Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation

El-Zonkoly, Amany

doi:10.1049/iet-gtd.2010.0676

Cited by 277 publications

(126 citation statements)

References 16 publications

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“…When DG unit is not connected to the system (i.e., base case system), the IMO is highest at one. The weights are intended to give the corresponding importance to each impact index for DG connection and depend on the required analysis (e.g., planning and operation) [25,39,[47][48][49]. Determining the appropriate weights will also rely on the experience of engineers and the concerns of distribution utilities.…”

Section: Multi-objective Indexmentioning

confidence: 99%

“…In future, if the impor-tance of voltage stability is increased due to a rise in load demands and system security concerns, the weights can be adjusted based on the priority. Considering the current concerns mentioned above and referring to previous papers [25,39,[47][48][49], this study assumes that the active power loss related to energy loss receives a signifi-cant weight of 0.7, leaving the reactive power loss related to volt-age stability at a weight of 0.3.…”

Section: Multi-objective Indexmentioning

confidence: 99%

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An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

2014

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highlights DG allocation for minimizing energy loss and enhancing voltage stability. Expressions to find the optimal power factor of DG with commercial standard size. A methodology for DG planning to recover investment for DG owners.Impact of technical and environmental benefits on DG investment decisions. Benefit-cost analysis to specify the optimal location, size and number of DG units. Keywords:Distributed generation; Emission reduction; Loss reduction; Network upgrade deferral; Optimal power factor; Voltage stability abstract This paper presents new analytical expressions to efficiently capture the optimal power factor of each Distributed Generation (DG) unit for reducing energy losses and enhancing voltage stability over a given planning horizon. These expressions are based on the derivation of a multi-objective index (IMO), which is formulated as a combination of active and reactive power loss indices. The decision for the optimal location, size and number of DG units is then obtained through a benefit-cost analysis. Here, the total benefit includes energy sales and additional benefits, namely energy loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The methodology was applied to a 69-bus industrial distribution system. The results showed that the additional benefits are imperative. Inclusion of these in the analysis would yield faster DG investment recovery.

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Section: Multi-objective Indexmentioning

confidence: 99%

Section: Multi-objective Indexmentioning

confidence: 99%

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

2014

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“…The objective function consists of system loading margin and the profit of the distribution companies (DISCOs) over the planning period, and it is solved using genetic algorithm (GA). In [46], a multi-objective index-based approach to optimally allocate DG units in distribution system with non-unity power factor considering different load models is discussed. The proposed multi-objective function considers a wide range of technical issues and particle swarm optimization technique is used.…”

Section: Network Dg Capacity Assessmentmentioning

confidence: 99%

Optimal capacity and location assessment of natural gas fired distributed generation in residential areas

Kamdar

Karady

2014

2014 Clemson University Power Systems Conference

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With ever increasing use of natural gas to generate electricity, installed natural gas fired microturbines are found in residential areas to generate electricity locally. This research work discusses a generalized methodology for assessing optimal capacity and locations for installing natural gas fired microturbines in distribution residential network.The overall objective is to place microturbines to minimize the system power loss occurring in the electrical distribution network; in such a way that the electric feeder does not need any up-gradation. The IEEE 123 Node Test Feeder is selected as the test bed for validating the developed methodology. Three-phase unbalanced electric power flow is run in OpenDSS through COM server, and the gas distribution network is analyzed using GASWorkS. The continual sensitivity analysis methodology is developed to select multiple DG locations and annual simulation is run to minimize annual average losses.The proposed placement of microturbines must be feasible in the gas distribution network and should not result into gas pipeline reinforcement. The corresponding gas distribution network is developed in GASWorkS software, and nodal pressures of the gas system are checked for various cases to investigate if the existing gas distribution network can accommodate the penetration of selected microturbines. The results indicate the optimal locations suitable to place microturbines and capacity that can be accommodated by the system, based on the consideration of overall minimum annual average losses as well as the guarantee of nodal pressure provided by the gas distribution network. The proposed method is generalized and can be used for any IEEE test feeder or an actual residential distribution network.ii ACKNOWLEDGMENTS

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“…The same authors in [2] have used circuit analysis to detemine the voltage stability index (VSI) for distribution networks connected in loop configuration.In this paper, we are more concerned with exploring PSO for optimizing the sizing and siting of DG units. Such an optimization problem canbe solved by different optimization algorithm [3], for instance Tuba Search (TS). However, is a time consuming algorithm and has a tendency to get trapped into local optimal value.…”

Section: Introductionmentioning

confidence: 99%

“…As compared to TS and SA, Genetic Algorithm (GA) has been widely used for finding the global (or near global) optimal solution of the problem. PSO has been proven to be better than GA in terms of computational time and convergence speed [3]. Hence, this paper surveys the use of PSO in placing DG units.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Particle Swarm Optimization for Use in Distributed Generation Placement and Sizing

2016

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Abstract. This paper surveys the research and development of Particle Swarm Optimization (PSO) algorithm for use in selecting a suitable position for Distributed Generation (DG) units within a distribution network. Our discussion first covers the algorithm development of PSO and its use in neural networks. After establishing the foundations of PSO, we then explore its use in sizing and sitting of DG units in distribution network. Combining PSO with other optimization techniques for attaining better results is also discussed in this paper.

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Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation

Cited by 277 publications

References 16 publications

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

Optimal capacity and location assessment of natural gas fired distributed generation in residential areas

A Survey on Particle Swarm Optimization for Use in Distributed Generation Placement and Sizing

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