Dispatching strategies for gas turbines (GTs) are changing in modern
electricity grids. A growing incorporation of intermittent renewable energy
requires GTs to operate more but shorter cycles and more frequently on partial
loads. Deep reinforcement learning (DRL) has recently emerged as a tool that
can cope with this development and dispatch GTs economically. The key
advantages of DRL are a model-free optimization and the ability to handle
uncertainties, such as those introduced by varying loads or renewable energy
production. In this study, three popular DRL algorithms are implemented for an
economic GT dispatch problem on a case study in Alberta, Canada. We highlight
the benefits of DRL by incorporating an existing thermodynamic software
provided by Siemens Energy into the environment model and by simulating
uncertainty via varying electricity prices, loads, and ambient conditions.
Among the tested algorithms and baseline methods, Deep Q-Networks (DQN)
obtained the highest rewards while Proximal Policy Optimization (PPO) was the
most sample efficient. We further propose and implement a method to assign GT
operation and maintenance cost dynamically based on operating hours and cycles.
Compared to existing methods, our approach better approximates the true cost of
modern GT dispatch and hence leads to more realistic policies.

对于加拿大阿尔伯塔省的案例研究中，通过将西门子公司提供的热力学软件纳入环境模型，并通过模拟不确定性，如电力价格、负载和环境条件的变化，我们实施了三种常见的深度强化学习算法来处理经济型燃气轮机调度问题。在所测试的算法和基准方法中，深度 Q 网络（DQN）获得了最高的奖励，而近端策略优化（PPO）是最高效的方法。我们进一步提出并实施了一种动态分配燃气轮机运行和维护成本的方法，基于运行时间和循环数，与现有方法相比，我们的方法更好地近似了现代燃气轮机调度的真实成本，因此导致了更现实的策略。