Sizing and control of residential solar panel and battery

Variable demand, dynamic electricity pricing and intermittent renewable energy are sources of variability and uncertainty in smart grid systems. Energy storage devices can be used to absorb these fluctuations and reduce the overall cost. The goal is to find the best policy for storing, buying, selling and using energy in the presence of this variability and uncertainty. This paper explores a comprehensive methodology to study the effect of integrating renewable sources and energy storage in reducing the cost of smart grid operation and the sizing and control of such systems. In this paper, we consider grid-connected dwellings with a solar panel and battery as an example scenario. Numerical results illustrate the performance of our proposed methodology.

[1]  Laurent Massoulié,et al.  Optimal Control of End-User Energy Storage , 2012, IEEE Transactions on Smart Grid.

[2]  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.

[3]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[4]  Hamid Sharif,et al.  A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges , 2013, IEEE Communications Surveys & Tutorials.

[5]  Jean C. Walrand,et al.  Optimal demand response with energy storage management , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[6]  Ufuk Topcu,et al.  Optimal power flow with distributed energy storage dynamics , 2011, Proceedings of the 2011 American Control Conference.

[7]  Clemens van Dinther,et al.  Estimating Economic Benefits of Electricity Storage at the End Consumer Level , 2009, Wirtschaftsinformatik.

[8]  Prashant J. Shenoy,et al.  SmartCharge: Cutting the electricity bill in smart homes with energy storage , 2012, 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy).

[9]  Leandros Tassiulas,et al.  Optimal energy storage control policies for the smart power grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[10]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[11]  H. Allcott,et al.  Rethinking Real Time Electricity Pricing , 2011 .

[12]  Jean-Yves Le Boudec,et al.  Optimal Generation and Storage Scheduling in the Presence of Renewable Forecast Uncertainties , 2014, IEEE Transactions on Smart Grid.

[13]  Han-I Su,et al.  Modeling and Analysis of the Role of Energy Storage for Renewable Integration: Power Balancing , 2013, IEEE Transactions on Power Systems.

[14]  Mathijs de Weerdt,et al.  The 2017 Power Trading Agent Competition , 2013 .

[15]  Anand Sivasubramaniam,et al.  Optimal power cost management using stored energy in data centers , 2011, PERV.

[16]  G. Goldman,et al.  A Survey of Utility Experience with Real Time Pricing , 2004 .

[17]  Visa Koivunen,et al.  Optimal Energy Consumption Model for Smart Grid Households With Energy Storage , 2013, IEEE Journal of Selected Topics in Signal Processing.

[18]  Hanne Sæle,et al.  Demand Response From Household Customers: Experiences From a Pilot Study in Norway , 2011, IEEE Transactions on Smart Grid.