Strategy for EV Charging Station Placement Using a Bootstrapping-Based Probabilistic Power Flow

The implementation of multiple distributed energy resources (DER) into the radial distribution networks which includes Photovoltaic (PV) systems, Electric Vehicle Charging Stations (EVCSs), and Battery Energy Storage Systems (BESS) presents a number of challenges that include voltage regulation and power loss as well as the challenges in managing load demand. In this paper, the author presents a superior optimization model that employs a new Hybrid Sine Cosine Gorilla Search Algorithm (HSCGSA) to effectively coordinate the addition of PV units, EVCS charging loads and BESS in the radial distribution systems. The offered approach will seek to maximize the position and size of DERS to reduce active and reactive power losses and ensure the stability of the voltage throughout the network. The HSCGSA is a hybrid approach to enhancement of the exploratory ability of the Sine Cosine Algorithm (SCA) and the exploitation power of the Gorilla Troops Optimizer (GTO), which provides a balanced solution to the intricate and complex optimization problems involving multiple planning criteria. The performance of the proposed algorithm is validated through simulations on IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The results demonstrate that the proposed HSCGSA significantly improves system performance compared with the conventional Genetic Algorithm (GA). For the IEEE 33-bus system, the active power loss is reduced from 3477.166kW to 1577.06kW, representing a reduction of approximately 54.6%, while the minimum bus voltage improves from 0.9224 pu to 0.9706 pu. In the IEEE 69-bus system, the active power loss decreases from 3859.99kW to 1381.28kW, corresponding to a reduction of about 64.2%, along with a substantial enhancement in the voltage stability index. Similarly, in the IEEE 118-bus system, the active power loss is reduced from 2279.42kW to 608.55kW, achieving nearly 73% reduction when multiple DER units are optimally allocated. These results confirm that the proposed HSCGSA provides superior capability for optimal placement and sizing of PV units, EV charging stations, and battery energy storage systems in radial distribution networks.

this paper presents Biogeography-based optimization (BBO) techniques for the excellent location to achieve minimum transmission active power loss, optimal cost, and optimal bus voltage. The proposed methods are authentic in the IEEE-30 bus system with power flow equation using a Newton-Raphson algorithm. The execution of IEEE-30 bus system is computed on different reactive loading condition. Here we are present the BBO and PSO’s comparative result using distinct optimization techniques. In the end, BBO is best for the transmission of active power loss minimization and total cost of the system at the various loading for the IEEE-30 bus system as compared to PSO (Particle Swarm optimization). Biogeography-base optimization is the best solution, is to reduce active power loss, system operating cost including the cost of FACTS devices and congestion in the transmission network.

this paper presents Biogeography-based optimization (BBO) techniques for the excellent location to achieve minimum transmission active power loss, optimal cost, and optimal bus voltage. The proposed methods are authentic in the IEEE-30 bus system with power flow equation using a Newton-Raphson algorithm. The execution of IEEE-30 bus system is computed on different reactive loading condition. Here we are present the BBO and PSO’s comparative result using distinct optimization techniques. In the end, BBO is best for the transmission of active power loss minimization and total cost of the system at the various loading for the IEEE-30 bus system as compared to PSO (Particle Swarm optimization). Biogeography-base optimization is the best solution, is to reduce active power loss, system operating cost including the cost of FACTS devices and congestion in the transmission network.

Purpose – The purpose of this paper is to present an improved approach to reactive power planning in electric power systems (EPS). It is based on minimization of a transmission network's active power losses. Several operating conditions have to be fulfilled to ensure stable operation of an EPS with minimal power losses. Some new limitations such as voltage instability detection and generator capability curve limit have been added to the existing method in order to improve the reliability of reactive power planning. The proposed method was tested on a model of the Slovenian power system. The results show the achievement of significant reduction in active power losses, while maintaining adequate EPS security.Design/methodology/approach – Optimal voltage profile has to be found in order to determine minimal possible active power losses of EPS. The objective function, used to find the optimal voltage profile, has integer and floating point variables and is non‐differentiable with several local minima. Additio...

