Bi-level multi-objective robust optimization for performance improvements in integrated energy system with solar fuel production

The authors design an integrated energy system with solar fuel preparation as the core, integrating cooling, heating, electricity and fuel cell vehicles, aiming to meet the multiple energy needs of users. Fluctuations associated with renewable energy and energy demand pose a challenge to operational planning of systems. Unfortunately, common models do not take into account the impact of such fluctuations on system operation. To mitigate this challenge, the authors develop a bi-level multi-objective robust optimization model to obtain the optimal resilient dispatch of the system. The above model is transformed into a minimax optimization problem to alleviate the difficulties that solving it directly, and two nested algorithms are employed to maximize the energy conversion efficiency, minimize CO2 emissions and energy cost of the system. The energy, environmental and economic effects of system are compared using a robust optimization model and a deterministic optimization model. The operation performances of system in the bi-level robust optimization model are not all better than the results obtained from the deterministic model, because the robust optimization model considers the volatility of solar, wind, and customer loads during system operation. This research provides a feasible method to ensure the stability of the system.