Multi-objective optimization of an integrated energy system with shared energy storage

To improve the operational efficiency and stability of an integrated energy system with shared energy storage, this study proposes a hierarchical optimization framework. A three-layer optimization model is constructed to address the problem that it is difficult to consider the interests of multiple parties and the synergy of multiple objectives in the traditional model. In the first layer, a fish eagle optimization algorithm is used to optimize the operator's energy supply revenue and subsidy cost. The second layer proposes a multi-objective osprey optimization algorithm (MOOOA) to solve the multi-objective optimization problem of the operational revenue and net load fluctuation of shared energy storage. The third layer is based on a mathematical solver to refine the optimization of demand response user agent cost. The proposed method reduces the user agent cost by 21.00% and the net load fluctuation by 49.99% with only 7.42% reduction in shared energy storage revenue, which verifies the role of layered optimization in synergizing the system with multiple actors. The operator-energy storage-user layered optimization architecture established in this study avoids the single optimization limitation of the traditional centralized model. The proposed MOOOA is verified to be universal by ZDT/UF test function. This study provides referable optimization ideas for the low-carbon and high-efficiency operation of integrated energy systems, which helps to promote energy structure transformation and sustainable development.