Networks of 0.4 kV are characterized by a large unbalance of loads in phases. Current unbalance leads to voltage unbalance and additional losses of electrical energy. As a result, the voltage at the consumer may not meet the quality standards of electrical energy. In addition, due to unbalance, the service life of electrical equipment is reduced. Since the effect of stress balancing significantly depends on the place of balancing loads on the line, the paper proposes to determine the places of balancing loads by solving a multicriteria optimization problem. The paper proposes an objective function that minimizes active power losses and contains the total index of active power losses and indices of voltage unbalance coefficients in the negative and zero sequence.
 
 The purpose of the study is to obtain an effective objective function for determining the places of balancing loads and voltages in the network, which ensures a minimum of active power losses and the values of the voltage unbalance factors within the required limits; conduct a study of balancing loads and voltages depending on the places of balancing.
 
 Materials and methods. In the work, methods for calculating electrical networks were used, taking into account voltage losses and active power. To study the places of balancing loads and voltages, the method of multicriteria optimization with restrictions was used. The study of the objective function was carried out on a mathematical model of a low voltage overhead line. All calculations were carried out in MATLAB.
 
 Research results. A review and analysis of modern tools and methods for balancing loads and voltages in low voltage networks has been carried out. As a result of the analysis, it was concluded that there is no algorithm for determining the places of load balancing in low-voltage networks that provide minimal active power losses and the values of the voltage unbalance factors within the required limits. The task of finding places for balancing loads and voltages is a multiobjective optimization problem with constraints. Therefore, an objective function was proposed that minimizes active power losses in the network and contains the total index of active power losses and indices of voltage unbalance coefficients for the reverse and zero sequence. To study the proposed objective function, a model overhead line of a 0.4 kV network with specified phase loads and voltages was used. For the model line, the calculation of active power losses and the values of the voltage unbalance coefficients in the initial mode before balancing was carried out. All calculations were carried out for each phase separately. At the first stage, the calculation of the sensitivity coefficients of active power losses and the sensitivity coefficients for the voltage unbalance coefficients was carried out. To study the balancing of loads, nodes were selected that have the maximum values of the sensitivity coefficients. It follows from the calculation results that the best effect from balancing is observed when balancing loads simultaneously in two nodes: in the node with the highest value of the total sensitivity factor of active power losses, and in the node with the maximum value of the phase sensitivity factor of active power losses. When balancing loads in only one of the nodes, the most optimal of the selected ones will be the node most remote from the TS. We also obtained weight coefficients that provide a minimum of the objective function.
 
 Conclusions. The proposed objective function is effective for determining the places of load and voltage balancing in low voltage networks. In this case, the best effect is observed when balancing loads in nodes that have the highest values of the sensitivity coefficients of total active power losses and by phases. The node most remote from the TP will be more optimal. When balancing loads in places determined using the proposed objective function, it is possible to reduce power losses and ensure the values of the unbalance coefficients in the nodes on the line less than the maximum allowable value.

A dominant statistical method, in which the best combination of factors’ levels are predicted by analyzing a few representative combinations of factors’ levels named as orthogonal experimental design (OED). The OED is an effective approach for analyzing the effect of multi-levels factors simultaneously and it works on orthogonal learning (OL) strategy. An evolutionary programming based heuristic method has two contradictory features such as exploration and exploitation, balancing in these features have significant impact on its optimization performance. We have applied an OED based auxiliary search strategy for enhancing performance of the bird swarm algorithm (BSA) by improving its exploitation search ability. It is a challenging task to keep balance among two contradictory features – exploration and exploitation of a heuristic approach, while addressing optimal power flow (OPF) problems in power systems. In this research study, we have proposed improved BSA (IBSA) for solving the OPF problems in thermal power systems. We have conducted a study of the OPF problems with objective functions - reducing electricity generation cost, emission pollution, and active power loss to measure the efficiency of proposed IBSA. In this work, we have utilized five benchmark functions and solved OPF problems using three IEEE test systems including IEEE-30 bus system, IEEE-57 bus system, and IEEE-118 bus system to verify stability, effectiveness, and performance of proposed IBSA. The statistical and simulation results have indicated that the proposed IBSA has better convergence, efficiency, and robustness features than the original BSA as well as other heuristic approaches. It is observed that lowest electricity generation cost 800.3975$/h on IEEE-30 bus system, 41663.5500$/h on IEEE-57 bus system, and 134941.0367$/h on IEEE-118 bus system have been achieved using proposed IBSA to address the OPF problems. Furthermore, in transmission lines of the power system network minimum active power loss 16.2869MW has been observed by conducting a case study on the IEEE 118-bus system based on the proposed IBSA approach.

