A Near-Optimal Model-Based Control Algorithm for Households Equipped With Residential Photovoltaic Power Generation and Energy Storage Systems

Integrating residential photovoltaic (PV) power generation and energy storage systems into the Smart Grid is an effective way of reducing fossil fuel consumptions. This has become a particularly interesting problem with the introduction of dynamic electricity energy pricing, since consumers can use their PV-based energy generation and controllable energy storage devices for peak shaving on their power demand profile, thereby minimizing their electricity bill. A realistic electricity pricing function is considered with billing period of a month, comprising both an energy price component and a demand price component. Due to the characteristics of electricity price function and energy storage capacity limitation, the residential storage control algorithm should 1)utilize PV power generation and load power consumption predictions and 2)account for various energy loss components during system operation, including energy loss components due to rate capacity effect in the storage system and power dissipation of the power conversion circuitry. A near-optimal storage control algorithm is proposed accounting for these aspects. The near-optimal algorithm, which controls the charging/discharging of the storage system, is effectively implemented by solving a convex optimization problem at the beginning of each day with polynomial time complexity. For further improvement, the reinforcement learning technique is adopted to adaptively determine the residual energy in the storage system at the end of each day in a billing period.

[1]  George Kesidis,et al.  Incentive-Based Energy Consumption Scheduling Algorithms for the Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

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

[3]  B.F. Wollenberg,et al.  Toward a smart grid: power delivery for the 21st century , 2005, IEEE Power and Energy Magazine.

[4]  Landis D. Kannberg,et al.  GridWiseTM: The Benefits of a Transformed Energy System , 2003, nlin/0409035.

[5]  Susan Vercheak,et al.  Re: Case 13-E-0030, Case 13-G-0031, and Case 13-S-0032; Proceeding on Motion of the Commission as to the Rates, Charges, Rules, and Regulations of Consolidated Edison Company of New York, Inc. for Electric, Gas and Steam Service , 2014 .

[6]  L. Zarzalejo,et al.  Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning , 2010 .

[7]  Naehyuck Chang,et al.  Hybrid electrical energy storage systems , 2010, 2010 ACM/IEEE International Symposium on Low-Power Electronics and Design (ISLPED).

[8]  Lawrence V. Snyder,et al.  Control Mechanisms for Residential Electricity Demand in SmartGrids , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[9]  Massoud Pedram,et al.  Accurate Component Model Based Optimal Control for Energy Storage Systems in Households with Photovoltaic Modules , 2013, 2013 IEEE Green Technologies Conference (GreenTech).

[10]  Yvonne Freeh,et al.  Handbook Of Batteries , 2016 .

[11]  Michael A. E. Andersen,et al.  High-Efficiency Isolated Boost DC–DC Converter for High-Power Low-Voltage Fuel-Cell Applications , 2010, IEEE Transactions on Industrial Electronics.

[12]  Shahin Nazarian,et al.  Profit maximization for utility companies in an oligopolistic energy market with dynamic prices , 2012, 2012 IEEE Online Conference on Green Communications (GreenCom).

[13]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[14]  Yanzhi Wang,et al.  A Hierarchical Control Algorithm for Managing Electrical Energy Storage Systems in Homes Equipped with PV Power Generation , 2012, 2012 IEEE Green Technologies Conference.

[15]  Naehyuck Chang,et al.  Maximum power transfer tracking for a photovoltaic-supercapacitor energy system , 2010, 2010 ACM/IEEE International Symposium on Low-Power Electronics and Design (ISLPED).

[16]  Wei Li,et al.  Short-Term Power Load Forecasting Using Improved Ant Colony Clustering , 2008, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008).

[17]  E. Ortjohann,et al.  Challenges in integrating distributed Energy storage systems into future smart grid , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[18]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[19]  Naehyuck Chang,et al.  Battery management for grid-connected PV systems with a battery , 2012, ISLPED '12.

[20]  Diane J. Cook,et al.  Energy Prediction Based on Resident's Activity , 2010 .

[21]  Takashi Hiyama,et al.  Neural network based estimation of maximum power generation from PV module using environmental information , 1997 .

[22]  T. Funabashi,et al.  One-Hour-Ahead Load Forecasting Using Neural Networks , 2002 .

[23]  Naehyuck Chang,et al.  Battery-supercapacitor hybrid system for high-rate pulsed load applications , 2011, 2011 Design, Automation & Test in Europe.