Multi-Objective Particle Swarm Optimization (MOPSO) for a Distributed Energy System Integrated with Energy Storage

Abstract

Distributed energy systems are considered as a promising technology for sustainable development and have become a popular research topic in the areas of building energy systems. This work presents a case study of optimizing an integrated distributed energy system consisting of combined heat and power (CHP), photovoltaics (PV), and electric and/or thermal energy storage for a hospital and large hotel buildings located in Texas and California. First, simulation models for all subsystems, which are developed individually, are integrated together according to a control strategy designed to satisfy both the electric and thermal energy requirements of a building. Subsequently, a multi-objective particle swarm optimization (MOPSO) is employed to obtain an optimal design of each subsystem. The objectives of the optimization are to minimize the simple payback period (PBP) and maximize the reduction of carbon dioxide emissions (RCDE). Finally, the energy performance for the selected building types and locations are analyzed after the optimization. Results indicate that the proposed optimization method could be applied to determine an optimal design of distributed energy systems, which reaches a trade-off between the economic and environmental performance for different buildings. With the presented distributed energy system, a peak shaving in electricity of about 300 kW and a reduction in boiler fuel consumption of 610 kW could be attained for the hospital building located in California for a winter day. For the summer and transition seasons, electricity peak shaving of 800 kW and 600 kW could be achieved, respectively.