Abstract
Distribution network is an essential part of electric power system, which however has higher power losses than transmission system. Distribution losses directly affect the operational cost of the system. Therefore, power loss reduction in distribution network is very important for distribution system users and connected customers. One of the commonly used ways for reducing losses is distribution system reconfiguration (DSR). In this process, configuration of distribution network changes by opening and closing sectional and tie switches in order to achieve the lowest level of power losses, while the network has to maintain its radial configuration and nodal voltage limits, and supply all connected loads. The DSR aiming loss reduction is a complex mixed-integer optimization problem with a quadratic term of power losses in the objective function and a set of linear and non-linear constraints. Accordingly, distribution network researchers have dedicated their efforts to developing efficient models and methodologies in order to find optimal solutions for loss reduction via DSR. In this paper, an efficient mathematical model for loss minimization in distribution network reconfiguration considering the system voltage profile is presented. The model can be solved by commercially available solvers. In the paper, the proposed model is applied to several test systems and real distribution networks showing its high efficiency and effectiveness for distribution systems reconfiguration.
Highlights
Active power loss minimization [1] is important for the distribution system efficiency and power quality
The results indicated that performances of quadratic programming (QP) and quadratically constrained programming (QCP) are better than the models formulated by Benders decomposition (BD)
NUMERICAL RESULTS AND CASE STUDIES The proposed model was applied to several test systems using CPLEX and the results were compared with decimal codification GA (DCGA) and solutions obtained by other formulations and methodologies, such as heuristic methods [30], [43]–[50], simulated annealing (SA) [51]–[54], tabu search (TS) [55], [56], modified TS (MTS) [57], genetic algorithms (GAs) [24], [52], [53], [58]–[73] (e.g. refined GA (RGA) [61], fuzzy GA (FGA) [62], binary GA (BGA) [63], GA based on Matroid theory (GAMT) [66], and SOReco [67]), particle swarm optimization (PSO) [74]–[79], plant growth simulation (PGS) [80], [81], ant colony optimization (ACO) [24], [52], [53], [82]–[86], harmony search algorithm (HSA) [25], [53], and honey bee mating optimization (HBMO) [53], [79], [87], [88]
Summary
Active power loss minimization [1] is important for the distribution system efficiency and power quality. Changing distribution system configuration by opening and closing switches, while the network maintains its radial operation satisfying all loads is an effective way for loss reduction and voltage improvement [2], [3]. Distribution system reconfiguration (DSR) can be formulated as a large-scale combinatorial optimization problem with constraints that can often contain nonlinearities. The associate editor coordinating the review of this manuscript and approving it for publication was Chenghong Gu. The feasible search space in DSR is typically large, nonconvex, and hard to explore. Determining good-quality solutions is always a challenging task. In order to cope with this issue, distribution system researchers have dedicated since a long time efforts to developing efficient models and methodologies for DSR
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