Power generation expansion planning with adaptive simulated annealing genetic algorithm

Yildirim, Mehmet Bayram; Erkan, Kadir; Öztürk, Sıtkı

doi:10.1002/er.1214

Cited by 43 publications

(23 citation statements)

References 15 publications

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“…Since DG units are not able to work under 100% availability and it also reduces year by year during the planning horizon, an availability factor is multiplied to the capacity of each unit, P Cap DG;i . The availability factor in Equation (17) is calculated for the end of the planning horizon, Av t , versus its availability at the beginning of the planning horizon [17].…”

Section: Cost Function Of Energy Not Suppliedmentioning

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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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: Cost Function Of Energy Not Suppliedmentioning

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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“…In the articles [10] and [9] it has been used jointly with the simulated annealing technique, in the [15] with the Taboo search technique and in the [17] with particles swarm technique. This technique, in optimization problems, falls less in local optimum solution than other techniques.…”

Section: Genetic Algorithms (Ga)mentioning

confidence: 99%

Comparative study of techniques used in the generation expansion planning

Delgado

Ortiz²,

Renedo³

et al. 2007

REPQJ

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Abstract:At the present time the generation expansion planning (GEP) has become a problem very difficult to solve for multiple reasons: many objectives, high uncertainties, very great planning horizon, etc. Its resolution by means of the exact traditional techniques, in numerous occasions, is not viable by the excessive time that is needed. For that reason, technical modern that allow the resolution in smaller time and with smaller accuracy in the solution are applied. Most of the approximate techniques are included inside a wider concept that is denominated Artificial Intelligence. In this article the more promising techniques of IA are studied indicating their applications in the PEG as well as their advantages and disadvantages

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“…To name a few, several artificial intelligence techniques have been applied to solve the problem, such as fuzzy theory [2,3], artificial neural network [4], genetic algorithm [5,6], simulated annealing [7], particle swarm optimization [8], etc. Also, in order to tackle the various uncertainties, stochastic programming [9], stochastic mixed-integer programming [10] and fuzzy-based mixed-integer programming [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimization of Combined Generation and Transmission Expansion Planning Considering Bus Voltage Limits

Kim¹,

Kim²

2014

Journal of Electrical Engineering and Technology

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-A novel integrated optimization method is proposed to combine both generation and transmission line expansion problem considering bus voltage limit. Most of the existing researches on the combined generation and transmission expansion planning cannot consider bus voltages and reactive power flow limits because they are mostly based on the DC power flow model. In this paper the AC power flow model and nonlinear constraints related to reactive power are simplified and modified to improve the computation time and convergence. The proposed method has been successfully applied to Garver's six-bus system which is one of the most frequently used small scale sample systems to verify the transmission expansion method.

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Power generation expansion planning with adaptive simulated annealing genetic algorithm

Cited by 43 publications

References 15 publications

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

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

Comparative study of techniques used in the generation expansion planning

Integrated Optimization of Combined Generation and Transmission Expansion Planning Considering Bus Voltage Limits

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