Network-Level Optimization for Unbalanced Power Distribution System: Approximation and Relaxation

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. existing scalable D-OPF algorithms either use approximations that are valid an system, or apply relaxation techniques the equations do guarantee a feasible solution. In this paper, we propose simultaneously achieve solutions by solving multiple iterations of approximate, relaxed, subproblems low complexity. first algorithm is based on successive linear approximation around current operating point, where solution obtained (LP) problem. second as conic constraints together with directional constraints, which achieves over second-order cone (SOCP) It demonstrated proposed able reach while significantly reducing computation time compared equivalent NLP model same system.