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The Distributed Generator types have different combinations of real and reactive power characteristics, which can affect the total power loss and the voltage support/control of the radial distribution networks (RDNs) in different ways. This paper investigates the impact of DG’s penetration level (PL) on the power loss and voltage profile of RDNs based on different DG types. The DG types are modeled depending on the real and reactive power they inject. The voltage profiles obtained under various circumstances were fairly compared using the voltage profile index (VPI), which assigns a single value to describe how well the voltages match the ideal voltage. Two novel effective power voltage stability indices were developed to select the most sensitive candidate buses for DG penetration. To assess the influence of the DG PL on the power loss and voltage profile, the sizes of the DG types were gradually raised on these candidate buses by 1% of the total load demand of the RDN. The method was applied to the IEEE 33-bus and 69-bus RDNs. A PL of 45–76% is achieved on the IEEE 33-bus and 48–55% penetration on the IEEE 69-bus without an increase in power loss. The VPI was improved with increasing PL of DG compared to the base case scenario.

Electrical energy is critical to a country’s socioeconomic progress. Distribution system expansion planning addresses the services that must be installed for the distribution networks to meet the expected load need, while also meeting different operational and technical limitations. The incorporation of distributed generation sources (DGs) alters the operating characteristics of modern power systems, resulting in major economic and technical benefits, such as simplified distribution network expansion planning, lower power losses, and improved voltage profile. Thus, in this study, an analytical method is used to design the expansion planning of the Addis North distribution network considering the integration of optimal sizes of distributed generations for the projected demand growths. To evaluate the capability of the existing Addis North distribution network and its capability to supply reliable power considering future expansion, the load demand forecast for the years 2020–2030 is done using the least square method. The performance evaluation of the existing and the upgraded network considering the existing and forecasted load demand for the years 2030 is done using ETAP software. Accordingly, the results revealed that the existing networks cannot meet the existing load demand of the town, with major problems of increased power loss and a reduced voltage profile. To mitigate this problem, the Addis North feeder-1 distribution network is upgraded and for each study case, the balanced and positive sequence load flow analysis was executed and the maximum total real and reactive power losses were found at bus 29. The result shows that the upgraded network of bus 29 was the optimal location of DG and its size was 9.93 MW. After the optimal size of DG was placed at this bus, the real and reactive power losses of the upgraded networks were 0.2939 MW and 0.219 MVAr, respectively. At bus 29 the maximum power losses reduction and voltage profile improvements were found. The active and reactive power losses were minimized by 21.285% and 19.633% respectively and the voltage profiles were improved by 8.78%. Thus, in the predicted year 2030, DG power sources could cover 61.12% of the feeder-1 power requirements.

Distribution systems pose a significant challenge within the power grid, primarily due to their high current, low voltage, and comparatively high ohmic resistance compared to transmission and sub-transmission systems. This results in substantial power losses, necessitating the need for effective mitigation strategies. To address this issue, a wide range of methods and algorithms have been proposed and continuously developed. Over the past half-century, reconfiguring the distribution network has emerged as a cost-effective and straightforward approach to reduce distribution losses. Distribution system reconfiguration has been extensively studied, with each study aiming to achieve distinct objectives. Additionally, numerous studies have explored the dynamics of distribution system reconfiguration, evaluating and comparing various approaches. This study comprehensively assesses both static and dynamic methods of reconfiguring distribution systems and introduces a novel dynamic reconfiguration technique. Unlike traditional methods that rely on real-time or hourly load models, this approach utilizes a load model to address the dynamic reconfiguration problem. Simulations were conducted on the well-established IEEE 33-bus test system, employing MATLAB software in conjunction with a genetic algorithm to minimize losses and optimize voltage profiles. Based on the simulation results, this novel dynamic reconfiguration method demonstrated superior performance compared to previously employed methods. It effectively reduced power losses and enhanced the voltage profile, demonstrating its potential for improving the overall efficiency of distribution systems.

The paper presents a decision-making algorithm that has been developed for the optimum size and placement of distributed generation (DG) units in distribution networks. The algorithm that is very flexible to changes and modifications can define the optimal location for a DG unit (of any type) and can estimate the optimum DG size to be installed, based on the improvement of voltage profiles and the reduction of the network’s total real and reactive power losses. The proposed algorithm has been tested on the IEEE 33-bus radial distribution system. The obtained results are compared with those of earlier studies, proving that the decision-making algorithm is working well with an acceptable accuracy. The algorithm can assist engineers, electric utilities, and distribution network operators with more efficient integration of new DG units in the current distribution networks.

This research takes on a crucial task- exploring the optimal placement of Renewable Distributed Generators such as Solar Photovoltaic, wind turbines and Electric Vehicles into the Radial Distribution System (RDS). This is a strategic move aimed at minimising power loss (P Loss ) and improving the voltage profile and stability index. The RDGs are integrated into RDS with and without considering the uncertainty of the different load demands for 24 h. The probability function of Beta and Weibull distribution functions are employed to attain the solar irradiance and wind speed in a particular region. In addition, EVs are also integrated into RDS, employing meta-heuristic algorithms intended to reduce power loss (PLoss) and improve the voltage profile. The study uses an Indian 28-bus test system mimicking a balanced radial distribution network to integrate distributed generators (DGs) and EV charging stations. The simulated results demonstrate that integrating DGs into power systems has offered considerable benefits, including reduced PLoss, heightened efficiency, decreased dependency on centralised generation, and improved environmental sustainability. It is discovered that the Multi-population Evolution Whale Optimization Algorithm (MEWOA) produces better results than other methods in the literature and is valuable and practical for handling these nonlinear optimisation situations.

