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

Soroudi, Alireza; Ehsan, Mehdi

doi:10.1002/etep.501

Cited by 32 publications

(16 citation statements)

References 30 publications

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“…This total incentive is more than the previously reported best result of 11.588 £/h [21]. After a number of careful experimentation, following optimum values of ICA parameters have finally been settled: N c = 100; crossover probability = 0.6, mutation probability=0.2 .…”

Section: A Determination Of Parameters For Icamentioning

confidence: 90%

“…The evolutionary methods include Ordinal Optimization (OO) [14], GA-OPF [13], Particle Swarm Optimization (PSO) [23], pure Genetic Algorithm (GA) [24], Immune Algorithm [25] and Immune Genetic Algorithm (IGA) [21].…”

Section: B Comparing With Other Methodsmentioning

confidence: 99%

“…Azadi Ave., P. O. Box 11365-9363 Tel : (Office) +98 (21) 66165954 Fax : +98 (21) 66023261 2 (DNOs) to meet the requirements of their customers. The main point of deregulation is to split generation, transport and consumption of electrical energy between independent parties.…”

Section: A Motivationmentioning

confidence: 99%

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Imperialist competition algorithm for distributed generation connections

Soroudi

Ehsan

2012

IET Gener. Transm. Distrib.

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This paper proposes an Imperialist Competition Algorithm (ICA) to maximize the benefits of distribution network operators (DNOs) accrued due to presence of distributed generation (DG) units in distribution networks.The sum of active loss reduction and network investment deferral incentives has been considered as objective function to be maximized in this study. The optimal location and size of DG units in the network are found considering various techno-economical issues. The application of the proposed methodology in the UK under current Ofgem financial incentives for DNOs is investigated. The strength of the proposed approach is validated by comparing the obtained results with other methods of the literature.

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Section: A Determination Of Parameters For Icamentioning

confidence: 90%

Section: B Comparing With Other Methodsmentioning

confidence: 99%

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Imperialist competition algorithm for distributed generation connections

Soroudi

Ehsan

2012

IET Gener. Transm. Distrib.

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“…The literature suggests a wide range of objectives, such as voltage stability improvement [5], risk aversion in load procurement [6], active loss reduction [7], [8], reactive loss reduction [9], reliability improvement, reducing the cost of energy required for serving the customers, increasing the incentives received by distribution network owners for using DGs, reducing the cost of energy not supplied, injecting power into the grid at peak load and emission reduction [10]. These studies have considered a variety of technical issues including voltage profile [11], [12], thermal limits of conductors [13], substation capacity [14], three phase and single phase to ground short circuit [13], [15], and load modeling [9].…”

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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“…For example, DG placement for maximizing the benefits of network upgrade deferral and loss reduction were reported using different approaches: hybrid GA-OPF [64], ordinal optimization [65] and Immune-Genetic Algorithm (IGA) [66]. Moreover, an OPF-based method was proposed for distribution system planning in the presence of DG over a given planning horizon [67].…”

Section: Network Upgrade Deferralmentioning

confidence: 99%

Smart integration of distributed renewable generation and battery energy storage

Hung¹

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Renewable energy (i.e., biomass, wind and solar) and Battery Energy Storage (BES) are emerging as sustainable solutions for electricity generation. In the last decade, the smart grid has been introduced to accommodate high penetration of such renewable resources and make the power grid more efficient, reliable and resilient. The smart grid is formulated as a combination of power systems, telecommunication communication and information technology. As an integral part of the smart grid, a smart integration approach is presented in this thesis. The main idea behind the smart integration is locating, sizing and operating renewable-based Distributed Generation (DG) resources and associated BES units in distribution networks strategically by considering various technical, economical and environmental issues. Hence, the aim of the thesis is to develop methodologies for strategic planning and operations of high renewable DG penetration along with an efficient usage of BES units.The first contribution of the thesis is to present three alternative analytical expressions to identify the location, size and power factor of a single DG unit with a goal of minimising power losses. These expressions are easily adapted to accommodate different types of renewable DG units for minimizing energy losses by considering the time-varying demand and different operating conditions of DG units. Both dispatchable and nondispatchable renewable DG units are investigated in the study. Secondly, a methodology is also introduced in the thesis for the integration of multiple dispatchable biomass and nondispatchable wind units. The concept behind this methodology is that each nondispatchable wind unit is converted into a dispatchable source by adding a biomass unit with sufficient capacity to retain the energy loss at a minimum level. Thirdly, the thesis studies the determination of nondispatchable photovoltaic (PV) penetration into distribution systems while considering time-varying voltage-dependent load models and probabilistic generation. The system loads are classified as an industrial, commercial or residential type or a mix of them with different normalised daily patterns. The Beta probability density function model is used to describe the probabilistic nature of solar irradiance. An analytical expression is proposed to size a PV unit. This expression is based on the derivation of a multiobjective index (IMO) that is formulated as a combination of iii three indices, namely active power loss, reactive power loss and voltage deviation. The IMO is minimised in determining the optimal size and power factor of a PV unit. Fourthly, the thesis discusses the integration of PV and BES units considering optimal power dispatch. In this work, each nondispatchable PV unit is converted into a dispatchable source by adding a BES unit with sufficient capacity. An analytical expression is proposed to determine the optimal size and power factor of PV and BES units for reducing energy losses and enhancing voltage stability. A self-correction algorith...

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

Cited by 32 publications

References 30 publications

Imperialist competition algorithm for distributed generation connections

Imperialist competition algorithm for distributed generation connections

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

Smart integration of distributed renewable generation and battery energy storage

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