multi-objective particle swarm optimization of a solar-wind hybrid power system with energy storage for better economic performance and reliability

More and more attention has been paid to the high penetration of renewable energy in recent years. the randomness and intermittency of solar and wind energy make it an inevitable trend that renewables are coupled with energy storage technologies. pumped hydro storage (phs) is the most widely-used storage form in the power grid but the capacity is limited by geographic condition. the concentrated solar power (csp) plant with thermal energy storage (tes) system can realize friendly grid connection and effective peak shaving. therefore, this paper proposes a solar-wind-hydro hybrid power system with phs-tes double energy storages, and investigates the optimal coordinated operation strategy and multi-objective sizing. the optimal sizing problem which considers the minimum levelized cost of energy (lcoe) and loss of power supply probability (lpsp) as objectives is solved by multi-objective particle swarm optimization. moreover, the seasonal uncertainties of renewables are considered by scenario-based analysis using k-means clustering. finally, the case study reveals the effectiveness of the coordinated operation strategy and double energy storages from the perspectives of economy and reliability. the comparisons of optimal sizing results show that the pv-wind-csp-phs system decreases the lcoe by 19.1% compared to pv-wind-csp system under the same lpsp, and reduces the lpsp compared to pv-wind-phs system with limited reservoir capacity, which indicates that the proposed system with double energy storages has better economy and reliability performance compared to either single storage.