Nonlinear demand response programs for residential customers with nonlinear behavioral models

Abstract To mitigate environmental issues of the thermal power plants, their greenhouse gas emissions are factored into the unit commitment (UC) problem. Moreover, demand side management as an effective strategy can relieve the energy security and environmental issues. Thus, the residential customers as one of the major groups of the customers, should be incorporated in the UC and generation scheduling problems. In this study, implementation of demand response (DR) programs in the UC problem are modeled. Herein, the implemented DR programs are entitled nonlinear DR (NDR) programs because nonlinear behavioral models for the residential customers are considered. In addition, the value of cost correlated with the implementation of the NDR programs in the UC problem (UC-NDR) are modeled. It is demonstrated that cooperation of the residential customers in the UC-NDR problem can be beneficial in decreasing cost and greenhouse gas emissions of the thermal power plants. In addition, it is concluded that comprehensive studies are needed to realistically model the residential customers behavior, since the different behavioral models result in different solutions and outcomes for the UC-NDR problem.

[1]  Shahram Jadid,et al.  Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system , 2015 .

[2]  Mahmud Fotuhi-Firuzabad,et al.  Load management in a residential energy hub with renewable distributed energy resources , 2015 .

[3]  D. Kirschen,et al.  Fundamentals of power system economics , 1991 .

[4]  Siamak Arzanpour,et al.  Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives , 2015 .

[5]  Atila Novoselac,et al.  Demand response for residential buildings based on dynamic price of electricity , 2014 .

[6]  C.D. Vournas,et al.  Reliability Constrained Unit Commitment Using Simulated Annealing , 2006, IEEE Transactions on Power Systems.

[7]  Mehdi Rahmani-andebili,et al.  Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets , 2016 .

[8]  M. Muslu Economic dispatch with environmental considerations: tradeoff curves and emission reduction rates , 2004 .

[9]  Mehdi Rahmani-andebili,et al.  Risk‐cost‐based generation scheduling smartly mixed with reliability‐driven and market‐driven demand response measures , 2015 .

[10]  S. M. Shahidehpour,et al.  Long-term transmission and generation maintenance scheduling with network, fuel and emission constraints , 1999 .

[11]  P. Cappers,et al.  Demand Response in U.S. Electricity Markets: Empirical Evidence , 2010 .

[12]  B. Hobbs,et al.  Value of Price Responsive Load for Wind Integration in Unit Commitment , 2014, IEEE Transactions on Power Systems.

[13]  Narayana Prasad Padhy,et al.  Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints , 2003 .

[14]  T.O. Ting,et al.  A novel approach for unit commitment problem via an effective hybrid particle swarm optimization , 2006, IEEE Transactions on Power Systems.

[15]  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.

[16]  Mehdi Rahmani-andebili,et al.  Combined emission and economic dispatch incorporating demand side resources , 2015, 2015 Clemson University Power Systems Conference (PSC).

[17]  Ayako Taniguchi,et al.  Estimation of the contribution of the residential sector to summer peak demand reduction in Japan using an energy end-use simulation model , 2016 .