Voltage Deviation Index Research Articles

Integration of Photovoltaic and DSTATCOM In the Distribution Network Using Rat Swarm Optimization Technique

Power loss minimization and improved voltage profile have been major challenges faced by the electrical distribution network (DN) mainly because of the long length of the feeders and the high resistance to reactance (R/X) ratio of the DN. A lot of techniques have been investigated to solve these problems. One of the most prominent is the optimal integration of distributed generation (DG) such as photovoltaic (PV) as well as the integration of Distribution Static Synchronous Compensator (DSTATCOM) into the network. The main challenge with this solution has been the determination of the optimal sizes and sites of the DG and/or DSTATCOM. This paper seeks to optimize the simultaneous allocation of multiple DSTATCOMs and PVs in the DN for power loss reduction and voltage profile improvement using the rat swarm optimization (RSO) technique, which is a simple, yet robust optimization technique. The optimization problem is formulated to minimize power loss, voltage deviation index, and maximize the voltage stability index. The IEEE 33 node DN is used as a test network and the simulation results show the effectiveness of the RSO technique in finding the best sizes and locations of the PVs and the DSTATCOMs. The power losses of the network are reduced from 210.996 kW, and 143.129 kVAr when there is no DSTATCOM nor PVs in the network to 26.155 kW, and 19.128 kVAr when DSTATCOM and PVs are simultaneously allocated into the network. A remarkable improvement in the voltage profile of the network is also observed with the minimum node voltage being 0.98 p.u. compared to 0.9038 p.u. when there are no DSTATCOMs or PVs. The RSO results were compared with other techniques from the literature, and it proved its superiority.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Integration of Distributed Generations and electric vehicles in the distribution system

Power quality challenges are a major problem these days because of the significant increase in load demand and the existence of various reasons for disruptions in the distribution system. A voltage sag or a decrease in the magnitude of the line voltage can be brought on by rapid fluctuations in load or distribution system issues. Enhancing the grid current profile, reducing grid voltage sag, and improving other performances can all be achieved through the combination of electric vehicles with distribution generation systems. In this paper, the integration of Distributed Generation with Electric Vehicles in the distribution system has been done to enhance system performances such as true power loss index, imaginary power loss index, voltage deviation index, short circuit current index, cost function, and environmental impact reduction index for 16 bus distribution system in constant impedance, constant current, and constant power load models, or ZIP load models through proper location, sizing, coordinated controlling, and types of Distributed Generation and Electric Vehicle pairs for both charging and discharging modes by adopting Hybrid Genetic Algorithm - Monte Carlo Simulation optimization methodologies. This study's primary goal is to determine the optimal distribution generation and electric vehicle pairing to improve the aforementioned parameters, which will assist researchers in their future planning. Researchers, industry professionals, scientists, and individuals working with Distributed Generation and Electric vehicles in the smart grid will find this statement to be of great use.

An improved particle swarm optimisation algorithm for optimum placement and sizing of biogas-fuelled distributed generators for seasonal loads in a radial distribution network

Optimal allocation is critical for effective planning and operation of grid connected distributed generator(s) (DGs) subject to efficient optimisation approach(es). In this paper, an improved particle swarm optimisation (IPSO) algorithm based on weighted randomised acceleration coefficients and adaptive inertia weight for dynamic movement of the particles towards an optimal solution is proposed. The technique is expected to determine optimal placement, sizing and power factor of biogas-fuelled distributed generators (B-DGs such as internal combustion engine (ICE) and fuel cells (FC)) in a radial distribution network. The proposed IPSO is applied to simultaneously optimise three technical objectives namely total active power loss (TAPL), voltage deviation (VD) and voltage stability index (VSI) considering load growth and seasonal voltage dependence. The proposed IPSO is validated using an IEEE 33 bus radial distribution network to evaluate its effectiveness through various combinations of B-DGs for different scenarios and cases with a maximum of 3 B-DGs. Some of the key findings indicate that operating 3 ICEs at optimal power factor reduces TAPL by 93.52 %, reduces VD by 99.62 % and improves VSI by 23.32 % compared to 61.80 % TAPL reduction, 94.77 % VD reduction and 17.89 % VSI improvement for 3 FCs operating at unity power factor. The proposed IPSO gives better results than the standard PSO in terms of solution quality, convergence speed and statistical results. It is also perceived to be better compared to most of the recent state-of-the-art optimization algorithms found in literature.

Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

AbstractThis paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

Optimal allocation of solar photovoltaic distributed generation for performance enhancement of electrical distribution networks considering optimal volt‐var regulation under uncertainty and high load variation

AbstractThis article proposes an optimal placement and sizing of photovoltaic (PV) power systems based distributed generation (DG) in radial electrical distribution networks considering the capability of PV inverters to regulate the voltage by optimal injecting and absorbing reactive power at the point of common coupling (PCC) using honey badger algorithm (HBA), as a recent and efficient optimization algorithm to solve the complicated optimal allocation. Several objective functions are achieved for distribution system performance enhancement: minimizing the power loss and the voltage deviation index (VDI) and maximizing the voltage stability index (VSI). Based on historical data and probabilistic models, seasonal hourly solar irradiance, ambient temperature, and load variation curves have been modeled, which simultaneously consider the light, normal, and heavy load demand. The essential components of distribution power systems have been characterized. To investigate the validity of the proposed approach, IEEE 33 and IEEE 69 BUS radial distribution test systems have been considered for power flow (PF) analyses, where Newton's Raphson method has been applied to solve the PF issue. The simulation results of different numerical scenarios have shown the effectiveness and validity of the newly proposed method to solve the optimal allocation problem considering optimal volt‐var regulation control of PV inverters compared to several valid and robust optimization algorithms.

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.

Modified jaya optimization and TOPSIS for determining the optimal frequency in LF-HVac

This study presents a novel approach of hybrid modified Jaya and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) optimization (MJTO) for efficiently determining the optimal frequency of low-frequency HVac lines. The proposed technique takes frequency constraints and static synchronous compensator (STATCOM) equations into the account. The objective functions in this study are the minimization of power losses, voltage deviation, and voltage stability index of transmission system. This study examines two types of objective functions: the traditional quadratic function and the enhanced quadratic function. These functions are used to incorporate more accurate modeling that considers low frequency. The MJTO is employed for global searching and gradient-free optimization to determine optimal control settings. The Modified Newton-Raphson method is utilized to minimize the mismatch power between power flow (PF) and STATCOM equations. The proposed method utilizes a hybrid technique to handle inequality constraints by preserving feasible solutions while keeping the original objective function intact. The suggested methodology is successfully implemented in extensive benchmark testing of IEEE systems on the 39 Bus and 14 Bus. The optimal solutions obtained are validated by comparing them to recent algorithms as mentioned in literature. The analytical and Statistical results indicates that the proposed optimization strategy is more optimistic for the LF-HVac system.

Optimal Placement of Superconducting Magnetic Energy Storages in a Distribution Network with Embedded Wind Power Generation

The prevalence of distributed generation in most power grids can negatively affect their performance in terms of power loss, voltage deviation, and voltage stability. Superconducting Magnetic Energy Storages (SMESs) can help in addressing this problem as long as they are optimally placed in the distribution network. This paper presents a hybrid Grasshopper Optimization Algorithm and a Simulated Annealing (GOA-SA) method to determine the optimal placement of SMESs in a distribution network with an embedded wind power generation system. The optimization was formulated as a multi-objective problem to minimize active power losses, reactive power losses, and voltage deviation and maximize the voltage stability index. An IEEE 57-node distribution network was employed and simulations were performed using MATLAB R2020b. Based on simulations using 200 kW SMESs in discharge mode, the active power loss decreased by 82.57%, the reactive power loss decreased by 80.71%, the average voltage deviation index decreased by 66.91%, and the voltage stability index improved by 34.97%. In the charging operation mode, the active power loss increased by 24.86%, the reactive power loss increased by 8.21%, the average voltage deviation increased by 12.86%, and the voltage stability index increased by 12.79%. These results show that SMESs can improve the technical performance of a distribution network.

Optimal allocation of controllable power factor distributed generation with network reconfiguration on electric vehicle charging station loaded distribution network

This paper presents a hybrid optimisation technique to solve the problem of optimal allocation of controllable power factor Distributed Generators (DGs) together with network reconfiguration on Electric Vehicle Charging Stations (EVCSs)-loaded distribution networks. The suggested hybrid optimisation strategy combines the Artificial Gorilla Troops Optimizer (GTO) algorithm with genetic operators to improve convergence characteristics and prevent premature convergence to local optima. The objective is to minimise power loss, bus voltage deviation and voltage stability index reduction, which are formulated as a multi-objective function. The effectiveness of the proposed approach is evaluated on 69 and 118-bus distribution networks. Additionally, a comparative analysis is performed to evaluate the efficacy of the suggested approach in comparison with standard GTO and other well-known optimisation algorithms. The results demonstrate that simultaneous optimal placement of controllable power factor DGs with network reconfiguration demonstrates superior effectiveness in mitigating the adverse impact of EVCSs on distribution networks compared to optimisation of fixed-power factor DG placement with network reconfiguration. Furthermore, the hybrid optimisation approach outperforms other applied algorithms in terms of solution quality.

