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This paper presents the integration of shunt capacitors in the radial distribution grids (RDG) with constant and time-varying load consideration for the reduction of power losses and total annual cost, which turns to enhance the voltage profile and annual net savings. To gather the stated goals, three objective functions are formulated with system constraints. To solve this identified problem, a novel optimization technique based on the modification of particle swarm optimization is proposed. The solution methodology is divided into two phases. In phase one, potential candidate buses are nominated using the loss sensitivity factor method and in phase two the proposed technique first selects the optimal buses for the capacitor placement among the potential buses then it decides the optimal sizing of the capacitors as well. To demonstrate the performance in terms of efficiency and strength, the proposed technique is tested on IEEE 15, 33, and 69 bus system for the optimal placement and sizing of capacitors (OPSC) problem. The results are achieved in terms of annual net savings for 15 bus (47.66%case−1, 32.76%case−2, 26.46%case−3), 33 bus (33.09% case−1, 27.06%case−2, 24.15%case−3), and 69 bus (34.51% case−1, 29.43%case−2, 25.83%case−3) which are comparable to other state of the art methods, and it also indicates the success of the proposed technique.

The optimal selection of conductors (OCS) in radial distribution networks is a critical aspect of system planning, directly impacting both investment costs and energy losses. This paper proposed a mixed-integer convex (MI-Convex) optimization framework to solve the OCS problem under balanced operating conditions, integrating the costs of conductor investment and energy losses into a single convex objective. This formulation leveraged second-order conic constraints and was solved using a combination of branch-and-bound and interior-point methods. Numerical validations on standard 27-, 33-, and 85-bus test systems confirmed the effectiveness of the proposal. In the 27-bus grid, the MI-Convex approach achieved a total cost of$550,680.25, outperforming or matching the best results reported by state-of-the-art metaheuristic algorithms, including the vortex search algorithm (VSA), Newton’s metaheuristic algorithm (NMA), the generalized normal distribution optimizer (GNDO), and the tabu search algorithm (TSA). The MI-Convex method demonstrated consistent and repeatable results, in contrast to the variability observed in heuristic techniques. Further analyses considering three-period and daily load profiles led to cost reductions of up to 27.6%, and incorporating distributed renewable generation into the 85-bus system achieved a total cost of $ 705,197.06—approximately 22.97% lower than under peak-load planning. Moreover, the methodology proved computationally efficient, requiring only 1.84 s for the 27-bus and 12.27 s for the peak scenario of the 85-bus. These results demonstrate the superiority of the MI-Convex approach in achieving globally optimal, reproducible, and computationally tractable solutions for cost-effective conductor selection.

This research proposes a strategy to minimize the active power loss in the standard IEEE 85-node radial distribution power grid by optimizing the placement of wind turbines in the grid. The osprey optimization algorithm (OOA) and walrus optimization algorithm (WOA) are implemented to solve the problem. The two algorithms are validated in three study cases of placing two wind turbines (WTs) in the system for power loss reduction. Mainly, in Case 1, WTs can only produce active power, while in Case 2 and Case 3, WTs can supply both active and reactive power to the grid with different ranges of power factors. In Case 4, the best-applied methods between the two are reapplied to reach the minimum value of the total energy loss within one year. Notably, this case focuses on minimizing the total power loss for each hour in a day under load demand variations and dynamic power supply from WTs. On top of that, this case uses two different sets of actual wind power data acquired from the Global Wind Atlas for the two positions inherited from the previous case. Moreover, the utilization of wind power is also evaluated in the two scenarios: (1) wind power from WTs is fully used for all values of load demand, (2) and wind power from WTs is optimized for each load demand value. The results in the first three cases indicate that the WOA achieves better minimum, mean, and maximum power losses for the two cases than the OOA over fifty trial runs. Moreover, the WOA obtains an excellent loss reduction compared to the Base case without WTs. The loss of the base system is 224.3 kW, but that of Case 1, Case 2, and Case 3 is 115.6, 30.6 kW, and 0.097 kW. The placement of wind turbines in Case 1, Case 2, and Case 3 reached a loss reduction of 48.5%, 84.3%, and 99.96% compared to the Base case. The optimal placement of WTs in the selected distribution power grid has shown huge advantages in reducing active power loss, especially in Case 3. For the last study case, the energy loss in a year is calculated by WSO after reaching hourly power loss, the energy loss in a month, and the season. The results in this case also indicate that the optimization of wind power, as mentioned in Scenario 2, results in a better total energy loss value in a year than in Scenario 1. The total energy loss in Scenario 2 is reduced by approximately 95.98% compared to Scenario 1. So, WOA is an effective algorithm for optimizing the placement and determining the power output of wind turbines in distribution power grids to minimize the total energy loss in years.

