Energy Storage Control for Dispatching Photovoltaic Power

The strong growth of the solar power generation industry requires an increasing need to predict the profile of solar power production over a day and develop highly efficient and optimized stand-alone and grid-connected photovoltaic systems. Moreover, the opportunities offered by battery energy storage systems (BESSs) coupled with photovoltaic (PV) systems require an ability to forecast the load power to optimize the size of the entire system composed of PV panels and storage devices. This paper presents a sizing and control strategy of BESSs for dispatching a photovoltaic generation farm in the 1-h ahead and day-ahead markets. The forecasting of the solar irradiation and load power consumption is performed by developing a predictive model based on a feed-forward neural network trained with the Levenberg–Marquardt back-propagation learning algorithm.

[1]  Jin Zhong,et al.  Optimal bidding strategy for demand response aggregator in day-ahead markets via stochastic programming and robust optimization , 2015, 2015 12th International Conference on the European Energy Market (EEM).

[2]  Ingo Kunold,et al.  Decentralized energy equalizer for a balancing aggregation of production and consumption of energy in scalable units , 2013, 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS).

[3]  Francesco Grimaccia,et al.  A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output , 2015 .

[4]  Mehdi Rahmani-Andebili,et al.  Investigating effects of changes in power market regulations on demand-side resources aggregators , 2015, 2015 IEEE Power & Energy Society General Meeting.

[5]  A. Hellal,et al.  Short term photovoltaic power generation forecasting using neural network , 2012, 2012 11th International Conference on Environment and Electrical Engineering.

[6]  Steven R. Weller,et al.  An optimization-based approach to scheduling residential battery storage with solar PV: Assessing customer benefit , 2015 .

[7]  Chul-Hwan Kim,et al.  Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[8]  Hong Yuan,et al.  A dynamic optimal control strategy for BESS considering wind power forecasting , 2014, 2014 IEEE International Conference on Mechatronics and Automation.

[9]  Djalel Dib,et al.  One-hour ahead electric load and wind-solar power generation forecasting using artificial neural network , 2015, IREC2015 The Sixth International Renewable Energy Congress.

[10]  Vikas Pratap Singh,et al.  Generalized neural network methodology for short term solar power forecasting , 2013, 2013 13th International Conference on Environment and Electrical Engineering (EEEIC).

[11]  A. Koyanagi,et al.  Study on maximum power point tracking of wind turbine generator using a flywheel , 2002, Proceedings of the Power Conversion Conference-Osaka 2002 (Cat. No.02TH8579).

[12]  Jan Kleissl,et al.  Solar Energy Forecasting and Resource Assessment , 2013 .

[13]  J. Kleissl,et al.  Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems , 2013 .

[14]  Sishaj P. Simon,et al.  Day-ahead prediction of solar power output for grid-connected solar photovoltaic installations using Artificial Neural Networks , 2014, 2014 IEEE 2nd International Conference on Emerging Electronics (ICEE).

[15]  Sishaj P. Simon,et al.  Artificial neural network predictor for grid-connected solar photovoltaic installations at atmospheric temperature , 2014, 2014 International Conference on Advances in Green Energy (ICAGE).

[16]  Hongbin Sun,et al.  A robust method based storage aggregator model for grid dispatch , 2015, 2015 IEEE Power & Energy Society General Meeting.

[17]  Yang Yan,et al.  Integrated Solutions for Photovoltaic Grid Connection: Increasing the Reliability of Solar Power , 2014, IEEE Power and Energy Magazine.

[18]  Azah Mohamed,et al.  Heuristic optimisation of state-of-charge feedback controller for hourly dispatch of hybrid PV/BES system , 2014, 2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[19]  F. Foiadelli,et al.  E-campus: The “sustainabilization” of engineering Bovisa Campus , 2016, 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC).

[20]  F. Foiadelli,et al.  Ultracapacitors application for energy saving in subway transportation systems , 2007, 2007 International Conference on Clean Electrical Power.

[21]  Subhashish Bhattacharya,et al.  Rule-Based Control of Battery Energy Storage for Dispatching Intermittent Renewable Sources , 2010, IEEE Transactions on Sustainable Energy.

[22]  Sonia Martínez,et al.  Storage Size Determination for Grid-Connected Photovoltaic Systems , 2011, IEEE Transactions on Sustainable Energy.

[23]  Chunqing Tan,et al.  Energy storage sizing for office buildings based on short-term load forecasting , 2012, 2012 IEEE 6th International Conference on Information and Automation for Sustainability.

[24]  Xiao-Ping Zhang,et al.  Aggregator service for PV and battery energy storage systems of residential building , 2015 .

[25]  A. Q. Jakhrani,et al.  A simple method for the estimation of global solar radiation from sunshine hours and other meteorological parameters , 2010, 2010 IEEE International Conference on Sustainable Energy Technologies (ICSET).

[26]  Karl Worthmann,et al.  Distributed and Decentralized Control of Residential Energy Systems Incorporating Battery Storage , 2015, IEEE Transactions on Smart Grid.

[27]  Sonia Leva,et al.  Urban Scale Photovoltaic Charging Stations for Electric Vehicles , 2014, IEEE Transactions on Sustainable Energy.

[28]  Zhigang Chen,et al.  An integrated control strategy of battery energy storage system in microgrid , 2013 .

[29]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .

[30]  R. Moreno,et al.  A framework for the energy aggregator model , 2013, 2013 Workshop on Power Electronics and Power Quality Applications (PEPQA).

[31]  Ming Ding,et al.  Control strategies of BESS for compensating renewable energy fluctuations , 2012 .

[32]  Binbin Chen,et al.  Battery capacity planning for grid-connected solar photovoltaic systems , 2014, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific.

[33]  Gooi Hoay Beng,et al.  Combination of renewable generation and flexible load aggregation for ancillary services provision , 2015, 2015 50th International Universities Power Engineering Conference (UPEC).

[34]  Charles W. Chase,et al.  Demand-Driven Forecasting: A Structured Approach to Forecasting , 2009 .

[35]  Michela Longo,et al.  Analysis of Ageing Effect on Li-Polymer Batteries , 2015, TheScientificWorldJournal.