Optimal Placement and Sizing of Distributed Generation Using Metaheuristic Algorithm

Nageswari, D.; Kalaiarasi, N.; Geethamahalakshmi, G.

doi:10.32604/csse.2022.020539

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…Figure 2 shows the distribution system with both of sending and receiving (feeder and load) parts. Branch current and voltage of receiver section are received by equations (14)(15)(16)(17):…”

Section: Line Flow Limitation (Lfl)mentioning

confidence: 99%

“…The multi objective performance index (MOPI) is introduced to optimal siting and sizing of DG in the DN. By implementing weighted coefficient, many technical constraints are combined and solved under various operating circumstance (15). Nafeh et al (16) studied changing parking lot (PL) to the intelligent parking lot (IPL)with installing WT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing a New Voltage Stability Index in Distribution Power System in Presence of Wind Turbine Units

Foomani,

Moradlou,

Nazarian

2024

IJE

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Equipping renewable energy resources generation units in the distribution network to reduce economical and emission concerns are the examples of active distribution network(ADN). The other advantages of utilizing distributed generators (DGs) are improving technical constraints of ADN. In this paper multibenefit functions are defined as main functions. Each of functions illustrates the positive impacts of utilizing wind turbines in the improving technical constraints of the ADN. Voltage stability (VS) is one of the main technical indices of the ADN. Several VSIs are defined to evaluate voltage stability of the ADN. The previous indices could not give the proper results about allocating DGs and accurate evaluating of voltage stability of ADN. This work proposes the new VSI. To this aim active power loss (APL), reactive power loss (RPL) and voltage stability index (VSI) are considered as technical constraints. In order to evaluate the presence of WT on improving APL and RPL, WTs are considered in two operational modes; unified power factor (UPF) and (APF). The main benefit function is solved by implementing genetic algorithm (GA). Multiplying weights to the APL, RPL and VSI (which are improved by attendance WTs) in benefit function formulation, make the multi-criteria decision formation to the proposed optimization problem. By employing analytical hierarchy process (AHP) technique and considering each technical constraints as main criteria, the obtained solutions are arranged. To verify the positive effectiveness of the proposed VSI, its results are compared with the results of other VSIs in the 33, 67 and 118 bus IEEE radial DN.

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Section: Line Flow Limitation (Lfl)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Utilizing a New Voltage Stability Index in Distribution Power System in Presence of Wind Turbine Units

Foomani,

Moradlou,

Nazarian

2024

IJE

View full text Add to dashboard Cite

show abstract

“…Multi objective index (MO) is the combination of active power loss index (API), reactive power loss index (RPI) and voltage deviation index (VD) [9]. The optimal allocation of DG can be achieved by minimizing the MO.…”

Section: Multi Objective Indexmentioning

confidence: 99%

“…Technical constraints are followed in [8] for optimal allocation of DGs without its violation and a selective particle swarm optimization algorithm (SPSO) is utilized in the presence of a non-uniformly distributed pattern of load. The study [9] involved the chaotic artificial flora optimization-based technique for multiple test scenarios of optimal allocation of DGs and system reconfiguration. In [10], multiple load models are considered for obtaining the optimal size and location of DGs using the student psychology-based optimization (SPBO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System Under A 24h Load Horizon

Abbas¹,

Qaisar²,

Waqar³

et al. 2022

Preprint

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Load variations in any power system result in losses escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in the priorly existing methods that ideally allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and allocation of DGs must be done in accordance to the variations of the connected load. Therefore, the current paper addressed the aforementioned problem by distinctive optimal allocation of DGs for each variability of 24hour load horizon. However, the single exclusive solution is considered among all allocations of 24 hours. The min-max regret concept has been utilized in order to deal with such methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royal optimization (BRO), accelerated particle swarm optimization (APSO) and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises of active and reactive power losses and voltage deviation. Hence, in order to find robustness of proposed technique, Conseil international des grands réseaux electriques (CIGRE’s) MV benchmark model incorporating 14 buses has been considerably used as a test network. In the end the results of three proposed algorithms have been compared.

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“…Technical constraints are followed in [8] for the optimal allocation of DGs without its violation, and a selective particle swarm optimization algorithm (SPSO) is utilized in the presence of a non-uniformly distributed pattern of load. The study in [9] involved the chaotic artificial flora optimization-based technique for multiple test scenarios of the optimal allocation of DGs and system reconfiguration. In [10], multiple load models are considered for obtaining the optimal size and location of DGs using the student psychology-based optimization (SPBO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon

et al. 2022

View full text Add to dashboard Cite

Load variations in any power system result in loss escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in existing methods that, ideally, the allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and the allocation of DGs must be done in accordance with the variations of the connected load. Therefore, the current paper addresses the aforementioned problem by the distinctive optimal allocation of DGs for each variability of 24 h load horizon. However, a single exclusive solution is considered among all allocations of 24 h. The min-max regret concept has been utilized in order to deal with such a methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royale optimization (BRO), accelerated particle swarm optimization (APSO), and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises active and reactive power losses and voltage deviation. Hence, in order to find the robustness of the proposed technique, Conseil international des grands reseaux electriques’ (CIGRE) MV benchmark model incorporating 14 buses has been used considerably as a test network. In the end, the results of three proposed algorithms have been compared.

show abstract

Optimal Placement and Sizing of Distributed Generation Using Metaheuristic Algorithm

Cited by 12 publications

References 22 publications

Utilizing a New Voltage Stability Index in Distribution Power System in Presence of Wind Turbine Units

Utilizing a New Voltage Stability Index in Distribution Power System in Presence of Wind Turbine Units

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System Under A 24h Load Horizon

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon

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