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A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N  − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages.

An analytical method for placement and sizing of distributed generation on power distribution systems for loss reduction is introduced. The proposed analytical method is developed based on a new formulation for the power flow problem, which is non-iterative, direct, and involves no convergence issues even for systems with high R/X branch ratios. Further, this power flow solution is extremely useful whenever fast and repetitive power flow estimations are required. A priority list based on loss sensitivity factors is developed to determine the optimal locations of the candidate distributed generation units. Sensitivity analysis is performed to estimate the optimal size and power factor of the candidate distributed generation units. Various types of distributed generators (DGs) have been dealt with and viable solutions are proposed to reduce total system loss. The proposed method has been tested on 33-bus and 69-bus distribution systems, which are extensively used as examples in solving the placement and sizing problem of DGs. Exhaustive power flow routines are also performed to verify the sizes obtained by the analytical method. The test results show that the proposed analytical method could lead to optimal or near-optimal solution, while requiring lower computational effort.

The optimization algorithms in electrical engineering present an important tool for improving the dynamic and static operation of electrical distribution networks (EDN) by supporting the regional operator system for distribution network reconfiguration with the presence of dispersed generation systems based on the PV and wind turbine hybrid system. The short circuit current, in particular of the phase-to-ground type, is the most prevalent and intense on the EDN and affects voltage stability and increases power losses. In this work, we propose a new optimization technique, the hybrid improved whale and sine cosine optimization algorithm, based on the randomly determined distance between each whale’s current position and the best position using probabilistic-based sine and cosine functions, to improve the balance between exploitation and exploration phases. The objectives implemented are to minimize total active power losses and maximize the voltage profile. Two test systems were conducted for this work: overhead and undergrounding 63 bus systems using private data on the algerian electricity and gas distribution company and verifying the effectiveness of the proposed method through its application in the IEEE 33 bus standard distribution system. We then compared the results obtained with the latest research.

In this paper, a hybrid droop coordination strategy is proposed to reduce total generation cost and total transmission power loss, simultaneously, for a class of DC microgrid. Generally, conventional droop control, which is known as a communication-less technique, is being used to ensure suitable power sharing among distributed generators. However, when both of the minimization of total generation cost and total transmission loss is intended, some modification should be taken in the droop strategy. In this regard, first, this paper presents a hybrid droop coordination strategy with the aim of loss and cost reduction in a DC microgrid by defining of weighting factors. Furthermore, an energy management system considering the proposed coordination strategy is used which has the responsibility to realize the best performance of the DC microgrid. A simulation study is conducted in MATLAB/Simulink environment under rapid load change and different weighting factors. Through comparison with the conventional droop scheme in different scenarios, it was found that the proposed method is able to reduce the total generation cost and the total transmission power loss by around 32.52% and 19.59% under defined assumptions.

To achieve economic and environmental benefit for the stand-alone microgrid consisting of diesel generators, wind turbine generators, photovoltaic generation system and lead-acid batteries, a multi-objective stochastic optimal planning method and a stochastic chance-constrained programming model are presented. In the model, the optimal objective is to simultaneously minimise the total net present cost and carbon dioxide emission in life cycle; the type and capacity of distributed generation units have been selected as the optimal variables; the loss of capacity is adopted as probability index constraint; the coordinated operation strategies between diesel generators and battery, the multi-unit operation constraints of diesel generators and the reserve capacity have been considered in the hard-circle operation strategy. Considering the uncertainties of wind speed, clearness index and load demand, Markov process transition probability matrix is adopted to synthesise those time series data. Optimal planning for an island microgrid system has been carried out by the planning system for microgrid (PSMG), a self-developed optimal planning software based on the multi-objective stochastic optimal planning method for stand-alone microgrid system.

The efficient and economical operation of a distribution power network (DPN) has been essential in recent times, considering the energy crisis and shortage of fossil fuels. A DPN is known to be efficient and economical if power losses are minimal, the voltage drop along the lines is less, and stability is maintained during different operating conditions. However, due to the crisis for primary fuel, all DPNs including radial power distribution networks (RPDN) are operated at threshold level. This has led to higher power losses, more voltage drops, and stability issues in RDPN. Hence, to reduce the power losses and voltage deviation and improve the stability of the power system network, distributed generation (DG) units are optimally allocated into radial DPN. In this study, an optimization technique using Jellyfish search optimizer (JSO) algorithm is proposed to optimize multiple DGs into RDPN to minimize a multi-objective function corresponds to real power loss (RPL) minimization, voltage stability (VS) enhancement, and total operating cost (TOC) minimization. The performance of the proposed technique is evaluated for multiple type I and type III DGs placement on an IEEE standard 33-bus RDPN. Besides, the effectiveness of the proposed technique is investigated considering a nominal and peak power demand. The efficacy of the research outcome of the suggested JSO approach has been compared with the outcome of other optimization algorithms presented in the literature. The comparison exemplifies that JSO gives more promising outcomes than other algorithms by delivering the least real power losses and better voltage profile enhancement at minimum operating cost.

