Risk-based distributed generation placement

Haghifam, Mahmoud Reza; Falaghi, Hamid; Malik, O.P.

doi:10.1049/iet-gtd:20070046

Cited by 182 publications

(127 citation statements)

References 14 publications

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“…In this category, the models are single or multi-objectives. The single-objective models are either originally singleobjective [5], or multi-objective which are converted into a single-objective (using a benefit to cost ratio index or an additive utility function [9]); multi-objective models of this category are solved using Pareto optimality concept [17]. The dynamic models of the literature are those which determine the optimal investment decisions to be made at each year (or time segment)…”

Section: B Literature Reviewmentioning

confidence: 99%

Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

Soroudi

Afrasiab

2012

IET Renew. Power Gener.

151

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A scenario reduction technique is used to reduce the computational burden of the model. The Pareto optimal solutions of the problem are found using a binary PSO algorithm and finally a fuzzy satisfying method is applied to select the optimal solution considering the desires of the planner. The effectiveness of the proposed model is demonstrated by applying it to a realistic 201-node distribution network. Index TermsDistributed generation, PSO, Dynamic planning, scenario reduction, Pareto optimal front. NOMENCLATURE Constants C ℓCost of reinforcement of feeder ℓ C

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Section: B Literature Reviewmentioning

confidence: 99%

Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

Soroudi

Afrasiab

2012

IET Renew. Power Gener.

151

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“…One of the appropriate models reported in the literature is calculating the difference between total loss of the system before and after DG placement [1], [5], [14], [16], [18].…”

Section: ) Total Incentive For Active Loss Reductionmentioning

confidence: 99%

“…In some models [16], the DNO should pay/receive equal to the electricity price multiplied by amount of loss reduction/increase and in some models [1], [5], [14], [18] a fix incentive, i.e. ψ, is paid to DNO for each MWh reduction of active losses .…”

Section: ) Total Incentive For Active Loss Reductionmentioning

confidence: 99%

“…This makes a chance for DNO to identify the technical characteristics of the network and provide economic signals for investors to know how much and where they should invest. Most of the reported models and methods of the literature deal with the DG sizing and placement problem in a DG-owned DNO environment, like [3], [9], [12], [15], [16]. In [3], a fuzzy goal programming is proposed to solve a multi-objective model which minimizes the active losses and improves voltage profile.…”

Section: Introductionmentioning

confidence: 99%

“…It is assumed that DNO can make investment in DG units and also he is responsible for purchasing energy from the main grid and provides it for the customers in its territory. In [16], a fuzzy model is proposed for DG placement which considers the effect of uncertainties of load and electricity price. The optimal placement is done for minimizing the cost of investment and operation of DG units and also the cost of purchasing electricity from the main grid for compensating the active losses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Soroudi

Ehsan

2010

Int Trans Elec Energy Syst

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This paper proposes a hybrid heuristic optimization method based on genetic algorithm and immune systems to maximize the benefits of distribution network operators (DNOs) accrued due to sizing and placement of distributed generation (DG) units in distribution networks. The effects of DG units in reducing the reinforcement costs and active power losses of distribution network have been investigated. In the presented method, the integration of DG units in distribution network is done considering both technical and economical aspects. The strength of the proposed method is evaluated by applying it on a small and a realistic large scale distribution network and the results are compared with analytical classic and other heuristic methods and discussed.

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HybridGA‐MCSbased optimization for distributed generation with dynamic voltage restorer planning in distribution networks

Vimlesh

Mukharjee

Singh

2021

Int Trans Electr Energ Syst

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The distributed generations (DGs) and dynamic voltage restorer (DVR) play an important role in the context of environmental problems and sustainable development throughout the world. The traditional DG and DVR locating and sizing method is based on a fixed network structure, which is assumed to be unchangeable during the planning period. The research presented here discusses a hybrid approach working on Monte Carlo simulation (MCS) and genetic algorithm (GA), which can be used for enhancement of system power factor (SPF) and voltage profile (VP) by the integration of DGs with DVR planning in distribution networks with ZIP load models from the minimization of total real power loss of the system viewpoint. The proposed methodology was applied to a 75-bus distribution network. This article is useful for researchers as well as practitioners who are working in the fields of integration of DG with DVR for improvement of system performances such as power factors and voltage profile from minimizing the losses.

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Risk-based distributed generation placement

Cited by 182 publications

References 14 publications

Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

HybridGA‐MCSbased optimization for distributed generation with dynamic voltage restorer planning in distribution networks

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