Sizing Of Multiple Distributed Generation Research Articles

Optimal placement and sizing of multiple distributed generators in power distribution networks

The primary objective of power systems is to meet the total power requirement, as well as to retain system reliability and security with the economic operation of the system. In recent years, exponential growth in power demand, lack of investments in up-gradation of electrical infrastructure, shortage in power generation and environmental concerns have been the key factors for unreliable and poor operation of the power systems. To tackle these problems, there is a need to rely on supplementary sources of generation and fortification of transmission capacity. Whereas, installation of large scale new generating units is not economical. An adequate number of small generating units incorporated is a more effective solution to meet the growing power demand. This research work focuses on the enhancement of voltage profiles while curtailing power loss by the optimal sizing and optimal placement of multiple distributed generators (DGs) at different buses. Different types of DG sizing and placement are carried out at 100%, 120% and 140% loading conditions.

Power supply enhancement in Onitsha distribution network using distribution generations

The research explored ways in which power supplied in Onitsha distribution line network would be enhanced using distributed generation. The problem of insufficient power supply in densely populated metropolis which is characterised by frequent load shedding was solved using optimal placement and sizing of multiple distributed generation. A gas plant was optimally placed at 33 kV injection substations at Awada and General Cotton Mill (GCM). The generators were optimally placed using voltage sensitivity index factor, while their optimum capacities were sized using step method. Simulations were done by power system analysis toolbox (PSAT) 2.1.11 simulation tool in MATLAB 2018a environment and the result of the findings revealed that 32 buses out of 34 buses were violated. The result of the simulation also showed that the optimal operating power output of the generators at Awada and GCM of 12.5 MW and 2.5 MW respectively could only clear 29 out of the 32 bus voltage violations. Overloading of Fegge injection substation transformer was identified as the cause and its upgrade from 7.5 MVA to 15.0 MVA cleared all the voltage violations. The system active loss and reactive loss were 1.82 MW and 35.03 MVar and the research was able to reduce them by 23.30% and 23.80% respectively. The statutory bus voltage limits of ±5% and the requirement that distributed generators installed must be less than the maximum demanded had been achieved in the research. The power supply in Onitsha distribution network was enhanced using distribution generations and the usage of optimal placement and sizing of multiple distributed generation is therefore recommended for problematic distribution networks not only for loss reduction also for better performance.

Optimal Planning of Electric Vehicle Charging Station along with Multiple Distributed Generator Units

Saving energy through the minimization of power losses in a distribution system is a key activity for efficient operation. Distributed Generation (DG) is one of the most efficient approaches to minimize losses. With increase in installation of Electric Vehicle Charging Stations (EVCSs) for Electrical Vehicles (EVs) in larger scale, optimal planning of EVCSs becomes a major challenge for distribution system operator. With increased EV load penetration in the electricity system, generation-demand mismatch and power losses increases. This results in poor voltage level, and deterioration in voltage stability margin. To mitigate the adverse impacts of increasing EV load penetration on Radial Distribution Systems (RDS), it is essential to integrate EVCSs at appropriate locations. The EVs integration into smart distribution systems involves Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) in charging and discharging modes of operation respectively for exchange of power with the grid thus resulting in energy management. The inappropriate planning of EVCSs causes a negative impact on the distribution system such as voltage deviation and an increase in power losses. In order to minimize this, DG units are integrated with EVCSs. The DGs assist in keeping the voltage profile within limitations, resulting in reduced power flows and losses, thereby enhancing power quality and reliability. Therefore, the DGs should be optimally allocated and sized along with the EVCS to avoid problems such as protection, voltage rise, and reverse power flow problems. This paper showcases a method to minimize losses using optimal location and sizing of multiple DGs and EVCS operating in G2V and V2G modes. The sizing and location of different types of DG units including renewables and non-renewables along with EV charging station is proposed in this study. This methodology overall reduces the power losses and also improves voltages of the network. The implementation is done by using the Simultaneous Particle Swarm Optimization technique (PSO) for IEEE 15, 33, 69 and 85 bus systems. The results indicate that the proposed optimization technique improves efficiency and performance of the system by optimal planning and operation of both DGs and EVs.

Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

This article presents the selection of location and sizing of multiple distributed generators (DGs) for boosting performance of the radial distribution system in the case of constant power load flow and constant impedance load flow. The consideration of placing and sizing of DGs is to meet the load demand. This article tries to overcome the limitations of existing techniques for determining the appropriate location and size of DGs. The selection of DG location is decided in terms of real power losses, accuracy, and sensitivity. The size of DG is measured in terms of real and reactive power. Both positioning and sizing of DG are analyzed with the genetic algorithm and the heuristic probability distribution method. The results are compared with other existing methods such as ant-lion optimization algorithm, coyote optimizer, modified sine-cosine algorithm, and particle swarm optimization. Further, the power quality improvement of the network is assessed by positioning D-STATCOM, and its location is decided on the basis of the nearby bus having poor voltage profile and high total harmonic distortion (THD). The switching and controlling of D-STATCOM are assessed with fuzzy logic controller (FLC) for improving the performance parameters such as voltage profile and THD at that particular bus. The proposed analytical approach for the system is tested on the IEEE 33 bus system. It is observed that the performance of the system with the genetic algorithm gives a better solution in comparison to heuristic PDF and other existing methods for determining the optimal location and size of DG. The introduction of D-STATCOM into the system with FLC shows better performance in terms of improved voltage profile and THD in comparison to existing techniques.

Optimal location and sizing of multiple distributed generators in radial distribution network using metaheuristic optimization algorithms

The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies more essential given its advantages such as reducing power losses and enhancing voltage profiles. This study addresses the optimal sizing and setting of Photovoltaic Distributed Generator (PVDG) connected to Radial Distribution Network (RDN) using various novel optimization algorithms. These algorithms are implemented to minimize the Multi-Objective Function (MOF), which devoted to optimize the Total Active Power Loss (TAPL), the Total Voltage Deviation (TVD), and the overcurrent protection relays (OCRs)?s Total Operation Time (TOT). The effectiveness of the proposed algorithms is validated on the test system standard IEEE 33-bus RDN. In this paper is presented a recent meta-heuristic optimization algorithm of the Slime Mould Algorithm (SMA), where the results reveal its effectiveness and robustness among all the applied optimization algorithms, in identifying the optimal allocation (locate and size) of the PVDG units into RDN for mitigating the power losses, enhance the RDN system's voltage profiles and improve the overcurrent protection system. Accordingly, the SMA approach can be a very favorable algorithm to cope with the optimal PVDG allocation problem.

전압 프로파일 향상 및 전력 손실 최소화를 고려한 여러 종류의 분산형 전원 최적 위치 및 용량 선정에 관한 연구

In recent years, the demand of electric power generation has increased significantly. Electrical distribution systems are progressively featured with multi types of Distributed Generation (DG) units to meet the sustainable energy requirements, improve voltage profile, and minimize energy losses. The aim of this work was to improve voltage profile and to reduce the power losses by optimal placing and sizing of multiple DGs, grouped in three types according to their capability in generating active and reactive powers, using iterative method. The load-flow method was used to simulate the test system i.e., IEEE-33 bus radial distribution system, using MATLAB programming. A notable improvement in voltage profile and reduction in active and reactive power losses of 69.66% and 64.14%, respectively, were achieved in comparison to the system without DGs.

Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0 – 2 MW and 0 – 3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0 – 2 MW power limit is consistent to the 0 – 3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses.

Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm

High energy losses and voltage instability issues in distribution system are prevalent for energy planners and utilities due to high penetration of Electric Vehicles (EVs). Battery Swapping Stations (BSS) are important component for energizing EVs equipped with battery charging mode. In this paper, energy loss reduction and voltage stability factor are enhanced by optimal allocation of Distributed Generation (DG) and BSS, using new Grasshopper Optimizer Algorithm (GOA). The zone based strategy with dispersed DG ensures sufficient distance among BSS's to adequately serve the motorists and enhance system performance. The operational constraints of BSS, DG and loads in each zone for 33-and 69-bus networks are considered. GOA method is compared with established and well-known techniques, namely Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Particle Swarm Optimization (PSO). Using GOA, energy losses are reduced to 82% and 92%, while loadability is increased to 13% and 19% in 33- and 69-bus systems, respectively. The results have shown superiority of GOA in terms of system performance and convergence characteristics. Optimal sites and sizes for DG-BSS placement in the system are beneficial to EV users, BSS developers, electricity grid and utility.

