Optimal Sizing Of Distributed Generation Research Articles

A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems

Distributed 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. In this paper an efficient technique is presented for optimal placement and sizing of DGs in a large scale radial distribution system. The main objective is to minimize network power losses and to improve the voltage stability. A detailed performance analysis is carried out on 33-bus, 69-bus and 118-bus large scale radial distribution systems to demonstrate the effectiveness of the proposed technique. Performing multiple power flow analysis on 118-bus system, the effect of DG sources on the most sensitive buses to voltage collapse is also carried out.

A Combination of Genetic Algorithm and Particle Swarm Optimization for Optimal Distributed Generation Location and Sizing in Distribution Systems with Fuzzy Optimal Theory

Distributed generation (DG) sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy. Locations and capacities of DG sources have profoundly impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. The objective is to minimize network power losses, to obtain better voltage regulation, and to improve the voltage stability within the framework of system operation and security constraints in radial distribution systems. This multi-objective optimization problem is transformed to single objective problem by employing fuzzy optimal theory. A detailed performance analysis is carried out on 33 and 69 bus systems to demonstrate the effectiveness of the proposed methodology.

Placement and Sizing of DG Using PSO&HBMO Algorithms in Radial Distribution Networks

Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. Many researchers have used evolutionary methods for finding the optimal DG placement and sizing. This paper proposes a hybrid algorithm PSO&HBMO for optimal placement and sizing of distributed generation (DG) in radial distri- bution system to minimize the total power loss and improve the voltage profile. The proposed method is tested on a standard 13 bus radial distribution system and simulation results carried out using MATLAB software. The simulation results indicate that PSO&HBMO method can obtain better results than the simple heuristic search method and PSO algorithm. The method has a potential to be a tool for identifying the best location and rating of a DG to be installed for improving voltage profile and line losses reduction in an electrical power system. Moreover, current reduc- tion is obtained in distribution system.

DG Placement and Sizing in Radial Distribution Network Using PSO&HBMO Algorithms

Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. Many researchers have used evolutionary methods for finding the optimal DG placement and sizing. This paper proposes a hybrid algorithm PSO&HBMO for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total power loss and improve the voltage profile. The proposed method is tested on a standard 13 bus radial distribution system and simulation results carried out using MATLAB software. The simulation results indicate that PSO&HBMO method can obtain better results than the simple heuristic search method and PSO algorithm. The method has a potential to be a tool for identifying the best location and rating of a DG to be installed for improving voltage profile and line losses reduction in an electrical power system. Moreover, current reduction is obtained in distribution system.

An Efficient Method for Optimal Placement and Sizing of Multiple Distributed Generators in a Radial Distribution Systems

This article presents a simple and efficient approach for the placement of multiple Distributed Generators (DG) in a radial distribution system. The approach determines the optimal locations and size of DGs with the objective of improving the voltage profile and loss reduction. Loss sensitivity factors (LSF) are used to select the candidate locations for the multiple DG placements and Simulated Annealing (SA) is used to estimate the optimal size of DGs at the optimal locations determined. The proposed method is applied to the 33bus and 69bus radial distribution systems. The solutions obtained by the proposed method are compared with other existing methods. The proposed method has outperformed the other methods in terms of the quality of solution.

Optimal Placement and Sizing of DG in Radial Distribution Networks Using SFLA

Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. Many researchers have used evolutionary methods for finding the optimal DG placement. This paper proposes a shuffled frog leaping algorithm (SFLA) for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total real power loss and to improve the voltage profile. The SFLA is a meta-heuristic search method inspired from the memetic evolution of a group of frogs when seeking for food. It consists of a frog leaping rule for local search and a memetic shuffling rule for global information exchange. The proposed SFL algorithm is used to determine optimal sizes and locations of multi-DGs. Test results indicate that SFLA method can obtain better results than the simple heuristic search method on the 33-bus radial distribution systems. Moreover, voltage profile improvement and branch current reduction are obtained.

Application of Tabu Search for Optimal Placement and Sizing of Distributed Generation for Loss Reduction

This paper presents the optimal and sizing of distributed generation (DG) placement in a radial distribution system for loss reduction. The main emphasis of this paper is to identify proper locations for installing DGs in a distribution system to reduce active power loss and improve bus voltages. Nevertheless, proper placement and sizing of DG units are not straightforward to be identified as a number of their positions and capacities need to be determined. It is therefore proposed in this paper to solve a DG placement problem based on a Tabu search algorithm. The objective function of the problem is to minimize the system loss subject to power flow constraints, bus voltage limits, pre specified number of DGs, and their allowable total installed capacity, and only one distributed generator for one installation position. The effectiveness of the methodology is demonstrated by a practical sized distribution system consisting of 69 bus and 48 load points. The results show that the optimal DG placement and sizing can be identified to give the minimum power loss while respecting all the constraints.

Multiobjective Optimization for Optimal Placement and Size of DG using Shuffled Frog Leaping Algorithm

There has been a great interest in integration of distributed generation (DG) units at distribution level in the recent years. DGs can provide cost-effective, environmental friendly, higher power quality and more reliable energy solutions than conventional generation. For maximum power loss reduction, proper sizing and position of distributed generators are ardently necessary. This paper presents a simple method for optimizing cost and optimal placement of generators. A simple vector based load flow technique is implemented on 38 bus distribution systems. This paper presents a new methodology using a new population based meta heuristic approach namely Shuffled frog leaping algorithm for the placement of Distributed Generators (DG) in the radial distribution systems to reduce the real power losses and cost of the DG. The paper also focuses on optimization of weighting factor, which balances the cost and the loss factors and helps to build up desired objectives with maximum potential benefit. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.

