Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation

Abstract The integration of energy storage technologies with renewable energy systems can significantly reduce the operating costs for microgrids (MG) in future electricity networks. This paper presents a novel energy management system (EMS) which can minimize the daily operating cost of a MG and maximize the self-consumption of the RES by determining the best setting for a central battery energy storage system (BESS) based on a defined cost function. This EMS has a two-layer structure. In the upper layer, a Convex Optimization Technique is used to solve the optimization problem and to determine the reference values for the power that should be drawn by the MG from the main grid using a 15 min sample time. The reference values are then fed to a lower control layer, which uses a 1 min sample time, to determine the settings for the BESS which then ensures that the MG accurately follows these references. This lower control layer uses a Rolling Horizon Predictive Controller and Model Predictive Controllers to achieve its target. Experimental studies using a laboratory-based MG are implemented to demonstrate the capability of the proposed EMS.

[1]  Stephen P. Boyd,et al.  Fast Model Predictive Control Using Online Optimization , 2010, IEEE Transactions on Control Systems Technology.

[2]  Dan Wei,et al.  Short term wind power prediction using ANFIS , 2016, 2016 IEEE International Conference on Power and Renewable Energy (ICPRE).

[3]  Josep M. Guerrero,et al.  Online Energy Management Systems for Microgrids: Experimental Validation and Assessment Framework , 2018, IEEE Transactions on Power Electronics.

[4]  G. Krajačić,et al.  Integration of renewable energy and demand response technologies in interconnected energy systems , 2018, Energy.

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

[6]  Shengbo Eben Li,et al.  Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles , 2016, IEEE Transactions on Transportation Electrification.

[7]  M. Jünger,et al.  50 Years of Integer Programming 1958-2008 - From the Early Years to the State-of-the-Art , 2010 .

[8]  Evangelos Rikos,et al.  A Model Predictive Control Approach to Microgrid Operation Optimization , 2014, IEEE Transactions on Control Systems Technology.

[9]  Ahmed M. Azmy,et al.  Optimizing distributed generation operation for residential application based on automated systems , 2015, 2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS).

[10]  Georgios B. Giannakis,et al.  An Online Convex Optimization Approach to Real-Time Energy Pricing for Demand Response , 2017, IEEE Transactions on Smart Grid.

[11]  Djalel Dib,et al.  One-Hour Ahead Electric Load Forecasting Using Neuro-fuzzy System in a Parallel Approach , 2015, Computational Intelligence Applications in Modeling and Control.

[12]  Bor Yann Liaw,et al.  On state-of-charge determination for lithium-ion batteries , 2017 .

[13]  Josep M. Guerrero,et al.  A rolling horizon rescheduling strategy for flexible energy in a microgrid , 2014, 2014 IEEE International Energy Conference (ENERGYCON).

[14]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[15]  Jiang-Wen Xiao,et al.  Distributed real-time demand response for energy management scheduling in smart grid , 2018, International Journal of Electrical Power & Energy Systems.

[16]  Liuping Wang,et al.  A Tutorial on Model Predictive Control: Using a Linear Velocity‐Form Model , 2008 .

[17]  Carlos Bordons,et al.  Combined environmental and economic dispatch of smart grids using distributed model predictive control , 2014 .

[18]  Xiang Ji,et al.  Autonomous optimized economic dispatch of active distribution system with multi-microgrids , 2018, Energy.

[19]  Oriol Gomis-Bellmunt,et al.  Trends in Microgrid Control , 2014, IEEE Transactions on Smart Grid.

[20]  Ketan Rajawat,et al.  Online algorithms for storage utilization under real-time pricing in smart grid , 2018 .

[21]  Ahmed M. Azmy,et al.  Operation optimization of distributed generation using artificial intelligent techniques , 2016 .

[22]  Hongmin Meng,et al.  Cooperative energy management optimization based on distributed MPC in grid-connected microgrids community , 2019, International Journal of Electrical Power & Energy Systems.

[23]  Arkadi Nemirovski,et al.  Robust Convex Optimization , 1998, Math. Oper. Res..

[24]  Amir Abtahi,et al.  Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system , 2018 .

