Maximization of System Benefits with the Optimal Placement of DG and DSTATCOM Considering Load Variations

Sannigrahi, Surajit; Acharjee, Parimal

doi:10.1016/j.procs.2018.10.446

Cited by 25 publications

(13 citation statements)

References 15 publications

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“…The PLFR curve for the IEEE 118 bus network is shown in Figure 19. According to the PLFR curve, buses of 70,104,78,68,106,108,65,31,69,67,89,64,103,101,42,46,58,30,23 and 47 are selected as candidate buses for reactive source installation. The simulation results, similar to results for the 118 bus network according to Figures 20-22, showed that the hybrid allocation has the least losses and voltage deviations.…”

Section: Results Of 118 Bus Ieee Networkmentioning

confidence: 99%

“…In [22], the optimal placement of DSTATCOM along with DG in the networks is applied for reducing power losses using cuckoo optimization algorithm (COA) as single objective optimization. In [23], the multi-objective allocation of DSTATCOM in load variations condition in distribution networks to reduce power losses and voltage deviations based on weighted coefficient method. In [24], optimal allocation of DSTATCOM is determined with the objective of power loss minimization and voltage stability improvement using whale optimization algorithm (WOA) based on weighted coefficient method.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Allocation of Capacitor and Distributed Static Compensator in Radial Distribution Networks Using Multi-Objective Improved Golden Ratio Optimization Based on Fuzzy Decision Making

et al. 2020

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In this paper, multi-objective reactive power compensation based on optimal and simultaneous allocation of capacitors and distribution static compensator (DSTATCOM) in radial distribution networks based on fuzzy decision making is proposed with the aim of reducing power losses cost, reactive investment cost and reducing voltage deviations considering power loss reduction factor (PLRF) to identify candidate buses. Optimal location and size of reactive resources is determined based using multi-objective improved golden ratio optimization algorithm (MOIGROM) inspired by Fibonacci series and improved using nonlinearly inertia weight strategy. This method is performed on a 13 bus distribution network, 69 and 118 buses IEEE standard networks in various scenarios including capacitor allocation only, DSTATCOM allocation only and optimal and simultaneous capacitor and DSTATCOM allocation (hybrid allocation). The simulation results showed that the hybrid allocation has less power losses and voltage deviations in different scenarios using fuzzy decision making based on Pareto front set. Also the impact of different loading conditions on problem solution cleared that with increasing the load level, especially during the heavy load, the power losses and voltage deviations of the networks increase and vice versa. In addition, the advantage of the MOIGROM method over the other methods is confirmed. INDEX TERMS Radial distribution network, optimal capacitor and DSTATCOM allocation, power loss reduction factor, fuzzy decision making, multi-objective improved golden ratio optimization algorithm.

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Section: Results Of 118 Bus Ieee Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Allocation of Capacitor and Distributed Static Compensator in Radial Distribution Networks Using Multi-Objective Improved Golden Ratio Optimization Based on Fuzzy Decision Making

et al. 2020

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“…A logistic transformation   i k SV is exploited to accomplish this modification that is written in Eq. (6) and (7).…”

Section: A Optimal Dg Placement Using Hybrid Bpso-fpamentioning

confidence: 99%

“…The presence of DG is a significant part in reconfiguration process which leads to save more power loss, decrease the drained current from central substations, provide stable feeder loads and enhancement in the voltage magnitude [6]. Power losses surely have an important effect on the revenues of electric power companies, greenhouse gases emission, and the cost of the energy supplied [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Distributed Generation Units in Radial Distribution System using Hybrid Techniques

Rani,

Reddy

2019

IJEAT

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Reconfiguration is a process that supports to eliminate the power loss from a distribution network and this process have the capability to reduce the losses up to a specific point. Additionally, loss minimization may be calculated through the presentation of Distributed Generation (DG) units. Conversely, the incorporation of DG into the distribution network at an improper position may cause higher in losses and fluctuations in voltage. In the meantime, the uncertainty in voltage may produce partial power failure in the system. For that reason, it is essential to deliberate the stability boundaries in DGs position and sizing in the Radial Distribution System (RDS). In this research paper, hybrid Binary Particle Swarm Optimization (BPSO) with Flower Pollination Algorithm (FPA) is proposed for the ideal reconfiguration process and placing the DG in the 69-bus RDS. BPSO is applied to identify the best DG reconfiguration and FPA is proposed to determine the optimal DG size. This technique narrowly changes the DG location in every load bus of the network that delivers the minimum value of the objective function, which is considered as the finest candidate for DG connection. The simulation outcomes indicate the proposed method is more effective in reducing the power loss from 224.9804 to 27.2183 KW with the reduction of 88.8972% when compared to existing algorithm

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“…[14] introduced the algorithm of adaptation in weight Particle swarm optimization in order to discover the optimal placements of various Distributed generation; though the impartial task was to somehow minimize the loss that is happening in the active power of the method. In [15], grouping of GAs and PSO was used to invent the most beneficial position of a restore functionality of Distributed generation units installed into the network. [16] Introduced method of Particle swarm optimization system so that losses can be minimized that is occurring at the active power in order to recognize numerous placements of the Distributed generation with power factor equal to non-unity.…”

Section: Introductionmentioning

confidence: 99%

Multiple DG Placement in Radial Bus System using Weight Adaptive Swarm Intelligence to Improve Voltage Profile

2019

IJITEE

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Methods found in the literature are regularly not capable of concurrently examine financial and technical oriented benefits for multi-location DG placement systems. Therefore an effective system is presented to such benefits as for multiple DG placements. Particle Swarm Optimization (PSO) is extensively used to optimize dimensional data in number of applications due to its fast searching and converging property to optimal solution and hence used in finding optimal location for multi DG placement. It is tested on 33 IEEE bus systems in which three initial locations are selected on random basis for DG placement and power flow analysis is done to evaluate electricity losses and voltage profile at all buses. Then PSO is used to find the optimal locations which give high values of voltage profile then initial configuration and proposed three best effective, beneficial locations where there are minimum electricity losses and high voltage profile indices. For speedy convergence of the algorithm adaptive weight parameter is used instead of fixed weight. Experimental results have been carried out for IEEE 33 radial bus in which type-1 DG placement is considered. With optimized locations, new switch-ties gives 70.4 % less power loss than the initial tied switches.

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Maximization of System Benefits with the Optimal Placement of DG and DSTATCOM Considering Load Variations

Cited by 25 publications

References 15 publications

Hybrid Allocation of Capacitor and Distributed Static Compensator in Radial Distribution Networks Using Multi-Objective Improved Golden Ratio Optimization Based on Fuzzy Decision Making

Hybrid Allocation of Capacitor and Distributed Static Compensator in Radial Distribution Networks Using Multi-Objective Improved Golden Ratio Optimization Based on Fuzzy Decision Making

Optimal Placement of Distributed Generation Units in Radial Distribution System using Hybrid Techniques

Multiple DG Placement in Radial Bus System using Weight Adaptive Swarm Intelligence to Improve Voltage Profile

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