Allocation Of Distribution Static Compensator Research Articles

Allocation and Sizing of DSTATCOM with Renewable Energy Systems and Load Uncertainty Using Enhanced Gray Wolf Optimization

Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.

Optimizing the allocation of renewable DGs, DSTATCOM, and BESS to mitigate the impact of electric vehicle charging stations on radial distribution systems

The increasing global adoption of Electric Vehicles (EVs) necessitates a greater supply of electricity for charging these cars. The popularity of EVs is also driven by their minimal maintenance requirements, enhanced performance, and eco-friendly nature. However, the expanding usage of EVs poses challenges to the distribution system's efficiency, thereby impacting its reliability. Consequently, ensuring the precise placement of electric vehicle charging stations (EVCS) becomes crucial for maintaining a dependable infrastructure. Solar and wind-based Renewable Distributed Generations (RDGs), Distribution STATic COMPensator (DSTATCOM), and Battery Energy Storage System (BESS) have become an important part of a Radial Distribution System (RDS) for mitigating the impact of EVCS as environmental sensitivity has grown and technology has advanced. Improper placement and sizing of components in can significantly impact the performance of a RDS. This research proposes a unique approach utilizing the Slime Mould Algorithm (SMA) and other optimization algorithms to identify the optimum positioning and sizing of RDG/DSTATCOM/EVCS/BESS within the RDS. The presented approach's efficacy is showcased by employing it on two commonly used IEEE RDSs: specifically, the 33-bus and 69-bus systems. The main objective of this research is to address actual power loss in these systems, subsequently enhancing voltage stability and bus voltage profiles. Findings from the test cases demonstrate that optimizing with the SMA algorithm produces more precise results in mitigating real power loss, enhancing bus voltage levels, and improving overall system stability when compared to existing algorithms.

A new robust modified capuchin search algorithm for the optimum amalgamation of DSTATCOM in power distribution networks

Very sensitive loads require the safe operation of electrical distribution networks, including hospitals, nuclear and radiation installations, industries used by divers, etc. To address this issue, the provided paper suggests an innovative method for evaluating the appropriate allocation of Distribution STATic COMpensator (DSTATCOM) to alleviate total power losses, relieve voltage deviation, and lessen capital annual price in power distribution grids (PDGs). An innovative approach, known as the modified capuchin search algorithm (mCapSA), has been introduced for the first time, which is capable of addressing several issues regarding optimal DSTATCOM allocation. Furthermore, the analytic hierarchy process method approach is suggested to generate the most suitable weighting factors for the objective function. In order to verify the feasibility of the proposed mCapSA methodology and the performance of DSTATCOM, it has been tested on two standard buses, the 33-bus PDG and the 118-bus PDG, with a load modeling case study based on real measurements and analysis of the middle Egyptian power distribution grid. The proposed mCapSA technique's accuracy is evaluated by comparing it to other 7 recent optimization algorithms including the original CapSA. Furthermore, the Wilcoxon sign rank test is used to assess the significance of the results. Based on the simulation results, it has been demonstrated that optimal DSTATCOM allocation contributes greatly to the reduction of power loss, augmentation of the voltage profile, and reduction of total annual costs. As a result of optimized DSTATCOM allocation in PDGs, distribution-level uncertainties can also be reduced.

Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm

Because of the increasing growth of Electric Vehicle (EV) in India, more electricity is required to power such vehicles. It is also gaining popularity because of its low maintenance, improved performance, and zero carbon impact. As the usage of electric vehicles grows, the distribution system’s performance is impacted. As an outcome, the reliability of the distribution system (DS) is dependent on the position of the electric vehicle charging station (EVCS). The fundamental difficulty is the deterioration of the DS due to an incorrect EVCS location. The DS is linked to the charging station and works with the distribution static compensator (DSTATCOM) to minimize the impact of the EVCS. A new nature-inspired Bald Eagle Search Algorithm (BESA) based optimization technique was utilized to find the optimal allocation of DSTATCOM and EVCS in the DS. The proposed strategy for mitigating the real power loss has been tested on practical Indian 28-bus and 108-bus distribution networks. Power loss reduction optimizes the system’s net savings, voltage stability, and bus voltage. The test case findings show that the BESA-based optimization is more accurate regarding power loss mitigation, bus voltage enhancement, and annual net saving improvement than the BA-based optimization in the DS.

