Optimal power procurement and demand response with quality-of-usage guarantees

In this paper, we propose a general operating scheme which allows the utility company to jointly perform power procurement and demand response so as to maximize the social welfare. Our model takes into consideration the effect of the renewable energy and the multi-stage feature of the power procurement process. It also enables the utility company to provide quality-of-usage (QoU) guarantee to the power consumers, which ensures that the average power usage level meets the target value for each user. To maximize the social welfare, we develop a low-complexity algorithm called the welfare maximization algorithm (WMA), which performs joint power procurement and dynamic pricing. WMA is constructed based on a two-timescale Lyapunov optimization technique. We prove that WMA achieves a close-to-optimal utility and ensures that the QoU requirement is met with bounded deficit. WMA can be implemented in a distributed manner and is robust with respect to system dynamics uncertainty.

[1]  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.

[2]  Miao He,et al.  Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration , 2011, 2011 Proceedings IEEE INFOCOM.

[3]  H. Vincent Poor,et al.  Scheduling Power Consumption With Price Uncertainty , 2011, IEEE Transactions on Smart Grid.

[4]  Daniel Pérez Palomar,et al.  Distributed Optimization of Coupled Systems With Applications to Network Utility Maximization , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[5]  Pravin Varaiya,et al.  Smart Operation of Smart Grid: Risk-Limiting Dispatch , 2011, Proceedings of the IEEE.

[6]  M. Neely,et al.  Max-Weight Achieves the Exact $[O(1/V), O(V)]$ Utility-Delay Tradeoff Under Markov Dynamics , 2010, 1008.0200.

[7]  Pravin Varaiya,et al.  Selling Random Wind , 2012, 2012 45th Hawaii International Conference on System Sciences.

[8]  Distributed Utility Associates.  Using Distributed Energy Resources, A How-To Guide for Federal Facility Managers , 2002 .

[9]  Multi-period Optimal Procurement and Demand Responses in the Presence of Uncertain Supply Libin Jiang , 2011 .

[10]  Na Li,et al.  Optimal demand response: Problem formulation and deterministic case , 2012 .

[11]  P. Varaiya,et al.  Bringing Wind Energy to Market , 2012, IEEE Transactions on Power Systems.

[12]  Leandros Tassiulas,et al.  Resource Allocation and Cross-Layer Control in Wireless Networks , 2006, Found. Trends Netw..

[13]  Alexandros G. Dimakis,et al.  Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

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

[15]  K. Mani Chandy,et al.  Optimal contract for wind power in day-ahead electricity markets , 2011, IEEE Conference on Decision and Control and European Control Conference.