Combined multi-objective optimization and agent-based modeling for a 100% renewable island energy system considering power-to-gas technology and extreme weather conditions

Abstract

Islands are constrained by geographical conditions in terms of energy delivery. Due to weak connections with the mainland and the power grid, the diversity of island energy demand leads to high economic costs and environmental pollution issues. This study proposes a 100% renewable island energy system, which integrates with power-to-gas, combined cooling, heating and power, and desalination technologies to supply electricity, heating, cooling, gas and fresh water to the local residents. A comprehensive approach for energy demand prediction, system design and dispatch optimization, as well as system evaluation is proposed. For energy demand prediction, agent-based modeling is used to simulate the demand of electricity, heating, cooling, gas and fresh water for the case study community on the island. The k-means clustering and scenario tree are further adopted to generate representative stochastic scenarios, which are applied to capture the uncertainty of energy demand. A multi-objective optimization model is developed to optimize the system design and scheduling strategy simultaneously. In order to demonstrate the effectiveness of the proposed approach and to evaluate the obtained optimal solutions for the case study, different objectives and extreme weather conditions are specifically considered. The optimal solution obtained shows that compared to battery storage, a 2.5% annual cost reduction can be achieved by using power-to-gas technology for energy storage. The findings also suggest that extreme weather conditions can be coped with by increasing the capacity of biogas generation, desalination, and energy storage equipment, thereby improving the resilience of the island energy system.