An MPC-Based Dual-Solver Optimization Method for DC Microgrids With Simultaneous Consideration of Operation Cost and Power Loss
Wenzhe Su, Samson S. Yu, Hong Li, Herbert Ho‐Ching Iu, Tyrone Fernando
Abstract
In this paper, a dual-solver framework based on model predictive control (MPC) is proposed, E-solver and L-solver. The economic scheduling problem is formulated using mixedinteger linear programming (MILP), which can be solved in an efficient way by using commercial solver (E-solver). While the transmission loss problem is formulated using non-linear programming (NLP), which can be solved in the interior point method, namely L-solver. The E-solver provides an economic priority power scheduling plan for the L-solver, and the L-solver solves the entire microgrid accurate power flow scheduling plan. The proposed planning model decomposition technique aims to solve the planning model in a time-sharing manner and combines the characteristics of the two optimizers with a reasonable matching algorithm to achieve economic, efficient, and fast real-time control. A case study of a DC microgrid is employed to assess the performance of the online optimization-based control strategy. Simulations based on eight-node DC microgrid show that the method reduces the operating cost by 12.10% and increases the calculation speed by 80.18% compared with the traditional interior point method. With a shorter delay, the proposed optimization method will facilitate the implementation of real-time control and optimization.