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In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector. • A multi-objective wind-solar-hydro complementary optimization model is developed. • Electricity supply and demand have gaps at different temporal and spatial scales. • Wind-solar-hydro complementary potential shows great temporal and spatial variation. • Renewable complementarity can improve China's future power system stability. [ABSTRACT FROM AUTHOR]

The construction of fast electric vehicle (EV) charging stations is critical for the development of EV industry. The integration of renewable energy into the EV charging stations comprises both threats and chances. A successful and reasonable capacity configuration and scheduling strategy is beneficial and significant. This paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy. Firstly, an EV charging load simulation model considering demand response is built, which dynamically modified charging expectation under time-of-use electricity price. Secondly, based on the system design, a multi-objective optimization model is proposed with minimum objectives of cost of electricity and pollution emissions. Then, this model is solved by a hybrid optimization algorithm which combines multi-objective particle swarm optimization (MOPSO) algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, the proposed optimization framework is applied to a case in Inner Mongolia, China. A scenario analysis is conducted and concludes that the renewable energy supplies, the connection with utility grid and demand response can help improve the performance on optimization objectives. A sensitivity analysis is also performed to verity the model's effectiveness. In addition, the proposed method is compared with simulated annealing and genetic algorithm to show its faster computation speed and higher solution quality. • A fast EV charging station with renewable energies and ESS is proposed. • An EV charging load simulation model based on demand response is built. • An optimization design model for FEVCS-WPE is proposed with two objectives. • A hybrid optimization algorithm combining MOPSO and TOPSIS method is used. [ABSTRACT FROM AUTHOR]