G. () “ Distributed generation-literature rev iew and outline of the Swiss station”: Internal Report, ETH Zurich; November Hadjsaid N, Canard JF, Du mas F. Dispersed generation impact on distribution networks Distributed generation literature review and outline of the Swiss situation Author(s): Koeppel, Gaudenz Publication Date: Permanent Link: blogger.com Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more Swiss Federal Institute of Technology (ETH) Zurich geidl@blogger.com 20th July This document is the result of a literature study and intends to give an overview of issues and current state concerning protection of DG. The flrst part gives a basic introduction to distributed generation and power systemprotection
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Download PDF Download Full PDF Package This paper. A short summary of this paper. Optimal Allocation and Sizing of Distributed Generation for Power Loss Reduction using Modified PSO for Radial Distribution Systems.
Journal of Energy Technologies and Policy www. org ISSN Paper ISSN Online Vol. uk Abstract For the purpose of improving the voltage profile and power losses reduction, this paper proposes allocation and sizing of Distributed Generation DG in radial distribution system 69 IEEE bus test system.
A simp le and effective approach for power loss reduction PLR value is employed for the allocation wh ile the sizing was by using the results fro m the allocation as local optimu m in a mod ified PSO called Ran ked Evolutionary particle swarm optimization REPSO in order to obtain the g lobal optimu m.
Load simu lations in power flow yielded improvement not only in power loss reduction but also in voltage profile. The proposed distributed generation literature review and outline of the swiss situation was found to be faster and gives more accurate results than the EP and PSO algorith ms. Keywords: Distributed Generat ion, Evo lutionary programming, Particle Swarm Optimization, Allocation and sizing, Power loss reduction.
Introducti on Energy resources in our modern fast paced world are fast depleting, hence it is indispensible that we find new ways of generating energy which is both self sustaining as well as easily manageable [1]. The rising concern about environmental pollution has also made DG to be a convenient substitute of the fast depleting fossil fuel centralized systems.
Their successfully integration into the network using new-generation technologies and power electronics have attracted many investors. Despite these advantages many issues are however, still pending concerning the integration of DGs within the existing power system networks; that require special attention [2—3]. Specifically the integration has changed the system fro m passive network to act ive networks and the change has serious impact on both the reliability and operation of the network as a whole [4], distributed generation literature review and outline of the swiss situation.
In addition to that, the non-optimal p lacement of DG can result in an increase in the system power losses and the consequence is that the voltage profile can fall below the allowab le limit [5]. Hence optimal p lacement of DG is h ighly required in order to minimize overall power system losses and therefore imp rove voltage profiles as utilities are seriously facing technical and non-technical issues, which may likely co mpound the situation.
Current and past researches have proposed many optimal placement methods ranging from analytical to optimization approaches that have successfully allocated and sized DG units [].
Several Analytical methods have been proposed by many authors for various objectives. The authors in [7], have presented analytical method that determines the optimal location based on loss min imization objective for both transmission and distribution and networks. Similar work in [8], have used the exact loss equation to find the optimu m location of DG and the DG was sized by using loss sensitivity equation based on minimu m losses. The authors in [9], distributed generation literature review and outline of the swiss situation, also presented the loss sensitivity factor based on equivalent current injection using two Bus-Injection to Branch-Current BIBC and Branch-Current to Bus-Vo ltage BCBV matrix.
In [10], optimal sizing and placement of DG for a network system was done based on two objectives which are losses and cost function as an objective using a simp le search algorith m. The method is simp le but consumes alot of t ime during the search processes for both the best location and optimu m size. Another author in distributed generation literature review and outline of the swiss situation, considers optimal size and location by minimizing loss and generation cost as a parameter together with DG power limits.
The site was then selected by considering the minimu m total power losses considering DG at each bus. Even though the approach is accurate but computation time is long and very tedious. For methods based on optimization, many algorith ms have been proposed by many researchers for optimal placement and sizing in distribution networks. A lso authors in [13] have introduced GA based optimizat ion algorithm to optimize size and allocate mu ltip le DG units for the purpose of minimizing power losses by taking into account the voltage limits of all the nodes in the system.
In this case optimal location of DG and reclosers were found based on system reliability. In [15], a hybrid of GA and PSO was employed for optimal location as well as capacity of DG, considering mult i- objective constraints such as voltage regulation improvement, voltage stability and system losses. In this paper, a hybridized PSO known as Ran ked Evolutionary Particle Swarm Optimization REPSO approach has been used to determine the optimal size and location the DG un its based on Power Loss Reduction value.
The effectiveness of this approach is demonstrated on test system. Overall, the method proposed is simp le and requires distributed generation literature review and outline of the swiss situation computational time for determining the optimu m placement and size of DG when co mpared to other optimization algorith ms.
Problem Formulati on Placement of DG units includes determination of the size, location as well as the number of units to be installed within the distribution system so that benefits are achieved while operational constraints are fully satisfied for varying load conditions.
