OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION
OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Summary/Abstract: Emphasizing the importance of Energy Management (EM)systems, the rise in Distributed Generation (DG)and the introduction of multicarrier energy networks have become key factors. An EMis a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units andheating and power storage systems. It focuses on modelingthe operational and organizing elements of the system. In addition, the modelingof optimal planning and scheduling for a multicarrier EMsystem considersthe unpredictable nature of wind and Photovoltaic (PV)units. An effective solution to the EMproblem, cost reduction, peak-to-average ratio (PAR),and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems(PSS). This work presents anOptimal Scheduling and Energy Management System utilizing HybridGenetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RESin EMsystems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and theproposed system. The results show that the HGA-PSO EM systemsignificantly decreases the cost, PAR, and carbon emissionby 58.74%, 57.19%, and 90%, respectively.

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