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An Intelligent Algorithm for Solving Unit Commitments Based on Deep Reinforcement Learning
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DOI：10.3390/su151411084.png)
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Abstract
En 中文
With the reform of energy structures, the high proportion of volatile new energy access makes the existing unit commitment (UC) theory unable to satisfy the development demands of day-ahead market decision-making in the new power system. Therefore, this paper proposes an intelligent algorithm for solving UC, based on deep reinforcement learning (DRL) technology. Firstly, the DRL algorithm is used to model the Markov decision process of the UC problem, and the corresponding state space, transfer function, action space and reward function are proposed. Then, the policy gradient (PG) algorithm is used to solve the problem. On this basis, Lambda iteration is used to solve the output scheme of the unit in the start-stop state, and finally a DRL-based UC intelligent solution algorithm is proposed. The applicability and effectiveness of this method are verified based on simulation examples.
Keywords:
safety restraint unit combination
Markov decision process
deep reinforcement learning