[25]  Leandros Tassiulas,et al.  Control and optimization meet the smart power grid: scheduling of power demands for optimal energy management , 2010, e-Energy.

[26]  Ralph E. White,et al.  Capacity Fade Mechanisms and Side Reactions in Lithium‐Ion Batteries , 1998 .

[27]  F. Silvestro,et al.  An Energy Resource Scheduler Implemented in the Automatic Management System of a Microgrid Test Facility , 2007, 2007 International Conference on Clean Electrical Power.

[28]  R. M. Nelms,et al.  Distributed Online Algorithm for Optimal Real-Time Energy Distribution in the Smart Grid , 2014, IEEE Internet Things J..

[29]  Peng Kou,et al.  Distributed Coordination of Multiple PMSGs in an Islanded DC Microgrid for Load Sharing , 2017, IEEE Transactions on Energy Conversion.

[30]  Ding Ming,et al.  Dynamic economic dispatch for microgrids including battery energy storage , 2010, The 2nd International Symposium on Power Electronics for Distributed Generation Systems.

[31]  Farzam Nejabatkhah,et al.  Overview of Power Management Strategies of Hybrid AC/DC Microgrid , 2015, IEEE Transactions on Power Electronics.

[32]  Peng Kou,et al.  Stable and Optimal Load Sharing of Multiple PMSGs in an Islanded DC Microgrid , 2018, IEEE Transactions on Energy Conversion.

[33]  Fang Li,et al.  CAN(Controller Area Network) Bus Communication System Based on Matlab/Simulink , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[34]  Carlos Bordons,et al.  Optimal Economical Schedule of Hydrogen-Based Microgrids With Hybrid Storage Using Model Predictive Control , 2015, IEEE Transactions on Industrial Electronics.

[35]  Eugenia Minca,et al.  Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources , 2015 .

[36]  Shaghayegh Bahramirad,et al.  Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid , 2012, IEEE Transactions on Smart Grid.

[37]  Carlos Álvarez-Bel,et al.  An optimisation algorithm for distributed energy resources management in micro-scale energy hubs , 2017 .

[38]  Hamid Falaghi,et al.  Joint optimization of day-ahead and uncertain near real-time operation of microgrids , 2019, International Journal of Electrical Power & Energy Systems.

[39]  Stefano Squartini,et al.  Collaborative Energy Management in Micro-Grid environments through multi-objective optimization , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[40]  Luiz A. de S. Ribeiro,et al.  Power Control in AC Isolated Microgrids With Renewable Energy Sources and Energy Storage Systems , 2015, IEEE Trans. Ind. Electron..

[41]  N. Kamel,et al.  Autoregressive method in short term load forecast , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[42]  Haotian Wu,et al.  Effect of gypsum crystallization characteristics on fine particle emission after desulfurization , 2017 .

[43]  Nima Amjady,et al.  Adaptive robust optimization framework for day-ahead microgrid scheduling , 2019, International Journal of Electrical Power & Energy Systems.

[44]  Heidar Ali Shayanfar,et al.  A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs , 2018, Energy.

[45]  Rodrigo Palma-Behnke,et al.  A Microgrid Energy Management System Based on the Rolling Horizon Strategy , 2013, IEEE Transactions on Smart Grid.

[46]  Ian A. Hiskens,et al.  Corrective Model-Predictive Control in Large Electric Power Systems , 2017, IEEE Transactions on Power Systems.

[47]  Efstratios N. Pistikopoulos,et al.  A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids , 2015 .

[48]  Pablo Sanchis,et al.  Implementation and Control of a Residential Electrothermal Microgrid Based on Renewable Energies, a Hybrid Storage System and Demand Side Management , 2014 .

[49]  Francesc Guinjoan,et al.  Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting , 2017 .

[50]  Laxman M. Waghmare,et al.  An Overview of Model Predictive Control , 2010 .

[51]  A. T. Holen,et al.  Operation planning of hydrogen storage connected to wind power operating in a power market , 2006, IEEE Transactions on Energy Conversion.

[52]  T. Logenthiran,et al.  Short term generation scheduling of a Microgrid , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.