The Proper Location and Sizing of Multiple Distributed Generators for Maximizing Voltage Stability Using PSO

Energy technologies and their efficient use plays a vital role in socio-economic development of any country. In the recent years, the restructuring of electricity market evolves some major improvements in the technologies of energy production and thus, it has paved the way for increasing the applications of Distributed Generation (DG) with renewable energy sources. In this research, the optimal placement and sizing of multiple DGs are achieved by a novel indicator United Bus and Line Voltage Firmness Factor (UBL_VFF). The objectives of this work are the minimization of system losses and maximization of voltage stability and they are achieved by identifying the weakest voltage bus due to the weakest link in the system. Particle Swarm Optimization (PSO) is used for solving this optimization problem. The effectiveness of this proposed approach is tested in 33 and 69 bus radial distribution test systems. The results of this proposed method is compared with the results reported in the contemporary literature. The results have proved to be robust in terms of reduction in system losses and maximization of bus and line voltage stability.

Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system

The role of distributed generation (DG) for empowering the performance of the distribution system is becoming better known in the power sector. This paper presents a competent optimization approach based on the Grey Wolf Optimizer (GWO) for multiple DG allocation (i.e. siting and sizing) in the distribution system. The multiple objectives are to minimize reactive power losses and improve the voltage profile of the distribution system, without violating power system constraints. GWO is a newly proposed meta-heuristic optimization algorithm, inspired by grey wolves (Canis lupus). Alpha, beta, delta, and omega are the four categories of grey wolves, which are utilized to simulate leadership hierarchy. Despite this, GWO takes three main steps in hunting: searching for prey, encircling prey and attacking prey in order to complete the optimization process. The proposed study, based on GWO, is compared with the Gravitational Search Algorithm (GSA) and the Bat Algorithm (BA) based meta-heuristic methods. The different case studies of multiple DG type allocations in a 69-bus distribution system are carried out to show the effectiveness of the proposed methodology and distribution system performance. The comparative numeric results, voltage profile and convergence characteristic curves indicate better performance of the GWO against the GSA and BA.

Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm

Abstract Distributed generation (DG) is becoming more important due to the increase in the demands for electrical energy. DG plays a vital role in reducing real power losses, operating cost and enhancing the voltage stability which is the objective function in this problem. This paper proposes a multi-objective technique for optimally determining the location and sizing of multiple distributed generation (DG) units in the distribution network with different load models. The loss sensitivity factor (LSF) determines the optimal placement of DGs. Invasive weed optimization (IWO) is a population based meta-heuristic algorithm based on the behavior of weeds. This algorithm is used to find optimal sizing of the DGs. The proposed method has been tested for different load models on IEEE-33 bus and 69 bus radial distribution systems. This method has been compared with other nature inspired optimization methods. The simulated results illustrate the good applicability and performance of the proposed method.

Optimal Placement and Sizing of Renewable Distributed Generation via Gravitational Search Algorithm

The installation of distributed generation (DG) gives advantages to the environment such as, it contribute in the reduction of non-peak operating cost, diversification of energy resources, lower losses thus improving overall organization. These advantages might be rescinded if no proper location and sizing of DGs are considered before the DG’s installation. This paper offers an optimal location and sizing of multiple DGs using heuristic method called gravitational search algorithm (GSA). The suggested algorithm is tested on 13-bus radial distribution system. This method is being compared with particle swarm optimization (PSO) in terms of system power loss, voltage deviation and total voltage harmonic distortion (THDv). GSA shows the ability to locate and sized DG optimally with a better performance and more reliable than PSO.

