2 0.993 0.99 0.999

3 0.988 0.984 0.993

4 0.984 0.979 0.988

5 0.979 0.974 0.983

6 0.976 0.969 0.979

6A 0.972 0.965 0.975

5A 0.969 0.961 0.972

4A 0.967 0.958 0.969

3A 0.965 0.956 0.967

2A 0.963 0.953 0.965

2B 0.962 0.952 0.963

3B 0.961 0.95 0.962

4B 0.961 0.95 0.961

5B 0.958 0.947 0.959

6B 0.956 0.944 0.957

6C 0.954 0.943 0.955

5C 0.953 0.941 0.954

4C 0.952 0.94 0.953

3C 0.952 0.94 0.952

2C 0.952 0.94 0.952

According to Table-3-11, voltage changes along the feeder can be compared 

which are illustrated in Figure-3-15 to Figure-3-17.

Also, power flow summary reports for all three scenarios are illustrated in 

Appendix-A (Table-A1 to Table-A3).
89

Figure-3-15: Voltage changes along the feeder (Base and first scenarios 

comparison)

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

22 1 2 3 4 5 6 6A 5A 4A 3A 2A 2B 3B 4B 5B 6B 6C 5C 4C 3C 2C

Voltage (PU)

Buses name 

Base

Scenario

1
90

Figure-3-16: Voltage changes along the feeder (Base and second scenarios 

comparison)

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

22 1 2 3 4 5 6 6A 5A 4A 3A 2A 2B 3B 4B 5B 6B 6C 5C 4C 3C 2C

Voltages(PU)

Buses names

Base

Scenario

2
91

Figure-3-17: Voltage changes along the feeder (All scenarios comparison)

0.9

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

22 1 2 3 4 5 6 6A 5A 4A 3A 2A 2B 3B 4B 5B 6B 6C 5C 4C 3C 2C

Voltage(PU)

Buses name

Base

Scenario

1

Scenario

2
92

Chapter 4:

Conclusion
93

The amount of permissible CVR program voltage reduction allowable on utility 

system varies depending on design, load characteristics, etc. However, in general

Rarely can one implement a “theoretical” across the board constant reduction. 

Most often, reduction affect only part each feeder. Most feeders can engage

reductions of averaging about 1-1.5% without too much re-engineering. There 

will be exceptions, feeders where no CVR can be applied without major reengineering and additions.

Reduction of about 3% can be effected through significant refinement of voltage 

engineering on feeders and the installation of properly tailored line regulators 

and switched capacitors.

Reduction of up to 5% and in some cases a bit more than 6% can be effected 

during emergencies, if the utility has formerly prepared to handle a 3% reduction 

without issues.

On many utilities systems, for the emergency condition listed above, the voltage 

delivered to customers near the end of feeders will drop markedly below the 

minimum dictated by the utility’s standards for normal voltage service. Such

conditions are acceptable for a brief period because the “emergency conditions” 

permit application of emergency voltage standards.

For instance, utilities using ANSI standard C84.1-1989 as their voltage standard 

can get beyond three additional volts for emergency CVR. The standard lists 
94

105% to 91.7% as allowable for certain temporary on occasional operation 

conditions, i.e., emergencies.

Undeniable reducing voltage reduces loads. This is easy to see with what is 

called a “notched test”: drop the voltage at the substation low side bus by 

adjusting LTC controls or regulation by a total reduction of 5% while monitoring 

current. The reduction in power usage is noticeable. System normally responds

slightly better than linearly: a 5% CVR gives about a 5-6% reduction in load. 

Raise the voltage to its original level fifteen minutes later and the load instantly 

goes up by a like amount. It is hard to argue with this type of “notch test” (shortterm drop and raise back in voltage). The voltage reduction reduces load by 

about or a little more than 1:1 ratio.

But this does not mean CVR works this well as an energy reduction measure.

What is often not recognized is that such “notch tests” do not mean that 

reduction lasts permanently. In fact, CVR cooperates with variety of appliance 

called “duty cycle rebound”.

At the end, in aspect of economical, CVR’s major cost impact of implementing 

CVR is at the distribution level, where it can increase cost of feeders in some 

cases. In other feeder cases, it can be applied at no cost, occasionally with the 

little more effort than the resetting of transformer taps and LDC setting. 

However, CVR reduces the voltage drop allowed to distribution planners to
95

distribute power, a consideration in systems that are well designed from a load 

reach standpoint and have no margin in voltage.

In some cases, CVR can be implements only by modifying the feeder system 

(adding regulators or reconductoring) to overcome voltage drop problems 

caused by CVR’s effective change in voltage standards. Beyond this, by limiting 

the voltage drop available, feeders will have fewer margins to accept new load 

growth before the (now revised) voltage drop criteria are exceeded. They will 

need to be reinforced more often and perhaps in a more costly manner.

