Real power loss minimisation of smart grid with electric vehicles using distribution feeder reconfiguration

Singh, Jyotsna G.; Tiwari, Rajive

doi:10.1049/iet-gtd.2018.6330

Cited by 66 publications

(35 citation statements)

References 42 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Real power losses in distribution networks are equal to the difference between the generated active power and the consumed real power. Therefore, (1) is selected as the objective function of the present real power optimization problem in this work [15][16][17]:…”

Section: Tap Value Formulationmentioning

confidence: 99%

Power Loss Minimization for Distribution Networks with Load Tap Changing Using Genetic Algorithm and Environmental Impact Analysis

et al. 2021

View full text Add to dashboard Cite

Section: Tap Value Formulationmentioning

confidence: 99%

Power Loss Minimization for Distribution Networks with Load Tap Changing Using Genetic Algorithm and Environmental Impact Analysis

et al. 2021

View full text Add to dashboard Cite

“…Thus, the search space of this optimisation problem is very large. In order to narrow down the search space, a heuristic initialisation is adopted in this paper [6, 19]. This initialisation method uses the concept of feasible strategy matrix (FSM) for each EV.…”

Section: Optimisation Frameworkmentioning

confidence: 99%

Electric vehicles reactive power management and reconfiguration of distribution system to minimise losses

Singh

Tiwari

2020

IET Generation Trans & Dist

Self Cite

View full text Add to dashboard Cite

This study investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in smart distribution system integrated with electric vehicles (EVs) and wind power generation. A two‐stage framework is proposed for real and reactive power management and DFR. In the first stage, demand dispatch of EVs is coordinated to utilise available wind power and minimise the total load variations. In the second stage, a comprehensive problem formulation is developed to minimise the losses in the system with simultaneous EVs reactive power management and DFR. A genetic algorithm is used to solve this two‐stage optimisation problem. A case study of 33‐bus radial distribution system is presented to highlight the efficacy of proposed approach. The obtained results are quantitatively assessed in terms of losses and node voltages. A comparative benchmarking analysis is presented to substantiate the role of DFR and reactive power capability of EVs in improving the system performance.

show abstract

“…However, the EV owner preferred charging time zones have not been handled. Another method introduced in [10] adopts a smart load management methodology for EVs and charging stations with losses minimization, but the presence of renewable energy sources with V2G technology has not been covered Another method introduced in [11] minimizes the real power losses over the distribution feeders but further considers the V2G technology and the charging cost. However, the random arrivals/departures of EVs were not considered.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Distributed Framework for Optimal Dynamic Load Management of Electric Vehicles With Vehicle-to-Grid Technology

Ahmed¹,

Abouelseoud

Abbasy

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The tendency towards carbon dioxide reduction greatly stimulates the popularity of electric vehicles against conventional vehicles. However, electric vehicle chargers represent a huge electric burden, which affects the performance and stability of the grid. Various optimization methodologies have been proposed in literature to enhance the performance of the distribution grids. However, existing techniques handle the raised issues from individual perspectives and/or with limited scopes. Therefore, this paper aims to develop a distributed controller-based coordination scheme in both medium and low voltage networks to handle the electric vehicles' charging impact on the power grid. The scope of this work covers improving the network voltage profile, reducing the total active and reactive power, reducing the load fluctuations and total charging cost, while taking into consideration the random arrivals/departures of electric vehicles and the vehicle owners' preferred charging time zones with vehicle-to-grid technology. Simulations are carried out to prove the successfulness of the proposed method in improving the performance of IEEE 31-bus 23 kV system with several 415 V residential feeders. Additionally, the proposed method is validated using Controller Hardware-in-the-Loop. The results show that the proposed method can significantly reduce the issues that appear in the electric power grid during charging with minor changes in the existing grid. The results prove the successful implementation of different types of charging, namely, ultra-fast, fast, moderate, normal and vehicle-to-grid charging with minimum charging cost to enhance the owner's satisfaction level.INDEX TERMS EV charging, quadratic programming, pattern search, energy management, V2G, V2V.

show abstract

Real power loss minimisation of smart grid with electric vehicles using distribution feeder reconfiguration

Cited by 66 publications

References 42 publications

Power Loss Minimization for Distribution Networks with Load Tap Changing Using Genetic Algorithm and Environmental Impact Analysis

Power Loss Minimization for Distribution Networks with Load Tap Changing Using Genetic Algorithm and Environmental Impact Analysis

Electric vehicles reactive power management and reconfiguration of distribution system to minimise losses

Hierarchical Distributed Framework for Optimal Dynamic Load Management of Electric Vehicles With Vehicle-to-Grid Technology

Contact Info

Product

Resources

About