The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economical grid scheduling method is designed. First, constraints on the safe and economical operation of renewable energy power systems are defined. Then, the quintuple of Markov decision process is defined under the framework of deep reinforcement learning, and the dispatching optimization problem is transformed into Markov decision process. To solve the problem of low sample data utilization in online reinforcement learning strategies, a PPO optimization algorithm based on the Kullback–Leibler (KL) divergence penalty factor and importance sampling technique is proposed, which transforms on-policy into off-policy and improves sample utilization. Finally, the simulation analysis of the example shows that in a power system with a high proportion of renewable energy generating units connected to the grid, the proposed scheduling strategy can meet the load demand under different load trends. In the dispatch cycle with different renewable energy generation rates, renewable energy can be absorbed to the maximum extent to ensure the safe and economic operation of the grid.

中文翻译：

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数据驱动的电力系统优化调度研究

出力的不确定性使得高比例可再生能源发电机组并网时难以有效解决电网经济安全调度问题。基于近端策略优化（PPO）算法，设计了一种安全、经济的电网调度方法。首先，定义了可再生能源电力系统安全和经济运行的约束条件。然后，在深度强化学习框架下定义马尔可夫决策过程的五元组，将调度优化问题转化为马尔可夫决策过程。为了解决在线强化学习策略中样本数据利用率低的问题，提出了一种基于 Kullback–Leibler (KL) 散度惩罚因子和重要性采样技术的 PPO 优化算法，将 on-policy 转换为 off-policy，提高了样本利用率。最后通过算例仿真分析表明，在可再生能源发电机组并网比例较高的电力系统中，所提调度策略能够满足不同负荷趋势下的负荷需求。在不同可再生能源发电率的调度周期内，最大限度消纳可再生能源，保障电网安全经济运行。算例仿真分析表明，在高比例可再生能源发电机组并网的电力系统中，提出的调度策略能够满足不同负荷趋势下的负荷需求。在不同可再生能源发电率的调度周期内，最大限度消纳可再生能源，保障电网安全经济运行。算例仿真分析表明，在高比例可再生能源发电机组并网的电力系统中，提出的调度策略能够满足不同负荷趋势下的负荷需求。在不同可再生能源发电率的调度周期内，最大限度消纳可再生能源，保障电网安全经济运行。