Integrating Firefly and Crow Algorithms for the Resilient Sizing and Siting of Renewable Distributed Generation Systems under Faulty Scenarios

This study aimed to optimize the sizing and allocation of renewable distributed generation (RDG) systems, with a focus on renewable sources, under N-1 faulty line conditions. The IEEE 30-bus power system benchmark served as a case study for us to analyze and enhance the reliability and quality of the power system in the presence of faults. The firefly algorithm (FFA) combined with the crow search (CS) optimizer was used to achieve optimal RDG sizing and allocation through solving the optimal power flow (OPF) under the most severe N-1 faulty line. The reason for hybridization lies in leveraging the global search capabilities of the CS optimizer for the sizing and allocation of RDGs and the local search proficiency of the FFA for OPF. Two severe N-1 faulty conditions—F27-29 and F27-30—were separately applied to the IEEE 30-bus distribution system. The most severe N-1 faulty line of these two faulty lines was F27-30, based on a severity ranking index including both the voltage deviation index and the overloading index. Three candidate buses, namely 27, 29, and 30, were considered in the optimization process. Our methodology incorporated techno-economic multi-objectives, encompassing overall costs, power losses, and voltage deviation. The optimizer can eliminate the impractical buses/solutions automatically while remaining the practical one. The results revealed that optimal RDG allocation at bus 30 effectively alleviated line overloading, ensuring compliance with the line flow limit, reducing costs, and enhancing voltage profiles, thereby improving system performance under N-1 faulty conditions compared to the equivalent case without RDGs.

Dynamic fuzzy learning based hybrid GWO-CSA for optimal planning of PV, BESS and DSTATCOM with network reconfiguration

The proposed approach is on the optimal planning issue of photovoltaic distributed generation (PV-DG) and DSTATCOM device with integrated battery energy storage systems (BESS) considering network reconfiguration by applying hybrid Grey-Wolf crow-search algorithm (GWO-CSA) encompassing dynamic fuzzy learning (DFL) optimization technique. Considering the stochastic nature of solar irradiance and variations, when solar energy is inadequate, BESS acts as backup energy storage device to meet essential load requirements. Network reconfiguration is to reduce power loss by changing the network tie switches using optimization algorithm. The principal objective of the proposed research work is to reduce the total power loss, enhance the voltage profile, improve the voltage stability index, and perform network reliability analysis. The beneficial effect of the proposed method is validated on Standard IEEE 69 and Standard 118 bus systems. It found that for the IEEE 69 bus system, the overall Real power loss values decreased to 12.82% and overall Reactive power loss increased to 78.18%. The total voltage deviation index (TTVDI), and total voltage stability index (TTVSI) values decreased to 17.94% and − 0.75% respectively. The reliability indices like SAIDI, SAIFI, CAIDI, CAIFI, and AENS values decreased by 56%, 3.54%, − 3.09%, 0.55% and 30% respectively. Similarly, for IEEE 118 bus system overall Real power loss, overall Reactive power loss, TTVDI, and TTVSI decreased to 9.53%, − 0.11%, 24.64%, and 3.87%. The reliability indices like SAIDI, SAIFI, CAIDI, CAIFI, and AENS values decreased by 2.4%, 1.0%, 1.54%, 0.94%, and 10.1% respectively. Further, the proposed approach of DFL-based hybrid GWO-CSA considers the reconfiguration with PV-DG, DSTATCOM and BESS enhances the overall performance compared to other scenarios.

