This paper proposes a three-layer capacity optimization configuration method for multi scenario wind-thermal-hydrogen-storage multi energy complementary systems based on analytic hierarchy process (AHP)-border collie optimization (BCO)-grey relationship analysis (GRA) to address the issues of grid fluctuations and insufficient peak shaving capacity caused by a high proportion of new energy grid integration. The improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique is utilized to decompose wind power output and optimize the adaptive capacity distribution among lithium iron phosphate batteries
alkaline electrolyzers
and proton exchange membrane electrolyzers. Taking into account the economic
environmental and reliability of thermal power units
the optimal capacity of thermal power units is determined using AHP for smoothing wind power fluctuations and deep peak shaving scenarios; An optimal capacity configuration model for energy storage systems is established using BCO with the goal of minimizing the cost of multi-energy complementary systems. From the perspective of safety in the electric hydrogen coupling system
GRA is used to verify the rationality of the capacity configuration results of the electric hydrogen coupling system. The effectiveness of this method is verified through actual operating data from a power station in China. The simulation results show that the grid connected fluctuation rate of 10 minutes obtained by the method proposed in this paper is the lowest at 15.58%
and the deep peak shaving depth of the auxiliary thermal power unit decreases from 30% to 24.99%.
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references
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College of Energy and Mechanical Engineering, Shanghai University of Electric Power
School of Mechanical and Power Engineering, Jiangsu Organic Solid Waste Deep Disposal and Hydrogen Production Engineering Research Center,Jiangsu Key Laboratory of Process Enhancement and New Energy Equipment Technology, Nanjing Tech University
Northwest Electric Power Design Institute Co., Ltd. of China Power Engineering Consultion Group
Electric Power Research Institute of Guangdong Power Grid Co., Ltd.
College of Electrical and Information Engineering, Hunan University