Furthermore, the utility needs to consider the impacts of CVR on load reach and 

the loss in value of that. In some cases, the loss in capability of the distribution 

system is considerable. And the overall cost of the future additions and 

expansion is considerable. This is not say that CVR is not economical or that it 

should not be examined as an option, just to point out that the present and future 

costs on distribution should be taken into account in developing evaluations of 

benefit/cost.
96

Appendix-A: Power flow summary reports

Table-A1: Power flow summary reports (For Base Scenario)

Mismatch Report

Iteration MW Mismatch MVAR Mismatch

Number Bus Name pu Bus Name pu

0 BUS-2_C 0.00000 BUS-2_C 0.00000

1 BUS-2_C 0.00000 BUS-2 0.00000

2 BUS-2_C 0.00000 BUS-3_C 0.00000

3 BUS-2_C 0.00000 BUS-3_C 0.00000

4 BUS-2_C 0.00000 BUS-3_C 0.00000

Generator Summary Report

Generator Scheduled Limits Solution

Name Typ

e

Rated

kVA

kW kVAR Vpu kVA

R 

Min

kVA

R 

Max

kW kVA

R

kVA Pf V

pu

Deg Eq'pu Deg

UTIL-1 Sw 1.000 4134 973 4247 0.973 1.

00

0

0.00 1.000 0.00

Load Summary Report

Bus Solution

Name Base kV kV Vpu Deg kW kVar kVA Pf

BUS-1 13.200 13.184 0.999 -1.24 0 0 0 0.000

BUS-2 13.200 13.111 0.993 -1.56 200 124 235 0.850

BUS-2_A 13.200 12.711 0.963 -4.06 200 124 235 0.850

BUS-2_B 13.200 12.695 0.962 -4.29 200 124 235 0.850

BUS-2_C 13.200 12.567 0.952 -5.16 200 -148 249 -0.804

BUS-3 13.200 13.044 0.988 -1.88 200 124 235 0.850

BUS-3_A 13.200 12.733 0.965 -3.82 200 124 235 0.850

BUS-3_B 13.200 12.684 0.961 -4.50 200 124 235 0.850

BUS-3_C 13.200 12.567 0.952 -5.13 200 124 235 0.850

BUS-4 13.200 12.983 0.984 -2.18 200 124 235 0.850

BUS-4_A 13.200 12.761 0.967 -3.57 200 124 235 0.850

BUS-4_B 13.200 12.679 0.961 -4.71 200 -1398 1412 -0.142

BUS-4_C 13.200 12.572 0.952 -5.09 200 124 235 0.850

BUS-5 13.200 12.927 0.979 -2.48 200 124 235 0.850

BUS-5_A 13.200 12.794 0.969 -3.31 200 124 235 0.850

BUS-5_B 13.200 12.646 0.958 -4.81 200 124 235 0.850

BUS-5_C 13.200 12.582 0.953 -5.03 200 124 235 0.850

BUS-6 13.200 12.877 0.976 -2.77 200 124 235 0.850

BUS-6_A 13.200 12.833 0.972 -3.04 200 124 235 0.850

BUS-6_B 13.200 12.620 0.956 -4.89 200 124 235 0.850

BUS-6_C 13.200 12.598 0.954 -4.97 200 124 235 0.850

BUS-22 69.000 69.000 1.000 0.00 0 0 0 0.000

System Summary Report

--

Total kW kVAR kVA PF

Generation in System 4134 973 4247 0.973

Load in System 4000 2480 4706 0.850

Shunt Load in System -0 -1794

Losses in System 134 287 - -

Check of Balance -0 -0
97

Table-A1: Power flow summary reports (For Base Scenario)-Continued

Transformer Tap Report

Transformer Connection Base kV Tap kV LTC Description

Name From

Bus Name

To

Bus Name

From To From To Typ

e

LT

C

LT

C

Typ

e

Contr

ol

Side

LTC

Side

Con

trol

Val

ue 

pu

Limits

Min 

kV

Limits

Max 

kV

TX-1 BUS-22 BUS-1 69.00

0

13.20

0

68.66

9

13.20

0

2W

nd

Yes kV To From 1.00

0

0.100 1500.