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the topology of distribution grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.

In a developing country, electricity has become the necessity of the growth industries; thus, the distribution system power quality and reliability are crucial. With low carbon initiatives, renewable energy or distributed generation (DG) is a promising source of electricity and leads the complex distribution system. Vital rises in DGs in power grids will significantly impact the system reliability and security, especially in power losses and voltage profiles parameters. This research focuses on an optimization placement and size of DGs in distribution systems to minimize power loss and improve voltage profile using the Modified Lightning Search Algorithm (MLSA). This research has modelled the practical 69-bus radial distribution system. Then MLSA with a weight summation approach is used to identify the suitable location and size for the DGs in the design proposal stage. The optimization objectives are to reduce power losses and improve the voltage profile, especially at the connection point of DGs. Besides that, load profile, DGs constant load and the solar load in distribution system modelled using MATLAB software. The results of the simulation using MLSA indicated that the optimization allocation and sizes of solar DGs applied with current load and load changes can minimize the power losses and improve voltage profile. These results verify the proposed approach’s effectiveness and success in determining the optimal location and sizing of solar DGs to reduce power losses as well as improve voltage profiles.

Plug-in electric vehicles (PEVs) impose considerable loads to existing power grids, and consequently, they can challenge power quality and reliability of power systems if their charging is not coordinated. To use existing distribution systems for PEV charging without upgrading them, coordinated charging is inevitable. In this study, an optimal PEV dynamic charging method is proposed observing power grid thermal ratings and voltage quality. The problem is formulated as a standard non-linear programming with minimising energy losses over the charging span as objective function subject to PEVs and power system security constraints. Node voltages and power losses are explicitly and precisely formulated in the proposed method. Coordinated charging span of PEVs starts from evening, when PEV owners arrive home, to the next day morning, when the owners need their car charged and ready to use. The IEEE 31-bus distribution system in highly stressed conditions is used to evaluate the performance of the proposed method in the worst voltage status. According to obtained results, discussed in detail, voltage quality constraints are more restricting than equipment thermal rating in PEV optimal charging because of mostly radial structure of distribution systems. The results confirm the efficiency of the proposed method.

In recent years, distributed photovoltaics (DPV) has ushered in a good development situation due to the advantages of pollution-free power generation, full utilization of the ground or roof of the installation site, and balancing a large number of loads nearby. However, under the background of a large-scale DPV grid-connected to the county distribution network, an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV. Therefore, a DPV orderly grid-connected method based on photovoltaics grid-connected order degree (PGOD) is proposed. This method aims to orderly analyze the change of voltage in the distribution network when large-scale DPV will be connected. Firstly, based on the voltage magnitude sensitivity (VMS) index of the photovoltaics permitted grid-connected node and the acceptance of grid-connected node (AoGCN) index of other nodes in the network, the PGOD index is constructed to determine the photovoltaics permitted grid-connected node of the current photovoltaics grid-connected state network. Secondly, a photovoltaics orderly grid-connected model with a continuous updating state is constructed to obtain an orderly DPV grid-connected order. The simulation results illustrate that the photovoltaics grid-connected order determined by this method based on PGOD can effectively analyze the voltage impact of large-scale photovoltaics grid-connected, and explore the internal factors and characteristics of the impact.

