Optimization of building integrated energy scheduling using an improved genetic whale algorithm

Renewable energy generation has become the general trend with increasing environmental problems. However, the instability of renewable energy generation and the diversification of user demand are highlighted and the optimization of energy scheduling has become the key to solve the problem. This study introduces an energy scheduling optimization model tailored for building integrated energy systems, encompassing elements like gas turbines, wind and solar modules, ground source heat pumps, electric vehicles, central air-conditioning, and energy storage. The model prioritizes economic efficiency and minimal carbon emissions by first collecting and pre-processing the data for the regional building conformance, and then utilizing an enhanced multi-objective genetic whale algorithm. Evaluations on a regional complex building highlighted the algorithm's robust convergence and stability. The resulting optimized scheduling effectively balances economic and environmental concerns, reducing costs by about 92.896 yuan per day on average and reducing carbon emissions by about 0.091 tons, promoting efficient system operation, reducing costs and mitigating environmental impacts.