Optimal Configuration of Rural Integrated Energy Storage Systems Considering Multiple Demand-Side Responses

In rural locations, energy storage options were limited to discrete forms. Complementary advantages among energy sources such as electricity, heat and gas cannot be fully utilized. Integrated energy systems can optimize the distribution of electricity, gas, heat, and energy storage to utilize different energy sources efficiently. This paper uses a multi-period demand response model that considers electricity prices. On this basis, an electric-gas-heat-coupled energy storage model is built. The purpose of the objective function is to decline the whole economic expenditure, maximize the consumption of renewable power, and limit the annual CO 2 emissions. This can be accomplished by employing advanced evolutionary algorithms for non-dominant classification. To solve the objective function, a reformative Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied. Upon analysis, the findings indicate that after considering the two categories of demand response, all loads reduce their energy consumption costs by shifting part of their load to hours with lower peak power values, and the curves for all load types are flatter than before incorporating demand response. The comparison of simulation data proves that the coupled electric-gas-heat energy storage model has the best economic performance.