Optimal sizing for a wind-photovoltaic-hydrogen hybrid system considering levelized cost of storage and source-load interaction

Hydrogen energy storage system (HESS) has excellent potential in high-proportion renewable energy systems due to its high energy density and seasonal storage characteristics. After detailing the volatility of wind speed, irradiance and load, this paper proposes a bi-level optimization model to analyze the economic operation of the wind-photovoltaichydrogen hybrid system (WPH-HS). First, the relationship between the source-load output matching and operating conditions of HESS is studied, two evaluation indicators are described, which can be adjusted by wind-solar complementarity on the source side and demand response on the load side. Second, considering the levelized cost of storage (LCOS), the total annual cost (TAC) calculation method of WPH-HS is presented, and this paper provides a new hybrid optimization technology of chaotic search, particle swarm optimization and non-dominated sorting genetic algorithm2. Finally, the system is simulated with the MATLAB software to determine the optimal sizing of components and minimize the LCOS while ensuring the optimal TAC. The simulation results are elaborated in detail. In particular, the added source-load interaction reduces the TAC and LCOS by 7.3% and 10.3%. When two indicators reach 0.03 and 0.1745, the system is economically viable with the LCOS of 0.276 USD/kWh. The hybrid optimization algorithm can achieve better result in fewer iterations. & COPY; 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper discusses the potential of hydrogen energy storage systems in high-proportion renewable energy systems and proposes a bi-level optimization model for the economic operation of a wind-photovoltaichydrogen hybrid system. The study investigates the relationship between the operation of the storage system and the matching of source-load output, and introduces wind-solar complementarity and demand response as adjustment factors. By using a hybrid optimization algorithm, the optimal sizing of components and the minimization of storage costs are achieved. The simulation results show that the system can reduce total costs by approximately 7.3% and storage costs by 10.3%, with a low storage cost of 0.276 USD/kWh.