Electrical Power and Energy Systems 55 (2014) 612–619

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Electrical Power and Energy Systems

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Multiobjective demand side management solutions for utilitieswith peak demand deficit

0142-0615/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.ijepes.2013.10.011

⇑ Corresponding author. Tel.: +91 75790 79231.E-mail addresses: nandkishor.kinhekar@gmail.com (N. Kinhekar), nppeefee@

iitr.ernet.in (N.P. Padhy), hariomgupt@gmail.com (H.O. Gupta).

Nandkishor Kinhekar a,⇑, Narayana Prasad Padhy a, Hari Om Gupta b

a Electrical Engineering Dept., IIT Roorkee, Roorkee, Uttarakhand 247 667, Indiab Jaypee Institute of Information Technology, Noida, Uttar Pradesh 201 307, India

a r t i c l e i n f o a b s t r a c t

Article history:Received 9 November 2012Received in revised form 14 September 2013Accepted 12 October 2013

Keywords:Demand side managementGenetic algorithmLoads shiftingTime of day tariff

Demand side management (DSM) is a growing concept around the world, particularly in urban India,recently due to presence of time of day (TOD) tariffs for the large commercial and industrial customers.Residential customers are not exposed to TOD tariff structure so far in India. This encourages commercialand industrial customers to schedule their flexible loads as per TOD tariff to extract maximum benefit ofit and helps utilities to reduce their peak load demand and reshape the load curve. For efficient DSMimplementation, this paper presents a multiobjective DSM solutions based on integer genetic algorithmto benefit both utilities and consumers. The proposed algorithm provides new directions on effectiveimplementation of DSM techniques for Indian utilities. Simulations were carried out on Indian practicaldistribution system with large commercial and industrial loads. The simulation results of the proposedalgorithm shows that the presented DSM technique comprehends reasonable savings to both utilityand consumers simultaneously, while reducing the system peak.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

DSM is the process of planning, implementation and monitoringof those utility’s activities that are designed to influence the periodof use of electricity and the amount of loads in consumer’s pre-mises so as to achieve desired peak load reduction. The reductionof the peak load is one of the objectives of DSM to avoid new gen-eration capacity addition and helps utility to reduce their opera-tional cost and environment to be free from excess carbon footprints. However, there are six objectives in the context of DSM:peak clipping, valley filling, load shifting, flexible load curve, stra-tegic conservation and strategic load growth [1]. Rapid industrial-ization and growing urbanization in developing countries like Indianeeds huge amount of power which cannot be met by presentgeneration capacity. In spite of continued growth in the powergeneration over years, the gap between demand and generationis growing every year [2]. Table 1 represents peak load demandshortage conditions in India for the last five years. The all Indiapeak demand deficit during the year 2011–12 was 10.6% [3]. Thescenario of peak demand shortage is even worst in the western re-gion of the country, the state of Maharashtra, where peak demanddeficit of 14.8% is anticipated. To bridge the gap between supplyand demand, construction of new generation plants are required.

But it is a costly affair and also causes climate changes due to greenhouse gas emissions. Hence, there is an urgent need for the utilitiesto focus on DSM options to save fuel for power generation and inturn, benefits customers in the form of reduced energy bills [4].Effective DSM implementation involves planned cooperation be-tween utilities and consumers to adjust load curve resulting inbenefits to the utility, consumers and society at large.

The government of India has taken a number of steps to initiateenergy efficiency/DSM in Indian electricity sector. These initiativeswere started in the 1980s and various inter ministerial workinggroup was formed on behalf of central government to drive na-tional energy efficiency efforts in 1983 [5]. In the mid-1990s, var-ious energy service companies (ESCOs) and energy auditorsworked for the market. DSM study revealed that an important areafor the implementation of DSM is the high-tension (HT) industrialsector which has the largest potential for energy conservation [4].DSM survey [6] indicated that most of the industries had installedenergy efficient motors, variable-speed drives, and efficient light-ing options were considered as a part of energy conservation ef-forts. The first major policy initiative on energy conservation andDSM in India was started in 2001 by endorsing Energy Conserva-tion Act 2001 [7]. Under this act, the Bureau of Energy Efficiency(BEE) was formed to promote and regulate implementation of en-ergy efficiency activities. In 2001, Indian government decided toimplement DSM in different states utilities by offering a TOD tariffand incentives for energy efficient plans. Utilities identified andimplemented various potential DSM programs in their respective

Table 1Peak load demand shortage conditions in India for last five years (Units in MegaWatts).

