Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Lakshmi, G. V. Naga; Jayalaxmi, A.; Veeramsetty, Venkataramana

doi:10.1007/s40866-021-00107-w

Cited by 28 publications

(6 citation statements)

References 43 publications

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“…Therefore, the optimally sized and located DG units significantly maximize PLR in distribution networks [ 12 – 15 ]. The copper losses are gaining importance in the distribution network due to higher resistance to reactance ratio [ 16 , 17 ]. Copper loss (i.e., active power loss) in the distribution networks is estimated to dissipate roughly 13% of total produced power [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

et al. 2022

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The installation of Distributed Generation (DG) units in the Radial Distribution Networks (RDNs) has significant potential to minimize active power losses in distribution networks. However, inaccurate size(s) and location(s) of DG units increase power losses and associated Annual Financial Losses (AFL). A comprehensive review of the literature reveals that existing analytical, metaheuristic and hybrid algorithms employed on DG allocation problems trap in local or global optima resulting in higher power losses. To address these limitations, this article develops a parallel hybrid Arithmetic Optimization Algorithm and Salp Swarm Algorithm (AOASSA) for the optimal sizing and placement of DGs in the RDNs. The proposed parallel hybrid AOASSA enables the mutual benefit of both algorithms, i.e., the exploration capability of the SSA and the exploitation capability of the AOA. The performance of the proposed algorithm has been analyzed against the hybrid Arithmetic Optimization Algorithm Particle Swarm Optimization (AOAPSO), Salp Swarm Algorithm Particle Swarm Optimization (SSAPSO), standard AOA, SSA, and Particle Swarm Optimization (PSO) algorithms. The results obtained reveals that the proposed algorithm produces quality solutions and minimum power losses in RDNs. The Power Loss Reduction (PLR) obtained with the proposed algorithm has also been validated against recent analytical, metaheuristic and hybrid optimization algorithms with the help of three cases based on the number of DG units allocated. Using the proposed algorithm, the PLR and associated AFL reduction of the 33-bus and 69-bus RDNs improved to 65.51% and 69.14%, respectively. This study will help the local distribution companies to minimize power losses and associated AFL in the long-term planning paradigm.

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Section: Introductionmentioning

confidence: 99%

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

et al. 2022

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“…Genetic operators are reproduction, crossover, recombination, and mutation [44]. The elitism operator is used to protect better individuals from older generations [45]. A certain time, consecutive iterations, and reaching a specific response or maximum iteration number can be selected as algorithm stopping criteria.…”

Section: A Genetic Algorithmmentioning

confidence: 99%

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Pürlü

Türkay

2022

IEEE Access

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In this paper, two metaheuristic methods, genetic algorithm and particle swarm optimization, are proposed to determine the optimal locations, sizes and power factors of single and double distributed generation units. In line with the 2050 carbon neutral goal, the aim was to integrate renewable distributed energy sources such as photovoltaic panels and wind turbines into the distribution system with a high penetration level. In contrast to most studies based on constant loads and dispatchable generations, an application considering the seasonal uncertainties of generation and consumption was performed to minimize the annual energy losses and voltage deviations of the distribution network. In addition, dispatchable, controllable and fuel-based conventional resources were allocated to compare the contributions of renewable resources. These seasonal case studies with various constraints were applied to IEEE 33-bus radial distribution network. To verify the feasibility and robustness of the proposed algorithms, case studies for peak loads were created and compared with the literature studies. While all distributed generation sources were operated at both unity and optimum power factor in all case studies, zero power factor and leading power factor scenarios were examined for a peak load only. Photovoltaic applications without energy storage technologies have not been very efficient because of the uneven daily distribution of solar irradiance, especially insufficient irradiation in the evening and excessive irradiation at noon. However, wind energy applications are more reliable and feasible, because the wind speed distribution is relatively more uniform than that of solar irradiation, both seasonally and daily. In all cases, operating distributed generation sources at the optimal power factor provided better results than those operating at unity power factor. As a result, wind turbines operating at optimal power factors have been found to contribute better than photovoltaic systems and are almost as good as conventional sources with controllable power output. While both proposed algorithms yielded better results than those in the literature, particle swarm optimization was better than genetic algorithm in terms of providing the best solution, faster convergence and shorter running time.

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“…The proposed approach considers both transmission and production cost component in the DG allocation process. There are various other researches which focuses on the optimal location of DG without including the effect of renewable DG inclusion on reliability of the system [2,9,21,28,33,37,57]. Ref [56] solves the OPF for hybrid power system while taking into account the risk factor due to variable nature of wind and solar.…”

Section: Introductionmentioning

confidence: 99%

Efficient Energy Management and Reliability Assessment by Optimal Placement of Renewable Energy Sources with Pump Storage Plant

Jain

Mahajan

2023

Smart Grids and Energy

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Deteriorating environment conditions due to global warming and depleting fossil fuels, has forced mankind to shift towards renewable energy sources (RES). The integration of RES in existing power system is challenging due to its intermittent nature. This study formulates novel sensitivity index i.e. nodal price of energy index (NPEI) to assist the location of sensitive buses for optimal placement of RES. Firstly, the proposed approach subjects power system to (N-1) contingency including both loss of generator and line outage. The NPEI is computed for every bus in various contingency conditions. The most severe contingency is identified based on highest value of sensitivity index in peak load period. The buses with highest values of NPEI is the optimal location for RES placement in the most critical contingency scenario. Secondly, the proposed approach focuses on efficient and cost-effective energy management by optimally scheduling disparate energy generation sources. The system incorporates pumped storage plant (PSP) based energy storage devices to effectively utilize wind and solar energy. The objective of this study is to minimize the total generation cost which includes the generation cost associated with conventional generators, its emission cost and valve-point loading effect, generation cost of RES, and the cost associated with energy supplied by PSP. The partial shading of PV modules is also implemented and its impact on system parameters are observed. The penetration of RES and energy storage effects system reliability hence, the proposed approach computes reliability indices expected energy not supplied (EENS) and loss of load expected (LOLE) for both presence and absence of RES and PSP. Finally, the proposed approach results in optimal placement of RES and optimal energy scheduling of disparate energy sources along with analyzing its effect on system reliability. The study is performed on IEEE 30 bus system.

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Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Cited by 28 publications

References 43 publications

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Efficient Energy Management and Reliability Assessment by Optimal Placement of Renewable Energy Sources with Pump Storage Plant

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