Cooperative operation optimization for rural multi-microgrid and county-integrated energy operators considering typical energy scenarios

Abstract

Despite being rich in renewable energy, China's rural areas are backwards in terms of energy use. Rural multi-microgrid cooperative operation optimization can effectively promote renewable energy use in rural areas. Many different microgrid energy scenarios have been developed for rural areas of China, and they have different resource endowments and unit compositions. Moreover, frequent power-related interactions occur between counties and villages. This study analyzes four typical microgrid energy scenarios in rural areas of China and optimizes their synergistic operation based on county-integrated energy operators. First, a mathematical model of rural microgrids for four energy scenarios and a trading mechanism between rural multi-microgrid and county-integrated energy operators were constructed. Subsequently, an upper-level optimization model that minimizes operating costs was developed for the county-integrated energy operator. A low-level optimization model was developed for rural multi-microgrid usage, and it minimized the operating costs. Finally, Stackelberg game theory was utilized to resolve the optimization issue. The results showed that the cooperative optimization of rural multi-microgrid and county-integrated energy operations can reduce the operating costs of both parties compared to that when each subject is operating alone. This optimization reduced the rural multi-microgrid cost from 12 773.64 yuan to 11 508.67 yuan and county-integrated energy operator cost from 3898.37 yuan to 1581.79 yuan. Moreover, it reduced both parties' dependence on external power grids; increased the self-balancing capacity of the rural multi-microgrid and county-integrated energy operator from 0.424 to 0.715 and 0.694 to 0.852, respectively; substantially increased the capacity of renewable energy consumption through power interaction; and reduced the risk of fluctuations in system operating costs.