An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

After the implementation of deregulation in a power system, an appreciable volume of renewable energy sources is used to generate electric power. Even though they are intended to improve the reliability of the power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand, and failures of other equipment lead to congestion in one or more transmission lines. There are several ways to alleviate this transmission congestion, such as the installation of new generation facilities in the place where the demand is high, the addition of a new transmission facility, generation rescheduling, and curtailment of load demand processes. Among the above methods generation rescheduling and load shedding are normally preferred, since the other methods require additional investments. However, some critical cases require improved methods to alleviate congestion. With the extensive application of distributed generation (DG), congestion management is also accomplished by the optimal placement of multiple DG units. It is well known that incorrect sizes and improper locations of DG undoubtedly create higher power losses and an undesirable voltage profile. Hence, this research effort employs the line flow sensitivity index to establish the optimal location of DG units and genetic algorithm-based optimization for determining the optimal sizes of DG units. The objective of this research is to minimize the total losses and real power flow performance index and to improve the voltage shape of the modified IEEE 30-bus test system. The results of this proposed approach are encouraging and help in anticipating higher efficiency by satisfying all the objectives.

Optimal multi objective placement and sizing of multiple DGs and shunt capacitor banks simultaneously considering load uncertainty via MOPSO approach

This study proposes a new application of multi objective particle swarm optimization (MOPSO) with the aim of determining optimal location and size of distributed generations (DGs) and shunt capacitor banks (SCBs) simultaneously with considering load uncertainty in distribution systems. The multi objective optimization includes three objective functions: decreasing active power losses, improving voltage stability for buses and balancing current in system sections. The uncertainty of loads is modeled by using fuzzy data theory. This method uses Pareto optimal solutions to solve the problem with objective functions and constraints. In addition, a fuzzy-based mechanism is employed to extract the best compromised solution among three different objective functions. The proposed method is implemented on IEEE 33 bus radial distribution system (RDS) and an actual realistic 94 bus Portuguese RDS and the results are compared with methods of Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Genetic Algorithm (NSGA), Multi-Objective Differential Evolution (MODE) and combination of Imperialist Competitive Algorithm and Genetic Algorithm (ICA/GA). Test results demonstrate that the proposed method is more effective and has higher capability in finding optimum solutions in cases where DG and SCB are located and sized simultaneously in a multi objective optimization.

A Heuristic Approach for Optimal Location and Sizing of Multiple DGs in Radial Distribution System

A distribution system is known as an interface between the central power system and its consumers. DGs are defined as small scale generation units that are connected near to customer load centres. DGs have the potential of altering power flows, system voltages, and even the performance of the integrated network. With the principle of minimizing line losses in the power systems, it is remarkably imperative to define the optimal size and location of local generations. This paper proposes Genetic Algorithm (GA) for optimal placement and sizing of distributed generation (DG) in radial distribution system by minimizing the real power loss and thus improving the voltage shape. The developed algorithm is tested on 33-bus radial distribution system. The proposed method has outperformed than the other methods in terms of the quality of solution and computational competence.

Optimal Access Point and Capacity of Distributed Generators in Radial Distribution Systems for Loss Minimization Including Load Models

ABSTRACTDistributed generation (DG) sources are predicated to play major role in distribution systems due to the demand growth for electrical energy. Location and sizing of DG sources found to be important on the system losses and voltage stability in a distribution network. This article proposes, an approach to determine the optimal sizing of multiple distributed generators in radial distribution system at unspecified power factor considering different load models. It is shown that load models can significantly affect the optimal sizing of DG units in radial distribution systems. The prior objective is to minimize network power losses hence to improve the voltage profile. Various optimization techniques are applied on the objective function. A detailed performance analysis is carried out on 38-bus radial distribution systems to demonstrate the effectiveness of the proposed techniques. Performing multiple power flow analysis on 38-bus system, the effect of DG sources on the most sensitive buses to voltage...

Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators in Power Distribution Network Using Cat Swarm Optimization

This article presents optimal placement and sizing of multiple distributed generators to achieve higher overall system reliability in large-scale primary distribution networks using a novel random search algorithm known as cat swarm optimization. A composite reliability index is used as the objective function in the optimization process. Furthermore, the effect of multiple distributed generator units on power transfer capacity and power loss reduction has been observed. Extensive simulations are carried out based on three practical distribution systems to demonstrate the effectiveness of the proposed method. Further, qualitative comparisons are made with adaptive weight particle swarm optimization, particle swarm optimization with constriction factor, and binary-coded genetic algorithm to show the efficacy of the proposed method for optimal placement and sizing of distributed generators in power distribution networks.