Minimisation of electric vehicle charging stations impact on radial distribution networks by optimal allocation of DSTATCOM and DG using African vulture optimisation algorithm

Electric vehicles (EVs) are becoming increasingly popular, as the results of fossil fuels are going to be exhausted in the future. Although, the excessive use of electric vehicles is advantageous because it reduces greenhouse gas emissions, but it also requires charging stations to recharge their batteries. In such a case, the Electric Vehicle Charging Station (EVCS) puts an additional burden on the utility grid which causes additional system power losses and voltage deviation. Furthermore, it also lowers the voltage stability index. To minimise the impact of EVCSs on the distribution system, charging stations are integrated into the distribution system in coordination with Distribution Static Compensator (DSTATCOM) and Distributed Generation (DG). In this paper, the African Vultures Optimisation Algorithm (AVOA) is used to determine the optimal position of EVCSs, DGs, and DSTATCOMs. The objective function is to reduce the real power loss index and voltage deviation index while enhancing the voltage stability index. Also, the AVOA results are validated by comparing them to the results of the grey wolf optimiser algorithm. The suggested approach is examined on 33 bus, 69 bus, and 136 bus systems. Researchers, academics, industrialists, and society all benefited from this research work.

Expansion Planning in Distribution Network With DSTATCOM Using Distance-Oriented Grasshopper Optimization Algorithm

This paper intends to consider a multi-objective problem for expansion planning in Power Distribution System (PDS) by focusing on (i) expansion strategy (ii) allocation of Circuit Breaker (CB), (iii) allocation of Distribution Static Compensator (DSTATCOM), (iv) Contingency Load Loss Index (CLLI), and power loss. Accordingly, the encoding parameters decide for expansion, Circuit Breaker (CB) placement, DSTATCOM placement, load of real and reactive powers of expanded bus or node are optimized using Grasshopper Optimization Algorithm (GOA) based on its distance and hence, the proposed algorithm is termed as Distance Oriented Grasshopper Optimization Algorithm (DGOA). The proposed expansion planning model is carried out in IEEE 33 test bus system. Moreover, the adopted scheme is compared with conventional algorithms and the optimal results are obtained.

Optimal allocation of DSTATCOM and PV array in distribution system employing fuzzy-lightning search algorithm

Increased electric energy demand is the major factor that cracks the researchers to focus on electrical energy demand to meet the challenge. Accordingly, the power loss should be minimized. Due to this, Distribution Flexible Alternating Current Transmission System (DFACTS) and Distributed Generation (DG) must be placed in an appropriate way in distribution systems. Fuzzy-Lightning Search Algorithm (FLSA) has been proposed to minimize the power loss in the radial distribution network by solving the optimal placement problem of Distribution STATic COMpensator (DSTATCOM) as DFACTS device and photo voltaic (PV) array unit as DG. The improved voltage profile values, less power loss and reduced stability problems have been attained by using FLSA. The proposed FLSA technique is evaluated through IEEE 30-bus system with MATLAB software. The power flow investigation has been done by Newton Raphson method. Optimum siting of DSTATCOM and PV array induced outstandingly decreased the losses to 7.0073 kW. The accomplished objectives and results have validated the FLSA is more advantageous than other optimization techniques.

Simultaneous allocation of photovoltaic DG and DSTATCOM for techno‐economic and environmental benefits in electrical distribution systems at different loading conditions using novel hybrid optimization algorithms

Enhancing distribution system operation accomplished with the integration of renewable energy resources (RERs) has several technical, economical, and environmental dimensions. In this regard, this paper presents an optimal integration procedure of Distributed Generation (DG) based on Photovoltaic panel (PV) and Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). The proposed procedure is formulated as a multi-objective function (MOF). The considered objectives that reflect the technical, economic, and environmental issues, are Active Power Loss Level (APLL), Short Circuit Level (SCL), Voltage Deviation Level (VDL), Net Saving Level (NSL), and Environmental Pollution Reduction Level (EPRL). The proposed procedure investigates several hybrid optimization methods that combine the firefly algorithm (FA) with various acceleration coefficients PSO algorithms to improve the overall solution quality of the hybrid algorithms compared with the individual algorithms. To prove the capability of the proposed procedure, four different cases are tested on IEEE 33-bus and 69-bus EDSs. Added to that, the proposed algorithms are extended to practical Algerian EDS in Adrar City 205-bus. Results obtained by the hybrid FA-SCAC-PSO algorithm showed that the simultaneous allocation of multiple DG and DSTATCOM in all standard and practical test systems significantly reduces the loss and enhances the voltage profile. An energy-efficient analysis to proceed for different cases studied based on the best hybrid FA-SCAC-PSO algorithm to reach the best value of MOF compared to other algorithms, moreover the capability to achieve the optimal allocation of DG and DSTATCOM by maintaining the voltages profile within the permissible limit, whatever the variation of load. Significant technical economic and environmental achievements are found for different case studies especially in the existence of DGs and DSTATCOM devices.