000

Line Overload Report

Overload Threshold = 10.00 %

Line Load

From Bus

Name

To Bus

Name

Branch

Name

Rated

Amps

Load

Amps

Loaded% OverLoaded% Comment

BUS-1 BUS-2 X-1 460.0 184.9 40.2% -59.8%

BUS-2 BUS-3 X-2 460.0 175.2 38.1% -61.9%

BUS-2_A BUS-3_A X-2_A 460.0 102.5 22.3% -77.7%

BUS-2_A BUS-2_B X-15 460.0 94.8 20.6% -79.4%

BUS-2_B BUS-3_B X-2_B 460.0 87.8 19.1% -80.9%

BUS-2_C BUS-3_C X-2_C 460.0 11.4 2.5% -97.5%

BUS-3 BUS-4 X-3 460.0 165.6 36.0% -64.0%

BUS-3_A BUS-4_A X-3_A 460.0 110.6 24.1% -75.9%

BUS-3_B BUS-4_B X-3_B 460.0 81.6 17.7% -82.3%

BUS-3_C BUS-4_C X-3_C 460.0 18.4 4.0% -96.0%

BUS-4 BUS-5 X-4 460.0 156.0 33.9% -66.1%

BUS-4_A BUS-5_A X-4_A 460.0 119.2 25.9% -74.1%

BUS-4_B BUS-5_B X-4_B 460.0 69.7 15.2% -84.8%

BUS-4_C BUS-5_C X-4_C 460.0 27.9 6.1% -93.9%

BUS-5 BUS-6 X-5 460.0 146.5 31.9% -68.1%

BUS-5_A BUS-6_A X-5_A 460.0 128.1 27.8% -72.2%

BUS-5_B BUS-6_B X-5_B 460.0 59.1 12.9% -87.1%

BUS-5_C BUS-6_C X-5_C 460.0 38.1 8.3% -91.7%

BUS-6_A BUS-6 X-14 460.0 137.2 29.8% -70.2%

BUS-6_B BUS-6_C X-16 460.0 48.6 10.6% -89.4%

Transformer Overload Report

Overload Threshold = 10.00 %

Transformer Load

Name From Bus

Name

To Bus

Name

Rated

Amps

Load

Amps

Loaded% OverLoaded% Comment

TX-1 BUS-22 BUS-1 125.5 35.5 28.3% -71.7%
98

Table-A1: Power flow summary reports (For Base Scenario)-Continued

Branch Losses Report

From Bus To Bus Losses

Name Base kV Name Base kV kW kVAR

BUS-1 13.200 BUS-2 13.200 17.9 27.3

BUS-2 13.200 BUS-3 13.200 16.1 24.5

BUS-2_A 13.200 BUS-3_A 13.200 5.5 8.1

BUS-2_A 13.200 BUS-2_B 13.200 4.7 6.9

BUS-2_B 13.200 BUS-3_B 13.200 4.0 5.8

BUS-2_C 13.200 BUS-3_C 13.200 0.1 -0.3

BUS-3 13.200 BUS-4 13.200 14.4 21.8

BUS-3_A 13.200 BUS-4_A 13.200 6.4 9.5

BUS-3_B 13.200 BUS-4_B 13.200 3.5 5.0

BUS-3_C 13.200 BUS-4_C 13.200 0.2 -0.2

BUS-4 13.200 BUS-5 13.200 12.8 19.3

BUS-4_A 13.200 BUS-5_A 13.200 7.5 11.1

BUS-4_B 13.200 BUS-5_B 13.200 2.5 3.5

BUS-4_C 13.200 BUS-5_C 13.200 0.4 0.2

BUS-5 13.200 BUS-6 13.200 11.3 17.0

BUS-5_A 13.200 BUS-6_A 13.200 8.6 12.9

BUS-5_B 13.200 BUS-6_B 13.200 1.8 2.4

BUS-5_C 13.200 BUS-6_C 13.200 0.8 0.7

BUS-6_A 13.200 BUS-6 13.200 9.9 14.9

BUS-6_B 13.200 BUS-6_C 13.200 1.2 1.5

BUS-22 69.000 BUS-1 13.200 4.8 95.2

Total System Losses 134.4 287.2

Voltage Drop Report

From Bus To Bus Drop

Name Base kV Name Base kV %

BUS-1 13.200 BUS-2 13.200 0.6%

BUS-2 13.200 BUS-3 13.200 0.5%

BUS-2_A 13.200 BUS-3_A 13.200 -0.2%

BUS-2_A 13.200 BUS-2_B 13.200 0.1%

BUS-2_B 13.200 BUS-3_B 13.200 0.1%

BUS-2_C 13.200 BUS-3_C 13.200 0.0%

BUS-3 13.200 BUS-4 13.200 0.5%

BUS-3_A 13.200 BUS-4_A 13.200 -0.2%

BUS-3_B 13.200 BUS-4_B 13.200 0.0%

BUS-3_C 13.200 BUS-4_C 13.200 -0.0%

BUS-4 13.200 BUS-5 13.200 0.4%

BUS-4_A 13.200 BUS-5_A 13.200 -0.3%

BUS-4_B 13.200 BUS-5_B 13.200 0.2%

BUS-4_C 13.200 BUS-5_C 13.200 -0.1%

BUS-5 13.200 BUS-6 13.200 0.4%

BUS-5_A 13.200 BUS-6_A 13.200 -0.3%

BUS-5_B 13.200 BUS-6_B 13.200 0.2%

BUS-5_C 13.200 BUS-6_C 13.200 -0.1%

BUS-6_A 13.200 BUS-6 13.200 -0.3%

BUS-6_B 13.200 BUS-6_C 13.200 0.2%

BUS-22 69.000 BUS-1 13.200 0.1%
99

Table-A2: Power flow summary reports (For Scenario-1)

Mismatch Report

Iteration MW Mismatch MVAR Mismatch

Number Bus Name pu Bus Name pu

0 BUS-2_C 0.00194 BUS-4_B 0.00007

1 BUS-2_C 0.00010 BUS-6_A 0.00002

2 BUS-2 0.00003 BUS-2_C 0.00002

3 BUS-2_C 0.00003 BUS-2_C 0.00002

4 BUS-2_C 0.00002 BUS-2_C 0.00001

5 BUS-2_C 0.00001 BUS-2_C 0.00000

6 BUS-2_C 0.00000 BUS-2_C 0.00000

Generator Summary Report

Generator Scheduled Limits Solution

Name Typ

e

Rated

kVA

kW kVAR Vpu kVA

R Min

kVA

R 

Max

kW kVA

R

kVA Pf V

pu

Deg Eq'pu Deg

UTIL-1 Sw 1.000 4567 1335 4758 0.960 1.

