An analytical approach for dg allocation in primary distribution network

In the current era of sustainable development, energy planning has become complex due to the involvement of multiple benchmarks like technical, social, economic and environmental. This in turn puts major constraints for decision makers to optimize energy alternatives independently and discretely especially in case of rural communities. In addition, topographical limitations concerning renewable energy systems which are mostly distributed in nature, the energy planning becomes more complicated. In such cases, decision analysis plays a vital role for designing such systems by considering various criteria and objectives even at disintegrated levels of electrification. Multiple criteria decision making (MCDM) is a branch of operational research dealing with finding optimal results in complex scenarios including various indicators, conflicting objectives and criteria. This tool is becoming popular in the field of energy planning due to the flexibility it provides to the decision makers to take decisions while considering all the criteria and objectives simultaneously. This article develops an insight into various MCDM techniques, progress made by considering renewable energy applications over MCDM methods and future prospects in this area. An extensive review in the sphere of sustainable energy has been performed by utilizing MCDM technique.

A review of multi criteria decision making (MCDM) towards sustainable renewable energy development

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

The integration of distributed generation (DG) units in power distribution networks has become increasingly important in recent years. The aim of the optimal DG placement (ODGP) is to provide the best locations and sizes of DGs to optimize electrical distribution network operation and planning taking into account DG capacity constraints. Several models and methods have been suggested for the solution of the ODGP problem. This paper presents an overview of the state of the art models and methods applied to the ODGP problem, analyzing and classifying current and future research trends in this field.

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

Distributed generation (DG) has been utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application. This paper presents a new optimization approach that employs an artificial bee colony (ABC) algorithm to determine the optimal DG-unit's size, power factor, and location in order to minimize the total system real power loss. The ABC algorithm is a new metaheuristic, population-based optimization technique inspired by the intelligent foraging behavior of the honeybee swarm. To reveal the validity of the ABC algorithm, sample radial distribution feeder systems are examined with different test cases. Furthermore, the results obtained by the proposed ABC algorithm are compared with those attained via other methods. The outcomes verify that the ABC algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems. The ABC algorithm has only two parameters to be tuned. Therefore, the updating of the two parameters towards the most effective values has a higher likelihood of success than in other competing metaheuristic methods.

Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm

Optimal distributed generation allocation for reliability, losses, and voltage improvement

It is difficult to estimate how much distributed generation (DG) capacity will be connected to distribution systems in the coming years; however, it is certain that increasing penetration levels require robust tools that help assess the capabilities and requirements of the networks in order to produce the best planning and control strategies. The work of this Task Force is focused on the numerous strategies and methods that have been developed in recent years to address DG integration and planning. This paper contains a critical review of the work in this field. Although there have been numerous publications in this area, widespread implementation of the methods has not taken place. The barriers to implementation of the advanced techniques are outlined, highlighting why network operators have been slow to pick up on the research to date. Furthermore, key challenges ahead which remain to be tackled are also described, many of which have come into clear focus with the current drive towards smarter distribution networks.

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State-of-the-Art Techniques and Challenges Ahead for Distributed Generation Planning and Optimization

This paper presents a model for active distribution systems expansion planning based on genetic algorithms, where distributed generation (DG) integration is considered together with conventional alternatives for expansion, such as rewiring, network reconfiguration, installation of new protection devices, etc. The novel approach of planning DG integration together with network expansion is a requirement for the modern active distribution network. However, the uncertainties related to DG power generation and load response growth must be taken into account in order to plan a safe system at a minimum cost. Thus, two different methodologies for uncertainties incorporation through the use of multiple scenarios analysis are proposed and compared. The multiple objectives optimization algorithm applied in the model takes into account the costs of reliability, losses, power imported from transmission, and network investments.

Active Distribution Network Integrated Planning Incorporating Distributed Generation and Load Response Uncertainties

https://www.matlabi.ir/wp-content/uploads/bank_papers/g_paper/g263_www.matlabi.ir_Multistage%20expansion%20planning%20for%20active%20distribution%20networks%20under%20demand.pdf

Multistage expansion planning for active distribution networks under demand and Distributed Generation uncertainties

This work presents a methodology for evaluating the impact of DG units installation on electric losses, reliability and voltage profile of distribution networks. The losses and voltage profile evaluation is based on a power flow method with the representation of generators as PV buses. The reliability indices evaluation is based on analytic methods modified to handle multiple generations. The methodology may be used to evaluate the influence of the local of installation and the capacity of DG on these system performance characteristics for different generation expansion planning alternatives. The results obtained with the proposed methodology for systems extracted from the literature demonstrates its applicability.

Carmen L. T. Borges

Papers

Optimal distributed generation allocation for reliability, losses, and voltage improvement

State-of-the-Art Techniques and Challenges Ahead for Distributed Generation Planning and Optimization

Active Distribution Network Integrated Planning Incorporating Distributed Generation and Load Response Uncertainties

Multistage expansion planning for active distribution networks under demand and Distributed Generation uncertainties

Impact of distributed generation allocation and sizing on reliability, losses and voltage profile