Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research

Georgilakis, Pavlos S.; Hatziargyriou, Nikos D.

doi:10.1109/tpwrs.2012.2237043

Cited by 742 publications

(337 citation statements)

References 85 publications

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“…considering all, the nonlinear programming, sequential quadratic and ordinal optimization methods are most efficient [20]. DG placement problem considering voltage stability analysis, author first used model analysis and continuous power flow method to find candidate bus for DG location, then an algorithm was carried on IEEE 33 bus radial system by [3].…”

Section: Methodsmentioning

confidence: 99%

Optimal Configuration of DG in Distribution System: An Overview

Kumar

Nallagownden

Elamvazuthi

2016

MATEC Web of Conferences

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Abstract. Distributed generation (DG) has increased ever attention in the distribution system from last few years. The main reason for DG in distribution system is increasing electric demand, deregulated power system and congested transmission network, which eventually declines the system performance. There is also increasing pressure of greenhouse gas emissions. For proper utilization of DG, the optimal placement and sizing is of main concern. Because improper DG location and size will increase the losses and decrease the system performance than existing. On the contrary, proper placement will maintain voltage profile, reduce power loss, and increase voltage stability in the distribution system. This paper presents overview of DG, the advances in DG technology and different optimization methods used for optimal placement and sizing problem. The key issues and challenges offered in the development of DG is also presented in this paper.

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Section: Methodsmentioning

confidence: 99%

Optimal Configuration of DG in Distribution System: An Overview

Kumar

Nallagownden

Elamvazuthi

2016

MATEC Web of Conferences

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“…The most commonly used traditional methods are Gradient Search, Linear Programming, Sequential Quadratic Programming, Nonlinear Programming and Dynamic Programming. Non-Traditional methods that authors use are Genetic Algorithm, Tabu Search, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, frog jumping methods, Evolutionary Programming Simulated Annealing and Bat Algorithm [6,7]. This paper consists of five chapters.…”

Section: Introductionmentioning

confidence: 99%

Review of Non-Traditional Optimization Methods for Allocation of Distributed Generation and Energy Storage in Distribution System

Šipoš¹,

Klaić²,

Fekete³

et al. 2018

Teh. vjesn.

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Abstract:The integration of distributed energy sources transforms passive distributed grid, in which the energy flows only in one direction (from the source to the consumer), in an active one, in which energy flows in both directions. To maximize positive impacts, which distributed generation (DG) can provide to the distribution network, it is necessary to determine the optimal allocation of distributed generation. The optimal allocation can be determined by using the optimization method. There are two main categories: exact methods (traditional) and heuristic (non-traditional) methods. Exact methods search for global optimum while heuristic methods achieve satisfactory solutions with greater computation speed. This paper gives a brief review of non-traditional methods used for determining optimal location and optimal power of DG with the aim to reduce real power losses and to improve voltage characteristics. Also, there is a review of the application of those methods in determining the optimal power, optimal location and optimal cycle of charging/discharging of electrical energy storage systems.

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“…miscoordination of protective devices, overvoltages during low load periods); see for instance [7]. Several surveys of the methods proposed for optimum allocation of distributed generation have been presented in the literature; see [8] and [9]. This work is related to the second perspective; namely, the optimum allocation of photovoltaic (PV) generation aimed at minimizing distribution system energy losses taking into account some constraints (e.g., there is a maximum voltage that should not be exceeded; there is a thermal limit for each system line section).…”

Section: Introductionmentioning

confidence: 99%

Optimum allocation of distributed generation in multi-feeder systems using long term evaluation and assuming voltage-dependent loads

Guerra

Martínez-Velasco

2016

Sustainable Energy, Grids and Networks

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The analysis of actual distribution systems with penetration of distributed generation requires powerful tools with capabilities that until very recently were not available in distribution software tools; for instance, probabilistic and time mode simulations. This paper presents the work made by the authors to expand some procedures previously implemented for using OpenDSS, a freely available software tool for distribution system studies, when it is driven as a COM DLL from MATLAB using a parallel computing environment. The paper details the application of parallel computing to the allocation of distributed generation for optimum reduction of energy losses in a multi-feeder distribution system when the system is evaluated during a long period (e.g., the target is to minimize energy losses for periods longer than one year) and voltage-dependent load models are used. The long term evaluation is carried out by assuming that the connection of the generation units is sequential, and using a divide and conquer approach to speed up calculations. The main goals are to check the viability of a Monte Carlo method in some studies for which parallel computing can be advantageously applied and propose a procedure for quasi-optimum allocation of photovoltaic generation in a multi-feeder distribution system.

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Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research

Cited by 742 publications

References 85 publications

Optimal Configuration of DG in Distribution System: An Overview

Optimal Configuration of DG in Distribution System: An Overview

Review of Non-Traditional Optimization Methods for Allocation of Distributed Generation and Energy Storage in Distribution System

Optimum allocation of distributed generation in multi-feeder systems using long term evaluation and assuming voltage-dependent loads

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