A wind-solar-storage optimization operation method

Abstract

The invention discloses a wind and solar energy storage optimal operation method. An inference rule set between working conditions and the optimal unit commitment is obtained by mining unit operation historical data, and the rule set is stored in a database; factors such as load changes are considered, the variable weight coefficient of a potential target unit configuration mode is calculated in a variable weight synthesizing mode of a balance function, and the optimal wind and solar energy storage unit configuration mode is selected by combining an existing comprehensive performance evaluation method. The wind and solar energy storage optimal operation method is more objective and accurate; meanwhile, unit configuration of wind and solar energy storage can be regulated in real time, wind and solar energy storage is made to work in the optimal state in real time, and therefore the working efficiency of wind and solar energy storage is improved effectively.

Description

technical field [0001] The invention belongs to a system optimization method, and particularly relates to a wind-solar storage optimization operation method. Background technique [0002] With the development of clean energy such as wind power and photovoltaics, more and more domestic and foreign experts have begun to study the operation optimization of wind-solar storage combined power generation. At present, the research on co-generation of multiple renewable energy sources at home and abroad mainly focuses on the optimal allocation of capacity, reliability assessment, and economic operation scheduling. However, the current optimization scheduling strategy does not consider the cost of switching between unit configuration modes, and with the extension of the operating time of the unit, it is difficult for the traditional intelligent optimization algorithm to establish an accurate mathematical model in the case of changing weather conditions and load requirements. The mode...

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Problems solved by technology

However, the current optimal scheduling strategy does not consider the cost of switching between unit configuration modes, and as the operating time of the unit increases, it is difficult for the traditional intelligent optimization algorithm to establish an accurate mathematical The model calculates the optimal configuration mode of the unit to dynamically adjust its operation strategy. Therefore, it is difficult to achieve the ideal goal derived by using the traditional optimization model and algorithm in actual operation, and the application of the algorithm is greatly limited.

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