Distributed Optimal Power Flow for Smart Microgrids
For microgrid operators, this provides a computationally tractable and distributed solution to a nonconvex OPF problem, enabling practical implementation with privacy and robustness benefits.
This paper addresses the optimal power flow problem for unbalanced microgrids, minimizing losses or generation costs while regulating voltage. The proposed semidefinite programming relaxation achieves globally optimal solutions in polynomial time, and a distributed algorithm using ADMM ensures scalability, robustness, and faster convergence than alternatives.
Optimal power flow (OPF) is considered for microgrids, with the objective of minimizing either the power distribution losses, or, the cost of power drawn from the substation and supplied by distributed generation (DG) units, while effecting voltage regulation. The microgrid is unbalanced, due to unequal loads in each phase and non-equilateral conductor spacings on the distribution lines. Similar to OPF formulations for balanced systems, the considered OPF problem is nonconvex. Nevertheless, a semidefinite programming (SDP) relaxation technique is advocated to obtain a convex problem solvable in polynomial-time complexity. Enticingly, numerical tests demonstrate the ability of the proposed method to attain the globally optimal solution of the original nonconvex OPF. To ensure scalability with respect to the number of nodes, robustness to isolated communication outages, and data privacy and integrity, the proposed SDP is solved in a distributed fashion by resorting to the alternating direction method of multipliers. The resulting algorithm entails iterative message-passing among groups of consumers and guarantees faster convergence compared to competing alternatives