In this paper, a novel optimal reactive power flow solution approach in multi-terminal HVDC (High Voltage Direct Current) systems is studied. ULTCs’ (under load tap changer transformers) full equivalent model for the DC converters’ are taken into account in the proposed AC-DC power unlike the similar studies in the literature. Thus, the proposed study provides real accurate results for practical AC-DC applications. Optimal reactive power flow for minimum active power loss is provided by Genetic Algorithm (GA). For the test of the proposed study, the IEEE 14-bus test system modified to AC-DC system is used in the study. The obtained test results prove that the proposed GA based optimization method is effective to reach the global optimum point of minimum active power loss without dropping to local minimum point through satisfying system constraints.

Distributed Generation (DG) is commonly used to reduce active power losses on a distribution network. The optimal location and size of DG will result in minimum active power losses and voltage profile improvement. This research proposes Novel Voltage Sensitivity Index (NVSI) and Stability Index (SI) methods to determine the optimal location and analytical expression to find optimal size and location of DG in Makassar distribution system, Feeder Kima, 76 buses to minimize active power losses and to improve voltage profile. Therefore, DG interconnection has a significant effect on improving the quality of the distribution network. The results show that the sensitivity method does not lead to the best placement DG in reducing active power losses. However, it is an analytical expression, which is very effective in determining optimal location and size of DG to reduce active power losses and to improve voltage profile in Makassar distribution system, Feeder Kima, 76 buses. The most optimal location for DG placement is on Bus 73 (Mega Sakti Pyramid), with a DG size of 0.8515MW. These combinations reduce 45.77% active power losses and increase 1.7336% voltage profile.

Introduction. Optimal planning of distributed generation (DG) units is a critical research topic due to the growing integration of renewable energy and the need to enhance distribution network performance. Classical optimization methods often struggle with the nonlinear, nonconvex, and highly coupled nature of DG allocation problems. Problem. The IEEE 33-bus distribution network experiences significant voltage drops and high active and reactive power losses under normal operating conditions. Determining the optimal placement and sizing of DG units is a complex problem involving multiple interacting variables and operational constraints. Goal. This study aims to improve technical performance by minimizing total active power losses and voltage deviation while ensuring voltage stability and network reliability. Methodology. The particle swarm optimization (PSO) algorithm is enhanced using the Dehghani method (DM) – a population-based modification framework allowing all individuals, including the worst member, to contribute in improving the best solution. The improved PSO-DM algorithm is applied to the IEEE 33 bus system under four cases: the base case without DG and scenarios with 2, 3 and 4 DG units. The objective function includes active power loss minimization and total voltage deviation. Results. The 4-DG configuration significantly improves system performance: active power losses decrease from 210.67 kW to 53.9 kW (74.4 % reduction), reactive losses drop from 142.84 kVAr to 38.42 kVAr (73.1 % reduction), the minimum bus voltage rises from 0.9037 to 0.9741 p.u. and total voltage deviation decreases from 1.8037 p.u. to 0.5129 p.u. (71.6 % improvement). These results demonstrate that PSO-DM effectively balances exploration and exploitation, yielding superior DG allocation solutions. Scientific novelty. Integrating DM into PSO introduces a cooperative solution-refinement mechanism that enhances convergence speed and search accuracy. Practical value. The PSO-DM framework provides a reliable and computationally efficient tool for DG planning in modern smart distribution networks. References 22, tables 1, figures 3.

