Slide 1

Comparison of Different Energy Consumption Scheduling SchemesM. Asghar KhanFA12-REE-0308/25/201311Performance ParametersBilling/Pricing MechanismPAR (Peak-to-Average Ratio) reduction and Total Energy Cost minimizationFairness Among UsersExecution Time of AlgorithmWaiting Time of Appliances8/25/201321.Billing/Pricing MechanismExisting billing techniques

Inclining Block Rates (IBR)Time Of Use Pricing (TOU)Critical Peak Pricing (CPP)Real Time Pricing (RTP) 8/25/20133a.IBR Example: IESCO General Supply Tariff- Residential8/25/20134

b. TOU Example: IESO Ontario (Canadian Province)8/25/20135

Smart meters track the energy use in your home on an hourly basis and send this information automatically to your local distribution company (LDC). By automating the meter-reading function, smart meters deliver a number of benefits:They support the implementation of time-of-use prices. By time-stamping your consumption data, local distribution companies will be able to determine how much electricity was used during off-peak times and how much was consumed during on-peak periods. This capability allows homeowners to find electricity savings by shifting their electricity use.They help LDCs to identify power theft and respond to meter failures and outages more quickly.They provide greater operational efficiencies in local distribution system management.statutory/staCHtr/AdjectiveRequired, permitted, or enacted by statute: "the courts did award statutory damages to each of the plaintiffs".(of a criminal offense) Carrying a penalty prescribed by statute: "statutory theft".Synonymslegal

5c.CPP (Critical Peak Pricing)

Very high critical peak prices are assessed for certain hours on event days (often limited to 10-15 per year) Prices can be 3-10 times as much during these few hours Typically combined with a TOU rate, but not always Typical goal of CPP is to more dramatically reduce load during the relatively few, very expensive hours Typically, a CPP is added to a TOU rate

8/25/20136

d.RTP (Real Time Pricing) Real-time pricing (RTP) is generally an hourly rate which is applied to usage on an hourly basis8/25/20137

The1 centeuro coin(0.01) has a value of one-hundredth of aeuroand is composed ofcopper-coveredsteel.7Q.Which one is better among all these pricing schemes?Towards Fairness: The alternative hour-by-hour billing mechanism presented in [1], is the most efficient billing mechanism, which incorporates the exact hour-by-hour load profile of each user. ; Bn is the bill of every user n ; ;

It charges users at a higher rate if they schedule their load at peak-hours and at a lower rate if they move their load to off-peak hours. In other words, the hour-by-hour alternative billing mechanism takes into account both total load and load flexibility. Therefore, we expect that it can improve fairness.Reference [2], also point out the truthfulness of the usersTowards PAR reduction & cost minimization: The most attractive pricing schemes regarding these two properties are presented in [2],[3] and also the combination of RTP with IBR [4] & [5].

; where8/25/20138

8

2.PAR (Peak-to-Average Ratio) reduction and Total Energy Cost minimization

All the schemes presented so for mainly focuses on reducing the peak-to-average ratioAs a consequence the total energy generation cost reduces and so the consumers pays less to the utilityThe best results according to the simulation are presented by [4],[6],[7] & [8].The percentage reduction in these objectives are given in the Table 1

8/25/201393.Fairness Among Users

Q: Is it beneficial for a user or a group of users to declare false ECS information to other users?Game-Theoretic Based ECS: The algorithm presented in [2] , promises to enforce the users not to be untruthful In fact, since every users individual payoff is nothing but the total energy cost multiplied by a negative constantTherefore, any false information leads to increasing the energy cost of cheating user as well as all other users in the system

8/25/201310

ContdAchieving Optimality and Fairness in Autonomous Demand Response:In this paper alternative hour-by-hour billing mechanism is presented in combination with RTPTwo important factors are considered while achieving fairnessLoad FlexibilityTotal LoadA fair billing mechanism is presented as

A fairness index is defined as

Here, the fairness index can be defined as the variational distance between normalized billing vector for billing mechanism B and normalized billing vector for B*This equation shows that a lower fairness index F point outs a fairer billing.

8/25/201311

4.Execution Time of Algorithm

This issue is more highlighted by [6],through experimental resultsMainly focusing on the search space traversingComparison of constrained and non-constrained cases are presentedAs contrast, the constrained search works with much smaller execution time by eliminating the unnecessary search space traversal and so shows stable behavior From the next slide, we can easily figure out that the execution time greatly depends on the search space size of the preemptive tasks and also on the constraint processingTool Used: Microsoft windows GetTickcount system call

8/25/201312Processing within a prescribed bound may be called a constraint processing.12Comparison of Experimental ResultsExecution Time of 1 Preemptive taskExecution Time of 2 Preemptive tasks

8/25/201313

5.Waiting Time of Appliances

The only scheme that explores the idea of waiting time of appliances regarding their actual schedule is [4].The cost of waiting can be modeled as