The efficient and economical operation of a distribution power network (DPN) has been essential in recent times, considering the energy crisis and shortage of fossil fuels. A DPN is known to be efficient and economical if power losses are minimal, the voltage drop along the lines is less, and stability is maintained during different operating conditions. However, due to the crisis for primary fuel, all DPNs including radial power distribution networks (RPDN) are operated at threshold level. This has led to higher power losses, more voltage drops, and stability issues in RDPN. Hence, to reduce the power losses and voltage deviation and improve the stability of the power system network, distributed generation (DG) units are optimally allocated into radial DPN. In this study, an optimization technique using Jellyfish search optimizer (JSO) algorithm is proposed to optimize multiple DGs into RDPN to minimize a multi-objective function corresponds to real power loss (RPL) minimization, voltage stability (VS) enhancement, and total operating cost (TOC) minimization. The performance of the proposed technique is evaluated for multiple type I and type III DGs placement on an IEEE standard 33-bus RDPN. Besides, the effectiveness of the proposed technique is investigated considering a nominal and peak power demand. The efficacy of the research outcome of the suggested JSO approach has been compared with the outcome of other optimization algorithms presented in the literature. The comparison exemplifies that JSO gives more promising outcomes than other algorithms by delivering the least real power losses and better voltage profile enhancement at minimum operating cost.

In this study, a probabilistic planning approach is proposed for optimally allocating different types of distributed generator (DG) (i.e. wind-based DG, solar DG and non-renewable DG) into a harmonic polluted distribution system so as to minimise the annual energy losses and reduce the harmonic distortions. The proposed planning methodology takes into consideration the intermittent nature of the renewable resources, load profile and the technical constraints of the system. The objective function is the total system annual power loss. The constraints include voltage limits at different buses (slack and load buses) of the system, feeder capacity, total harmonic distortion (THD) limits and maximum penetration limit of DG units. The optimisation process is achieved using the genetic algorithm optimisation method. This proposed approach has been applied to a typical rural distribution system with different scenarios including all possible combinations of distributed energy resources. The simulation results using Matlab programming environment show that significant reductions in the energy losses and THD are achieved for all the proposed scenarios. Also simulation results depict that the proposed method is robust and computationally efficient.

Radial distribution networks (RDNs) often experience high power losses, voltage instability, and operational inefficiencies because of their low‐voltage, high‐current characteristics, and fluctuating load behavior. This study investigates the optimization of capacitor placement and sizing using the Crow Search Algorithm (CSA) to enhance voltage stability, minimize power losses, and reduce operational costs. Power flow analysis was conducted via the Backward/Forward Sweep (BFS) method on the IEEE 33‐bus system, incorporating four load models: Constant impedance (CZ), constant current (CI), constant power (CP), and composite ZIP. The optimization objective was to minimize the total operating annual cost (TOAC), which includes active power loss costs and annual capacitor installation costs. CSA’s performance was benchmarked against invasive weed optimization (IWO), teacher learner‐based optimization (TLBO), and artificial bee colony (ABC) algorithms. Simulation results demonstrated that CSA improved the voltage stability index (VSI) from 0.61 to 0.68 (CP), 0.69 (CI), 0.65 (CZ), and 0.66 (ZIP), and reduced active power losses by 30.41%, 26.01%, 36.45%, and 33.78% for CP, CI, CZ, and ZIP loads, respectively. Corresponding cost savings achieved using CSA were 30.28% (CP), 25.85% (CI), 36.35% (CZ), and 33.67% (ZIP), confirming superior performance over the compared methods. The highest TOAC was observed for CZ loads ( $303,570.2), while the lowest was for CI loads ($ 195,386.4), reflecting the economic influence of load model variation. To ensure practical applicability and robustness, a sensitivity analysis of optimization algorithms under varying load models was performed. This included evaluating the impact of varying capacitor sizing ranges, system sizes, load compositions, and algorithm initialization conditions. Results confirmed that CSA maintained stability and effectiveness under diverse scenarios, validating its reliability and adaptability in real‐world RDN planning. Overall, the proposed CSA‐based approach provided a robust and cost‐effective solution for optimizing capacitor placement and sizing in RDNs, supporting both technical performance and economic sustainability.

Bidirectional power flow based smart grid system is implemented in the Distributed Generation (DG) sources using Renewable Energy Generators (REG) like solar, wind, etc. Moreover, the unsuitable connection of a load to a grid and DGs can reduce Power Quality (PQ) and bidirectional power flow. Consequently, the existing power generation system has a limited number of power-generation sources, which are linked as millions of end consumers as well as transmission grid. Also, power generation sources has small controller performance over power loss from producing plants to end customers. Moreover, the power-generation sources are having injection points to transfer the power. In the current and future conditions, power systems must accommodate more power from renewable energy sources, be capable of handling bidirectional power flow with distributed generation, and use automatic metering infrastructure, phasor measurement units, power quality conditioners, electric vehicle charging infrastructure, cyber security, and so on.In recent times, the renewable energy-based distribution system is a challenging task in the Battery Energy Storage System (BESS). However, in many cases, power loss, and harmonic moderation are significant issues. Hence in this research, a novel Intelligent Neural Herd Based Water Drop Optimization (INH-WDO) is proposed to control the bidirectional power flow of the converter. The simulation of these proposed methods is actualized in MATLAB platform; subsequently, the projected performance results such as power loss (0.2MV), THD (2.5%) are compared with existing control techniques for proving the significance of the developed controller design. Moreover, the system efficiency in terms of power quality is based on power loss minimization while reducing the Total Harmonic Distortion (THD).