Static and dynamic convex distribution network expansion planning

Abstract

This chapter presents static and dynamic optimization-based models for planning the electric distribution network. Based on a branch flow model, two Mixed-Integer Conic Quadratic Programming (MICQP) convex formulations are proposed to solve the network expansion planning models including high modeling fidelity of the intrinsic interaction of the manifold elements of the networks. The objective of the presented models is to minimize investment and operation costs by optimally deciding on installing new feeders and/or changing existing ones for others with larger capacities, installing new substations or expanding existing ones and, finally, installing capacitor banks and voltage regulators, modifying the network topology. In addition, discrete tap settings of voltage regulators are modeled as a set of mixed-integer linear equations, which are embedded in an ac optimal power flow. The presented MICQP models are convex optimization problems. Therefore globality and convergence are guaranteed. Computational results to verify the efficiency of the proposed methodology are obtained for a 24-node test system. Finally, conclusions are duly drawn