A Framework for Profit Maximization in a Grid-Connected Microgrid with Hybrid Resources Using a Novel Rule Base-BAT Algorithm

M. Elgamal, N. Korovkin, A. Elmitwally, A. Abdel Menaem, Zhe Chen
2020 IEEE Access  
In this paper, an energy management system (EMS) is proposed for optimal operation of a microgrid (MG). Dispersed photovoltaic arrays (PVs) and wind turbine generators (WTs) as renewable energy sources (RES) supply a major part of the network demanded energy. Also, an energy storage system (ESS), a micro-turbine unit (MT), and a fuel cell unit (FC) are integrated. The uncertainty and stochastic nature of the network load and RES data are treated via probabilistic modeling and scenarioselection
more » ... pproach. The predicted day-ahead data of the most diverse hourly scenarios are entered into the proposed EMS to determine the active and reactive power (P-Q) participations of local distributed resources. Likewise, it specifies the discharging/charging power and state of the ESS in addition to the exchanged active/reactive power amounts with the main network. The main goal is to maximize the profit of the secondary grid while satisfying all technical constraints. In the proposed EMS, the day-ahead energy management is developed as a comprehensive optimization problem. Moreover, the paper proposes novel modifications to improve the BAT optimization technique. The optimization problem of the energy management in the microgrid is implemented using a new integrated rule base-improved BAT method. Furthermore, the proposed EMS competence is proven by comparing its performance to recent literature. INDEX TERMS Grid-tied microgrid, renewable energy resources, optimization, energy management. NOMENCLATURE MG Microgrid EMS Energy management system RES Renewable energy sources DG Distributed generation PV Photovoltaic array WT Wind turbine FC Fuel cell MT Microturbine ESS Energy storage system OLTC On load tap changer of main grid transformer P-Q Active power and reactive power PSO Particle swarm optimization MCS Monte Carlo simulation
doi:10.1109/access.2020.2987765 fatcat:ldmgbaa2mngblfuzd4qm3btbaa