Year Requirement Availability Shortage Percentage

2007–08 1,08,866 90,793 18,073 16.62008–09 1,09,809 96,785 13,024 11.92009–10 1,19,166 1,04,009 15,157 12.72010–11 1,22,287 1,10,256 12,031 9.82011–12 1,30,006 1,16,191 13,815 10.6

Source: Load generation balance reports 2008–09 to 2012–13, Central ElectricityAuthority, Ministry of Power, Government of India [2].

N. Kinhekar et al. / Electrical Power and Energy Systems 55 (2014) 612–619 613

states like replacement of incandescent lamps with energy savingCompact Fluorescent Lamps (CFLs), replacement of old inefficientagricultural water pump sets with energy efficient pump sets,use of solar water heating systems, installation of automatic powerfactor correction panels, use of BEE star labelled equipments withminimum efficiency standards. Implementation of such DSM pro-grams provides energy saving up to certain extent but does notcontribute much to reduce peak as there is not much active partic-ipation by consumers in India. Most of the current DSM activitiesin India are utility driven. There is a need of more consumerinvolvement and consumer driven DSM activities in near future.

DSM program include two main activities, energy efficiencyprograms and demand response (DR) programs. One of the majorgoals of DR program is reducing consumption during peak hoursand shifting demand to off-peak hours [8–14]. DR can be catego-rized as a direct load control (DLC) which allows utilities to controlportion of the consumers load directly with their permission andindirect load control which allows consumers to control their loadsindependently according to the price signal sent by the utilities[15,16]. Several algorithms and techniques for DR have been re-ported in the literature. Rapid growth in electricity demand hasmade the use of DR programs more beneficial to both utilitiesand consumers [17–19]. An optimization technique based on dy-namic programming is presented [20] for the optimal dispatch ofDLC so that the system production cost is minimized. A linear pro-gramming model for optimizing the amount of system peak loadreduction through customer direct load control programs is pre-sented [21] that are applied to specific programs at Florida Powerand Light Company. General modelling techniques which may beapplied to load management programs involving thermal storageare discussed [22] for the benefit of utilities to reduce system peakand energy production cost reduction.

In India, utilities commonly experience peak demand shortageand have to buy energy with high cost from the whole sale marketto satisfy their customers. To avoid such huge energy purchasecost, utility wish to induce a change in consumer’s load patternto reduce the overall peak demand of the system. Consumers allowthe utility to control their flexible i.e. shiftable loads remotelybased on prior agreements between them. Flexible loads are theloads whose runtime can be shifted to other period without muchloss in convenience to consumers such as water heaters, washingmachines. The consumer in return gets incentives in terms of re-bate in electricity bill as per agreement conditions and it is alsopossible to provide incentives with a direct payment by action.

The control of the loads will be carried out using inbuilt actua-tors and sensors that allow automatic turn ON and OFF afterreceiving control signal from the utility. Unfortunately consumershad no scope for disobeying the utilities due to the prior agreementand it says that consumers have no objection with the utility incontrolling the devices. Ideally the consumers only approve se-lected devices to be controlled by the utility. In our simulation,only 10–12% of the total connected loads have been treated as con-trollable load. Even in urban India sometimes both commercial and

industrial consumers face load shedding. So, the utility may notface high resistance from the consumer side participating in theproposed mechanism.

Most of the DSM techniques presented in the literature areapplicable for one type of appliance [21–28]. But, a few of themare applicable to practical systems consisting of different varietyof appliances [29–32]. Most of the techniques in the literaturewere developed using dynamic programming [20,23] and linearprogramming [16,21] which cannot handle a large number of con-trollable devices. A DSM strategy [29] based on load shifting tech-nique proposed for large number of devices of several types. Theprimary objective of the DSM techniques, discussed in the litera-ture, is reduction of system peak load demand and operational costof utilities [16,29]. It had paid more attention to the utilities profitrather than customers benefit. Several simulation tools are avail-able for DSM analysis. But a DSM based simulation tool is intro-duced to illustrate customer-driven DSM operation thatestimates electricity consumption of household appliances to min-imize the customer’s cost [33].