Electric vehicles with unique characteristics like lower noise, energy saving and pollution free due to reduction of carbon dioxide are consider to be the best choice for future automobile industry. The battery of electric vehicle (EV) must get recharged at charging stations when they are used for driving in urban area. Since electric charging stations will be used simultaneously by many EV. The existing distribution system might not be highly affected by the installation of charging stations if the numbers of electric vehicles are small. However, with the increase in the number of electric vehicles the characteristics of the electric vehicle charging patterns may have considerable impact on distribution systems which depends particularly on the electric vehicle charging location. There may be significant impacts like overloading and power losses in the system. These impacts could be mitigated by proper system planning and through strategic placement of Electric Vehicle Charging Station (EVCS) in the existing radial distribution network. In this research the optimal location EVCS are identified in radial distribution network and also the active power losses and system voltage profile are examined. This research proposes a heuristic algorithm called Particle Swarm Optimization (PSO) to optimize the IEEE 33 bus radial distribution system with electric vehicle charging stations. The prime objective is to place the EVCS at optimal location in existing radial distribution network by considering the real (active) power losses and also the voltage at the buses of the system.

<span lang="EN-US">Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High </span><em><span lang="EN-US">R</span></em><span lang="EN-US">/</span><em><span lang="EN-US">X</span></em><span lang="EN-US"> ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.</span>

Multi-Objective Optimization for Distributed Generator and Shunt Capacitor Placement Considering Voltage-Dependent Nonlinear Load Models

The photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) based hybrid system design and optimal placement using chaotic quasi‐oppositional crayfish optimization algorithm (CQOCOA) in a radial distribution network (RDN) under load uncertainty is the main objective of this study. Here, crayfish optimization algorithm (COA) is modified and improved by adding quasi‐oppositional behavior to it. Then chaos theory is added to speed up the convergence pace and avoid the local optimality. For optimal placement of hybrid PV/WT/BES system, simultaneous active power loss and annual operation costs minimization is taken as the objective to enhance the efficacy of the RDN. The uncertainty modeling of PV and WT distributed generation (DG) is considered for power generation as solar irradiance and wind speed can change. This algorithm is validated on 69‐bus and 94‐bus to establish the potency of the suggested CQOCOA algorithm. The active power loss cost is also evaluated after the installation of hybrid PV/WT/BES system. Adjusting the growing load demand, 25% increased load and 10% decreased load is considered for load uncertainty modeling. In both (69‐bus and 94‐bus) systems, the placement of hybrid PV/WT/BES system using the CQOCOA method reduces the active power loss by 58.93%, 60.53%, 53.53%, and 62.19%, 65%, 62.99% for normal, 25% increased, and 10% decreased loading conditions, respectively. In yearly running cost of hybrid system design by CQOCOA method for 69‐bus at normal and 10% decreased load gives yearly savings of 25 364$, 31 951$, 88 951$ and 16 511$, 1527$, 25 608$ than COA, DAOA, and AOA methods. The comparative study of results revealed that the CQOCOA algorithm is better than several optimization algorithms.

The reactive power provided by the wind turbine (WT) has the function to adjust the node voltage. However, if the reactive power output by WTs is not reasonably allocated, it is easy to cause problems such as the node voltage exceeding the limit and excessive active power loss. Therefore, a hierarchical multi-objective optimization strategy is proposed, which divides the wind farm cluster into three parts: grid layer, inter-field layer, and inner-field layer. According to the dispatching instructions of the grid layer, the inter-field layer uses the minimum voltage difference of the point of common coupling (PCC) and the minimum active power loss as the optimization goal. Then, the reactive power output by the wind farm and SVG is coordinated. The inner-field layer receives the instructions issued by the inter-field layer and coordinates WTs for reactive power compensation with the goal of reducing voltage difference and active power loss. The calculation examples show that this strategy can efficaciously ameliorate the situation of excessive voltage at key nodes and exorbitant active power loss.

sonmez, yusuf/0000-0002-9775-9835; GUVENC, Ugur/0000-0002-5193-7990; Duman, Serhat/0000-0002-1091-125X