关键词: 区域多微网, 风电消纳, 分层协调优化, 需求响应, 合作博弈, 鲁棒优化

Abstract: With the continuous deepening of the reform of the power market, it is of great significance to study the energy transaction among multiple microgrids on the distribution side to improve regional economy. To reduce the economic cost of microgrid operation, promote the consumption of renewable energy, and reduce the adverse impact of large-scale microgrid access on distribution network operation, taking the energy trading among multiple microgrids in the adjacent range as the research object, this paper establishes a hierarchical coordinated energy optimization management model for multi-microgrid system applying cooperative game. The upper layer of the model is the unified pricing of the management center, and the lower layer is the autonomous optimization of each microgrid. In the autonomous optimization scheduling of each microgrid, the demand response of different types of loads is considered, and a coordinated operation with users is formed through the signing of an agreement. The non-convex non-linear programming problem with power flow constraints is transformed into a second-order cone optimization problem that can be effectively solved by using the second-order cone relaxation technology, and the original bi-level programming model is transformed into an easy-to-solve single-level linear programming model by using decomposition theory such as dual theory and Big M method. The upper management center formulates the electricity price and conducts multiple volume and price interactions with the microgrids to find the equilibrium state of the coordinated operation of the regional multi-microgrid system. And then the income of the regional multi-microgrid alliance is distributed through the income distribution method based on the Shapley value. Finally, an example is given to verify the effectiveness of the model for improving the benefits of microgrids and promoting the consumption of renewable energy.

Key words: regional multi-microgrid system, wind power consumption, hierarchical coordinated optimization, demand response, cooperative game, robust optimization