In this paper, a load shifting DSM technique is used to schedulecontrollable devices of both commercial and industrial consumersat various hours of the day. Half hourly forecasted load data andhalf hourly forecasted pool market price and TOD tariff are the in-puts given to the DSM program. Various types of controllable de-vices from commercial and industrial consumers are identified.Approximately 1700 devices from commercial consumers and160 devices from industrial consumers are available for control.Earlier research demonstrates benefits to utilities through DSMimplementation [16,29]. In this paper, the main objective is notonly to benefit the utility but also the consumers from urban India.

The rest of the paper is organized as follows. A DSM methodol-ogy for the benefits of utilities and consumers is given in Section 2.Section 3 provides the details about data simulation. Simulation re-sults and discussion are presented in Section 4. Section 5 concludesthe paper.

2. Proposed DSM methodology

In most of the urban cities of India especially in Mumbai, thereare multiple distribution utilities such as TATA Power Ltd., RelianceInfrastructure Ltd., BrihanMumbai Electric Supply and Transport(BEST) and Maharashtra State Electricity Distribution CompanyLtd. These distribution utilities also own their generating unitsand are responsible for maintaining the distribution network.There exists a stiff competition to survive by accommodating rea-sonable percentage of customers to their networks. In the processof maximising their connections the utility suffers with peak loadmanagement and most of the times the distribution utilities haveto buy power with high price during peak period. So, a multiobjec-tive optimization is being formulated and simulated to benefitboth the utilities and customers through DSM. In a long term,the incentive signal may motivate more customers to join the loadshifting program and help the utility to reduce the peak demand.

2.1. Problem formulation

The proposed DSM technique works on the principle of the con-nection time shifting of each controllable device in the system tobring the final load curve closer to the objective load curve. Math-ematical formulation of the proposed multiobjective DSM tech-nique is given as follows.

Minimize

XN

t¼1

ðACPðtÞ � ðW1 � OCuðtÞ þW2 � OCcðtÞÞÞ2 ð1Þ

614 N. Kinhekar et al. / Electrical Power and Energy Systems 55 (2014) 612–619

Eq. (1) is the minimization of the actual power to the objectivecurves by least squares, where ACP(t) is the actual consumption ofpower at time t which represents the final desired load consump-tion curve. OCu(t) represents the utility objective curve at time tthat was chosen to be inversely proportional to pool electricitymarket prices which aims to benefit utility. OCc(t) represents theconsumer objective curve at time t that was chosen to be inverselyproportional to TOD tariff in the commercial and industrial sectorwhich aims to benefit consumers. N is the number of half hourlytime steps. W1 and W2 are the weights given to individual objectivecurve such that,

W1 þW2 ¼ 1 ð2Þ

When, W1 = 1 and W2 = 0, utility objective curve OCu(t) is generated.When, W1 = 0 and W2 = 1, consumer objective curve OCc(t) is gener-ated. In our proposed multiobjective formulation W1 and W2 areconsidered to be equal to 0.5 such that, the objective curve gener-ated benefits both utility and consumers. This objective curve iscalled the proposed objective curve OCp(t) at time t which is gener-ated by taking an average of OCu and OCc. The proposed objectivecurve is given by following equation:

OCpðtÞ ¼ ðOCuðtÞ þ OCcðtÞÞ=2 ð3Þ

The ACP(t) is given by the following equation [29]:

ACPðtÞ ¼ ForecastðtÞ þ ConnectðtÞ � DisconnectðtÞ ð4Þ

where Forecast(t) is the forecasted consumption at time t, and Con-nect(t) and Disconnect(t) are the amount of loads connected and dis-connected at time t respectively during the load shifting. Detailsabout Connect(t) and Disconnect(t) and the constraints related toabove minimization problem are given in Appendix A. If a dayahead forecasted load curve having peak and off-peak periods isknown in advance then by disconnecting some loads at peak periodand connecting i.e. shifting them to the off-peak period gives actualdesired consumption curve of tomorrow that is used to schedulethe control actions of various devices in real time using smart sen-sors, control and communication technologies available.

