Day-ahead real-time pricing strategy based on the price-time-type elasticity of demand

The curtailment of the peak demand has great economic and environmental benefits. In this paper, an efficient price profile under the Real-Time Pricing (RTP) option is found out to optimize the regional domestic daily electric load curve. The domestic electric appliances are divided into eight categories, with respect to the difference of their self-price elasticity and cross-price elasticity. In order to set up a reasonable pricing strategy model, both the users' satisfaction and the price are taken into account. The program of RTP is taken and the Particle Swarm Optimization (PSO) algorithm is used to optimize the electricity price profile. Under the optimized price profile, the load curve tends to be more flat and the average price for the customer is lower than before, after the variation and shift of the electric power demand.

[1]  Li Hui,et al.  Indoor Dynamic Thermal Comfort Control Method Based on Particle Swarm Optimization , 2013 .

[2]  P. Ferrao,et al.  The impact of demand side management strategies in the penetration of renewable electricity , 2012 .

[3]  Pedro Faria,et al.  Particle swarm optimization applied to integrated demand response resources scheduling , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[4]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[5]  Arindam Ghosh,et al.  Smart demand side management of low-voltage distribution networks using multi-objective decision making , 2012 .

[6]  Alessandro Di Giorgio,et al.  An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management , 2012 .

[7]  Ju Hehua,et al.  Time-Optimal Trajectory Planning Algorithm for Manipulator Based on PSO , 2011 .

[8]  Yuan Jia-hai,et al.  Customer Response Under Time-of-Use Electricity Pricing Policy Based on Multi-Agent System Simulation , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[9]  J. Contreras,et al.  Price-maker strategies of a hydro producer in a day-ahead electricity market , 2012, 2012 16th IEEE Mediterranean Electrotechnical Conference.

[10]  R. Faranda,et al.  Distributed Interruptible Load Shedding and Micro-Generator Dispatching to Benefit System Operations , 2012, IEEE Transactions on Power Systems.

[11]  Jingrui Zhang,et al.  Small Population-Based Particle Swarm Optimization for Short-Term Hydrothermal Scheduling , 2012, IEEE Transactions on Power Systems.

[12]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  Xiaoying Wang,et al.  A User Satisfaction Level Based Multi-objective Optimization Method for Grid Task Scheduling , 2006, 2006 Fifth International Conference on Grid and Cooperative Computing (GCC'06).

[14]  Mohammed H. Albadi,et al.  Demand Response in Electricity Markets: An Overview , 2007, 2007 IEEE Power Engineering Society General Meeting.