Power Generation Optimization of the Combined Cycle Power-Plant System Comprising Turbo Expander Generator and Trigen in Conjunction with the Reinforcement Learning Technique

Abstract

In this paper, a method that utilizes the reinforcement learning (RL) technique is proposed to establish an optimal operation plan to obtain maximum power output from a trigen generator. Trigen is a type of combined heat and power system (CHP) that provides chilling, heating, and power generation, and the turbo expander generator (TEG) is a generator that uses the decompression energy of gas to generate electricity. If the two are combined to form a power source, a power generation system with higher efficiency can be created. However, it is very difficult to control the heat and power generation amount of TEG and trigen according to the flow rate of natural gas that changes every moment. Accordingly, a method is proposed to utilize the RL technique to determine the operation process to attain an even higher efficiency. When the TEG and trigen are configured using the RL technique, the power output can be maximized, and the power output variability can be reduced to obtain high-quality power. When using the RL technique, it was confirmed that the overall efficiency was improved by an additional 3%.