2.2. The proposed algorithm

DSM algorithm needs to be flexible enough to handle number ofcontrollable devices of diversified nature. Designed algorithmshould be able to address complex nature of these devices in termsof their consumption pattern and duration. Various optimizationtechniques such as linear and dynamic programming are commonlyused in this domain. However, these approaches have limitations todeal with such complexity. Evolutionary computation algorithmshave shown potential for solving such complex problems [34]. Ge-netic algorithm (GA) offers many advantages in obtaining optimalsolutions for complex mathematical objectives. Hence, in this paper,an evolutionary integer genetic algorithm is proposed.

As discussed in Appendix A, the maximum number of possibletime steps N can be found using the Eq. (A.7). For this proposedalgorithm number of half hourly time intervals is taken as 48.

Chromosomes of the evolutionary algorithm represent the solu-tions to the problem. In this study, the chromosome is constructedas an array of integers.

Length of chromosome ¼ N ð5Þ

A fitness function is chosen such that the algorithm attains ac-tual consumption power curve as close to the objective load curveas possible, which is given as follows:

Fitness ¼X48

t¼1

ðACPðtÞ � ðW1 � OCuðtÞ þW2 � OCcðtÞÞÞ2 ð6Þ

MATLAB GA tool function is used to execute the proposed evo-lutionary algorithm. To start with the GA, initial population of 160chromosomes is chosen randomly. Then the fitness of each chro-mosome is evaluated with the constraints given in Appendix A.New populations of chromosomes are generated using uniformcrossover technique with the mutation rate of 0.04. Best crossoverfrequency of 0.7 is found experimentally for this minimizationproblem. Maximum numbers of generations used are 500. Thealgorithm is terminated when the stipulated number of genera-tions is reached. The major steps in proposed algorithm is summa-rised as:

Step 1 – Data input from utility such as forecasted load, fore-casted pool electricity prices, and TOD tariffs.

Step 2 – Data input from consumers such as device type, con-sumption pattern of devices, and duration of deviceconsumption.

Step 3 – Preparation of respective objective load curves such asOCu(t), OCc(t), and OCp(t).

Step 4 – Data given in steps 1, 2 and 3 provided as input to inte-ger genetic algorithm.

Step 5 – Output of algorithm is in the form of desired load curveand device load shifting intimation to consumers.

Step 6 – Output of algorithm reveals three DSM objectives.(1) Utility driven DSM that confirm benefits to utilities and sys-

tem peak demand reduction.(2) Consumer driven DSM that confirm benefits to consumers

and system peak demand reduction.(3) Proposed DSM that confirms benefits to both utilities and

consumers along with system peak demand reduction.

2.3. Proposed DSM architecture

The proposed DSM strategy is based on a day-ahead load shift-ing and can be applied for large distribution systems. Fig. 1 demon-strates the proposed DSM architecture that can be used for Indiandistribution systems in the future. Forecasted half hourly loads,pool prices, TOD tariffs and device consumption patterns areknown in advance, and they are processed in central server ofthe utility distribution centre. The desired load curve can be gener-ated using proposed DSM strategy for next twenty-four hours. Thedevice rescheduling information obtained from generated curvewill be transmitted to smart meters using GPRS technology. Thencontrol signals will be sent to the devices for execution by DSMcontroller using ZigBee technology. The devices can either turnedON or OFF automatically using inbuilt actuators and sensors afterreceiving the control signal in real time.

Real time implementation of the proposed architecture is onlypossible using smart distribution grid in which Advanced MeteringInfrastructure (AMI) plays an important role. Currently Indian util-ities are developing AMI infrastructure in their distribution systemautomation program. Smart grid initiatives are in beginning stagein India [35]. Hence, the proposed DSM architecture can help In-dian utilities to reduce peak demand deficits of future.

3. Details of data simulation

To illustrate the usefulness of the proposed technique, simula-tions are carried out on a practical distribution system of a utilityin western part of India which contains commercial and industrialloads. The test case system belongs to a metropolitan city of Maha-rashtra state of Western India – Mumbai. It is the capital city ofMaharashtra state with approximate population of 12.5 millionand spread over 437.5 km2 area. It is located at 18.96�N latitudeand 72.82�E longitude. The test case system belongs to a utility

Utility Distribution Centre

Available Data Base Forecasted Load

Forecasted Pool Electricity Market Price TOD Tariff

Central Server

GPRSGPRS Desired Consumption Curve (with device load shifting information to DSM controller)

Device Type Consumption pattern

Device Type Consumption pattern

Smart Meter and DSM Controller

Dryer Kettle Oven Fan/AC Central AC

Lights Coffee Maker

Different Controllable Devices

----

Commercial Consumers

ZigBee

Smart Meter and DSM Controller

Lathe Machine

Welding Machine

InductionMotor

DC Motor

Fan / AC

Water Heater

----

Different Controllable Devices

Industrial Consumers

ZigBee

Fig. 1. Proposed architecture for DSM.