This paper presents an effective biogeography-based optimization (BBO) for optimal location and sizing of solar photovoltaic distributed generation (PVDG) units to reduce power losses while maintaining voltage profile and voltage harmonic distortion at the limits. This applied algorithm was motivated by biogeography, that the study of the distribution of biological species through time and space. This technique is able to expand the searching space and retain good solution group at each generation. Therefore, the applied method can significantly improve performance. The effectiveness of the applied algorithm is validated by testing it on IEEE 33-bus and IEEE 69-bus radial distribution systems. The obtained results are compared with the genetic algorithm (GA), the particle swarm optimization algorithm (PSO) and the artificial bee colony algorithm (ABC). As a result, the applied algorithm offers better solution quality and accuracy with faster convergence.

This study evaluates electrochemical voltage-range and voltage-profile regarding electrodes of insertion (intercalation) batteries. The phrase “voltage-range” expresses the difference between obtained maximum and minimum potential for the cells. It also can be called as operating voltage-range, working voltage-range, electrochemical voltage-range, or voltage window. This paper proposes a new notion regarding electron density of states, i.e. trans-band, which can be implemented to justify the voltage -range and -profile, by means of Fermi levels’ alignment. Voltage -range and -profile of a number of insertion electrode materials are clarified by the proposed theoretical approach, namely LiMn 2 O 4 , Li 2 Mn 2 O 4 , ZnMn 2 O 4 , LiFePO 4 , LiCoO 2 , Li 2 FeSiO 4 , LiFeSO 4 F, and TiS 2 . Moreover, the probable observed difference between charge and discharge profile is explained by the approach. The theoretical model/approach represents a number of important concepts, which can meet some scientific fields, e.g. electrochemistry, energy storage devices, solid state physics (DFT), and phase diagrams. By means of DFT calculations, this paper deals with quantizing the energy of electrochemical reactions, justifying the configuration of voltage-profile, and explaining the origin of the voltage-range. Accordance with the experimental observations suggests that this paper can extend boundary of quantum mechanics toward territories of classical thermodynamics, and boundary of the modern thermodynamics toward kinetics. Opening a new horizon in the related fields, this paper can help tuning, engineering, and predicting cell-voltage behavior.

In response to the market’s increasing need for electricity and the escalating technical and environmental challenges, the Power System (PS) sector has strongly emphasised the escalating technical and ecological challenges, and the PS sector has placed a strong emphasis on integrating distributed energy resources (DERs) into distribution systems (DS). However, if not allocated optimally, integrating DERs can provide various technical topics such as power quality, stability, reliability, and voltage management concerns. Therefore, creating effective and efficient optimisation techniques to solve issues of DER integration is crucial. In this paper, the integration of DERs into DS is utilised by the Multi-Objective Evolution of the Whale Optimisation Algorithm (MEWOA). MEWOA is an optimisation method inspired by humpback whales’ hunting strategies and has demonstrated promising results in solving challenging optimisation issues. The suggested approach tries to optimise the location and sizing of DERs in DS while considering several factors, including voltage variation, Power loss (P Loss ) reduction, and operational cost. The extensive simulations are run on the Indian 28-bus and IEEE 69-bus distribution systems to show the viability of the suggested approach. The findings demonstrate that the proposed method can significantly enhance the voltage profile, lessen P Loss , lower the annual operating expenses and save revenue from P Loss minimisation. The results also show that MEWOA outperforms other optimisation techniques like the Grasshopper Optimisation Algorithm (GOA), the Dragonfly Algorithm (DA), and the Whale Optimisation Algorithm (WOA) in terms of convergence speed and solution quality. As a result, the suggested way for integrating DERs into DS utilising MEWOA is a successful and efficient optimisation technique. The results demonstrate that the proposed approach can enhance distribution system performance while lowering operational expenses and environmental impact.

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently.

In electrical power systems, FACTS devices effectively control power flow and change bus voltages, leading to lower system losses and excellent system stability. The article discusses the research from the last decade that evaluated various methods for placing FACTS devices using the meta-heuristic approach to address the positioning of FACTS devices to maintain proper bus voltages and control line flow and improve the overall system efficiency. The need for more efficient electricity systems management has given rise to innovative technologies in power generation and transmission. The combined cycle power station is a good example of a new development in power generation and flexible AC transmission systems, generally known as FACTS, are controllers that improve transmission systems. Worldwide transmission systems are undergoing continuous changes and restructuring. They are becoming more heavily loaded and are being operated in ways not originally envisioned. Transmission systems must be flexible to react to more diverse generation and load patterns. In addition, the economical utilization of transmission system assets is of vital importance to enable utilities in industrialized countries to remain competitive and to survive. In developing countries, the optimized use of transmission systems investments is also important to support industry, create employment and utilize efficiently scarce economic resources. FACTS controller is a technology that responds to these needs. It significantly alters the way transmission systems are developed and controlled together with improvements in asset utilization, system flexibility and system performance. Several models and techniques suggest that devices can be placed in a particular location with different parameter settings. Finally, the optimization problem improved system performance by decreasing power loss, improving the voltage profile and power angle at each bus, raising the L-index, and minimizing generating costs. FACTS devices can increase the transmission line’s capacity for transferring power by increasing the voltage at its terminals at both ends and reducing line reactance. The FACTS controller must be installed in the distribution and transmission lines to maximize the power flow. Various techniques are used for the best placement of FACTS controllers, including analytical methods, arithmetic programming approaches, meta-heuristic optimization approaches, and hybrid approaches—this paper analyses numerous analytical and meta-heuristic optimization techniques to place FACTS controllers in the most advantageous locations. The fundamental problems in intelligent power systems, such as improving stability, power quality, and managing congestion, are discussed in this study, along with several applications of FACTS devices. The cutting-edge power systems of today provide users with constant, high-quality power through smart grids and smart meters.