00

0

0.00 1.000 0.00

Load Summary Report

Bus Solution

Name Base kV kV Vpu Deg kW kVar kVA Pf

BUS-1 13.200 13.158 0.997 -1.37 0 0 0 0.000

BUS-2 13.200 13.072 0.990 -1.71 220 136 259 0.850

BUS-2_A 13.200 12.584 0.953 -4.37 220 136 259 0.850

BUS-2_B 13.200 12.561 0.952 -4.61 220 136 259 0.850

BUS-2_C 13.200 12.404 0.940 -5.55 220 -129 255 -0.863

BUS-3 13.200 12.993 0.984 -2.05 220 136 259 0.850

BUS-3_A 13.200 12.613 0.956 -4.12 220 136 259 0.850

BUS-3_B 13.200 12.544 0.950 -4.84 220 136 259 0.850

BUS-3_C 13.200 12.404 0.940 -5.52 220 136 259 0.850

BUS-4 13.200 12.920 0.979 -2.37 220 136 259 0.850

BUS-4_A 13.200 12.649 0.958 -3.85 220 136 259 0.850

BUS-4_B 13.200 12.534 0.950 -5.06 220 -1351 1369 -0.161

BUS-4_C 13.200 12.410 0.940 -5.47 220 136 259 0.850

BUS-5 13.200 12.854 0.974 -2.69 220 136 259 0.850

BUS-5_A 13.200 12.691 0.961 -3.58 220 136 259 0.850

BUS-5_B 13.200 12.497 0.947 -5.17 220 136 259 0.850

BUS-5_C 13.200 12.423 0.941 -5.42 220 136 259 0.850

BUS-6 13.200 12.793 0.969 -2.99 220 136 259 0.850

BUS-6_A 13.200 12.739 0.965 -3.29 220 136 259 0.850

BUS-6_B 13.200 12.466 0.944 -5.26 220 136 259 0.850

BUS-6_C 13.200 12.441 0.943 -5.35 220 136 259 0.850

BUS-22 69.000 69.000 1.000 0.00 0 0 0 0.000

System Summary Report

--

Total kW kVAR kVA PF

Generation in System 4567 1335 4758 0.960

Load in System 4400 2728 5177 0.850

Shunt Load in System -0 -1752

Losses in System 167 360 - -

Check of Balance 0 0
100

Table-A2: Power flow summary reports (For Scenario-1)-Continued

Transformer Tap Report

Transformer Connection Base kV Tap kV LTC Description

Name From

Bus Name

To

Bus Name

From To From To Typ

e

LT

C

LT

C

Typ

e

Contr

ol

Side

LT

C

Side

Contr

ol

Value 

pu

Limits

Min 

kV

Limits

Max 

kV

TX-1 BUS-22 BUS-1 69.00

0

13.20

0

68.66

9

13.20

0

2W

nd

Yes kV To Fro

m

1.000 0.100 1500.

000

Voltage Violation Report

Limits ( MAX: 1.05, Min: 0.95)