0

1

2

3

4

5

6

7

8

Ele

ctri

city

Pri

ce (

Rup

ees/

kWh)

Time (hours)

Pool Price TOD Tariff (Commercial Consumer) TOD Tariff (Industrial Consumer)

Fig. 3. Half hourly pool electricity price and TOD tariff data.

N. Kinhekar et al. / Electrical Power and Energy Systems 55 (2014) 612–619 615

that has a customer base of more than 0.35 million in Mumbai city,including 50,000 LT (low tension) and HT (high tension) commer-cial, industrial consumers. Sample data has been created out of theinformation provided by the utility to demonstrate the results. Theentire distribution network operates at a voltage of 11 kV.

The measured half hourly load consumption data recorded byAutomatic Meter Reading (AMR) system which is provided bythe utility has been used as forecasted load data for simulationand is shown in Fig. 2. TOD tariff is a tariff structure in which dif-ferent rates are applicable for use of electricity at different time ofthe day. Half hourly pool electricity price and TOD tariff data pro-vided by the utility is considered for simulation which is shown inFig. 3. Simulations were carried out with a maximum allowabledelay of 6 h. The maximum load demands of commercial andindustrial consumers in this study are 6.5 MW and 8.5 MWrespectively.

0100020003000400050006000700080009000

For

ecas

ted

Loa

d (k

Wh)

Time (hours)

Commercial Consumer Industrial Consumer

Fig. 2. Forecasted load data for commercial and industrial consumers.

On a typical day, the control period is considered from 9th hr ofcurrent day to 9th hr of the next day as the peak is normally startedaround 9th hr of the day for given distribution areas. Each area ofdistribution system has different types of controllable devices, thedetails of which are given in the following subsection.

3.1. Commercial and Industrial area

The consumption patterns of the loads assumed to be undercontrol for both commercial and industrial areas are given in Ta-bles 2 and 3 respectively. The assumptions are made based onexperienced utility engineers view. Their views are based onspecific characteristics of the service each device provides, so thatconsumers do not experience any inconvenience due to shifting ofthe device runtime. This is based on the load survey carried outby the utility. There are six different types of customers under

Table 2Data of controllable devices in the commercial area.

Device type Half hourly consumption of devices (kW) Number of devices

1st Half hour 2nd Half hour 3rd Half hour 4th Half hour 5th Half hour 6th Half hour

Dryer 0.8 0.8 – – – – 440Water heater 2.4 2.0 – – – – 155Kettle 1.2 1.0 1.0 1.0 – – 296Oven 1.2 1.0 – – – – 258Coffee maker 0.75 0.75 0.75 0.75 – – 226Fan/AC 2.25 2.0 2.0 2.0 – – 115Central AC 3.75 3.5 3.25 3.25 3.0 3.0 50Lights 1.5 1.25 1.0 1.0 0.75 0.75 114Total – – – – – – 1654

Table 3Data of controllable devices in the industrial area.

Device type Half hourly consumption of devices (kW) Number ofdevices

1st Halfhour

2nd Halfhour

3rd Halfhour

4th Halfhour

5th Halfhour

6th Halfhour

7th Halfhour

8th Halfhour

9th Halfhour

10th Halfhour

Lathemachine

8.5 8.5 8.5 8.5 8.5 8.5 – – – – 27

Weldingmachine

12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5 – – 36

Water heater 4.8 4.8 4.8 4.8 4.8 4.8 4.8 4.8 – – 34Fan/AC 15 15 15 15 15 15 15 15 15 15 19Induction

