A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

Abstract

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.

An Innovative Approach to Radiality Representation in Electrical Distribution System Reconfiguration: Enhanced Efficiency and Computational Performance

“…The primary goal of our research is to reconcile the need for accurate radial network representation with the computational demands of the DPSR. Our findings indicate significant improvements in both processing times and the quality of solutions, suggesting a promising direction for future research in the optimization of power distribution systems [2,4].…”

“…Borges et al [4] adopted a similar approach to Lavorato et al in expressing radiality conditions as a single equality between the number of active branches and the number of nodes minus the number of substations, with an additional constraint to ensure current flows in only one direction. This final set of radiality constraints has been widely accepted by researchers in the field, as demonstrated by Pareja et al [2] who employed a mathematical model closely resembling that described by Borges et al, incorporating the presence of distributed generation in the network along with the optimization problem of strategically placing such distributed generation.…”

“…For instance, Borges et al [4] utilized piecewise linear approximations to linearize quadratic active and reactive power, as well as quadratic voltage. This approach has been widely accepted, with small variations by Pareja et al [2], and has proven to be an effective way to reduce computational effort.…”

“…Reconfiguration entails modifications in the topology of the power distribution network, primarily aimed at minimizing losses, enhancing load balancing, and improving fault response capabilities. This strategy is not merely vital for the effective operation of power grids but is also integral to incorporating renewable energy sources and adjusting to the dynamic characteristics of contemporary energy demands [2].…”

Simultaneously Distributed Generation Allocation and Network Reconfiguration in Distribution Network Considering Different Loading Levels

“…A new technique entitled the three-dimensional group search optimizer (3D-GSO) technique was proposed for the simultaneous planning of DG units and NR in [63]. In [64], A MILP model for simultaneous planning of DG units and NR to minimize TAPL and VD in DNs.…”