Optimal Scheduling of Renewable Energy Resources in Energy Management Systems Using Hybrid Genetic Algorithm and Particle Swarm Optimization

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 EM is 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 and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, 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 an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic 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 RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.