Unit commitment optimization of gas-fired generation at black point power station incorporating the efficiency, flexibility, reliability & cost

Abstract

Gas-fired generation has been considered one of the mainstream generation technologies nowadays and has become a vital part of the decarbonisation journey in Hong Kong. Having a significant impact on the business and environment, the utilisation of generation units has to be optimised so that additional gas generation can be accommodated and the performance of efficiency and reliability for the existing gas-fired generation units can be enhanced. Currently, at Black Point Power Station (BPPS) in Hong Kong, the generation scheduling is done manually based on the traditional approach. Each unit has its specific characteristic in terms of efficiency, cost, flexibility, reliability and performance according to its individual hardware condition, degradation and running regime. If those costs and various machine factors are not considered effectively, efficiently and intelligentially, the overall generation costs, as well as the carbon emission reduction, will not be optimised as a result. This study is to develop a Unit Commitment Optimisation (UCO) model with the help of state-of-the-art Artificial Intelligence and Machine Learning for optimising the daily scheduling of eight gas-fired generation units at BPPS. It illustrates the comparison and advantage of the newly developed optimisation model with the existing method. Moreover, a holistic review of the real-life generation cost, efficiency, maximum output power and condition-related data is completed to minimize modelling uncertainty. All the factors that affect the scheduling are further validated during the testing and tuning phase. The simulation result shows that the model is practical with sound good accuracy in efficiency, and cost optimisation for the BPPS machine scheduling.