Hybrid Siting and Sizing of Distributed Generators and Shunt Capacitors with System Reconfiguration using Wild Horse Optimizer

Injection of Distributed generators (DGS) and Shunt capacitors (SCS) simultaneously with system reconfiguration significantly promotes smart grid performance. In addition, system reconfiguration increases the injected distributed generation capacity in the system. This work proposes a wild horse optimizer (WHO) for the optimal siting and sizing of DGS and SCS in parallel with network reconfiguration. The proposed method aims to attain single and multi-objectives: minimizing active power loss, maximizing voltage stability index (VSI), and minimizing voltage deviation index (VDI). Five operational cases are introduced to elucidate the superior performance of the proposed method. The five cases are executed on IEEE 33-bus standard radial distribution test system. The single-objective function results are compared with other optimization algorithms. The simulation results belay that the proposed WHO optimizer has the best results for unfixed DGS and SCS locations.

Machine learning-based optimal distributed generation and electric vehicle load management

The load profile of radial distribution networks (RDNs) is significantly impacted when plug-in electric vehicles (PEVs) are connected to them in large numbers. The disturbances in the load profile may lead to increased power losses in distribution lines and deterioration of the network voltage profile. The provision of distributed generation at strategic locations in the distribution network can help to compensate for the impact on the electrical network due to PEV loads. This paper proposes the use of machine learning (ML)-based models for determining the optimal location of distributed generators (DGs) in an RDN. The proposed method considered time-varying load in addition to PEV load. The suggested method determines optimal placement of DGs based on the power loss reduction index and voltage deviation index reduction index. Four distinct types of ML models were used in the proposed approach using synthesised data on the Institute of Electrical and Electronics Engineers' 33-bus RDN. The performance of the ML models was evaluated with respect to mean squared error and mean absolute percentage error and, for the ML models considered, the random forest ML model gave the best performance.

A novel optimal allocation of STATCOM to enhance voltage stability in power networks

Utilizing a static synchronous compensator (STATCOM) in the electrical power grid greatly improves the grid's voltage profile by enhancing voltage stability. This article proposes a novel approach based on Mixed Integer Distributed Ant Colony Optimization (MIDACO) to determine the optimal STATCOM installation in the electrical power grid. This approach has two control variables to optimize: the STATCOM size and location. This optimization aims to enhance voltage stability with minimum cost by minimizing two objectives: the voltage deviation index and the STATCOM cost. Also, this article presents a sensitivity analysis to show the stochastic nature of MIDACO and to explain the effect of MIDACO parameters on the optimization approach and the process of reaching the optimal solution. The proposed method has been evaluated on three standard test systems: IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus. In addition, the MIDACO results are compared to those of the artificial bee colony algorithm, the genetic algorithm, and particle swarm optimization.

Impact of DSTATCOM penetration level on technical benefits in radial distribution network

This paper investigates the impact of DSTATCOM on the losses, voltage stability, and voltage deviation of the radial distribution networks (RDNs) at different DSTATCOM penetration levels (DPLs). A novel voltage stability improvement index (VSII) is modeled to assign an index value to represent the proximity of the voltage stability index (VSI) of all the network buses to the ideal value. The VSII allows a fair comparison of the VSI profile obtained for different events of DSTATCOM penetration levels. Due to the numerous buses of the RDN, the new voltage stability index (NVSI) selects the most sensitive buses to reactive compensation for DSTATCOM integration after performing the load flow for the base case before integration of DSTATCOM. The DSTATCOM is placed on the selected bus, turn by turn, and DPL increases from zero to the maximum reactive load of the RDN in the step of 1 %. This methodology was implemented on the standard 33–bus and Nigerian Imalefalafia 32–bus RDNs. The outcome establishes that a DPL of 30–55 % and 70–100 % were attained for standard 33–bus and Imalefalafia 32–bus RDNs respectively without increasing the power loss. The VSII was continually improved, and average voltage deviation index (AVDI) was reduced with increasing DPL.

Optimized planning framework of solar photovoltaic based generation with EV charging station in a rural distribution network considering uncertainties

To address the adverse impacts due to rapid growth of electric vehicles (EVs), a robust planning framework is developed in this paper for optimal deployment of EV charging stations and solar energy resources in the distribution network. Uncertainty modeling of EV is done using probability density function considering stochastic parameters extracted from real National Household Travel Survey (NHTS)datasheet. Considering solar irradiance as the uncertainty parameter, a practical Photovoltaic (PV) model is developed using beta probability function. To solve the problem of optimal allocation of EV charging stations and PV in the distribution network, proposed Teaching Learning Based Optimization algorithm is used. The problem is formulated to minimize the power loss reduction index and the voltage deviation index while considering system constraints. Here this proposed approach is tested to Indian 28 bus rural distribution network and standard IEEE 69 bus system in MATLAB. Also to assess the efficiency of the proposed technique, it is compared with three different algorithms, i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) in terms of convergence characteristics and computational time. The system indices, i.e., voltage profile, line loss, voltage stability and the penetration level of EV charging station are improved after simultaneously optimally deploying EV charging station and PV units both in rural and standard 69 bus distribution networks. Different case studies were conducted and it was observed that deployment of EV charging station in the network leads to deterioration of voltage profile, voltage stability and line loss. The simulation outcome further reveals that the addition of PV panels concurrently with EV charging stations enhances the system performances and the penetration level of EV charging station in the network.