The current Ii is determined fro m load flow using Newton-Raphson method. For single sources network all the power is supplied by the single sources but with DG penetration that are optimally located power loss reduction is achieved. This power losses reduction due to DG connection is determined as the difference of the power losses with DG and without DG connection.
The emphasis is to place the DG at a location that will g ive maximu m loss reduction. The optimal location of the Distributed generation literature review and outline of the swiss situation is bus i for maximu m power loss reduction. Ranked Evol utionary Particle Swarm Opti mization Algorithm The Rank Evolutionary Particle Swarm Optimizat ion REPSO is a hybrid of Evo lutionary Programming EP and PSO.
Evolutionary Programming EP is a heuristic population-based search technique that is used for both random variation and selection.
The search for an optimal solution is based on the natural process of biological evolution and is acco mplished by using a parallel method in parameter space. EP exp lores the problem space by using a population of trials, as opposed to a single point, to demonstrate potential solutions to a problem.
Th is makes EP less likely to get trapped in local minima. EP emp loys the tournament scheme in order to choose the survivals for the next generation. This selection is used to identify the candidates that can pass into the next generation fro m the comb ined population of the parents and offspring. The population of individuals with better fitness functions are then sorted in ascending order.
The first half of the population is then retained as the new individuals or parents to the next generation, and the others are removed fro m the pool. This process continues until the solution converges [16].
Th is is the reason why EP is comb ined with PSO to achieve a global optimal solution within a short time. Th is new approach is called REPSO algorithm that combines the merits of the EP and the particle swarm optimisation algorith m.
The advantages are that of speed and accuracy when compared to traditional PSO [17]. The algorithm is as shown in Fig. Results and Discussions A program is written in MATLAB for the calculation of power loss for optimal DG placement and the optimu m sizing was done by using REPSO. A bus system as LV feeder with the line, bus data and load modelled as a constant power type are used for the simu lations. The line loss without DG connection is as shown in Fig.
The DG penetration will result in reduction of these losses in the network especially at the most critical locations. The result shows that adding more than four DG to the network is not economical considering the power loss reduction. Table I: Single DG placement Iteration No. Bus DG size PLR 1 61 1. The locations where the DG units are placed are considered as optimal locations which are busses 61, 17, 50 and With these locations sizes are determine using the REPSO algorith m.
The DG sizes obtained are global optimu m sizes that corresponds optimu m location obtained during single placement. The sizes of the DGs are dependent on the number of DG locations. It is better to distribute the DGs to various busses rather than concentrating them on a single bus. In this radial network only 4 DG units were installed without violating the system constraints. In the first placement only one was installed.
In the second two DG un its while third and fo rth three and four DG units were installed respectively as ind icated in Table II with the power losses before and after DG installation.
It can be observed from the table that as the number of DG units installed increases the power loss reduction also increases. In all the cases voltage profile has improved and the improvement is significant.
The lowest voltage profile for all the buses is above statutory lowest limit. Typically during the first placement the bus with the highest PLR value is Bus 61 and is the best candidate for DG p lacement. The third and the forth placement power loss reduction difference is found to be insignificant when co mpared distributed generation literature review and outline of the swiss situation the first two placements. Conclusions In this paper, a methodology for finding the optimal locations and sizes of DGs fo r Po wer loss reduction of radial distribution systems is presented.
The DG placement method proposed is based on power loss reduction and a REPSO algorith m is proposed for finding the optimal DG sizes with all the necessary optimization constraints. This methodology was tested on IEEE 69 bus system. The results show that DG installation at the optimal locations can improve voltage profile and at the same time reduces power losses of the network. The proposed algorith m g ives faster and more accurate results when compared to EP and PSO algorithms.
References S. JoshiA. Mathur, A. Jain, S. Gupta, N. Jani, B. Dispersed generation impact on distribution networks. IEEE Co mput Appl Po wer ;—8 Tuitemwong K, Premrudeepreechacharn S. Int J Electr Po wer ; 33 pp—71 Griffin T, To mosovic K, Secrest D, Law A.
Distributed Energy Resources – Microgrids
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G. () “ Distributed generation-literature rev iew and outline of the Swiss station”: Internal Report, ETH Zurich; November Hadjsaid N, Canard JF, Du mas F. Dispersed generation impact on distribution networks Jan 01, · Literature review on Distributed Generation allocation In recent years several researchers tried to explore different possibilities for optimal allocation of DG, which are reviewed in this section. Benefits of DG from economical point of view include reduction or avoidance of the need to build new T&D lines and up gradation of existing power lines As the electric utility industry continues to restructure, driven both by rapidly evolving regulatory environments and by market forces, the emergence of a number of new generation technologies also profoundly influences the industry's outlook. While it is certainly true that government public policies and regulations have played a major role in the rapidly growing rate at which distributed
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