NSGA-based multiobjective optimisation for modular strategical planning of electric distribution systems

Ippolito, Mariano Giuseppe; Morana, Giuseppe; Sanseverino, Eleonora Riva; Vuinovich, F.

doi:10.1109/icsmc.2001.971998

Cited by 10 publications

(7 citation statements)

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“…Individual minima of each objective functionðf Ã i Þ Each objective function has a minimum value in its objective space called individual minima. This value is obtained independently by minimizing the single objective function given by Equation (2) with respect to the equality and inequality constraints of Equation (1).…”

Section: Normal Boundary Intersection Algorithmmentioning

confidence: 99%

“…Nowadays, the need for more flexible electric power systems, the changes in the regulatory and economic scenarios, the importance of harnessing energy savings and minimizing the environmental impacts have been the incentives for development of distributed generation (DG) systems [1][2][3]. There is an increasing demand for the development of distribution network from a passive network to an active one in order to facilitate the increasing levels of DG.…”

Section: Introductionmentioning

confidence: 99%

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Normal boundary intersection and benefit–cost ratio for distributed generation planning

Zangeneh

Jadid

Rahimi-Kian

2008

Int Trans Elec Energy Syst

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SUMMARYDistributed generation (DG) and its planning are becoming more important in power systems. This paper presents a DG planning strategy based on multi-objective optimization approaches. The main goal of this paper is to determine a Pareto set of locations and sizes of the new DG units by minimizing different cost functions considering the technical constraints, and then determining the best planning strategy. The Pareto set will help the DG planner make a better decision in choosing the best plan from the Pareto set. The best scheme is chosen by the benefit-cost ratio (BCR). The objectives of a DG planner in this paper are assumed to be the cost of investment and operation, cost of energy purchased, cost of energy losses and the cost of energy not supplied. Two market scenarios for the planner are considered: (1) electricity auctions and (2) bilateral contracts of energy. Several case studies are presented to demonstrate the applicability of the proposed method.

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Section: Normal Boundary Intersection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Normal boundary intersection and benefit–cost ratio for distributed generation planning

Zangeneh

Jadid

Rahimi-Kian

2008

Int Trans Elec Energy Syst

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“…a multiobjective optimization to solve static planning problems (using a Non-dominated Sorting Genetic Algorithm, NSGA, [11]) in which the solutions show minimum losses and installation costs and minimum unavailability; 2. a single objective optimization to find the minimum cost expansion strategies (using an ACS algorithm).…”

Section: The Problem Of Optimal Planning Of Distribution Systemsmentioning

confidence: 99%

“…In [10], a single objective optimisation strategy (minimum cost) has been implemented for a system organised on two voltage levels. In [11], a multiobjective strategy for a three levels system, two of which work in a.c. and one in direct current was implemented.…”

Section: Introductionmentioning

confidence: 99%

“…With reference to a static optimisation, Genetic Algorithms (GAs) allow, with a limited computational effort, the attainment of good sub-optimal solutions by means of a process of evolution. As far as static strategical planning is concerned, in [10,11], some GA-based optimisation procedures for a two or more level distribution system design have been implemented. In [10], a single objective optimisation strategy (minimum cost) has been implemented for a system organised on two voltage levels.…”

Section: Introductionmentioning

confidence: 99%

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Ant Colony Search Algorithm for Optimal Strategical Planning of Electrical Distribution Systems Expansion

et al. 2005

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Strategical planning is one of many research fields in the design of electrical distribution systems. The problem of strategical planning is a multiobjective combinatorial problem and the search space may often be quite large concerning to the options. The aim is to identify a strategy of expansion of a given distribution system in a given timeframe. For this problem, the search space is created beforehand by running a multiobjective optimisation algorithm for the optimal design of distribution networks for different load levels related to different years. The sets of Pareto-optimal solutions obtained for each load level at each year are equivalent in terms of the considered objectives, these being minimum losses, installation costs, and minimum unavailability. The problem of the identification of the optimal expansion strategy through these chronologically intermediate solutions leading to the final target configuration at the last year has been solved herein using an ACS (Ant Colony Search) algorithm. In order to verify the efficiency of the ACS algorithm, a small size application has been carried out and results have been compared to those obtained with enumeration. Then, a Simulated Annealing (SA) approach was used for a larger size test problem and results were compared to those obtained using the ACS. For this problem, the ACS demonstrated to be more robust than SA with higher quality results.

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Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm

Niu

Wan

et al. 2022

Journal of Cleaner Production

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NSGA-based multiobjective optimisation for modular strategical planning of electric distribution systems

Cited by 10 publications

References 1 publication

Normal boundary intersection and benefit–cost ratio for distributed generation planning

Normal boundary intersection and benefit–cost ratio for distributed generation planning

Ant Colony Search Algorithm for Optimal Strategical Planning of Electrical Distribution Systems Expansion

Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm

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