--

Bus

Name

Base kV Vpu kV

BUS-2_C 13.200 0.940 12.404

BUS-3_C 13.200 0.940 12.404

BUS-4_B 13.200 0.950 12.534

BUS-4_C 13.200 0.940 12.410

BUS-5_B 13.200 0.947 12.497

BUS-5_C 13.200 0.941 12.423

BUS-6_B 13.200 0.944 12.466

BUS-6_C 13.200 0.943 12.441

Line Overload Report

Overload Threshold = 10.00 %

Line Load

From Bus

Name

To Bus

Name

Branch

Name

Rated

Amps

Load

Amps

Loaded% OverLoaded% Comment

BUS-1 BUS-2 X-1 460.0 207.1 45.0% -55.0%

BUS-2 BUS-3 X-2 460.0 196.2 42.7% -57.3%

BUS-2_A BUS-3_A X-2_A 460.0 112.5 24.5% -75.5%

BUS-2_A BUS-2_B X-15 460.0 103.3 22.5% -77.5%

BUS-2_B BUS-3_B X-2_B 460.0 94.7 20.6% -79.4%

BUS-2_C BUS-3_C X-2_C 460.0 11.9 2.6% -97.4%

BUS-3 BUS-4 X-3 460.0 185.3 40.3% -59.7%

BUS-3_A BUS-4_A X-3_A 460.0 122.2 26.6% -73.4%

BUS-3_B BUS-4_B X-3_B 460.0 86.8 18.9% -81.1%

BUS-3_C BUS-4_C X-3_C 460.0 20.5 4.5% -95.5%

BUS-4 BUS-5 X-4 460.0 174.5 37.9% -62.1%

BUS-4_A BUS-5_A X-4_A 460.0 132.3 28.8% -71.2%

BUS-4_B BUS-5_B X-4_B 460.0 78.3 17.0% -83.0%

BUS-4_C BUS-5_C X-4_C 460.0 31.4 6.8% -93.2%

BUS-5 BUS-6 X-5 460.0 163.8 35.6% -64.4%

BUS-5_A BUS-6_A X-5_A 460.0 142.6 31.0% -69.0%

BUS-5_B BUS-6_B X-5_B 460.0 66.5 14.5% -85.5%

BUS-5_C BUS-6_C X-5_C 460.0 43.0 9.3% -90.7%

BUS-6_A BUS-6 X-14 460.0 153.1 33.3% -66.7%

BUS-6_B BUS-6_C X-16 460.0 54.7 11.9% -88.1%
101

Table-A2: Power flow summary reports (For Scenario-1)-Continued

Transformer Overload Report

Overload Threshold = 10.00 %

Transformer Load

Name From Bus

Name

To Bus

Name

Rated

Amps

Load

Amps

Loaded% OverLoaded% Comment

TX-1 BUS-22 BUS-1 125.5 39.8 31.7% -68.3%

Branch Losses Report

From Bus To Bus Losses

Name Base kV Name Base kV kW kVAR

BUS-1 13.200 BUS-2 13.200 22.5 34.4

BUS-2 13.200 BUS-3 13.200 20.2 30.9

BUS-2_A 13.200 BUS-3_A 13.200 6.6 9.9

BUS-2_A 13.200 BUS-2_B 13.200 5.6 8.2

BUS-2_B 13.200 BUS-3_B 13.200 4.7 6.9

BUS-2_C 13.200 BUS-3_C 13.200 0.1 -0.3

BUS-3 13.200 BUS-4 13.200 18.0 27.5

BUS-3_A 13.200 BUS-4_A 13.200 7.8 11.7

BUS-3_B 13.200 BUS-4_B 13.200 4.0 5.7

BUS-3_C 13.200 BUS-4_C 13.200 0.2 -0.1

BUS-4 13.200 BUS-5 13.200 16.0 24.3

BUS-4_A 13.200 BUS-5_A 13.200 9.2 13.8

BUS-4_B 13.200 BUS-5_B 13.200 3.2 4.6

BUS-4_C 13.200 BUS-5_C 13.200 0.5 0.4

BUS-5 13.200 BUS-6 13.200 14.1 21.4

BUS-5_A 13.200 BUS-6_A 13.200 10.7 16.1

BUS-5_B 13.200 BUS-6_B 13.200 2.3 3.2

BUS-5_C 13.200 BUS-6_C 13.200 1.0 1.1

BUS-6_A 13.200 BUS-6 13.200 12.3 18.6

BUS-6_B 13.200 BUS-6_C 13.200 1.6 2.0

BUS-22 69.000 BUS-1 13.200 6.0 119.4

Total System Losses 166.5 359.6

Voltage Drop Report

From Bus To Bus Drop

Name Base kV Name Base kV %

BUS-1 13.200 BUS-2 13.200 0.6%

BUS-2 13.200 BUS-3 13.200 0.6%

BUS-2_A 13.200 BUS-3_A 13.200 -0.2%

BUS-2_A 13.200 BUS-2_B 13.200 0.2%

BUS-2_B 13.200 BUS-3_B 13.200 0.1%

BUS-2_C 13.200 BUS-3_C 13.200 -0.0%

BUS-3 13.200 BUS-4 13.200 0.6%

BUS-3_A 13.200 BUS-4_A 13.200 -0.3%

BUS-3_B 13.200 BUS-4_B 13.200 0.1%

BUS-3_C 13.200 BUS-4_C 13.200 -0.0%

BUS-4 13.200 BUS-5 13.200 0.5%

BUS-4_A 13.200 BUS-5_A 13.200 -0.3%

BUS-4_B 13.200 BUS-5_B 13.200 0.3%

BUS-4_C 13.200 BUS-5_C 13.200 -0.1%

BUS-5 13.200 BUS-6 13.200 0.5%

BUS-5_A 13.200 BUS-6_A 13.200 -0.4%

BUS-5_B 13.200 BUS-6_B 13.200 0.2%

BUS-5_C 13.200 BUS-6_C 13.200 -0.1%

BUS-6_A 13.200 BUS-6 13.200 -0.4%

BUS-6_B 13.200 BUS-6_C 13.200 0.2%

BUS-22 69.000 BUS-1 13.200 0.3%
102

Table-A3: Power flow summary reports (For Scenario-2)

Mismatch Report

Iteration MW Mismatch MVAR Mismatch

Number Bus Name pu Bus Name pu

0 BUS-2_C 0.00102 BUS-2_C 0.00076

1 BUS-2 0.00013 BUS-2_C 0.00010

2 BUS-2_C 0.00015 BUS-2_C 0.00009

3 BUS-2_C 0.00012 BUS-2_C 0.00007

4 BUS-2_C 0.00010 BUS-2_C 0.00005

5 BUS-2_C 0.00008 BUS-2_C 0.00004

6 BUS-2_C 0.00006 BUS-2_C 0.00003

7 BUS-2_C 0.00005 BUS-2_C 0.00003

8 BUS-2_C 0.00004 BUS-2_C 0.00002

9 BUS-2_C 0.00003 BUS-2_C 0.00002

10 BUS-2_C 0.00002 BUS-2_C 0.00001

11 BUS-6_A 0.00005 BUS-2_C 0.00004

12 BUS-2_C 0.00001 BUS-2 0.00000

13 BUS-2_C 0.00000 BUS-2 0.00000

Generator Summary Report

Generator Scheduled Limits Solution

Name Typ

e

Rated

kVA

kW kVAR Vpu kVA

R 

Min

kVA

R 

Max

kW kVA

R

kVA Pf V

pu

Deg Eq'pu Deg

UTIL-1 Sw 1.000 4562 1198 4717 0.967 1.

