This paper presents a method for data- and model-driven control optimisation for industrial energy supply systems (IESS) by means of deep reinforcement learning (DRL). The method consists of five steps, including system boundary definition and data accumulation, system modelling and validation, implementation of DRL algorithms, performance comparison and adaptation or application of the control strategy. The method is successfully applied to a simulation of an industrial cooling system using the PPO (proximal policy optimisation) algorithm. Significant reductions in electricity cost by 3% to 17% as well as reductions in CO₂ emissions by 2% to 11% are achieved. The DRL-based control strategy is interpreted and three main reasons for the performance increase are identified. The DRL controller reduces energy cost by utilizing the storage capacity of the cooling system and moving electricity demand to times of lower prices. Additionally, the DRL-based control strategy for cooling towers as well as compression chillers reduces electricity cost and wear-related cost alike.

本文提出了一种通过深度强化学习 (DRL) 对工业能源供应系统 (IESS) 进行数据和模型驱动控制优化的方法。该方法包括系统边界定义和数据积累、系统建模和验证、DRL算法的实现、性能比较和控制策略的适配或应用五个步骤。该方法已成功应用于使用 PPO（近端策略优化）算法的工业冷却系统的模拟。电力成本显着降低 3% 至 17% 并减少 CO ₂实现了 2% 至 11% 的排放量。解释了基于 DRL 的控制策略，并确定了性能提高的三个主要原因。DRL 控制器通过利用冷却系统的存储容量并将电力需求转移到价格较低的时期来降低能源成本。此外，基于 DRL 的冷却塔和压缩式冷却器控制策略降低了电力成本和与磨损相关的成本。