Performance analysis and multi-objective optimization of biomass co-firing power plants using multi-objective genetic algorithm

Indonesia has set a target to achieve net-zero emissions (NZE) by 2060. To support this goal, a co-firing approach has been implemented in coal-fired power plants (CFPPs) to reduce the use of coal as the primary energy source. This study assessed the performance and impact of fuel modifications on objective functions (exergy efficiency, generated cost, and CO₂ emissions) of a coal-fired steam power plant. The objective of evaluating the performance of CFPPs is to improve exergy efficiency, lower the cost of electricity generation, and reduce CO₂ emissions. Previous studies lacked discussion of coal-fired power facilities that use biomass co-firing. The objective function was optimized using a Multi-Objective Genetic algorithm (MOGA). A parametric analysis was performed by varying the power plant load and coal usage to observe how they affected the changes in the objective function. As the load value increased, the exergy efficiency also increased, decreasing the generated cost (GC) required to produce 1 kWh (Kilowatt hours). This phenomenon occurs because of the auxiliary power usage of the Paiton CFPP. The optimization results indicate that the ideal point for the Paiton CFPP is an exergy efficiency of 32.05 %, a GC of 471.49 IDR/kWh, and CO₂ emissions of 27,291.77 tonnes. This result was obtained using a MOGA-based ANN method with nine hidden neurons. In the MOGA RSM analysis, the most favorable Pareto front yields the following respective values for exergy efficiency, generation cost, and CO₂ emissions: 33.09 %, 473.33 IDR/kWh, and 24,349.01 tonnes.