00

0

0.00 1.000 0.00

Load Summary Report

Bus Solution

Name Base kV kV Vpu Deg kW kVar kVA Pf

BUS-1 13.200 13.271 1.005 -1.46 0 0 0 0.000

BUS-2 13.200 13.190 0.999 -1.80 220 136 259 0.850

BUS-2_A 13.200 12.733 0.965 -4.48 220 136 259 0.850

BUS-2_B 13.200 12.713 0.963 -4.72 220 136 259 0.850

BUS-2_C 13.200 12.570 0.952 -5.67 220 -45 225 -0.980

BUS-3 13.200 13.114 0.993 -2.14 220 136 259 0.850

BUS-3_A 13.200 12.759 0.967 -4.23 220 136 259 0.850

BUS-3_B 13.200 12.699 0.962 -4.96 220 136 259 0.850

BUS-3_C 13.200 12.572 0.952 -5.65 220 136 259 0.850

BUS-4 13.200 13.045 0.988 -2.46 220 136 259 0.850

BUS-4_A 13.200 12.791 0.969 -3.96 220 136 259 0.850

BUS-4_B 13.200 12.691 0.961 -5.18 220 -1389 1406 -0.156

BUS-4_C 13.200 12.581 0.953 -5.61 220 -45 225 -0.979

BUS-5 13.200 12.982 0.983 -2.78 220 136 259 0.850

BUS-5_A 13.200 12.829 0.972 -3.68 220 136 259 0.850

BUS-5_B 13.200 12.657 0.959 -5.29 220 136 259 0.850

BUS-5_C 13.200 12.591 0.954 -5.55 220 136 259 0.850

BUS-6 13.200 12.925 0.979 -3.09 220 136 259 0.850

BUS-6_A 13.200 12.874 0.975 -3.39 220 136 259 0.850

BUS-6_B 13.200 12.629 0.957 -5.39 220 136 259 0.850

BUS-6_C 13.200 12.607 0.955 -5.48 220 136 259 0.850

BUS-22 69.000 69.000 1.000 0.00 0 0 0 0.000
103

Table-A3: Power flow summary reports (For Scenario-2)-Continued

System Summary Report

--

Total kW kVAR kVA PF

Generation in System 4562 1198 4717 0.967

Load in System 4400 2728 5177 0.850

Shunt Load in System -0 -1888

Losses in System 162 358 - -

Check of Balance -0 0

Transformer Tap Report

Transformer Connection Base kV Tap kV LTC Description

Name From

Bus Name

To

Bus Name

From To From To Typ

e

LT

C

LT

C

Typ

e

Contr

ol

Side

LT

C

Side

Contr

ol

Value 

pu

Limits

Min 

kV

Limits

Max 

kV

TX-1 BUS-22 BUS-1 69.00

0

13.20

0

70.82

5

13.72

8

2W

nd

Yes kV To Fro

m

1.000 0.100 1500.

