Intelligent Controller for Energy Storage System in Grid-Connected Microgrid

This paper presents the design of a fuzzy logic-based controller to be embedded in a grid-connected microgrid with renewable and energy storage capability. The objectives of the controller is to control the charge and discharge rate of the energy storage system (ESS) to reduce the end-user operating cost through arbitrage operation of the ESS and reducing the power exchange between the main and microgrid. Instead of using a forecasting-based approach, the proposed methodology takes the difference between the available renewable generation and load, state-of-charge of ESS, and electricity market price to determine the charge and discharge rate of the ESS in a rolling horizon. A comparison with other controllers with similar objectives shows that the proposed controller can achieve a lower operating cost and reduce the power exchange between the main and microgrid.

[1]  Nicanor Quijano,et al.  Dynamic Population Games for Optimal Dispatch on Hierarchical Microgrid Control , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Martin Braun,et al.  Local Voltage Control Strategies for PV Storage Systems in Distribution Grids , 2014, IEEE Transactions on Smart Grid.

[3]  Mohammad Shahidehpour,et al.  Fuzzy-Logic Based Frequency Controller for Wind Farms Augmented With Energy Storage Systems , 2016, IEEE Transactions on Power Systems.

[4]  Alexis Kwasinski,et al.  Availability Evaluation of Micro-Grids for Resistant Power Supply During Natural Disasters , 2012, IEEE Transactions on Smart Grid.

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

[6]  Brian Elmegaard,et al.  Optimal operation strategies of compressed air energy storage (CAES) on electricity spot markets with fluctuating prices , 2009 .

[7]  Vincenzo Piuri,et al.  A Decision Support System for Wind Power Production , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[8]  T. Logenthiran,et al.  Fuzzy logic control of energy storage system in microgrid operation , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[9]  Toru Namerikawa,et al.  Real-Time Pricing Mechanism for Electricity Market With Built-In Incentive for Participation , 2015, IEEE Transactions on Smart Grid.

[10]  Hadi Khani,et al.  Real-Time Optimal Dispatch and Economic Viability of Cryogenic Energy Storage Exploiting Arbitrage Opportunities in an Electricity Market , 2015, IEEE Transactions on Smart Grid.

[11]  Shahin Nazarian,et al.  Optimal control of PEVs for energy cost minimization and frequency regulation in the smart grid accounting for battery state-of-health degradation , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).

[12]  Mikihiko Matsui,et al.  Fuzzy-Logic-Based $V/f$ Control of an Induction Motor for a DC Grid Power-Leveling System Using Flywheel Energy Storage Equipment , 2009, IEEE Transactions on Industrial Electronics.

[13]  Francesc Guinjoan,et al.  Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids , 2018, IEEE Transactions on Smart Grid.

[14]  T. Logenthiran,et al.  Forecasting of photovoltaic power using extreme learning machine , 2015, 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[15]  Rachid Beguenane,et al.  Energy Management and Control System for Laboratory Scale Microgrid Based Wind-PV-Battery , 2017, IEEE Transactions on Sustainable Energy.

[16]  Warren B. Powell,et al.  Tutorial on Stochastic Optimization in Energy—Part II: An Energy Storage Illustration , 2016, IEEE Transactions on Power Systems.

[17]  Vassilios G. Agelidis,et al.  Distributed Cooperative Control of Microgrid Storage , 2015, IEEE Transactions on Power Systems.

[18]  David J. Hill,et al.  Coordinated Dispatch of Virtual Energy Storage Systems in Smart Distribution Networks for Loading Management , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[19]  Panagiotis Papadopoulos,et al.  A Probabilistic Method Combining Electrical Energy Storage and Real-Time Thermal Ratings to Defer Network Reinforcement , 2017, IEEE Transactions on Sustainable Energy.

[20]  R. Moreno,et al.  Opportunities for Energy Storage: Assessing Whole-System Economic Benefits of Energy Storage in Future Electricity Systems , 2017, IEEE Power and Energy Magazine.

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

[22]  Feng Zhang,et al.  Battery ESS Planning for Wind Smoothing via Variable-Interval Reference Modulation and Self-Adaptive SOC Control Strategy , 2017, IEEE Transactions on Sustainable Energy.

[23]  Ye Tian,et al.  An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[24]  M Castilla,et al.  Hierarchical Control of Intelligent Microgrids , 2010, IEEE Industrial Electronics Magazine.

[25]  Kit Po Wong,et al.  Efficient real-time residential energy management through MILP based rolling horizon optimization , 2015, 2015 IEEE Power & Energy Society General Meeting.

[26]  Bowen Zhou,et al.  Local Storage Meets Local Demand: A Technical Solution to Future Power Distribution System , 2016 .

[27]  Chongqing Kang,et al.  Evaluating the Contribution of Energy Storages to Support Large-Scale Renewable Generation in Joint Energy and Ancillary Service Markets , 2016, IEEE Transactions on Sustainable Energy.

[28]  P. Denholm,et al.  Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects , 2009 .

[29]  Ju Liu,et al.  Solution to short-term frequency response of wind farms by using energy storage systems , 2016 .

[30]  Massoud Pedram,et al.  A Near-Optimal Model-Based Control Algorithm for Households Equipped With Residential Photovoltaic Power Generation and Energy Storage Systems , 2016, IEEE Transactions on Sustainable Energy.