Robust Scheduling Scheme for Energy Storage to Facilitate High Penetration of Renewables

This paper presents a robust scheduling scheme for energy storage systems (ESSs) deployed in distribution networks to facilitate high penetrations of renewable energy sources (RES). This scheme schedules the charging and discharging of an ESS cognizant of state-of-charge (SoC) limits, transmission line real time thermal ratings (RTTR), and voltage constraints. Robust optimization (RO) has been adopted to deal with the uncertainty of RES output, load, and RTTR. Two methods have been introduced to estimate the tradeoff between the cost and the probability of constraint violations. The proposed scheduling scheme is tested on the IEEE 14 and 118 busbar networks with real load, generation, and RTTR profiles through Monte Carlo simulation (MCS). Test results show that the proposed scheme is able to minimize or curtail the probability of constraint violation to a desired level. In contrast, classical optimal power flow (OPF) approaches which do not consider uncertainty, when coupled with RTTR and ESS, result in a low PoS. At the same time, compared to conservative OPF approaches, the proposed scheme reduces the power and energy requirement of ESS.

[1]  Alfonso Damiano,et al.  Real-Time Control Strategy of Energy Storage Systems for Renewable Energy Sources Exploitation , 2014, IEEE Transactions on Sustainable Energy.

[2]  Jake P. Gentle,et al.  A Comparison of Real-Time Thermal Rating Systems in the U.S. and the U.K. , 2014, IEEE Transactions on Power Delivery.

[3]  Rob J Hyndman,et al.  Short-Term Load Forecasting Based on a Semi-Parametric Additive Model , 2012, IEEE Transactions on Power Systems.

[4]  Neal Wade,et al.  Design and analysis of electrical energy storage demonstration projects on UK distribution networks , 2015 .

[5]  Johan Driesen,et al.  Multiobjective Battery Storage to Improve PV Integration in Residential Distribution Grids , 2013, PES 2013.

[6]  Ruiwei Jiang,et al.  Robust Unit Commitment With Wind Power and Pumped Storage Hydro , 2012, IEEE Transactions on Power Systems.

[7]  Andrea Michiorri,et al.  Investigation into the influence of environmental conditions on power system ratings , 2009 .

[8]  J. Kleissl,et al.  Geostrophic Wind Dependent Probabilistic Irradiance Forecasts for Coastal California , 2013, IEEE Transactions on Sustainable Energy.

[9]  S. Fan,et al.  Short-term load forecasting based on an adaptive hybrid method , 2006, IEEE Transactions on Power Systems.

[10]  M. Lange On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors , 2005 .

[11]  Ross Baldick,et al.  Variation of distribution factors with loading , 2002 .

[12]  Xu Andy Sun,et al.  Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem , 2013, IEEE Transactions on Power Systems.

[13]  Jianhua Chen,et al.  A Robust Wind Power Optimization Method for Look-Ahead Power Dispatch , 2014, IEEE Transactions on Sustainable Energy.

[14]  Rui Zhang,et al.  Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine , 2013 .

[15]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[16]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[17]  Yannig Goude,et al.  Local Short and Middle Term Electricity Load Forecasting With Semi-Parametric Additive Models , 2014, IEEE Transactions on Smart Grid.

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

[19]  Claudio A. Cañizares,et al.  Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids , 2015, IEEE Transactions on Smart Grid.

[20]  S. Nahavandi,et al.  Prediction Intervals for Short-Term Wind Farm Power Generation Forecasts , 2013, IEEE Transactions on Sustainable Energy.

[21]  Melvyn Sim,et al.  The Price of Robustness , 2004, Oper. Res..

[22]  K. Nose-Filho,et al.  Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network , 2011, IEEE Transactions on Power Delivery.

[23]  M. Trovato,et al.  Planning and Operating Combined Wind-Storage System in Electricity Market , 2012, IEEE Transactions on Sustainable Energy.

[24]  Miltiadis Alamaniotis,et al.  Evolutionary Multiobjective Optimization of Kernel-Based Very-Short-Term Load Forecasting , 2012, IEEE Transactions on Power Systems.

[25]  Pengfei Wang,et al.  Distribution network voltage control using energy storage and demand side response , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).