基于最优潮流的含多微网的主动配电网双层优化调度

多个微电网并入主动配电网(Active Distribution Network, ADN)会对ADN系统的经济性和可靠性产生影响。采用传统的多微网与ADN全网统一优化调度方法时，若微网中风/光出力一旦出现波动，难以高效精确地求出系统的最优潮流，甚至造成无解。因此提出一种基于双层规划的多微网并网优化调度模型。上层模型中各微网作为电源并入ADN，以ADN系统潮流平衡为约束，建立最优潮流模型。运用二阶锥松弛技术将非凸非线性的潮流模型转化为凸可行域的二阶锥规划模型，并调用Gurobi求解器求解。下层模型以上层优化出的联络线功率为约束，建立并网微电网内可控电源的调度模型，并采用结合Tent映射混沌和NDX交叉技术的改进遗传算法(GA)求解。以并入多微网的调整IEEE33节点系统为算例，仿真算例表明双层规划的调度模型及算法具有可行性且在此模型下含多微网的ADN系统有更好的经济性。同时当风/光发电出现波动时，下层模型仍然可以进行局部调整优化，从而降低了微电网波动对ADN系统的影响，提高了系统的可靠性和鲁棒性。

Bi-level programming optimal scheduling of ADN with a multi-microgrid based on optimal power flow

A multi-microgrid in an Active Distribution Network (ADN) will influence the economy and reliability of an ADN system. When the traditional unified optimal dispatching method of a multi-microgrid and the whole ADN network is adopted, it is difficult to calculate the optimal power flow of the system efficiently and accurately once the wind power output in the microgrid fluctuates, and may even cause no solution being obtained. Therefore, an optimal scheduling model for a grid-connected multi-microgrid based on bi-level programming is proposed. Each microgrid in the upper model is incorporated into the ADN as a power source. The optimal power flow model is established with the ADN system power flow balance as the constraint. Using the second-order cone relaxation technique, the non-convex non-linear power flow model is transformed into a second-order cone programming model for the convex feasible region and the solution is obtained using the Gurobi solver. The lower model takes the power of the line optimized from the upper model as the constraint, sets up within the MG interconnection of a controllable power supply scheduling model, and uses the combination of Tent map chaotic and NDX cross technology improved Genetic Algorithm (GA) to solve the problem. The feasibility of the algorithm is verified by the example of the adjustment IEEE-33 nodes system incorporated into the multi-microgrid. The simulation results show that the scheduling model and algorithm of bi-level programming are feasible and the ADN system with multi-microgrid is more economical. When the wind/sun output fluctuates, the lower model can still be locally adjusted and optimized to reduce the influence of microgrid fluctuation on the ADN system and improve the reliability and robustness of the system. This work is supported by National Natural Science Foundation of China (No. 61364027) and Natural Science Foundation of Guangxi (No. 2019GXNSFAA185011).