A novel modified Archimedes optimization algorithm for optimal placement of electric vehicle charging stations in distribution networks

The number of electric vehicle charging stations (EVCSs) is increasing alongside electric vehicles (EVs). The unplanned rollout of EVCSs can lead to problems such as deterioration of the voltage profile, increased active power losses, and maximum load demand in distribution networks (DNs). Therefore, EVCSs should be placed in optimum locations on the DNs. The complexity of the problem requires efficiency and performance. In this paper, a novel modified Archimedes optimization algorithm (MAOA) is proposed for optimal placement of EVCSs in DNs. Five parameters of the AOA were modified using the Honey Badger Algorithm (HBA). This placement is based on a combination of indexes: power loss, voltage deviation, and voltage stability index (VSI). Next, Newton-Raphson based load flow analysis is used to compute the minimum cost. MAOA, AOA and eight other optimization algorithms are compared. The results provide the most appropriate buses in the DN. The efficiency of the proposed algorithm was tested on IEEE 33, 69, and 10-bus Hatay power network systems. In all test systems, the proposed MAOA provided the best results with consistently low standard deviation values. The results suggest that the proposed method is more effective in the integration of EVCSs into the DN. © 2017 Elsevier Inc. All rights reserved.

Distributed generator and capacitor‐embedded reconfiguration in three‐phase unbalanced distribution systems using teaching learning‐based optimization

AbstractIn the transmission and distribution networks, both active and reactive power play significant roles. While active power does the beneficial work, reactive power maintains the voltage that necessitates management from a system reliability perspective. The voltage variation from the nominal range may result in unintentional operation and early component failure. To maximize the amount of real power that can be transported over the power‐transmitting media, the system must also control reactive power flow. In a three‐phase unbalanced distribution system reinforced with distributed generator units (DGUs) and shunt capacitor units (SCUs), this paper suggests a teaching learning‐based optimization (TLBO) approach to be applied to combinatorial problems for simultaneous reduction of real power loss and net reactive power flow, enhance voltage stability index (VSI) and minimize aggregated voltage deviation index. A multiobjective formulation based on TLBO has been implemented to choose the ideal sizes and placements of DGUs and SCUs in large distribution networks. The case studies have been carried out on an IEEE 33‐bus standard system and a large 123‐bus unbalanced system. The analysis of the IEEE 33‐ and 123‐bus test systems reveals the economic efficiency of the suggested TLBO algorithm. Notably, by applying DG and capacitor simultaneously in the 33‐bus system, a significant reduction of 59.35%, 55.05%, and 50.76% (phases a, b, and c), and for the 123‐bus system, a significant reduction of 43.65%, 19%, and 48.65% (phases a, b, and c) in total active power losses have been achieved. The results suggest that the proposed method renders significant improvement in voltage stability and reduces power losses concerning the base case for both test systems.

Multi-objective distributed generation integration in radial distribution system using modified neural network algorithm

This paper introduces a new approach based on a chaotic strategy and a neural network algorithm (NNA), called chaotic-based NNA (CNNA), to solve the optimal distributed generation allocation (ODGA), in the radial distribution system (RDS). This consists of determining the optimal locations and sizes of one or several distributed generations (DGs) to be inserted into the RDS to minimize one or multiple objectives while meeting a set of security limits. The robustness of the proposed method is demonstrated by applying it to two different typical RDSs, namely IEEE 33-bus and 69-bus. In this regard, simulations are performed for three DGs in the cases of unity power factor (UPF) and optimal power factor (OPF), considering single and multi-objective optimization, by minimizing the total active losses and improving the voltage profile, voltage deviation (VD) and voltage stability index (VSI). Compared to its original version and recently reported methods, the CNNA solutions are more competitive without increasing the complexity of the optimization algorithm, especially when the RDS size and problem dimension are extended.