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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.

In the present work the problem of Optimal Placement of remote controlled switches, through the replacement of installed manual switches, in Electric Power Distribution Systems with radial configuration is approached, considering economic, technical, operational and social aspects. The approach used for the identification of strategic points is based on the implementation of meta-heuristic algorithms for optimization with multiple objectives. For the evaluation of each solution, an evaluating function based on the Monte Carlo method is implemented, which estimates the defined indices through the simulation of failures in the network, whose probabilities are obtained using the Reliability Block Diagram method. Three different multi-objective algorithms were implemented and their results compared using the hypervolume metric, having the Generalized Differential Evolution 3 algorithm the best performance. [ABSTRACT FROM AUTHOR]

High penetration levels of electric vehicles (EVs) in distribution networks will create significant operational challenges to distribution system operators and demand robust methods to optimize the charging problem. This work addresses the problem of controlling the charging of electric vehicles in low voltage distribution systems using an optimization framework formulated as a scheduling problem. A computationally efficient procedure with a detailed modeling of the network is sought. The objective is to obtain a charging schedule for all the EVs connected to the network satisfying a set of network operational constraints. The approach uses a complete AC three-phase modeling of the network and deploys a time-series power flow coupled with a greedy heuristic to obtain a smart charging procedure over a fixed planning period considering the variable behavior of loads and EV connection patterns. Distribution network constraints consider phase unbalance, transformer and line limitations, and node voltage limits. The approach is tested on an IEEE low voltage distribution test feeder. [ABSTRACT FROM AUTHOR]

This paper presents a solution strategy for planning and project distribution system problems. This model applies concepts of Tabu Search (TS) metaheuristic to provide a complete solution for the problem, from distribution substations to individual consumers, making it able to be implemented by distribution companies. A 50-bus network was used to evaluate this method that obtained feasible and good solutions for planning and project distribution systems. [ABSTRACT FROM PUBLISHER]

In this paper, a technique based on nondominated sorting genetic algorithm-II (NSGA-II) is presented for solving the service restoration problem in an electric power distribution system. Due to the presence of various conflicting objective functions and constraints, the service restoration task is a multiobjective, multiconstraint optimization problem. In contrast to the conventional genetic-algorithm (GA)-based approach, this approach does not require weighting factors for the conversion of such a multiobjective optimization problem into an equivalent single objective function optimization problem. In this work, various practical distribution system operation issues, such as the presence of priority customers, presence of remotely controlled, as well as manually controlled switches, etc. have also been considered. Based on the simulation results on four different distribution systems, the performance of the NSGA-II-based scheme has been found to be significantly better than that of a conventional GA-based method. Besides, to reduce the software runtime of the NSGA-II algorithm, a faster version of NSGA-II has also been implemented. [ABSTRACT FROM AUTHOR]