A New Approach to Optimal Location and Sizing of DSTATCOM in Radial Distribution Networks Using Bio-Inspired Cuckoo Search Algorithm

This article proposes a new approach based on a bio-inspired Cuckoo Search Algorithm (CSA) that can significantly envisage with several issues for optimal allocation of distribution static compensator (DSTATCOM) in Radial Distribution System (RDS). In the proposed method, optimal locations of the DSTATCOM are calculated by using the Loss Sensitivity Factor (LSF). The optimal size of the DSTATCOM is simulated by using the newly developed CSA. In the proposed method, load flow calculations are performed by using a fast and efficient backward/forward sweep algorithm. Here, the mathematically formed objective function of the proposed method is to reduce the total system power losses. Standard 33-bus and 69-bus systems have been used to show the effectiveness of the proposed CSA-based optimization method in the RDS with different load models. The simulated results confirm that the optimal allocation of DSTATCOM plays a significant role in power loss minimization and enhanced voltage profile. The placement of DSTATCOM in RDS also plan an important role for minimizing uncertainties in the distribution level. The proposed method encourages one to use renewable-based resources, which results in affordable and clean energy.

Optimal Allocation of Multiple D-STATCOM in Distribution System Using PSO

In this paper, the Particle Swarm Optimization (PSO) is proposed to obtain the optimal number of Distribution Static Compensator (D-STATCOM) devices, sizes and locations in order to minimize total power losses, bus voltage deviations, and overloaded lines in the radial distribution system with Distributed Generations (DGs). The reactive power compensation is implemented using an effective technique based on the Loss Sensitivity Factor (LSF) as well as the proposed PSO technique. The validity of the proposed technique is examined on IEEE-33 bus radial distribution systems and modified with DG connection at different buses representing various cases studies. All simulations are done using a coded program using MATLAB where the modeling of D-STATCOM is tested and verified. The DG penetration levels, the voltage profile and total power losses of the distribution system are compared before and after D-STATCOM compensation

Optimal allocation of D-STATCOM in distribution networks including correlated renewable energy sources

Gradual depletion of fossil fuel resources, poor energy efficiency, and environmental pollution problems have led to the use of renewable energy sources (RESs) such as wind turbines (WTs) and solar photovoltaic (PV) cells in distribution networks all around the world. The uncertain nature of these sources, along with network power demands, necessitates probabilistic evaluation to extract results with high applicability and efficiency. Distribution network flexible AC transmission system (D-FACTS) devices such as distribution static compensator (D-STATCOM) can be efficiently used for making the modern distribution networks with high penetration of RESs more flexible.This paper presents a probabilistic technique for optimal allocation of the D-STATCOM, considering the correlation between uncertain variables. The proposed solution method helps to mitigate expected active power losses, improve expected voltage deviation index (VDI), and decrease D-STATCOM expected installation cost for radial/mesh distribution networks. The k-means based data clustering method (DCM) and Latin hypercube sampling (LHS) method are used for probabilistic evaluation of distribution networks. In addition, the particle swarm optimization (PSO) algorithm is employed as the optimization tool. The proposed algorithm is applied to the IEEE 69 node test network, and the results are compared with the Monte Carlo simulation (MCS) method. Also, the efficacy of the proposed study method has been investigated for a real meshed distribution network.

Multi-objective model for simultaneous distribution networks reconfiguration and allocation of D-STATCOM under uncertainties of RESs

This paper proposes an approach for simultaneous placement of distribution static compensator (D-STATCOM) and renewable energy sources (RESs) and distribution system reconfiguration (DSR) based on stochastic multi-objective modelling. Associated uncertainties of RESs in generating power, market price errors and load forecasting are modelled in this problem. The problem is optimised by non-dominated sorting genetic algorithms-II (NSGA-II) and then the best compromise solution is specified with fuzzy set theory. The aim of optimisation is to minimise installation and operation costs of distributed generation (DG) units, cost of D-STATCOM installation, the cost of procured energy from the transmission grid, costs of reactive and active power losses, line upgrade cost and DSR expense. A risk-based model of energy not supplied (ENS) as a reliability index is considered in the objective function to improve the reliability of the system. The proposed approach is validated on the IEEE 33-bus test grid.

