Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations

Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude. This will make delivering reliable power, on demand, a major challenge. In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas. Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility. Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCO's profit from energy transactions, system planning and operation cost savings. The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated.

[1]  J. Buckley,et al.  Fuzzy genetic algorithm and applications , 1994 .

[2]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[3]  X. Guan,et al.  Purchase allocation and demand bidding in electric power markets , 2002 .

[4]  L. Yaan,et al.  Purchase Allocation and Demand Bidding in Electric Power Markets , 2002, IEEE Power Engineering Review.

[5]  D. Sutanto,et al.  Storage power flow controller using battery storage , 2003 .

[6]  A. Ott,et al.  Experience with PJM market operation, system design, and implementation , 2003 .

[7]  A. Jofré,et al.  A distribution company energy acquisition market model with integration of distributed generation and load curtailment options , 2005, IEEE Transactions on Power Systems.

[8]  Antonio J. Conejo,et al.  Energy procurement for large consumers in electricity markets , 2005 .

[9]  A. Philpott,et al.  Optimizing demand-side bids in day-ahead electricity markets , 2006, IEEE Transactions on Power Systems.

[10]  A. Oudalov,et al.  Optimizing a Battery Energy Storage System for Primary Frequency Control , 2007, IEEE Transactions on Power Systems.

[11]  Tomonobu Senjyu,et al.  Unit commitment computation by fuzzy adaptive particle swarm optimisation , 2007 .

[12]  Zuyi Li,et al.  A Multiperiod Energy Acquisition Model for a Distribution Company With Distributed Generation and Interruptible Load , 2007, IEEE Transactions on Power Systems.

[13]  Q. Wang,et al.  The Design of Battery Energy Storage System in a Unified Power-Flow Control Scheme , 2008, IEEE Transactions on Power Delivery.

[14]  X.Y. Wang,et al.  Determination of Battery Storage Capacity in Energy Buffer for Wind Farm , 2008, IEEE Transactions on Energy Conversion.

[15]  Hans-Georg Beyer,et al.  Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Lamine Mili,et al.  Economic Market Design and Planning for Electric Power Systems , 2009 .

[17]  S. Bhattacharya,et al.  Control Strategies for Battery Energy Storage for Wind Farm Dispatching , 2009, IEEE Transactions on Energy Conversion.

[18]  Kankar Bhattacharya,et al.  A generic operations framework for discos in retail electricity markets , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[19]  T.T. Lie,et al.  A Statistical Approach to the Design of a Dispatchable Wind Power-Battery Energy Storage System , 2009, IEEE Transactions on Energy Conversion.

[20]  Ke Meng,et al.  Self-adaptive radial basis function neural network for short-term electricity price forecasting , 2009 .

[21]  A. Oudalov,et al.  Optimizing a Battery Energy Storage System for Frequency Control Application in an Isolated Power System , 2009, IEEE Transactions on Power Systems.

[22]  S. Palamarchuk,et al.  Dynamic programming approach to the bilateral contract scheduling , 2010 .

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

[24]  Zhao Yang Dong,et al.  Use of High-performance Graphics Processing Units for Power System Demand Forecasting , 2010 .

[25]  Alireza Khaligh,et al.  Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art , 2010, IEEE Transactions on Vehicular Technology.

[26]  B. F. Hobbs,et al.  Opportunity Cost Bidding by Wind Generators in Forward Markets: Analytical Results , 2011, IEEE Transactions on Power Systems.

[27]  Q Li,et al.  On the Determination of Battery Energy Storage Capacity and Short-Term Power Dispatch of a Wind Farm , 2011, IEEE Transactions on Sustainable Energy.

[28]  K. Zare,et al.  Risk-Based Electricity Procurement for Large Consumers , 2011, IEEE Transactions on Power Systems.

[29]  L. S. Belyaev,et al.  Electricity Market Reforms , 2011 .

[30]  S. N. Singh,et al.  AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network , 2012, IEEE Transactions on Sustainable Energy.

[31]  K. J. Tseng,et al.  Determination of Short-Term Power Dispatch Schedule for a Wind Farm Incorporated With Dual-Battery Energy Storage Scheme , 2012, IEEE Transactions on Sustainable Energy.

[32]  M. Stanley Whittingham,et al.  History, Evolution, and Future Status of Energy Storage , 2012, Proceedings of the IEEE.

[33]  J. W. Taylor,et al.  Short-Term Load Forecasting With Exponentially Weighted Methods , 2012, IEEE Transactions on Power Systems.