A Human-Machine Cooperative Scheduling Method for Integrated Energy System Based on Fuzzy Reasoning

Optimizing integrated energy systems' day-ahead scheduling lowers operating expenses. Human scheduling is more accurate and flexible, but machine scheduling can handle the unpredictability of thermoelectric demands and renewable energy sources better. For integrated energy systems, we suggest a human-machine cooperation scheduling approach to fully leverage the benefits of both human and machine intelligence. By keeping the human involved, the human-machine cooperation scheduling approach prevents the decline of human proficiency and excessive reliance on the computer. Both human and computer scheduling decisions are assigned weights using fuzzy reasoning. The differences in human-machine decision-making and prediction are taken into consideration in the proposed method. Simulation trials illustrate the efficacy of the suggested human-machine cooperative scheduling strategy. The trials' findings show that the proposed method can successfully lower the integrated energy system's operating costs.