Hybrid Allocation of Capacitor and Distributed Static Compensator in Radial Distribution Networks Using Multi-Objective Improved Golden Ratio Optimization Based on Fuzzy Decision Making

In this paper, multi-objective reactive power compensation based on optimal and simultaneous allocation of capacitors and distribution static compensator (DSTATCOM) in radial distribution networks based on fuzzy decision making is proposed with the aim of reducing power losses cost, reactive investment cost and reducing voltage deviations considering power loss reduction factor (PLRF) to identify candidate buses. Optimal location and size of reactive resources is determined based using multi-objective improved golden ratio optimization algorithm (MOIGROM) inspired by Fibonacci series and improved using nonlinearly inertia weight strategy. This method is performed on a 13 bus distribution network, 69 and 118 buses IEEE standard networks in various scenarios including capacitor allocation only, DSTATCOM allocation only and optimal and simultaneous capacitor and DSTATCOM allocation (hybrid allocation). The simulation results showed that the hybrid allocation has less power losses and voltage deviations in different scenarios using fuzzy decision making based on Pareto front set. Also the impact of different loading conditions on problem solution cleared that with increasing the load level, especially during the heavy load, the power losses and voltage deviations of the networks increase and vice versa. In addition, the advantage of the MOIGROM method over the other methods is confirmed.

Sizing and Location Optimization of DSTATCOM in Radial Distribution System

A huge review on greatest allocation of Distribution Static Compensator (DSTATCOM) strategies in Radial Distribution system (RDS) device for compensation of reactive power (Q), mitigation of electricity losses and enhancement in voltage profile is presented. DSTATCOM compensates bus voltage to restriction the strength factor, in addition with energetic and additionally reactive power flows in the RDS. It can additionally provide immediate and non-stop capacitive (C) and inductive (L) mode compensation. This system also injects quantity of lead or lagging compensating current, when it is connected with a same load or varying load. Various IEEE buses are used for checking the achievability of the optimization methods in distribution system. In few papers the presented approach is evaluated through evaluating it with previous techniques and benefits are shown by means of simulation results.

Optimal allocation of DSTATCOM considering the uncertainty of photovoltaic systems

Rational allocation of the distribution static compensator (DSTATCOM) can effectively solve the impact of high penetration of photovoltaic (PV) systems on an active distribution network. Taking the intermittent output of a PV system into consideration, this paper establishes an optimal allocation model of DSTATCOM, in which active power loss, voltage deviation, and total allocation cost are considered as the objective functions. Monte Carlo simulation (MCS) is employed to simulate the random output of PV. Moreover, an opportunity constraint is introduced into the optimal model to evaluate the reliability of the allocation scheme. Multiobjective differential evolution gray wolf optimization (MODEGWO) is proposed to determine the optimal location and capacity of the DSTATCOM. A new optimization method integrating MCS, opportunity constraint, and MODEGWO is proposed to deal with the optimal allocation model of DSTATCOM. The proposed method is validated on an IEEE 33‐bus system. Simulation results confirm the rationality and validity of the proposed model. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Simultaneous Allocation of DG and DSTATCOM Using Whale Optimization Algorithm

This paper proposed a new methodology based on nature-enthused meta-heuristic optimization algorithm named as whale optimization algorithm (WOA), which can significantly envisage problems for simultaneous allocation of distributed generation (DG) and Distribution STATic COMpensator (DSTATCOM) in the radial distribution systems (RDS). In the proposed method, optimal locations and sizing of the DG and DSTATCOM can be determined by loss sensitivity factor and WOA, respectively. The objective function of the proposed method is to minimize the system’s total power losses and total operating cost of DG and DSTATCOM. The proposed method is applied to well-known IEEE 33-bus and large 136-bus RDS. The attained simulation results and comparison of different cases considered leads to allocation of DG and DSTATCOM combination resulting in significant power loss reduction with good voltage profile enhancement.

