Comparison of Genetic and Reinforcement Learning Algorithms for Energy Cogeneration Optimization

Large process plants generally require energy in different forms: mechanical, electrical, or thermal (in the form of steam or hot water). A commonly used source of energy is cogeneration, also defined as Combined Heat and Power (CHP). Cogeneration can offer substantial economic as well as energy savings; however, its real-time operation scheduling is still a challenge today. Multiple algorithms have been proposed for the CHP control problem in the literature, such as genetic algorithms (GAs), particle swarm optimization algorithms, artificial neural networks, fuzzy decision making systems and, most recently, reinforcement learning (RL) algorithms.This paper presents the comparison of a RL approach and a GA for the control of a cogenerator, using as a case study a thermal power plant serving a factory during the year 2021. The two methods were compared based on an earnings before interest, taxes, depreciation, and amortization (EBITDA) metric. The EBITDA that could be obtained using the RL algorithm, exceeds both the EBITDA that could be generated using a per-week genetic algorithm and the one from the manual scheduling of the CHP. Thus, the RL algorithm proves to be the most cost-effective strategy for the control of a CHP.