Distribution network energy loss reduction under
            <scp>EV</scp>
            charging schedule

Bouhouras, Aggelos S.; Kothona, Despoina; Gkaidatzis, Paschalis A.; Christoforidis, Georgios C.

doi:10.1002/er.7727

Cited by 17 publications

(5 citation statements)

References 30 publications

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“…The former performs this task based on the movement of potential solutions, known as particles, in the problem space to avoid the problem of local minima and later by an iterative method inspired by an evolutionary process. Thus, the authors of [59,60] used PSO for evaluating the EV integration impacts such as network security, voltage and energy losses. Whereas, a multi-objective differential algorithm is used in [61] for EV charging control to improve the power quality of the network.…”

Section: Optimisation-based Methodsmentioning

confidence: 99%

A review of electric vehicle hosting capacity quantification and improvement techniques for distribution networks

Fatima,

Püvi,

Lehtonen

et al. 2023

IET Generation Trans & Dist

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The electrification of the transport sector to control the carbon footprint has been gaining momentum over the last decade with electric vehicles (EVs) seen as the replacement for conventional internal combustion engines. Economic incentives, subsidies, and tax exemptions are also paving the way for rising EV penetration in the power distribution networks. However, the exponential EV adoption requires careful technical and regulatory analysis of traditional networks to satisfy the network reliability constraints. Therefore, it would be vital to find EV hosting capacity (HC) limits of networks from a multifaceted approach involving various market players, mainly distribution system operators and EV owners. This review provides a systematic categorization of EV hosting capacity evaluation and improvement methods, thus enabling researchers and industry personnel to navigate the advancing landscape of EVs. This novel framework extends beyond the theoretical implication of diverse objective functions and HC improvement methods to the actual numerical values of EV HC across varying geographical settings. Therefore, this unique synthesis of varying aspects of EV HC facilitates the in‐depth understanding of the integration of sustainable energy and transport sector.

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Section: Optimisation-based Methodsmentioning

confidence: 99%

A review of electric vehicle hosting capacity quantification and improvement techniques for distribution networks

Fatima,

Püvi,

Lehtonen

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…Their algorithm improved the deviation term by 27% compared to the PSO algorithm. Zhang et al 9 proposed the generalized space transformation evolution technique and combined it with an improved particle swarm optimization algorithm. Their generalized space was based on contrastive learning, which not only improves the utilization of the current space, but also enhances the exploration of the current space.…”

Section: Related Workmentioning

confidence: 99%

Reconfiguration of low-voltage distributed power sources within electric power's distribution network based on improved particle swarm-fish swarm fusibility algorithm

Xu,

Nie,

et al. 2024

Sci Rep

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With the development of distributed power sources in the distribution network, the algorithm of distribution network reconfiguration is gaining attention from experts and scholars. Its goal is to reduce the power loss during power transmission, so as to reduce the power grid loss during power transmission. And weaken the electric heating effect in the process of electric energy transmission, thus maintaining the safety of the surrounding residents. Due to the wire impedance effect, a lot of electric energy of the circuit is lost to electric heating, which is easy to cause local overheating and lead to fire. This will not only cause power loss, but also endanger the safety of surrounding residents. To address the issue, experiments on distribution grid reconstruction are performed using the enhanced particle swarm-fish swarm algorithm with the Elecgrid self-constructed dataset. Initially, low-voltage distributed power sources in parallel are connected to the circuit, thereby decreasing internal resistance and electrical heat. Then, by controlling the circuit in the system, the double separation relay adjusts the inductance and capacitance of the conductor, thus reducing the reactance length. Additionally, particle swarm particles are mutated to enable them to jump out of the local optimum, and elite fish approach is used to expand the search area. Finally, the proposed fusion algorithm is applied to the self-built data set of Elecgrid and compared with the other three algorithms. The fusion algorithm serves as the standard test system for this comparison. The active power loss of the hybrid algorithm is 63 kW at an operating voltage of 0.74 V. The loss work of the other three algorithms is 74 kW, 97 kW and 109 kW respectively. The mixed algorithm has the lowest loss among the four algorithms. The experiments are repeated for six times, and the linear fitting degrees of the four algorithms are 0.9804, 0.9527, 0.9612 and 0.9503, respectively. The experimental results show that the application of this algorithm can effectively reduce the active loss in the process of distribution network reconfiguration, thus reducing energy consumption; At the same time, it can reduce the electric heating in the process of electric energy transmission, and then prevent the occurrence of fire. There are three main contributions of this study. Firstly, the resistance in the transmission path is reduced by using this algorithm, so that the power transmission efficiency can be analyzed more accurately. Secondly, the new algorithm enriches the power safety maintenance method; Finally, the fire caused by local overheating of the line is reduced by fusion algorithm.

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“…In [41], the operation costs in parking lots with PV systems were considered, and the optimal charging rates were computed in 30 min intervals. The work of [42] proposed a particle swarm optimization-based algorithm to optimally schedule the EV charging process hourly to reduce energy losses and improve the voltage profiles in the distribution system. A mixedinteger programming optimization problem was formulated in [43] to optimize the EV hourly charging schedule to minimize the operating cost of the power system.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed Real-Time Feedback Optimization for Renewable Energy Sources and Vehicle-to-Grid Power Compensation of Electric Vehicle Chargers in Distribution Systems

Cheng,

Ching

2024

Sustainability

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A novel distributed feedback optimization-based controller for electric vehicle (EV) chargers and renewable energy sources (RESs) in distribution systems is proposed. The proposed controller utilizes the flexibility in EV chargers’ active and reactive power consumption to offer the desirable vehicle-to-grid services. Instead of using the conventional cascaded PI controllers, a new optimization-based approach is proposed to control RESs to track their power injection setpoints. The proposed controller formulates the control targets as a single constrained optimization problem, i.e., to minimize the critical bus voltage magnitude deviations while driving RESs to follow their power setpoints, thereby fulfilling the EV charging requirements and regulating their power outputs and bus voltage magnitudes to stay within their limits. A distributed feedback optimization-based control algorithm is designed for EV chargers and RESs to steer the system trajectories of the distribution systems towards the optimal solution of the formulated optimization problem. Simulation results show that the proposed controller can always steer the test system to the optimal solution of the optimization problem. The advantages of the real-time vehicle-to-grid power compensation of EV chargers are also demonstrated.

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Distribution network energy loss reduction under EV charging schedule

Cited by 17 publications

References 30 publications

A review of electric vehicle hosting capacity quantification and improvement techniques for distribution networks

A review of electric vehicle hosting capacity quantification and improvement techniques for distribution networks

Reconfiguration of low-voltage distributed power sources within electric power's distribution network based on improved particle swarm-fish swarm fusibility algorithm

Distributed Real-Time Feedback Optimization for Renewable Energy Sources and Vehicle-to-Grid Power Compensation of Electric Vehicle Chargers in Distribution Systems

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