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

The distribution flexible alternating current system (D-FACT) devices are being increasingly placed in the distribution system due to their several practical and environmental benefits. In this work, a novel grey wolf optimizer (GWO) algorithm as a meta-heuristic technique is applied to solve the reconfiguration problem, the installation of distribution static compensator (D-STATCOM) problem, and the combination of these two problem. The aim of this research is to diminish the total power loss and the load-balancing (LB) index of the radial distribution system. First, the index vector (IV) method is employed to suggest the candidate busses for D-STATCOM allocation. Then, the GWO algorithm is applied to establish the locations and sizes of the D-STATCOM from the candidate busses. An overall accurateness and consistency of the proposed GWO algorithm has been validated and experienced on a 33-bus, a 69-bus, and a real-time 31-bus radial distribution system, India with three kinds of load levels. In addition, the proposed method results are ideal as compared with other intellectual methods like genetic algorithm (GA) and fuzzy GA.

Application of Bacterial Foraging Algorithm in The Allocation of DSTATCOM in 50-bus Canteen Feeder

Voltage instability has been identified as the most critical factor responsible for poor power quality in electric power systems. The high losses experienced at the distribution level of these systems has become a major concern to power system operators, with about 10-13% of the total generation being dissipated as heat. Maintaining the system voltage within an acceptable limit will go a long way in reducing these losses and enhancing the overall system operational capability. The objective of this paper is to improve the voltage magnitude and reduce overall power losses in an existing 50-bus radial distribution feeder via the allocation of Distribution Static Compensator (DSTATCOM) using an established bacterial foraging algorithm (BFA) based model. The application of the swarm-based meta-heuristic model is extended to a three-quarter (75%) loading condition of the standard IEEE 33-bus test network and then, employed on the 50-bus Canteen feeder for both normal (100%) and three-quarter (75%) loading conditions. Comprehensive analysis was performed for both networks and the results were compared with their respective base-case scenarios. The final results of the evaluation obtained through simulation showed appreciable reduction in power losses and improvement in overall voltage profile with the allocation of DSTATCOM in both networks using the BFA based model. Voltage improvement in the region of 20.04% and active power loss reduction of 24.86% were recorded for three-quarter loading of the IEEE test network. For the 50-bus Canteen feeder, an overall voltage profile improvement of 6.13% and active power loss reduction of 22.84% were achieved for normal loading condition, whereas 2.99% and 19.71% improvement in total voltage profile and active power loss respectively were attained under three-quarter loading condition.

Analysis of Distribution System with D-STATCOM by Gravitational Search Algorithm (GSA)

In order to analyze the distribution system (DS) to minimize the power losses and improve power quality, finding accurate position size of distribution static compensator (D-STATCOM) is essential. In this paper, a metaheuristic optimization technique, the gravitational search algorithm (GSA) is proposed for the analysis of DS with accurate placing and sizing of distributed static synchronous compensator (D-STATCOM) for power loss reduction, minimum voltage profile index, improvement in voltage profile and annual energy saving for distribution network operator. The effectiveness of the proposed algorithm is tested on IEEE 33 and IEEE 69 bus systems, and performance of GSA has been compared with other techniques such as immune algorithm, bat algorithm and sensitivity approach method for optimal allocation of D-STATCOM. It is found that the best performance is achieved by the proposed method. Forward–backward sweep method is used for the load flow solutions in this paper.

Multi-objective simultaneous DG and DSTATCOM allocation in radial distribution networks using cuckoo searching algorithm

In this paper, authors proposed a new methodology based on bio-inspired Cuckoo Search Algorithm (CSA), which can significantly be envisaged with simultaneous allocation of Distributed Generation (DG) and Distribution STATic COMpensator (DSTATCOM) in the radial distribution systems (RDS). In the proposed method, optimal locations of the DG and DSTATCOM are determined by using Voltage Stability Index (VSI) and Loss Sensitivity Factor (LSF) respectively. The optimal size of DG and DSTATCOM are found by using a newly developed nature-inspired CSA in the RDS. Further, in the proposed method load flow calculations are carried out by using a fast and efficient Backward/Forward Sweep (BFS) algorithm. The objective function of the proposed method is derived in such a way that to minimize the system total power losses and enhancing the bus voltages. The effectiveness of the proposed method is tested by considering a well-known IEEE 33-bus and large 136-bus RDS. The attained results of the proposed method and comparison of different cases for allocation of DG and DSTATCOM are presented in result section. From the presented results, it can be clearly understood that the proposed method is able to significantly reduce the power losses and enhance the voltage profile of the RDS.