000

Line Overload Report

Overload Threshold = 10.00 %

Line Load

From Bus

Name

To Bus

Name

Branch

Name

Rated

Amps

Load

Amps

Loaded% OverLoaded% Comment

BUS-1 BUS-2 X-1 460.0 203.6 44.3% -55.7%

BUS-2 BUS-3 X-2 460.0 192.9 41.9% -58.1%

BUS-2_A BUS-3_A X-2_A 460.0 111.9 24.3% -75.7%

BUS-2_A BUS-2_B X-15 460.0 103.2 22.4% -77.6%

BUS-2_B BUS-3_B X-2_B 460.0 95.2 20.7% -79.3%

BUS-2_C BUS-3_C X-2_C 460.0 10.3 2.2% -97.8%

BUS-3 BUS-4 X-3 460.0 182.3 39.6% -60.4%

BUS-3_A BUS-4_A X-3_A 460.0 121.2 26.3% -73.7%

BUS-3_B BUS-4_B X-3_B 460.0 88.1 19.1% -80.9%

BUS-3_C BUS-4_C X-3_C 460.0 20.6 4.5% -95.5%

BUS-4 BUS-5 X-4 460.0 171.7 37.3% -62.7%

BUS-4_A BUS-5_A X-4_A 460.0 130.8 28.4% -71.6%

BUS-4_B BUS-5_B X-4_B 460.0 75.6 16.4% -83.6%

BUS-4_C BUS-5_C X-4_C 460.0 30.4 6.6% -93.4%

BUS-5 BUS-6 X-5 460.0 161.2 35.1% -64.9%

BUS-5_A BUS-6_A X-5_A 460.0 140.7 30.6% -69.4%

BUS-5_B BUS-6_B X-5_B 460.0 64.0 13.9% -86.1%

BUS-5_C BUS-6_C X-5_C 460.0 41.2 9.0% -91.0%

BUS-6_A BUS-6 X-14 460.0 150.9 32.8% -67.2%

BUS-6_B BUS-6_C X-16 460.0 52.5 11.4% -88.6%

Transformer Overload Report

Overload Threshold = 10.00 %

Transformer Load

Name From Bus

Name

To Bus

Name

Rated

Amps

Load

Amps

Loaded% OverLoaded% Comment

TX-1 BUS-22 BUS-1 125.5 39.5 31.4% -68.6%
104

Table-A3: Power flow summary reports (For Scenario-2)-Continued

Branch Losses Report

From Bus To Bus Losses

Name Base kV Name Base kV kW kVAR

BUS-1 13.200 BUS-2 13.200 21.7 33.3

BUS-2 13.200 BUS-3 13.200 19.5 29.8

BUS-2_A 13.200 BUS-3_A 13.200 6.6 9.7

BUS-2_A 13.200 BUS-2_B 13.200 5.6 8.2

BUS-2_B 13.200 BUS-3_B 13.200 4.8 6.9

BUS-2_C 13.200 BUS-3_C 13.200 0.1 -0.3

BUS-3 13.200 BUS-4 13.200 17.4 26.6

BUS-3_A 13.200 BUS-4_A 13.200 7.7 11.5

BUS-3_B 13.200 BUS-4_B 13.200 4.1 5.9

BUS-3_C 13.200 BUS-4_C 13.200 0.2 -0.1

BUS-4 13.200 BUS-5 13.200 15.5 23.5

BUS-4_A 13.200 BUS-5_A 13.200 9.0 13.5

BUS-4_B 13.200 BUS-5_B 13.200 3.0 4.2

BUS-4_C 13.200 BUS-5_C 13.200 0.5 0.3

BUS-5 13.200 BUS-6 13.200 13.6 20.7

BUS-5_A 13.200 BUS-6_A 13.200 10.4 15.7

BUS-5_B 13.200 BUS-6_B 13.200 2.1 2.9

BUS-5_C 13.200 BUS-6_C 13.200 0.9 0.9

BUS-6_A 13.200 BUS-6 13.200 11.9 18.1

BUS-6_B 13.200 BUS-6_C 13.200 1.4 1.8

BUS-22 69.000 BUS-1 13.200 6.3 124.9

Total System Losses 162.3 357.9

Voltage Drop Report

From Bus To Bus Drop

Name Base kV Name Base kV %

BUS-1 13.200 BUS-2 13.200 0.6%

BUS-2 13.200 BUS-3 13.200 0.6%

BUS-2_A 13.200 BUS-3_A 13.200 -0.2%

BUS-2_A 13.200 BUS-2_B 13.200 0.2%

BUS-2_B 13.200 BUS-3_B 13.200 0.1%

BUS-2_C 13.200 BUS-3_C 13.200 -0.0%

BUS-3 13.200 BUS-4 13.200 0.5%

BUS-3_A 13.200 BUS-4_A 13.200 -0.2%

BUS-3_B 13.200 BUS-4_B 13.200 0.1%

BUS-3_C 13.200 BUS-4_C 13.200 -0.1%

BUS-4 13.200 BUS-5 13.200 0.5%

BUS-4_A 13.200 BUS-5_A 13.200 -0.3%

BUS-4_B 13.200 BUS-5_B 13.200 0.3%

BUS-4_C 13.200 BUS-5_C 13.200 -0.1%

BUS-5 13.200 BUS-6 13.200 0.4%

BUS-5_A 13.200 BUS-6_A 13.200 -0.3%

BUS-5_B 13.200 BUS-6_B 13.200 0.2%

BUS-5_C 13.200 BUS-6_C 13.200 -0.1%

BUS-6_A 13.200 BUS-6 13.200 -0.4%

BUS-6_B 13.200 BUS-6_C 13.200 0.2%

BUS-22 69.000 BUS-1 13.200 -0.5%
105

References

ACT, V.Challenge AND opportunity: Charting A new energy future.

Albadi, M. H., & El-Saadany, E. (2008). A summary of demand response in electricity 

markets. Electric Power Systems Research, 78(11), 1989-1996. 

Balijepalli, V. M., Pradhan, V., Khaparde, S., & Shereef, R. (2011). Review of demand 

response under smart grid paradigm. Paper presented at the Innovative Smart 

Grid Technologies-India (ISGT India), 2011 IEEE PES, 236-243. 

Chiu, W., Sun, H., & Poor, H. (2012). Demand-side energy Storage management in 

smart grid. IEEE Third International Conference on Smart Grid Communications.

De Steese, J., Merrick, S., & Kennedy, B. (1990). Estimating methodology for a large 

regional application of conservation voltage reduction. Power Systems, IEEE 

Transactions On, 5(3), 862-870. 

