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

Šipoš, Mario; Klaić, Zvonimir; Fekete, Krešimir; Stojkov, Marinko

doi:10.17559/tv-20170703135143

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Other methods that have been employed in the evaluation of hosting capacities especially in multiobjective optimisation applications include genetic algorithm, particle swarm optimisation, artificial neural network, bee colony optimisation, differential evolution, etc. (Venter, 2010;Pratihar, 2012;Khamees, Badra & Abdelaziz, 2016;Šipoš et al, 2018;Bajaj & Singh, 2021;.…”

Section: Hosting Capacity Methodologiesmentioning

confidence: 99%

Design and application of a distributed generation hosting capacity algorithm

Sam

Oyedokun

Akpeji

2021

J. energy South. Afr.

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Distribution networks in Southern Africa and elsewhere are witnessing an unprecedented growth of consumer-side distributed generation (DG) courtesy of governmental interventions to maximise the utilisation of renewable energy resources through low-carbon grid-edge technologies. To deal with the increasing adoption of consumer-side DG, distribution network operators need to conduct technical studies to foster an understanding of the benefits and impacts of DG and the hosting capacity (HC) of existing distribution networks. This will aid the implementation of measures to manage grid exports. Using a distribution network in Namibia as a case study, this paper presents an algorithm for assessing the HC of consumer-side DG in existing distribution networks that are situated in areas anticipating high and uniform uptake of DG. The algorithm is a hybrid of deterministic and probabilistic methods. The uniqueness of the algorithm is the concept of calculating monthly HC. The algorithm was tested on a real existing residential distribution network and the results confirmed that HC varies monthly. However, the practical implementation of monthly HC requires upgrades to existing inverter technology, which currently contains a single export limit functionality. This opens the possibility to drive innovation in the inverter technology to develop a date-based multiple export limit functionality.

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Section: Hosting Capacity Methodologiesmentioning

confidence: 99%

Design and application of a distributed generation hosting capacity algorithm

Sam

Oyedokun

Akpeji

2021

J. energy South. Afr.

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“…This algorithm made further advances in the field, such as the main duality theorem, the Farkas lemma, the Motzkin transfer theorem and others [19]. The traditionally employed model of optimization includes linear programming, sequential quadratic programming, nonlinear programming, and dynamic programming [20]. In 1939, the first formulation of the linear programming problem and the method for solving this problem were proposed by Leonid Kantorovich.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Attribute Decision-Making Methods as a Part of Mathematical Optimization

Vinogradova

2019

Mathematics

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Optimization problems are relevant to various areas of human activity. In different cases, the problems are solved by applying appropriate optimization methods. A range of optimization problems has resulted in a number of different methods and algorithms for reaching solutions. One of the problems deals with the decision-making area, which is an optimal option selected from several options of comparison. Multi-Attribute Decision-Making (MADM) methods are widely applied for making the optimal solution, selecting a single option or ranking choices from the most to the least appropriate. This paper is aimed at providing MADM methods as a component of mathematics-based optimization. The theoretical part of the paper presents evaluation criteria of methods as the objective functions. To illustrate the idea, some of the most frequently used methods in practice—Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Complex Proportional Assessment Method (COPRAS), Multi-Objective Optimization by Ratio Analysis (MOORA) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)—were chosen. These methods use a finite number of explicitly given alternatives. The research literature does not propose the best or most appropriate MADM method for dealing with a specific task. Thus, several techniques are frequently applied in parallel to make the right decision. Each method differs in the data processing, and therefore the results of MADM methods are obtained on different scales. The practical part of this paper demonstrates how to combine the results of several applied methods into a single value. This paper proposes a new approach for evaluating that involves merging the results of all applied MADM methods into a single value, taking into account the suitability of the methods for the task to be solved. Taken as a basis is the fact that if a method is more stable to a minor data change, the greater importance (weight) it has for the merged result. This paper proposes an algorithm for determining the stability of MADM methods by applying the statistical simulation method using a sequence of random numbers from the given distribution. This paper shows the different approaches to normalizing the results of MADM methods. For arranging negative values and making the scales of the results of the methods equal, Weitendorf’s linear normalization and classical and author-proposed transformation techniques have been illustrated in this paper.

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Genetic algorithm for complex optimization of power system mode

Muhamediyeva,

Safarova

2024

Iii International Scientific and Practical Symposium “Materials Science and Technology” (Mst-Iii-2023)

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

Cited by 3 publications

References 31 publications

Design and application of a distributed generation hosting capacity algorithm

Design and application of a distributed generation hosting capacity algorithm

Multi-Attribute Decision-Making Methods as a Part of Mathematical Optimization

Genetic algorithm for complex optimization of power system mode

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