Optimal Network Reconfiguration (NR) is a well-accepted approach to minimize power loss and enhance voltage profile in the Electrical Distribution Networks (EDN). Since the NR problem contains huge combinational search space, most researchers consider the meta-heuristic techniques to attain NR solution. However, these meta-heuristic techniques do not guarantee to obtain the optimal solution besides they require large processing time to converge. This is mainly due to (1) random initialization and updating of population and (2) the continuous verification of population during the search process. With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and enhancing the voltage profile of the EDN, this work proposes a new method based on two-stage optimizations. The proposed method introduces an approach to simplify the network into simplified network graph. Then, this approach is utilized for guided initializations and generations of the population and for the proper population’s codification. The proposed method is implemented using the firefly algorithm and verified on 33-bus and 118-bus test systems. The results show the ability of the proposed method to obtain the optimal solution within fast computational time and with superior consistency compared to the conventional methods.

Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization.

As power distribution systems evolve from conventional radial structures to more complex ring networks, driven by the widespread integration of distributed generation (DG), the challenge of ensuring reliable and effective protection becomes increasingly critical. Directional Overcurrent Relays (DOCRs) are pivotal in this context, protect the network through precise coordination between primary and backup protection schemes. However, optimizing these settings is a daunting task due to the nonlinear nature of the problem and the myriad constraints involved. This paper embarks on a comprehensive exploration of cutting-edge optimization techniques deployed to tackle DOCR coordination challenges. The paper delves into Nature-Inspired Algorithms (NIAs), metaheuristic approaches, mathematical formulations, and artificial intelligence methods, critically assessing their capabilities in navigating the complex landscape of relay coordination. While NIAs excel in exploring vast search spaces, their slower convergence can be a drawback. Metaheuristics offer robust solutions but at the cost of high computational demand, and traditional mathematical methods, though precise, may fall short in dynamic environments. Pushing the boundaries of current methodologies, the paper investigates hybrid optimization techniques that fuse the strengths of multiple approaches. These hybrids show promise in transcending the limitations of individual methods, offering innovative solutions for optimizing relay coordination in increasingly intricate network configurations. Moreover, this paper sheds light on the profound impact of emerging energy technologies on existing protection frameworks, emphasizing the urgent need for adaptive strategies to ensure stable and reliable power delivery. Through this investigation, actionable insights and forward-thinking solutions are proposed to bolster the safety and resilience of modern distribution networks, positioning them to meet the demands of a rapidly transforming energy landscape.

Common power system restoration planning strategy is based on a ‘build up’ approach, where a blackout system is sectionalized among several islands for parallel restoration prior to resynchronization. In order to speed up the resynchronization of the islands, each island must have similar energizing times. However, there is a huge number of possible combinations of islands that can be formed. Thus, this paper proposes a method to determine optimal islands that have similar energizing times. The method involves identifying transmission lines that should not be connected to form the islands. The proposed method is based on the combination of heuristic and discrete optimization methods. The heuristic technique is proposed to find initial solution that is close to the optimal solution. This solution will guide the optimization technique, which is the discrete Artificial Bee Colony optimization method, to find the optimum solution. The proposed method also considers restoration constraints including black start generator availability, load-generation balance, and the maintenance of acceptable voltage magnitude within each island. The proposed method is validated via simulation using IEEE 39, 118-bus and 89-bus European systems. The advantage of the proposed method in terms of restoration time is demonstrated through a comparison with other literature.

The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m2 and 84.04 W/m2, while using the MLR method they are 141.18 W/m2 and 104.87 W/m2 respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.