Optimal sizing of distributed generation using particle swarm optimization

Kumari, Reena; Kumar, G. V. Nagesh; Nagaraju, S. Siva; Jain, M. Babita

doi:10.1109/icicict1.2017.8342613

Cited by 29 publications

(12 citation statements)

References 8 publications

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“…The values are as shown in Tables 5-7. Utilizing the coefficient values from Tables 5-7, Equations ( 9)-( 14) were rewritten to give Equations ( 15)- (20).…”

Section: Determining the Coefficients Values Using Gamentioning

confidence: 99%

“…The best fit function with the least amounts of errors compared with the reference V PCC -X/R PCC data points was obtained for Equations ( 15)- (20). As stated in Section 2.4.3, the output parameter for GA objective functions was considered to be the error between reference and predicted values and was calculated by SD formulas.…”

Section: Finding the Fit Functionsmentioning

confidence: 99%

“…However, the main drawback of the GA-based method proposed in [19] is that it requires modelling the entire power system. Other Artificial intelligence-Based approaches for optimal sizing and siting of DG units presented in literature includes Particle Swarm Optimisation (PSO) [20], simulated annealing (SA) [21], the hybrid genetic algorithm (HGA) [21], an Artificial Bee Colony (ABC) [22] and the decision-making algorithm [23]. These approaches require modelling and simulating complicated test distribution systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Developing a Mathematical Model for Wind Power Plant Siting and Sizing in Distribution Networks

et al. 2020

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Wind Power Plants (WPPs) are generally located in remote areas with weak distribution connections. Hence, the value of Short Circuit Capacity (SCC), WPP size and the short circuit impedance angle ratio (X/R) are all very critical in the voltage stability of a distribution system connected WPP. This paper presents a new voltage stability model based on the mathematical relations between voltage, the level of wind power penetration, SCC and X/R at a given Point of Common Coupling (PCC) of a distribution network connected WPP. The proposed model introduces six equations based on the SCC and X/R values seen from a particular PCC point. The equations were developed for two common types of Wind Turbine Generators (WTGs), including: the Induction Generator (IG) and the Double Fed Induction Generator (DFIG). Taking advantage of the proposed equations, design engineers can predict how the steady-state PCC voltage will behave in response to different penetrations of IG- and DFIG-based WPPs. In addition, the proposed equations enable computing the maximum size of the WPP, ensuring grid code requirements at the given PCC without the need to carry out complex and time-consuming computational tasks or modelling of the system, which is a significant advantage over existing WPP sizing approaches.

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“…The values are as shown in Tables 5-7. Utilizing the coefficient values from Tables 5-7, Equations ( 9)-( 14) were rewritten to give Equations ( 15)- (20).…”

Section: Determining the Coefficients Values Using Gamentioning

confidence: 99%

Section: Finding the Fit Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Developing a Mathematical Model for Wind Power Plant Siting and Sizing in Distribution Networks

et al. 2020

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“…Selecting the best places for installing DG units and their preferable sizes in large distribution systems is a complex combinatorial optimization problem. Different formulations have been used based on calculus-based methods, search-based methods and combinations of various approaches [40] , such as, gradient and second-order algorithms [41] , Here-ford Ranch algorithm [42] , heuristic iterative search meth-od [43] , analytical method [44] , hybrid fuzzy-Genetic Algorithm (GA) method [45] .…”

Section: Optimum Dg Installation Based On Voltage Stabilitymentioning

confidence: 99%

Reliability / Security of Distribution System Network under Supporting by Distributed Generation

Eltamaly

Mossa

El‐Sayed

et al. 2019

I-ES

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<p>This paper discusses the impact of Distributed Generator (DG) on the power system for enhancing the power system quality by improving the voltage profile and power losses reduction. With the increasing on the demand power and the power system extension, the distributed renewable energy technologies are becoming increasingly important in the energy supply systems of many countries. DGs units can be defined as a small-units that generate electric power near to the location of customers based on the renewable energy techniques, including wind energy, solar energy, and geothermal energy. Interconnecting DG to an existing distribution system provides various benefits to several entities as for example the owner, utility and the final user. DG provides an enhanced power quality, higher reliability of the distribution system and can peak shaves and fill valleys. However, the integration of DG into existing networks has associated several technical, economic and regulatory questions. Also, this paper uses the power system IEEE-12 busses for an example to illustrate the voltage control and decreases the active and reactive power losses by adding the wind generation DGs with the distribution network. </p>

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“…Optimal location & Sizing of DG's using Backtracking Search Algorithm in IEEE 33-bus Distribution System [7]. In [8], proposing a novel methodology using the population depends on heuristic approach namely Particle Swarm Optimization (PSO) and New Particle Swarm Optimization (NPSO) for selecting the optimal sizing of Distributed Generator (DG) in the distribution systems. in [9], producing an optimization algorithm that employs modified flower pollination algorithm (MFPA) to select the optimal DGs allocation to minimize the system power losses, the performance of the proposed MFPA is investigated on three standard test systems; IEEE 33-bus, IEEE 69-bus and IEEE 136-bus.…”

Section: Introductionmentioning

confidence: 99%

Optimal Distributed Generation Placement and Sizing to Reduce Active Power Loss Using Ga and Aco Algorithm

Zakaria

El-Amary

Swief

et al. 2019

Journal of Al-Azhar University Engineering Sector

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In this paper, Genetic Algorithm (GA) and Ant colony Algorithm (ACO) optimization techniques are proposed to find optimal sizing and location for distributed generation in electrical networks. The objective function of the work relies upon a linearized model to compute the active power losses as a function of power generators. This strategy based on a strong coupling between active power and power flow taking into consideration the voltage angles. With the end goal to exhibit the adequacy of the proposed method, the proposed strategy is applied on IEEE 30-bus standard systems. Different maximum penetration level capacity of DG units with three ranges such as, 10%, 20% and 30% of maximum power load and various possible places of DG units among several types of DG (active, reactive or active and reactive power) are considered. Results show that the optimization tools employing GA and ACO are effective in reducing active power losses and cost loss by finding the optimal placement and sizing of DG units.

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Optimal sizing of distributed generation using particle swarm optimization

Cited by 29 publications

References 8 publications

Developing a Mathematical Model for Wind Power Plant Siting and Sizing in Distribution Networks

Developing a Mathematical Model for Wind Power Plant Siting and Sizing in Distribution Networks

Reliability / Security of Distribution System Network under Supporting by Distributed Generation

Optimal Distributed Generation Placement and Sizing to Reduce Active Power Loss Using Ga and Aco Algorithm

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