; where waiting parameter The following model can be used for waiting parameter ;;The higher the value of A.C.P. the higher will be the cost of waiting.In practice, three choices of A.C.P. can be

Consequentially, we can say that the waiting time of appliances is inversely proportional to Adjustable Control Parameter (ACP) and to the total payments of user to the utility.8/25/201314

8/25/20131515ReferencesZahra Baharlouei, Student Member, IEEE, Massoud Hashemi, Member, IEEE, Hamed Narimani, Student Member, IEEE, and Hamed Mohsenian-Rad, Member, IEEE, Achieving Optimality and Fairness in Autonomous Demand Response: Benchmarks and Billing Mechanisms, IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 968-975, June 2013.Amir-Hamid Mohsenian-Rad, Member, IEEE, Vincent W. S. Wong,Senior Member, IEEE, Juri Jatskevich, Senior Memjber, IEEE, Robert Schober, Fellow, IEEE, and Alberto Leon-Garcia, Fellow, IEEE, Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid, IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 320-331, December 2010.Mohsenian-Rad, A-H., et al. Optimal and Autonomous Incentive-Based Energy Consumption Scheduling Algorithm for Smart Grid, Innovative Smart Grid Technologies (ISGT), 2010, IEEE, (2010).Amir-Hamid Mohsenian-Rad, Member, IEEE,, and Alberto Leon-Garcia, Fellow, IEEE, Optimal Resedential Load Control with Price Prediction in Real-Time Electricity Pricing Environmrnts, IEEE Trans. Smart Grid, vol. 1, no. 2, pp. 120-133, September 2010.Pedram Samadi, Student Mameber, IEEE, Hamed Mohsenian-Rad, Member, IEEE, Vincent W.S. Wong, Senior Member, IEEE, and Robert Schober, Fellow, IEEE, Tacking the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid, IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 1007-1016, June 2013.Junghoon Lee, Hye-Jin Kim, Gyung-Leen Park and Mikyung Kang, Energy Consumption Scheduler for Demand Response Systems in the Smart Grid, Journal of Information Science and Engineering 28, 955-969, (2012).Pedram Samadi, Student Mamber, IEEE, Hamed Mohsenian-Rad, Member, IEEE, Robert Schober, Fellow, IEEE, Wong, Senior Member, IEEE, Advanced Demand Side Management for the Future Smart Grid Using Mechsnism Design, , IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1170-1180, September 2012.Pedram Samadi, Student Member, IEEE, Hamed Mohsenian-Rad, Member, IEEE, Vincent W.S. Wong, Senior Member, IEEE, and Robert Schober, Fellow, IEEE, Tacking the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid, IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 1007-1016, June 2013.

8/25/2013167Scheme NameAlgorithm AdoptedPricing MechanismSubscribersTypeFairnessGain

ApplicationCoverage Area

Simulation Setup

Peak Load ReductionEnergy Cost MinimizationExecution Time of Algorithm

Waiting Time of AppliancesUtilityEnd-User

IncentiveBased ECSDistributed Algorithm Based Optimization Based on Convex and Increasing Cost FunctionsResidentialNA38.1%37.8%24 hrMoreYesYesLocalMATLAB

Game- Theoretic Based ECSDistributed Algorithm Based OptimizationBased on Convex and Increasing Cost FunctionsResidentialApplicable17%19.6%NAMoreYesYesGlobalMATLAB

Heuristic Optimization Based ECSHeuristic Based Evolutionary AlgorithmDay-Ahead Load ShiftingResidentialNA18.3%5.0%2 hrInversely Related to DelayYesYesGlobalMATLAB

Commercial18.3%5.8%

Industrial14.2%10%

Backtracking Based ECSBacktracking Based ECSRTPResidentialNA23.1%NILessMoreYesYesLocalVisual C++ 6.0, MS Window GetTrick Count

Vickrey-Clarke-Groove (VCG) Based ECSVCGBased on Convex, Differentiable & Increasing FunctionResidentialApplicable19.3%37.8%MoreMoreYesYesGlobalMATLAB

ORLC With Price PredictionWeighted Avg. Filter Based Price PredictionRTP + IBRResidentialNA38%25%NA Inversely Related to A.C.P &PaymentsYesYesLocalMATLAB

A Layered Architecture for DSMSpring AlgorithmRTP,CPP & TOUResidentialNAApplicable7.53%NAMoreYesYesLocalMATLAB/Simulink

Tackling the Load Uncertainty Challenge for ECSOptimization Based SchedulingRTP + IBResidentialApplicable25.6%-28.9%15.8%-17.6%NAMoreYes YesGlobalMOSEK

Optimality and Fairness Based ECS ModelGame Theory Based OptimizationRTP with Hour-by-Hour Billing MechanismResidential73% More Efficient & Inv.Related to OptimilityApplicableApplicable NANAYesYes GlobalMATLAB

TABLE1. Comparison of the Scheduling Schemes for Energy Consumption in Smart Grid