Power grid optimal carbon energy composite flow obtaining method based on swarm intelligence reinforcement learning

Abstract

The invention discloses a power grid optimal carbon energy composite flow obtaining method based on swarm intelligence reinforcement learning. The method comprises the following steps: S1, establishing a multi-object optimal carbon energy composite flow model object function, S2, setting a reward function according to the object function, S3, updating a Q value matrix of each main body according to an eligibility trace, S4, calculating a greed action of each main body, S5, updating an action probability matrix of each main body, S6, randomly selecting a pre-judgment action of each main body at a current state, S7, inputting the multiple main bodies in a coordinative manner, and solving an optimal action of a swarm, S8, performing updating and then obtaining action values after correction, S9, determining a control variable matrix, and performing load flow calculation, and S10, after the load flow calculation, judging whether the Q value matrix is convergent, taking a result obtained by last load flow calculation as a power grid optimal carbon energy composite flow if the Q value matrix is convergent, and returning to the S2 if the Q value matrix is not convergent. The method enables loss of an energy flow and loss of a carbon discharge flow in a power grid to reach minimums; and the good global optimization capability is guaranteed, and the convergence speed of an algorithm is obviously improve at the same time.

Description

technical field [0001] The invention relates to the technical field of power grid reactive power optimization, in particular to a method for obtaining the optimal carbon-energy composite flow of the power grid based on swarm intelligence reinforcement learning. Background technique [0002] With the increasingly serious impact of the greenhouse effect on the environment, low-carbon economy has gradually become the key development direction of various energy-consuming industries. Among them, the power industry, as the largest CO2 emitter, will play an important role in the development of a low-carbon economy. There are many related studies on low-carbon electricity, including issues such as optimal power flow, economic dispatch, unit combination, carbon storage and carbon capture. However, these studies mainly focus on the optimization of carbon emissions on the power generation side, but there is a lack of related research on how to reduce the carbon emissions of the power ...

AI Technical Summary

Problems solved by technology

Naturally, some scholars will think of combining reinforcement learning with particle swarm and ant colony algorithm, and general improved swarm intelligence algorithms have emerged one after another. However, these methods only stop group optimization and reinforcement learning in the algorithm process. Simple serial combination, the advantages of the two different types of algorithms have not been truly integrated and brought into play

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