motor30 30 30 30 30 30 30 30 30 30 20

DC motor 40 40 40 40 40 40 – – – – 18Total – – – – – – – – – – 154

616 N. Kinhekar et al. / Electrical Power and Energy Systems 55 (2014) 612–619

the commercial area category. Those are a 5 star hotel, multispe-ciality hospital, commercial office, IT office, 2–3 stars hotel, andmunicipal pumping. There are around 1700 controllable devicesavailable for control in this area from 8 different types. Thoseare dryer, water heater, kettle, oven, coffee maker, fan/air-condi-tioner, central air-conditioner and lights. Runtime of dryer, waterheater and oven is of short duration whereas, that of central airconditioner and lights are of longer duration. Case study is takenfrom commercial capital of India, where large number of com-mercial offices, IT offices and hotels are located. Hence, centralair conditioner and lights are contributing the major controllableload. It can be switched off for short duration during peak loadcondition without loss in convenience to consumers. The totalcontrollable load made available from commercial area is 10%of the total connected load.

There are five different types of customers under the industrialarea category. Those are Data Centre, Manufacturing Unit, IT Com-pany, Air Catering Company, and large commercial offices. Thereare around 160 controllable devices available for control in thisarea from 6 different types. Those are lathe machine, welding ma-chine, water heater, fan/air-conditioner, induction motor and dcmotor. The number of devices available for control is small as mostof the loads from industries are critical in nature. However, the de-vices have largest consumption ratings and longest consumptionperiods. The total controllable load made available from industrialarea is 12% of the total connected load.

4. Results and discussion

Three different case studies were carried out using the proposedDSM strategy. The first case study is carried out by consideringutility objective curve (OCu) in which actual consumption curveafter load shifting follows OCu. The second case study is carriedout by considering consumer objective curve (OCc) in which actualconsumption curve after load shifting follows OCc. The third case

study is carried out by considering proposed objective curve(OCp) in which actual consumption curve after load shifting followsOCp that is the average of utility and consumer objective curves.These case studies are discussed in detail as follows.

4.1. Case study 1 – utility driven DSM (W1 = 1, W2 = 0)

In this case study, OCu is generated as inversely proportional topool market price. Load shifting DSM technique is used to shift thecontrollable loads to next valley hours so that the actual powerconsumption curve follows the OCu. The simulation results ob-tained for commercial and industrial consumers are shown inFigs. 4 and 5 respectively. Simulation results show that the finaldesired consumption curve for industrial consumers is more closeto the OCu than that for commercial consumers. It is observed thatthe flexibility of device shifting is less in case of commercial area.For example, devices such as water heater, oven and coffee makerare of short runtime and must run during early hours of the daywhereas, central air conditioner and lights are of longer durationwhich has been displaced during later hours of the day. In caseof industrial consumers, there is wide scope of device displacementas they are running in different production shifts. Simulationresults achieved the system peak demand reduction of 6.45% and5.03% for the commercial and industrial area respectively. Simula-tion results show that the utility bill of the commercial and indus-trial area is reduced with the proposed DSM technique. But, in thisstrategy benefits to end consumers has not been taken intoaccount. The utility gained the maximum profit by shifting endconsumers loads as per their requirement without giving anydirect benefits to consumers.

4.2. Case study 2 – consumer driven DSM (W1 = 0, W2 = 1)

In this case study, OCc is generated as inversely proportional toTOD tariff offered to commercial and industrial consumers. Load

5 10 15 20 25 30 35 40 45

4000

4500

5000

5500

6000

6500

Time (hours)

Loa

d (k

Wh)

Forecasted Load

OCu

ACP

Fig. 4. Utility driven DSM results of the commercial area.

5 10 15 20 25 30 35 40 45

4500

5000

5500

6000

6500

7000

7500

8000

8500

Time (hours)

Loa

d (k

Wh)

Forecasted Load

OCu

ACP

Fig. 5. Utility driven DSM results of the industrial area.

5 10 15 20 25 30 35 40 45

4000

4500

5000

5500

6000

6500

Time (hours)

Loa

d (k

Wh)

Forecasted Load

OCc

ACP

Fig. 6. Consumer driven DSM results of the commercial area.

5 10 15 20 25 30 35 40 45

4500

5000

5500

6000

6500

7000

7500

8000

8500

Time (hours)

Loa

d (k

Wh)

Forecasted LoadOCcACP

Fig. 7. Consumer driven DSM results of the industrial area.