A New Approach for Optimal Location and Sizing of Distributed Generation Using an Exact Solution Method

Extensive penetration of distributed generation (DG) is one of the critical challenges near future for efficient and low carbon energy utilization in the world. Since DG installation greatly influences existing power systems, it is required to install DG with suitable location and adequate size, thus some decision-making tools for optimal DG installation are necessary. This paper presents a new approach for optimal location and sizing of DG. Although approximate methods are generally adopted in this area because of the problem difficulty arising from the combinational nature, an exact enumerative method is proposed in this paper because of its simplicity and versatility. The usefulness of this enumerative method is demonstrated in a simple pilot system and some quantitative/qualitative characteristics in DG installation are also clarified.

A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems

Distributed generation (DG) sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy. Locations and capacities of DG sources have profoundly impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. The objective is to minimize network power losses, better voltage regulation and improve the voltage stability within the frame-work of system operation and security constraints in radial distribution systems. A detailed performance analysis is carried out on 33 and 69 bus systems to demonstrate the effectiveness of the proposed methodology.

Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties

Some uncertainties, such as the uncertain output power of a plug-in electric vehicle (PEV) due to its stochastic charging and discharging schedule, that of a wind generation unit due to the stochastic wind speed, and that of a solar generating source due to the stochastic illumination intensity, volatile fuel prices, and future uncertain load growth could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distribution system planning. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal siting and sizing of DGs. First, a mathematical model of CCP is developed with the minimization of the DGs' investment cost, operating cost, maintenance cost, network loss cost, as well as the capacity adequacy cost as the objective, security limitations as constraints, and the siting and sizing of DGs as optimization variables. Then, a Monte Carlo simulation-embedded genetic-algorithm-based approach is employed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is used to verify the feasibility and effectiveness of the developed model and method, and the test results have demonstrated that the voltage profile and power-supply reliability for customers can be significantly improved and the network loss substantially reduced.

Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems

Introducing distributed generation (DG) in a distribution network has considerable advantages such as reducing power loss and cost, environmental friendliness, voltage improvement, postponing system upgrades and enhancing system reliability and continuity of service. Practical application of the DG, however, proves difficult. Social, economic and political factors affect the final optimal solution. Solution techniques for DG deployment rely on optimisation methods. The technique proposed here finds the optimal location and size of the DG to minimise the total system power loss for radial distribution feeder systems by solving two independent sub-problems: (i) location and (ii) size. A sufficient sensitivity test for the first problem is suggested. Determining the optimal DG size is done using a new heuristic curve-fitted technique that reduces the search-space by selecting fewer DG-tests. Four DG sizes, which are carefully selected based on the system's total load demand percentages, are used to determine the optimal solution. To validate the proposed technique, the 33-bus and 69-bus feeder systems are examined and the results obtained by the presented technique are compared with those obtained using other competing methods.

Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process; so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy; therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi- DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method.

Distributed Generation Planning Strategy with Load Models in Radial Distribution System

This paper proposes a new approach for distributed generation (DG) planning from the perspective of a distribution company (disco). Optimal sizing and siting decisions for distributed generator is obtained through different approaches: Power loss minimization, System MVA minimization, System cost minimization and System Energy loss minimization. The methodology adopted permits the planner to decide optimal location and size of DG with compromise between Power loss, System MVA, System cost and System energy loss. Normally a constant power (real and reactive) load model is assumed in most of the studies. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. A comparative study of optimal location and size of distributed generator provided by installing DG resources with different type of loads models has been performed. utility scenarios as an optimization problem. The objective function was based on supply-demand chain which aimed to minimize the investment and operating costs of local candidate DGs, payments towards purchasing the required extra power by the DISCO, payments toward loss compensation services, as well as the investment cost of other chosen new facilities for different market scenarios. Caisheng Wang et al. (4) proposed an analytical method to determine the best location of candidate DGs for minimum loss configuration. A rigorous analysis for uniformly distributed, centrally distributed, and increasingly distributed loads has been carried out for constant and time varying loads on a feeder. The proposed approach was non-iterative unlike power flow programs. Therefore, there is no convergence problem involved, and results could be obtained quickly. However, authors have indicated that other constraints such as voltage and line limits may affect the DG placement. V ictor H. M endez Quezada et al. (5) discussed such knowledge gap, through the analysis of the impact of DG on distribution losses by studying different scenarios with several DG penetration and concentration levels. Losses are evaluated on an annual basis. Different DG technologies, such as CHP, wind power, and photovoltaic cells, are modeled. For each type of technology, different penetration levels, corresponding with different amounts of DG installed capacity, and different concentration levels, corresponding with several DG units connected along the feeder, are studied.

Minimising voltage deviation in distribution feeders by optimising size and location of distributed generation

A new emerging trend of distribution networks is to use small generating units, known as distributed generation (DG), operating in parallel with the main grid. This kind of distribution networks has enabled DG to support power systems in fulfilling their requirements to increase power output as well as quality of power supply. In order to maximise benefits from the DG system, proper DG planning is necessary. The main purpose of this research is to maximise voltage support through optimal sizing and location of DG. A new methodology is developed to determine an optimal DG size and DG location on the distribution feeder for optimising system voltages. The developed technique is tested on a long radial feeder of a practical system and results are reported.