Ellens, W., Berry, A., & West, S. (2012). A quantification of the energy savings by 

conservation voltage reduction. Paper presented at the Power System Technology 

(POWERCON), 2012 IEEE International Conference On, 1-6. 

Ertem, S., & Baghzouz, Y. (1989). Simulation of induction machinery for power 

system studies. Energy Conversion, IEEE Transactions On, 4(1), 88-94. 
106

Federal Energy Regulatory Commission. (2010). National action plan on demand 

response. Docket no.AD09-10, 

Frost, Sullivan. (2003). Unit shipment forecasts and business care analysis for electric 

utilities

Goldman, C. A., Kintner-Meyer, M., & Heffner, G. (2002). Do" enabling technologies" 

affect customer performance in price-responsive load programs?

Kennedy, B., & Fletcher, R. (1991). Conservation voltage reduction (CVR) at 

snohomish county PUD. Power Systems, IEEE Transactions On, 6(3), 986-998. 

Kirshner, D., & Giorsetto, P. (1984). Statistical test of energy saving due to voltage 

reduction. Power Apparatus and Systems, IEEE Transactions On, (6), 1205-1210.

Kundur, P., Balu, N. J., & Lauby, M. G. (1994). Power system stability and control

McGraw-hill New York.

Lefebvre, S., Gaba, G., Ba, A., Asber, D., Ricard, A., Perreault, C., & Chartrand, D. 

(2008). Measuring the efficiency of voltage reduction at hydro-québec 

distribution. Paper presented at the Power and Energy Society General MeetingConversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE, 1-7. 
107

Nejad, M. F., Saberian, A., Hizam, H., Radzi, M., Amran, M., Kadir, A., & Abidin, M. 

Z. (2013). Application of smart power grid in developing countries. Paper 

presented at the Power Engineering and Optimization Conference (PEOCO), 2013 

IEEE 7th International, 427-431. 

Office of Energy. (2010). Demand Management. Government of Western Australia

n.d web.

Preiss, R., & Warnock, V. (1978). Impact of voltage reduction on energy and demand.

Power Apparatus and Systems, IEEE Transactions On, (5), 1665-1671. 

Price, W., Casper, S., Nwankpa, C., Bradish, R., Chiang, H., Concordia, C., . . . Wu, G. 

(1995). Bibliography on load models for power flow and dynamic performance 

simulation. IEEE Power Engineering Review, 15(2), 70. 

Price, W., Chiang, H., Clark, H., Concordia, C., Lee, D., Hsu, J., . . . Mansour, Y. 

(1993). Load representation for dynamic performance analysis. IEEE Transactions 

on Power Systems (Institute of Electrical and Electronics Engineers);(United States), 

8(2)

Price, W. W., Wirgau, K. A., Murdoch, A., Mitsche, J. V., Vaahedi, E., & El-Kady, M. 

(1988). Load modeling for power flow and transient stability computer studies.

Power Systems, IEEE Transactions On, 3(1), 180-187. 
108

Sabir, S., & Lee, D. (1982). Dynamic load models derived from date acquired during 

system transients. Power Apparatus and Systems, IEEE Transactions On, (9), 3365-

3372. 

Schweppe, F. C. (1982). Frequency Adaptive, Power-Energy Re-Scheduler, 

Singh, R., Tuffner, F., Fuller, J., & Schneider, K. (2011). Effects of distributed energy 

resources on conservation voltage reduction (CVR). Paper presented at the Power 

and Energy Society General Meeting, 2011 IEEE, 1-7. 

Sinitsyn, N. A., Kundu, S., & Backhaus, S. (2013). Safe protocols for generating power 

pulses with heterogeneous populations of thermostatically controlled loads.

Energy Conversion and Management, 67, 297-308. 

Sonderegger, R. C. (1978). Dynamic models of house heating based on equivalent

thermal parameters.

Torriti, J., Hassan, M. G., & Leach, M. (2010). Demand response experience in europe: 

Policies, programmes and implementation. Energy, 35(4), 1575-1583. 

U.S Department of Energy. (2012). Smart Grid/Department of Energy.

Warnock, V., & Kirkpatrick, T. (1986). Impact of voltage reduction on energy and 

demand: Phase II. Power Systems, IEEE Transactions On, 1(2), 92-95. 
109

Willis, H. L. (2010). Power distribution planning reference book CRC press.

Wilson, N., Wagner, B., & Colborne, W. (1985). Equivalent thermal parameters for an 

occupied gas-heated house. ASHRAE Trans.;(United States), 91(CONF-850606-)

Wilson, T. (2002). Energy conservation with voltage reduction-fact or fantasy. Paper 

presented at the Rural Electric Power Conference, 2002. 2002 IEEE, C3-C3_6. 

 
110

Date: May 2014

Vita

Ali Dorrody 

Current Address

555 E Silverado ranch Blvd.

#2165

Las Vegas, Nevada, 89183

Cell Phone: (702) 666-1622

E-mail address: Dorrodya@unlv.nevada.edu

Education 

University of Nevada, Las Vegas (UNLV)

Master of Science in Engineering-Electrical Engineering, 2014

Tehran Azad University, Tehran, Iran

Bachelor of Science in Engineering-Electrical Engineering, 2000