Distributed Real-Time Pricing Scheme for Local Power Supplier in Smart Community

In this paper, we consider the real-time pricing problem for a small scale local power supplier (LPS) in a smart energy community. The LPS supplies power to the residential users (RUs) in a local area and sells the remaining power to the main grid. Since the selling price to the main grid is relative low, LPS intends to sell more power to the RUs with an appropriate price. The LPS determines the price based on the proposed pricing scheme to maximize its revenue. The price is informed to RUs through the communication infrastructure. According to the announced price of LPS, each RU schedules its power consumption to maximize its utility. We model the interactions between the local power supplier and all users as a one-leader multi-followers Stackelberg game, where the LPS acts as the leader and RUs act as the followers. To address this problem, a distributed algorithm based on information exchange between the LPS and RUs is proposed. Simulation results show that the distributed algorithm converges to the Stackelberg equilibrium.

[1]  Richard G. Newell,et al.  Environmental and Technology Policies for Climate Mitigation , 2008 .

[2]  David M. Auslander,et al.  Residential electricity auction with uniform pricing and cost constraints , 2009, 41st North American Power Symposium.

[3]  H. Vincent Poor,et al.  Three-Party Energy Management With Distributed Energy Resources in Smart Grid , 2014, IEEE Transactions on Industrial Electronics.

[4]  Walid Saad,et al.  Economics of Electric Vehicle Charging: A Game Theoretic Approach , 2012, IEEE Transactions on Smart Grid.

[5]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[6]  Chen Chen,et al.  An innovative RTP-based residential power scheduling scheme for smart grids , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Chau Yuen,et al.  Peak-to-Average Ratio Constrained Demand-Side Management With Consumer's Preference in Residential Smart Grid , 2014, IEEE Journal of Selected Topics in Signal Processing.

[8]  Tongdan Jin,et al.  Ordering electricity via Internet and Its potentials for smart grid systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[9]  H. Morais,et al.  Intelligent multi-player smart grid management considering distributed energy resources and demand response , 2010, IEEE PES General Meeting.

[10]  Peter Luh,et al.  Load adaptive pricing: An emerging tool for electric utilities , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[11]  Xiao-Jun Zeng,et al.  A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid , 2013, Soft Comput..

[12]  Daniel Pérez Palomar,et al.  Demand-Side Management via Distributed Energy Generation and Storage Optimization , 2013, IEEE Transactions on Smart Grid.

[13]  Vincent W. S. Wong,et al.  Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[14]  Hongbin Sun,et al.  Active Demand Response Using Shared Energy Storage for Household Energy Management , 2013, IEEE Transactions on Smart Grid.

[15]  Zhi Chen,et al.  Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization , 2012, IEEE Transactions on Smart Grid.

[16]  E. McKenna,et al.  Photovoltaic metering configurations, feed-in tariffs and the variable effective electricity prices that result , 2013 .

[17]  Quanyan Zhu,et al.  Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach , 2013, IEEE Transactions on Smart Grid.

[18]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[19]  Ha T. Nguyen,et al.  Quantifying Rooftop Solar Photovoltaic Potential for Regional Renewable Energy Policy , 2010, Comput. Environ. Urban Syst..

[20]  Peter Xiaoping Liu,et al.  A game-theoretical decision-making scheme for electricity retailers in the smart grid with demand-side management , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[21]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.