Optimization Solution for Unit Power Generation Plan Based on the Integration of Constraint Identification and Deep Reinforcement Learning

In response to the complexity of renewable energy and the numerous safety constraints in actual power grid scenarios, which result in a large model size and difficulties in developing rapid solutions, this paper proposes an accelerated algorithm for solving the optimization of large-scale unit generation plans by combining deep reinforcement learning and security constraint identification. Firstly, this paper constructs an optimization model of a unit generation plan and incorporates conditional risk values to quantify the risk cost caused by operational uncertainty. Secondly, this paper uses a stacked noise-reduction automatic encoder to identify the effective constraint set in the optimization model of the power generation plan. Then, this paper transforms the model into Markov decision processes, designs a reward mechanism with the identified constraints, and uses the proximal policy optimization algorithm to solve it. Finally, this paper takes IEEE30 and a regional power grid in northwest China as examples and performs simulation analyses in various scenarios. The results show that it can greatly reduce the model training time, and the application effect on large-scale systems is obvious. In particular, the online solution time is effectively reduced by 15,837.09 s.