5 10 15 20 25 30 35 40 45

4000

4500

5000

5500

6000

6500

Time (hours)

Loa

d (k

Wh)

Forecasted Load

OCp

ACP

Fig. 8. Proposed DSM results of the commercial area.

N. Kinhekar et al. / Electrical Power and Energy Systems 55 (2014) 612–619 617

shifting DSM technique is used to shift the controllable loads tonext valley hours so that the actual power consumption curve fol-lows the OCc. The simulation results obtained for commercial andindustrial consumers are shown in Figs. 6 and 7 respectively. It isobserved from the simulation results that the proposed DSM strat-egy has managed to bring final desired consumption curve close tothe OCc. Simulation results show that the final desired consump-tion curve for industrial consumers is more close to the OCc thanthat for commercial consumers. Simulation results achieved thesystem peak demand reduction of 6.14% and 12.92% for the com-mercial and industrial area respectively. Industrial consumers aremore attracted towards TOD tariff that resulted higher peak reduc-tion compared to commercial consumers. Simulation results showthat the consumer’s bill of the commercial and industrial area isreduced with the proposed DSM technique. But, in this strategybenefits to utilities has not been taken into account.

4.3. Case study 3 – proposed DSM (W1 = 0.5, W2 = 0.5)

In this case study, OCp is generated by taking average of OCu andOCc so as to benefit both utilities and end consumers. Load shiftingDSM technique is used to shift the controllable loads to next valleyhours so that the actual power consumption curve follows the OCp.The simulation results obtained for commercial and industrial con-sumers are shown in Figs. 8 and 9 respectively. It is observed fromthe simulation results that the proposed DSM strategy has man-aged to bring final desired consumption curve close to the OCp.Simulation results show that the final desired consumption curvefor industrial consumers is more close to the OCp than that for com-mercial consumers mainly due to TOD tariff. Simulation resultsshow that the behaviour of devices shifted is influenced by boththe pool market price and TOD tariff in the proposed DSM strategy.It reveals that around noon, devices are shifted and influenced bypool price while during night time devices are shifted and influ-enced by TOD tariff as electricity price is cheaper. Simulation re-sults show that, this DSM strategy managed to achieve reductionin utility costs as well as reduction of the commercial and indus-trial consumer’s electricity bills simultaneously.

Supply peak demand reductions due to three different objec-tives is summarised graphically in Fig. 10. Peak demand reductionof 10.56% for commercial consumers and 12.27% for industrial con-sumers is achieved using proposed DSM that is reasonably higherthan utility and consumer driven DSM. Importantly the peakreduction is quite robust and significant. Hence, the proposedDSM strategy can help Indian utilities to reduce deficit significantlywhich is 12.32% over last five years.

Electricity bill savings to utility and commercial, industrial con-sumers due to three different objectives is summarised in Table 4.It is observed that the proposed DSM strategy which is based onthe average of OCu and OCc, offers electricity bill saving of 0.19%for commercial consumers and 0.29% for industrial consumers is

5 10 15 20 25 30 35 40 45

4500

5000

5500

6000

6500

7000

7500

8000

Time (hours)

Loa

d (k

Wh)

Forecasted LoadOCpACP

Fig. 9. Proposed DSM results of the industrial area.

6.45

5.0376.14

12.92

10.56

12.27

0

2

4

6

8

10

12

14

% P

eak

Dem

and

Red

ucti

on

Commercial Industrial Commercial Industrial Commercial Industrial

Utility driven DSM Consumer driven DSM Proposed DSM

Fig. 10. Percentage peak demand reductions.

Table 5Analysis of multiobjective DSM methodology.

W1 W2 Case studies Benefits

1 0 Utility driven DSM Utility companies0.25 0.75 Consumer biased

DSMBenefits are biased to consumers

0.5 0.5 Proposed DSM Utility companies and consumers0.75 0.25 Utility biased DSM Benefits are biased to utility

companies0 1 Consumer driven

DSMConsumers

618 N. Kinhekar et al. / Electrical Power and Energy Systems 55 (2014) 612–619

lesser than the savings offered by just considering the TOD tariffs.Similarly, the utility operational cost reduction of 0.68% and 4.87%is observed which is lesser than the cost reductions offered to util-ity by just considering pool electricity market price. It is true thatin case of multiobjective optimization, the benefits are being dis-tributed between both the utilities and end consumers. The overallbenefit to consumer will certainly be lower compared to optimiz-ing with respect to TOD tariff only. So, the consumers may notbe very much happy rather not attracted to the proposed multiob-jective algorithm temporarily. It is equally true for the private util-ities that they may not actively participate in a DSM program thatonly extend benefits to the consumers. So, we felt that the DSMprogram especially in developing countries can only be successfulif both the participating entities such as distribution licensees andconsumers experience financial benefits due to their activeparticipation. The proposed algorithm attempted to attract boththe utility and consumers to participate through cooperation inDSM program and hence the nation can experience reduction inpeak deficit.

