A demand-side pricing strategy considering thermal comfort

This paper proposes a demand-side pricing strategy to purchase regulation service. The demand-side regulation service has the advantage of lower cost and faster response to reduce the utility company's cost comparing with the traditional automatic generation control (AGC). Thus, it is necessary for the utility company to make a tradeoff between the demand-side regulation and the AGC to minimize the regulation cost. In this study, we establish a cost model based on the thermal comfort and formulate a cost minimization problem. Specifically, we determine the purchase of the regulation service within regulating range provided by the consumers. In general, the cost minimization problem is nonconvex, and we use the particle swarm optimization (PSO) algorithm to obtain the globally optimal price for the demand-side regulation service. Numerical results demonstrate the effectiveness of the proposed pricing strategy, and the demand-side regulation service can reduce the cost of the utility company significantly.

[1]  Maojiao Ye,et al.  Solving Potential Games With Dynamical Constraint , 2016, IEEE Transactions on Cybernetics.

[2]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[3]  Andrea Manuello Bertetto,et al.  Home automation systems and PMV classification for moderate confined environments , 2014, 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD).

[4]  Xudong Ding,et al.  PMV-based fuzzy algorithms for controlling indoor temperature , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[5]  Guoqiang Hu,et al.  A Cooperative Demand Response Scheme Using Punishment Mechanism and Application to Industrial Refrigerated Warehouses , 2014, IEEE Transactions on Industrial Informatics.

[6]  Vincent W. S. Wong,et al.  Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design , 2012, IEEE Transactions on Smart Grid.

[7]  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.

[8]  Asuman E. Ozdaglar,et al.  Price and Capacity Competition , 2006, Games Econ. Behav..

[9]  Guoqiang Hu,et al.  Energy Management Considering Load Operations and Forecast Errors With Application to HVAC Systems , 2018, IEEE Transactions on Smart Grid.

[10]  Guoqiang Hu,et al.  Distributed Energy Consumption Control via Real-Time Pricing Feedback in Smart Grid , 2014, IEEE Transactions on Control Systems Technology.

[11]  Nathan Mendes,et al.  PMV-Based Predictive Algorithms for Controlling Thermal Comfort in Building Plants , 2007, 2007 IEEE International Conference on Control Applications.

[12]  Johanna L. Mathieu,et al.  Price and capacity competition in zero-mean storage and demand response markets , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[13]  Na Li,et al.  Optimal demand response based on utility maximization in power networks , 2011, 2011 IEEE Power and Energy Society General Meeting.

[14]  Quanyan Zhu,et al.  Demand Response Management in the Smart Grid in a Large Population Regime , 2016, IEEE Transactions on Smart Grid.

[15]  Xiaofeng Liao,et al.  Reinforcement Learning for Constrained Energy Trading Games With Incomplete Information , 2017, IEEE Transactions on Cybernetics.

[16]  Georgios B. Giannakis,et al.  Residential Load Control: Distributed Scheduling and Convergence With Lost AMI Messages , 2012, IEEE Transactions on Smart Grid.

[17]  Joseph H. Eto Demand Response Spinning Reserve Demonstration -- Phase 2 Findings from the Summer of 2008 , 2010 .

[18]  G. A. Chown,et al.  Implementation of regulation as an ancillary service in Eskom and the use of Eskom internal web for this service , 1999 .

[19]  Fairus Muhamad Darus,et al.  Thermal environment of natural ventilated preschool buildings in warm-humid climates , 2012, 2012 IEEE Symposium on Business, Engineering and Industrial Applications.

[20]  Peter Cappers,et al.  Demand Response for Ancillary Services , 2013, IEEE Transactions on Smart Grid.