Imperialistic competitive algorithm-based carbon-energy combined flow solving method

Abstract

The invention relates to an imperialistic competitive algorithm-based carbon-energy combined flow solving method. Carbon-energy combined flow of a power system is optimized by adopting a multicultural transfer-based imperialistic competitive reinforcement learning algorithm; each empire adopts a value function matrix in the reinforcement learning algorithm as a cultural matrix of the empire; each optimization nation updates the cultural matrix through interaction with the environment and performs action selection according to the cultural matrix to improve the globality of a policy; and knowledge in the cultural matrix is stored in real time and multicultural transfer is performed to improve the optimization efficiency of subsequent new tasks. According to the adopted method, the cultural transfer-based imperialistic competitive reinforcement learning algorithm is a relatively new intelligent algorithm and has the advantages of high convergence speed, better global convergence, higher stability and the like, so that quick carbon-energy combined flow optimization of the large-scale power system is realized.

Description

Technical field: [0001] The invention relates to the field of optimization of carbon-energy composite flow in electric power system, in particular to a solution method of carbon-energy composite flow based on imperialist competition algorithm. Background technique: [0002] In recent years, by CO 2 The problem of environmental degradation caused by mainly greenhouse gases is becoming more and more serious, and carbon emission reduction and low-carbon economy have become the focus of widespread attention in the industry and academia. The power industry is CO 2 As one of the major emitters of China, it is obliged to be more responsible to carry out low-carbon power construction. However, many current researches on low-carbon electricity, such as unit combination taking into account low-carbon resources, economic dispatch taking into account low-carbon resources, and carbon capture and From the perspective of the grid side, low-carbon grid dispatching is carried out. At pre...

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Problems solved by technology

However, in the above model, the power generation side and the grid side only care about their own carbon footprint and carbon emissions, which will undoubtedly cause double calculation of carbon emissions. Reasonable apportionment between sides

[0003] In addition, the optimal carbon-energy composite flow model of the power system is a nonlinear programming problem with multiple constraints and multiple variables. The traditional Newton method, interior point method, and quadratic programming method rely too much on certain mathematical models; while the emerging Artificial intelligence algorithms such as ant colony algorithm, artificial bee colony algorithm, genetic algorithm, particle swarm algorithm, and reinforcement learning algorithm, etc., are slow to iterate because of the complexity of the problems to be solved, and even cannot be optimized due to the "curse of dimensionality".

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