Table 5 summarise analysis of multiobjective DSM methodol-ogy. It is observed that if a weight given to individual objectivecurves (i.e., W1 and W2) varies between 0 and 1, then benefitsreceived by utilities and consumers varies. For W1 = 0.25 andW2 = 0.75, benefits are biased to consumer side while for

Table 4Percentage electricity bill savings.

Case studies DSM for commercial consumers

Utility Commercial

Utility driven DSM 1.57 0Consumer driven DSM 0 0.35Proposed DSM 0.68 0.19

W1 = 0.75 and W2 = 0.25, benefits are biased to utility companies.In case of proposed DSM (W1 = W2 = 0.5) both utilities and consum-ers are treated equally.

Though the cost reduction of utility and consumers obtained bythe proposed DSM strategy is low, the main objective of this anal-ysis which is peak load reduction is achieved significantly. Thedeployment of present technology seems to be unrealistic todaybecause of low cost reductions. But, it is the need for future Indiato reduce large peak demand deficit, to provide reliable power tohuge population, and to compliance of strict environmental laws.In light of this, we felt that the deployment of this technology willbe feasible in future.

5. Conclusion

In this paper, a new DSM strategy has been proposed and sim-ulated for Indian utility. The proposed algorithm helps in motivat-ing both the utility and the connected consumers to participate inthe DSM and system peak demand reduction. It is a day-ahead loadshifting strategy which can be implemented in real time usingsmart grid technology with the support of AMI in the future. Final-ly, it has been observed that a successful implementation of theproposed scheme can help the urban India to free from loadshedding.

Acknowledgements

The first author acknowledges support by the Quality Improve-ment Programme (QIP) Scheme of the All India Council forTechnical Education (AICTE), Government of India. The authorsacknowledge the guidance of Mr. Shekhar Khadilkar, ChiefManager – DSM cell from TATA Power Limited, Mumbai for hiskind support.

Appendix A

The following equations describe the formulation of DSM strat-egy [30]:

The Connect(t) is given by the following equation:

ConnectðtÞ ¼Xt�1

i¼1

XD

k¼1

Xkit � P1k þXj�1

l¼1

Xt�1

i¼1

XD

k¼1

Xkiðt�1Þ � Pð1þlÞk ðA:1Þ

The Disconnect(t) is given by the following equation:

DSM for industrial consumers

consumer Utility Industrial consumer

6.87 00 0.544.87 0.29

N. Kinhekar et al. / Electrical Power and Energy Systems 55 (2014) 612–619 619

DisconnectðtÞ ¼Xtþm

q¼tþ1

XD

k¼1

Xktq � P1k þXj�1

l¼1

Xtþm

q¼tþ1

XD

k¼1

Xkðt�1Þq

� Pð1þlÞk ðA:2Þ

Minimization problem is subject to the following constraints:The number of devices shifted cannot be a negative value.

Xkit > 0 8i; j; k ðA:3Þ

The number of devices shifted away from a time step cannot bemore than the number of devices available for control at that timestep.

XN

t¼1

Xkit 6 CtrlableðiÞ ðA:4Þ

Connection times of devices can only be delayed, and notbrought forward, which can be expressed as

Xkit ¼ 0 8i > t ðA:5Þ

The contract options specify the maximum allowable time delay forall devices and limit the possible number of time steps that devicescan be shifted to, thus,

Xkit ¼ 0 8ðt � iÞ > m ðA:6Þ

where m is the maximum permissible delay.Taking the above into consideration, the maximum number of

possible time steps N can be found using the following equation:

N ¼ ð24�mÞ �mþXm�1

n¼1

n

!� k ðA:7Þ

where k is number of different types of devices.

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