Optimization of interconnected absorption cycle heat pumps with micro-genetic algorithms

Production of hot water in district heating plants needs to be adjusted on a day-to-day basis to match the expected demand and availability and prices of energy resources. However, such plants are often highly nonlinear and complex. It therefore makes sense to attempt to automate optimization of the operating conditions within the physical boundaries set by the plant equipment. In this work, we investigate the use of micro-genetic algorithms to achieve constrained global set-point optimization based on a dedicated simulation model. The model is based on a real district heating plant consisting of four interconnected LiBr–water based absorption cycle heat pumps primarily driven by a geothermal reservoir and wood chip boilers. Various scenarios are considered, and it is found that the proposed genetic algorithm is able to find combinations of valid set-points that provide savings of several percent of current operating costs compared to a baseline scenario in reasonable computation time.