Gas-fired generation has been considered one of the mainstream generation technologies nowadays and has become a vital part of the decarbonization journey in Hong Kong. Having a significant impact on the business and environment, the utilization of generation units has to be optimized so that additional gas generation can be accommodated and the performance of efficiency and reliability for the existing eight 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. As those...[ Read more ]

Gas-fired generation has been considered one of the mainstream generation technologies nowadays and has become a vital part of the decarbonization journey in Hong Kong. Having a significant impact on the business and environment, the utilization of generation units has to be optimized so that additional gas generation can be accommodated and the performance of efficiency and reliability for the existing eight 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. As those costs and various machine factors are not considered effectively, efficiently and intelligentially, the overall generation costs, as well as the carbon emission reduction, have not been optimized as a result.

This thesis is to develop a unit commitment optimization (UCO) model with the help of state-of-the-art artificial intelligence and machine learning for optimizing the daily scheduling of eight gas-fired generation units at BPPS. It illustrates the comparison and advantage of the newly developed optimization 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 optimization for the BPPS machine scheduling.