The topic of distribution NETWORK RECONFIGURATION has been recognized as a common problem in the operation of distribution NETWORKs. Up to now, RECONFIGURATION of the distribution NETWORK has been used to reduce power loss, issues with reliability and emergency problems. As a result of the increasing installation rate of Distributed Generations (DGs) and the increasingly increasing number of Electric Vehicles (EVs), distribution NETWORK operators have faced new problems as a result of high sensitive loads. These new facets of the problem of NETWORK operation consist of the problem of congestion and enable the business mechanism to be executed by an existing distribution market. With the application of Distribution Locational Marginal Price (DLMP), using Particle Swarm Optimization (PSO), this paper introduces a new methodology to solve this problem. As a suitable case study, the standard IEEE 33-bus NETWORK has been selected and simulation results show the compatibility of the proposed system. In the case of not considering DGs and the problem of congestion, comparisons with other papers were also discussed. The participation of DGs in the electricity distribution industry, the issue of congestion and the numerical results have been shown and discussed below.

Distribution NETWORK RECONFIGURATION (DNR) is an important challenge in the operation of distribution NETWORKs which may be influenced by factors such as Wind Turbine Generators (WTG). In this paper, a novel policy is implemented to solve the DNR problem in presence of WTGs. The objectives of proposed DNR policy are minimization of active power losses, total electrical energy costs, and total emissions of the NETWORK. To solve the problem, an improved version of Honey Bee Mating Optimization (IHBMO) algorithm is implemented. Moreover, a stochastic scenario-based model is considered to meet the uncertainty of WTGs and loads. The bases of the proposed stochastic model are generation of stochastic scenarios using the roulette wheel mechanism, and a scenario reduction technique to decrease the computation burden of the problem. For each scenario, a multi-objective mechanism is employed to save non-dominated solutions extracted by IHBMO. A decision-making procedure based on fuzzy clustering technique is used to rank the obtained non-dominated solutions according to the decision-maker preferences. Finally, an 84-bus distribution test NETWORK is considered to evaluate the feasibility and effectiveness of the proposed method. Obtained results show that the proposed method can be a very promising potential method for solving the stochastic multi-objective RECONFIGURATION problem in distribution systems.

This paper presents an algorithm based on multi-objective approach for NETWORK RECONFIGURATION. Multiple objectives are considered for reduction in the system power loss, deviations of the nodes voltage and transformers loading imbalance, while subject to a radial NETWORK structure in which all the loads must be energized and no branch current constraint is violated. These three objectives are integrated into an objective function through weighting factors and the configuration with minimum objective function value is selected for each tie-switch operation. Heuristic rule is also incorporated in the algorithm for selecting the sequence of tie-switch operation. The effectiveness of the proposed method is demonstrated through examples.

The efficient operation of electrical distribution systems is critical in modern industry, as it directly impacts the reliability, stability, and cost-effectiveness of delivering electricity to consumers. Capacitors are included in radial distribution systems to improve voltage profile and minimize losses by providing reactive power. Consequently, losses are reduced as the reactive power flow component is compensated. Furthermore, re-configuration of the distribution NETWORK, which involves altering the open/closed status of switches, is a vital approach that affects the steady flow of electricity through the NETWORK. NETWORK RECONFIGURATION and optimal capacitor placement are essential techniques for enhancing the performance of the distribution NETWORKs. This study utilizes the Cuckoo Search Algorithm (CSA) to solve the problems of NETWORK RECONFIGURATION and optimal capacitor placement. The primary objective is to minimize power losses while ensuring that the voltage profile and reliability of the distribution system satisfy industry-level standards. The proposed method was tested on the IEEE 33 bus NETWORK. Five different scenarios were considered. The simulations were conducted in MATLAB software. The acquired improvements in the power loss reduction and voltage profile corroborate the effectiveness of this novel approach. Compared to previously explored methods, the results of the proposed scheme for solving optimal capacitor placement and NETWORK RECONFIGURATION individually were found to be more effective in terms of reducing power loss (3.12% and 4.02%, respectively).

Power system engineers are forced to place more emphasis on reducing losses at the distribution level. From an economic perspective, Joint NETWORK RECONFIGURATION and capacitor placement is one of the best ways to save energy.Application of heuristic technique is unavoidable, because of expansion of distribution NETWORKs and becoming more complex connections in these grids. In view of this, for the first this paper investigates the ability of Bacterial Foraging Algorithm (BFA) for Joint NETWORK RECONFIGURATION and capacitor placement.