Customer-driven demand response model for facilitating roof-top PV and wind power integration

Integrating wind and solar energy resources poses intermittency to power systems, which faces independent system operators with new technical and economic challenges. This study proposes a novel model to integrate the uncertainties of wind power on the supply side and roof-top solar photovoltaic (PV) on the demand side. To cope with their uncertainties, a demand response (DR) aggregator is proposed, which is enabled to participate in reserve markets. To this end, a new DR model is developed considering both customers' options to reduce and increase load through the DR aggregator. As such, besides improving the existing DR models (load shifting and curtailment), two DR programmes, i.e. load growth and load recovery, are mathematically modelled. Numerical studies indicate the effectiveness of the proposed model to reduce the total operation cost of the system and facilitate the integration of wind power and roof-top PV.

[1]  Thomas L. Acker,et al.  IEA Wind Task 24 Integration of Wind and Hydropower Systems; Volume 1: Issues, Impacts, and Economics of Wind and Hydropower Integration , 2011 .

[2]  Jamshid Aghaei,et al.  Robust n–k contingency constrained unit commitment with ancillary service demand response program , 2014 .

[3]  Masood Parvania,et al.  Integrating Load Reduction Into Wholesale Energy Market With Application to Wind Power Integration , 2012, IEEE Systems Journal.

[4]  Abhijit R. Abhyankar,et al.  Joint Energy and Spinning Reserve Market Clearing Incorporating Wind Power and Load Forecast Uncertainties , 2015, IEEE Systems Journal.

[5]  Abdullah Abusorrah,et al.  Demand Response Exchange in the Stochastic Day-Ahead Scheduling With Variable Renewable Generation , 2015, IEEE Transactions on Sustainable Energy.

[6]  Silvano Martello,et al.  Decision Making under Uncertainty in Electricity Markets , 2015, J. Oper. Res. Soc..

[7]  Nadali Mahmoudi,et al.  Wind Power Offering Strategy in Day-Ahead Markets: Employing Demand Response in a Two-Stage Plan , 2015, IEEE Transactions on Power Systems.

[8]  M. P. Moghaddam,et al.  Risk constrained offering strategy of wind power producers considering Intraday Demand Response Exchange , 2016, T&D 2016.

[9]  Tapan Kumar Saha,et al.  Modelling demand response aggregator behavior in wind power offering strategies , 2014 .

[10]  M. Klobasa Analysis of demand response and wind integration in Germany's electricity market , 2010 .

[11]  G. Ault,et al.  Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response , 2010 .

[12]  Enrico Zio,et al.  Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System , 2012, ArXiv.

[13]  M. Parvania,et al.  ISO's Optimal Strategies for Scheduling the Hourly Demand Response in Day-Ahead Markets , 2014, IEEE Transactions on Power Systems.

[14]  T. Saha,et al.  Demand Response Application by Strategic Wind Power Producers , 2016, IEEE Transactions on Power Systems.

[15]  Jamshid Aghaei,et al.  Critical peak pricing with load control demand response program in unit commitment problem , 2013 .

[16]  N. Growe-Kuska,et al.  Scenario reduction and scenario tree construction for power management problems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[17]  S. Oren,et al.  Large-Scale Integration of Deferrable Demand and Renewable Energy Sources , 2014, IEEE Transactions on Power Systems.

[18]  Masood Parvania,et al.  Demand Response Scheduling by Stochastic SCUC , 2010, IEEE Transactions on Smart Grid.

[19]  A. Soroudi,et al.  Possibilistic-Scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment , 2012, IEEE Transactions on Power Systems.

[20]  Vassilios G. Agelidis,et al.  Power Smoothing of Large Solar PV Plant Using Hybrid Energy Storage , 2014, IEEE Transactions on Sustainable Energy.

[21]  A. Rahimi-Kian,et al.  Aggregated wind power and flexible load offering strategy , 2011 .

[22]  E.F. El-Saadany,et al.  Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization , 2010, IEEE Transactions on Power Systems.

[23]  Javier Contreras,et al.  Stochastic Unit Commitment in Isolated Systems With Renewable Penetration Under CVaR Assessment , 2016, IEEE Transactions on Smart Grid.

[24]  Hieu Trinh,et al.  An Approach for Wind Power Integration Using Demand Side Resources , 2013, IEEE Transactions on Sustainable Energy.

[25]  Ozan Erdinc,et al.  Qualification and Quantification of Reserves in Power Systems Under High Wind Generation Penetration Considering Demand Response , 2015, IEEE Transactions on Sustainable Energy.

[26]  T. Niknam,et al.  Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources , 2012, IEEE Transactions on Power Delivery.

[27]  Lei Wu,et al.  Impacts of High Penetration Wind Generation and Demand Response on LMPs in Day-Ahead Market , 2014, IEEE Transactions on Smart Grid.

[28]  J. Watson,et al.  Multi-Stage Robust Unit Commitment Considering Wind and Demand Response Uncertainties , 2013, IEEE Transactions on Power Systems.

[29]  Thomas L. Acker,et al.  Integration of Wind and Hydropower Systems: Results of IEA Wind Task 24 , 2012 .

[30]  N. Aparicio,et al.  Daily Solar Energy Estimation for Minimizing Energy Storage Requirements in PV Power Plants , 2013, IEEE Transactions on Sustainable Energy.

[31]  Paul Denholm,et al.  Evaluating the limits of solar photovoltaics (PV) in electric power systems utilizing energy storage and other enabling technologies , 2007 .