Energy Management System for a Grid-Connected Microgrid with Photovoltaic and Battery Energy Storage System

A microgrid (MG) is an energy system composed of renewable resources, energy storage unit and loads that can operate in either islanded or grid-connected mode. Renewable resources should be scheduled to manage load demand and power flow within MG. This paper presents a MG energy management system (M-EMS) for grid-connected photovoltaic (PV) and battery energy storage system (BESS) based hybrid MG. The proposed M-EMS consists of two modules, namely, forecasting and optimisation. The forecasting module is responsible for predicting solar irradiance, temperature and load demand, whereas the optimisation module performs optimal day-ahead scheduling of power generation and load demand in a grid-connected MG for economical operation. The proposed M-EMS for grid-connected hybrid PV-BESS MG is verified using MATLAB/Simulink. Simulation results indicate the efficiency and effectiveness of the proposed method for understudy case.

[1]  Sang-Won Min,et al.  Optimal Scheduling and Operation of the ESS for Prosumer Market Environment in Grid-Connected Industrial Complex , 2018 .

[2]  Hossam A. Gabbar,et al.  Enhanced MG with optimum operational cost of pumping water distribution systems , 2017, 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE).

[3]  Usman Bashir Tayab,et al.  A review of droop control techniques for microgrid , 2017 .

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

[5]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[6]  Luu Ngoc An,et al.  Optimal energy management for grid connected microgrid by using dynamic programming method , 2015, 2015 IEEE Power & Energy Society General Meeting.

[7]  Mehdi Savaghebi,et al.  Generation and demand scheduling for a grid-connected hybrid microgrid considering price-based incentives , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[8]  Ricardo Torquato,et al.  Towards a real-time Energy Management System for a Microgrid using a multi-objective genetic algorithm , 2015, 2015 IEEE Power & Energy Society General Meeting.

[9]  Zhang Jianhua,et al.  Energy management system, generation and demand predictors: a review , 2018 .

[10]  Sudhansu Kumar Mishra,et al.  A Review of Short Term Load Forecasting using Artificial Neural Network Models , 2015 .

[11]  V. Lo Brano,et al.  Forecasting daily urban electric load profiles using artificial neural networks , 2004 .