The paper introduces a new hybrid optimization algorithm, HWCAPSO, for optimal distributed generator (DG) placement and soft-open point (SOP) size determination along with network reconfiguration. The hierarchical algorithm combining the Water Cycle Algorithm (WCA) and Particle Swarm Optimization (PSO) is introduced to solve this nonconvex problem. WCA excels in global exploration due to its water-cycle-inspired diversification, while PSO’s velocity-based update mechanism ensures rapid local convergence. Their hybrid synergy mitigates premature convergence in challenging problems. The proposed HWCAPSO algorithm uniquely integrates the global exploration capability of WCA with the local exploitation strength of PSO in a hierarchical framework, addressing the mixed-integer nonlinear programming (MINLP) challenges of simultaneous DG/SOP allocation and reconfiguration gap in existing hybrid methods. It aims to optimize total active power losses while fulfilling operational constraints such as voltage limits, thermal capacities, and radiality. The efficiency of the HWCAPSO is confirmed by exhaustive case studies from the 33-bus test system and the 69-bus test system, where its performance is compared with that of individual WCA and PSO. Findings show that HWCAPSO yields better loss reduction (up to 92.4% for the 33-bus network as and 92.7% for the 69-bus network), enhanced voltage profiles, as well as satisfactory convergence characteristics. Results are statistically validated over 30 independent runs, with 95% confidence intervals confirming robustness. The versatility of the algorithm to deal with intricate, multi-objective optimization applications make it an efficient option for real distribution network planning and operation.

Power losses and voltage profiles in electricity distribution networks are a problem, particularly in developing nations. Many techniques have reportedly been used in the previous ten years to address this issue. Among other solutions, network reconfigurations (NRs) are regarded as one of the most practical. It is important to conduct a periodic update survey on this subject because the electricity radial distribution networks (RDNs) are continually evolving. Therefore, a thorough evaluation of the various techniques to address the issues with NRs along distribution networks is provided in this manuscript. There is discussion of several mathematical, traditional, heuristic-based, and machine-learning strategies. It is important to understand how the radiality is achieved as well as methods for resolving distribution load flow, particularly with greater R/X ratios. The most typical test cases used in the literature are listed. In order to enrich this review and make it useful to others, more than 200 articles (the majority of which were published in the last five years) are referenced inside the body of this text. The final conclusions and related future insights are presented. At last, this work is an invaluable resource for anyone involved in this field of study because it offers a comprehensive literary framework that can serve as the foundation for any future research on NRs and its prospective difficulties. Therefore, academics can use this framework to enhance previous formulations and approaches as well as suggest more effective models.

The increasing share of distributed generation aggravates voltage limit compliance at customers’ delivery points. Currently, grid operators validate compliance with the voltage limits specified in Grid Codes by conducting load flow simulations at the medium voltage level, considering the connected low voltage grids as ‘loads’ to reduce the modeling effort. This approach does not support the accurate validation of limit compliance, as the voltage drops at the low voltage level are unknown. Nevertheless, to guarantee acceptable voltages even under worst-case conditions, safety margins are involved that impair the utilization of the electricity infrastructure. This study conducts load flows simulations in a test distribution grid, revealing the variable character of the voltage limits at different system boundaries. The conventional load model is extended by new parameters—the boundary voltage limits—to enable the consideration of variable voltage limits in load flow analysis of LINK-based smart grids. The standardized structure of the LINK-architecture allows for the systematic and accurate validation of voltage limit compliance by reducing the required modeling data to the technically necessary minimum. Use cases are specified that allows smart grids to increase the utilization of the electricity infrastructure by day-ahead scheduling and short-term adaptation of boundary voltage limits.