Reinforcement of Power System Performance Through Optimal Allotment of Distributed Generators Using Metaheuristic Optimization Algorithms

Mirsaeidi, Sohrab; Li, Shangru; Devkota, Subash; He, Jinghan; Li, Meng; Wang, Xiaojun; Konstantinou, Charalambos; Said, Dalila Mat; Muttaqi, Kashem M.

doi:10.1007/s42835-022-01080-9

Cited by 2 publications

(5 citation statements)

References 16 publications

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“…Te thermal capacity must not exceed the allowable limit of each conductor. Tus, the current limit is defned in the following model [9]:…”

Section: Generation Limits Of Photovoltaic Distributedmentioning

confidence: 99%

“…User-Written DLLs objectives, including total power loss and voltage deviation index, which are obtained by employing equations ( 7) and (9), respectively. On the other hand, penalty terms can be calculated by comparing dependent variables and their limits as follows:…”

Section: Scripts Com Interfacementioning

confidence: 99%

“…Most studies have applied popular optimization algorithms such as genetic algorithm (GA) and particle swarm optimization algorithm (PSO) to fnd the location and capacity of DGUs before connecting them to the distribution system. Te main purpose of this is to reduce power loss on branches and improve the voltage profle [5][6][7][8][9]. Te authors in [5,6] suggested GA and [7][8][9] applied PSO for determining the integration of DGUs with objective functions of improving voltage stability, reducing losses with the lowest investment cost.…”

Section: Introductionmentioning

confidence: 99%

“…Te main purpose of this is to reduce power loss on branches and improve the voltage profle [5][6][7][8][9]. Te authors in [5,6] suggested GA and [7][8][9] applied PSO for determining the integration of DGUs with objective functions of improving voltage stability, reducing losses with the lowest investment cost. Trough the application of technology-economic factors, the suitable solution of DGUs is found with the smallest generated cost.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Placement of Photovoltaic Distributed Generation Units in Radial Unbalanced Distribution Systems Using MATLAB and OpenDSS-Based Cosimulation and a Proposed Metaheuristic Algorithm

Pham

Nguyen

Kien

2022

International Transactions on Electrical Energy Systems

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In this paper, an improved slime mould algorithm (ISMA) is proposed for finding the optimal location and sizing of photovoltaic distributed generation units (PVDGUs) in unbalanced distribution systems (UDSs). The proposed method is developed by changing the control variable update mechanism of the original slime mould algorithm (SMA). Total power losses on distribution lines and voltage deviation index of all buses are minimized under the consideration of various constraints. The location and sizing of PVDGUs found by ISMA are added into the cosimulation between MATLAB and OpenDSS for reaching other remaining parameters of UDSs. In addition, sunflower optimization (SFO), social-ski drive (SSD), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), bonobo optimizer (BO), and SMA are also run for finding PVDGUs placement solution on the unbalanced three-phase IEEE 123-bus test feeder. As a result, the proposed ISMA can reduce the power loss up to 78.88% and cut the voltage deviation up to 1.4779 pu while that of others is from 69.10% to 78.87% and from 1.5759 to 1.4996 pu. Thus, ISMA should be used to place PVDGUs in UDSs meanwhile the cosimulation between MATLAB and OpenDSS should be applied as a power flow calculation tool for UDSs.

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“…Te thermal capacity must not exceed the allowable limit of each conductor. Tus, the current limit is defned in the following model [9]:…”

Section: Generation Limits Of Photovoltaic Distributedmentioning

confidence: 99%

Section: Scripts Com Interfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Placement of Photovoltaic Distributed Generation Units in Radial Unbalanced Distribution Systems Using MATLAB and OpenDSS-Based Cosimulation and a Proposed Metaheuristic Algorithm

Pham

Nguyen

Kien

2022

International Transactions on Electrical Energy Systems

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“…A comparative analysis of all these methods is presented in Table 1. Other than conventional optimization techniques, some hybrid techniques such as simulated annealing [25,38,41], sunflower optimization [12], bibliography originated [27], Slap swarm [35], and so forth are also obtainable with satisfactory performance indices. Moreover, it is beyond the necessity of the parameter selection for its operation irrespective of the size of the problem and the iteration requirement [22,32].…”

Section: Introductionmentioning

confidence: 99%

Distributed generator and capacitor‐embedded reconfiguration in three‐phase unbalanced distribution systems using teaching learning‐based optimization

Mehroliya,

Arya

2023

Comp Applic In Engineering

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In the transmission and distribution networks, both active and reactive power play significant roles. While active power does the beneficial work, reactive power maintains the voltage that necessitates management from a system reliability perspective. The voltage variation from the nominal range may result in unintentional operation and early component failure. To maximize the amount of real power that can be transported over the power‐transmitting media, the system must also control reactive power flow. In a three‐phase unbalanced distribution system reinforced with distributed generator units (DGUs) and shunt capacitor units (SCUs), this paper suggests a teaching learning‐based optimization (TLBO) approach to be applied to combinatorial problems for simultaneous reduction of real power loss and net reactive power flow, enhance voltage stability index (VSI) and minimize aggregated voltage deviation index. A multiobjective formulation based on TLBO has been implemented to choose the ideal sizes and placements of DGUs and SCUs in large distribution networks. The case studies have been carried out on an IEEE 33‐bus standard system and a large 123‐bus unbalanced system. The analysis of the IEEE 33‐ and 123‐bus test systems reveals the economic efficiency of the suggested TLBO algorithm. Notably, by applying DG and capacitor simultaneously in the 33‐bus system, a significant reduction of 59.35%, 55.05%, and 50.76% (phases a, b, and c), and for the 123‐bus system, a significant reduction of 43.65%, 19%, and 48.65% (phases a, b, and c) in total active power losses have been achieved. The results suggest that the proposed method renders significant improvement in voltage stability and reduces power losses concerning the base case for both test systems.

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Reinforcement of Power System Performance Through Optimal Allotment of Distributed Generators Using Metaheuristic Optimization Algorithms

Cited by 2 publications

References 16 publications

Optimal Placement of Photovoltaic Distributed Generation Units in Radial Unbalanced Distribution Systems Using MATLAB and OpenDSS-Based Cosimulation and a Proposed Metaheuristic Algorithm

Optimal Placement of Photovoltaic Distributed Generation Units in Radial Unbalanced Distribution Systems Using MATLAB and OpenDSS-Based Cosimulation and a Proposed Metaheuristic Algorithm

Distributed generator and capacitor‐embedded reconfiguration in three‐phase unbalanced distribution systems using teaching learning‐based optimization

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