Combined Approach for Power Loss Minimization in Distribution Networks in the Presence of Gridable Electric Vehicles and Dispersed Generation

Suresh, Velamuri; Cherukuri, S. Hari Charan; Sudabattula, Suresh Kumar; Prabaharan, Natarajan; Hossain, Eklas

doi:10.1109/jsyst.2021.3123436

Cited by 21 publications

(9 citation statements)

References 24 publications

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“…The article's objective is to reduce power loss in the distribution system when DG is present along with more strategic planning of G2V and V2G operating modes of EVs. Velamuri et al (2022). To identify the optimal size of the DGs to be placed in the system, the suggested method includes a smart charging mechanism, a voltage stability index, and an EGOA.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of plug-in electric vehicles on grid integration with distributed energy resources: A review

et al. 2023

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The rise of electric vehicles (EVs) has a massive impact on the electricity grid due to the electrification of vehicles in the transportation sector. As a result, various techniques are needed to minimize the effects of charging on the grid. One of these techniques is having intelligent coordination between the various components of the EV charging network. This ensures that the network has enough electricity to support the charging needs of the vehicles. This article provides an overview of the many aspects of the EV industry and its charging infrastructure. It also provides a step-by-step approach for implementing the Vehicle to Grid (V2G) deployment, the utilization of recordings from the data by the EV battery through Artificial Intelligence and the cost-benefit analysis from effective utilization of the V2G method. The paper also explores the various aspects of the EV market and the role of aggregators and consumers. Finally, it assesses the possibility of expansion of the EV charging and grid integration system and outlines its challenges and solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Impact of plug-in electric vehicles on grid integration with distributed energy resources: A review

et al. 2023

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“…To ensure optimal allocation of DGs and electric vehicles on IEEE 33-bus RDN in 24 hours, enhanced grasshopper optimization algorithm was proposed to minimize power losses and improve voltage profile [33].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Also, comparison of the convergence and maximum iteration numbers with those in literature is listed in Table 9. No DG GA [8] EGOA [33] DAPSO [2] ALOA [59[ ALOA [36] PPA [14] GA PSO GA [8] EGOA [33] PSO GA DAPSO [2] ALOA [36] PPA [14] PPSO [1] 1DG 2DG…”

Section: B Case Study 1: For Peak Loadmentioning

confidence: 99%

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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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“…Hybrid optimizations select an optimization technique from a collection of different algorithms that dynamically perform the same optimization at compile time. As per this work, the Genetic Algorithm (GA) and Monte Carlo Simulation (MCS) should be used in the IEEE 16 bus system for loss reduction in both Static models (SLMs) and constant impedance (Z), constant current (I), and constant power (P) load models (ZIP-LMs). Recent studies have aided in the development of ideas for analyzing the effect of DGs on EVs in distribution networks: Patel et al [1] proposed a novel GA-based multi-objective optimization overview of the distinct sorts of DGs in power systems with ZIP-LMs from the perspective of minimizing the full real power loss of the system to improve power system efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Comparative Analysis of Distributed Generations and Electric Vehicles in Distribution Systems

Singh

Dubey

Kumar

2022

Preprint

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Electric vehicles were introduced to the market as a way to reduce dependency on internal combustion engine-driven transportation systems. However, this method increased the burden on the current electrical grid rather than reducing it. In the power grid, distributed generation ideas are presented to reduce this burden. This research shows how to combine hybrid optimization algorithms for optimum location and sizing of different sorts of Distributed Generation (DGs) with Electric Vehicle (EV) scheduling in distribution systems using static and ZIP load models to reduce the system's total real power loss. For assessment, many types of DGs and EVs are evaluated. For DGs with EVs scheduling in the distribution system, multiple static and ZIP load models are examined. Real power loss mitigation is achieved via hybrid optimization strategies that include Genetic algorithms and Monte Carlo simulation methodologies. The suggested approach's practicality was tested using a 16-bus distribution test system. At both static and ZIP load models, the more effective kinds of DGs with EVs couples are DG2 with Fuel cell electric vehicles.

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Combined Approach for Power Loss Minimization in Distribution Networks in the Presence of Gridable Electric Vehicles and Dispersed Generation

Cited by 21 publications

References 24 publications

Impact of plug-in electric vehicles on grid integration with distributed energy resources: A review

Impact of plug-in electric vehicles on grid integration with distributed energy resources: A review

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

A Novel Approach for Comparative Analysis of Distributed Generations and Electric Vehicles in Distribution Systems

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