Development of a three-phase battery energy storage scheduling and operation system for low voltage distribution networks

Three phase battery energy storage (BES) installed in the residential low voltage (LV) distribution network can provide functions such as peak shaving and valley filling (i.e. charge when demand is low and discharge when demand is high), load balancing (i.e. charge more from phases with lower loads and discharge more to phases with higher loads) and management of distributed renewable energy generation (i.e. charge when rooftop solar photovoltaics are generating). To accrue and enable these functions an intelligent scheduling system was developed. The scheduling system can reliably schedule the charge and discharge cycles and operate the BES in real time. The scheduling system is composed of three integrated modules: (1) a load forecast system to generate next-day load profile forecasts; (2) a scheduler to derive an initial charge and discharge schedule based on load profile forecasts; and (3) an online control algorithm to mitigate forecast error through continuous schedule adjustments. The scheduling system was applied to an LV distribution network servicing 128 residential customers located in an urban region of South East Queensland, Australia.

[1]  B. Dunn,et al.  Electrical Energy Storage for the Grid: A Battery of Choices , 2011, Science.

[2]  Gaetano Zizzo,et al.  A Self-Adapting Approach for Forecast-Less Scheduling of Electrical Energy Storage Systems in a Liberalized Energy Market , 2013 .

[3]  Zhengen Ren,et al.  Modelling future uptake of distributed energy resources under alternative tariff structures , 2014 .

[4]  Rodney Anthony Stewart,et al.  Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system , 2014 .

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

[6]  Daniel Masa Bote,et al.  PV self-consumption optimization with storage and Active DSM for the residential sector , 2011 .

[7]  Daniel Masa Bote,et al.  Neural network controller for active demand side management with PV energy in the residential sector , 2012 .

[8]  Y Riffonneau,et al.  Optimal Power Flow Management for Grid Connected PV Systems With Batteries , 2011, IEEE Transactions on Sustainable Energy.

[9]  Federico Silvestro,et al.  Optimal Management Strategy of a Battery-Based Storage System to Improve Renewable Energy Integration in Distribution Networks , 2012, IEEE Transactions on Smart Grid.

[10]  Zhe Chen,et al.  Optimal operation strategy of battery energy storage system to real-time electricity price in Denmark , 2010, IEEE PES General Meeting.

[11]  Gerard Ledwich,et al.  Improving voltage profile of residential distribution systems using rooftop PVs and Battery Energy Storage systems , 2014 .

[12]  Peter Grindrod,et al.  A new error measure for forecasts of household-level, high resolution electrical energy consumption , 2014 .

[13]  Peter Wolfs,et al.  An optimal management strategy for distributed storages in distribution networks with high penetrations of PV , 2014 .

[14]  C. Singh,et al.  Optimal scheduling and operation of load aggregator with electric energy storage in power markets , 2010, North American Power Symposium 2010.

[15]  Matthew Rowe,et al.  The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction , 2014 .

[16]  M. Shahidehpour,et al.  Short-term scheduling of battery in a grid-connected PV/battery system , 2005, IEEE Transactions on Power Systems.

[17]  Junwei Lu,et al.  Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks , 2014 .

[18]  A. Oudalov,et al.  Sizing and Optimal Operation of Battery Energy Storage System for Peak Shaving Application , 2007, 2007 IEEE Lausanne Power Tech.

[19]  S. M. Shahidehpour,et al.  Short term generation scheduling in photovoltaic-utility grid with battery storage , 1997 .

[20]  E. Caamaño-Martín,et al.  Neural network controller for Active Demand-Side Management with PV energy in the residential sector , 2012 .

[21]  K. M. Muttaqi,et al.  Mitigation of Rooftop Solar PV Impacts and Evening Peak Support by Managing Available Capacity of Distributed Energy Storage Systems , 2013, IEEE Transactions on Power Systems.

[22]  Tsung-Ying Lee.  Operating Schedule of Battery Energy Storage System in a Time-of-Use Rate Industrial User With Wind Turbine Generators: A Multipass Iteration Particle Swarm Optimization Approach , 2007, IEEE Transactions on Energy Conversion.

[23]  Jeremy Woyak,et al.  Economic optimal operation of Community Energy Storage systems in competitive energy markets , 2014 .

[24]  Matthew Rowe,et al.  A Peak Reduction Scheduling Algorithm for Storage Devices on the Low Voltage Network , 2014, IEEE Transactions on Smart Grid.

[25]  Martin Kumar Patel,et al.  On the electrification of road transport - Learning rates and price forecasts for hybrid-electric and battery-electric vehicles , 2012 .