Analyzing Electric Vehicle Load Impact on Power Systems: Modeling Analysis and a Case Study for Maldives

Suski, Adam; Remy, Tom; Chattopadhyay, Debabrata; Song, Chong Suk; Jaques, Ivan; Keskes, Tarek; Li, Yanchao

doi:10.1109/access.2021.3111001

Cited by 23 publications

(8 citation statements)

References 42 publications

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“…In [135], a coordinated framework with an ESS was proposed that optimizes economic benefits. The coordinated charging greatly reduces additional generation capacity to just 1.8% in the Maldives by 2030 and will lessen the incremental expense to 25 million USD in discounted terms over 2021-2030 [132]. Another case study in China between 2020 and 2030 indicates that ideal EV coordination successfully lowers the peak load and smooths the load curve [136].…”

Section: Coordinated/uncoordinated Frameworkmentioning

confidence: 99%

“…To flatten the load, unidirectional and bidirectional V2G reduced their peak loads to 1236 and 1210 GW, respectively. If EV charging is uncoordinated, a very minor rise in electricity consumption from EVs of 3.1% may result in a 26.1% increase in demand for power generation and, therefore, 15.7% more investment by 2030, according to a case study in the Maldives [132].…”

Section: Charger Classificationmentioning

confidence: 99%

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A critical review on contemporary power electronics interface topologies to vehicle‐to‐grid technology: Prospects, challenges, and directions

Tasnim,

Riana,

Shams

et al. 2023

IET Power Electronics

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With the evolution of the smart grid concept, the production of electric vehicles (EVs) is predicted to rise because of environmental concerns, technological advancements, and improvements in EV management. Vehicle‐to‐grid (V2G) is an enabling, realistic, and affordable technology to cope with a large number of EVs, increase energy sustainability, provide economical solutions, satisfy user‐side consumers, and facilitate power flow to the grid. Power electronics (PE) converters, particularly bidirectional power converters, are promising interfaces for V2G infrastructure because they determine the characteristics and functionalities of V2G. Therefore, this study provides an extensive review of the characteristics, technological aspects, and visions of V2G infrastructure. This review helps to identify the current state, most recent developments, and problems related to bidirectional interface topologies and control strategies in V2G infrastructure. It further examines the classification of chargers or dischargers based on numerous factors, including limitations and impacts. Furthermore, the benefits, challenges with possible mitigation solutions, and future outlooks in the implementation of V2G technology are discussed. This review is planned to serve as a reference for existing work in V2G frameworks, PE interfacing topologies, and control strategies, and to also facilitate a guideline for future work that can be implemented to flourish V2G technology.

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Section: Coordinated/uncoordinated Frameworkmentioning

confidence: 99%

Section: Charger Classificationmentioning

confidence: 99%

A critical review on contemporary power electronics interface topologies to vehicle‐to‐grid technology: Prospects, challenges, and directions

Tasnim,

Riana,

Shams

et al. 2023

IET Power Electronics

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“…Coordinated charging can be implemented through several incentives to shift the charging load away from the peak. A modeling analysis conducted by the World Bank for Maldives using the EPM model comprehensively demonstrated that coordinated charging can lead to substantial reduction in peaking generation capacity [56].…”

Section: Typical Outputsmentioning

confidence: 99%

Analysis of Long-Term Variable Renewable Energy Heavy Capacity Plans Including Electric Vehicle and Hydrogen Scenarios: Methodology and Illustrative Case Study for Turkey

et al. 2023

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Following COP26, many countries are embarking on decarbonization strategies for the power sector that may include inter alia storage, hydrogen and carbon capture (and storage). There is also significant increase in load that may come in the form of electric vehicles (EV) charging and hydrogen requirement for decarbonization of other sectors. While there is a growing literature around long-term decarbonization strategies, there is still ample room for a practical methodology to rigorously test capacity plans to include a range of options inter alia re-optimization of the mix of renewable technologies, better coordination of the (EV) load from a system perspective, or augmenting the plan with battery energy storage (BESS) and hydrogen. This paper presents our research on EV load and green hydrogen modeling including how they can be integrated into long-term electricity models. We present a methodology that allows planners to undertake a rigorous assessment that can be readily implemented using the World Bank Electricity Planning Model (EPM). The application of the model is illustrated through a case study for Turkey (Türkiye) for 2050. The case study shows how an incumbent policy-driven capacity plan for 2050 that included 33% contribution from variable renewable energy (VRE) may be prone to unserved energy risk during winter months due to seasonal variability of VRE. The analysis goes on to demonstrate how the plan can be reinforced with additional peaking gas turbines, re-optimization of wind and solar, BESS and hydrogen. Coordinated charging of EVs is also shown to bring significant relief to investment requirements.

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“…The purpose of the PSMDO is to determine the maximum phase power demand at each node with particle representation given in (10). The objective function in (11) is to maximise the total maximum demand in the system with consideration of the charging behaviour of EV users in terms of the charging opportunity of each node and time defined by (12), subject to the lower and upper bounds of node voltage given in ( 13) and ( 14), and feeder capacity shown in (15) and (16).…”

Section: Particle Swarm-based Maximum Demand Optimisation (Psmdo)mentioning

confidence: 99%

Optimal electric vehicle scheduling in unbalanced distribution system by spatial and temporal data mining and bi‐level particle swarm optimisation

Piamvilai,

Sirisumrannukul

2023

IET Generation Trans & Dist

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This research proposes a new comprehensive methodology aimed at optimally managing electric vehicle (EV) charging in an unbalanced distribution system with consideration of grid capacity and voltage quality constraints as well as the stochastic driving and charging behaviour of EV users. A data mining algorithm is designed to generate suitable spatial and temporal EV and charger input data for EV scheduling that is decomposed into two subproblems. The lower‐level subproblem identifies the maximum load at each node and time and the upper‐level subproblem allocates charging slots for each EV being charged. Both subproblems are solved by developed particle swarm optimisation (PSO) algorithms. The effectiveness and robustness of the algorithms have been thoroughly validated by conducting rigorous tests on a modified IEEE 37‐bus distribution system with different EV penetration scenarios. The test results have confirmed the algorithms' performance in accommodating the increasing load associated with EV charging and successfully improving the system performance by maximising the system load factor, minimising load unbalance, and reducing the system power loss. All operational constraints on node voltages, and distribution transformer and feeder capacities of the existing power distribution infrastructure are fully respected while maintaining high user satisfaction represented by the average state of charge (SoC) of all EVs.

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Analyzing Electric Vehicle Load Impact on Power Systems: Modeling Analysis and a Case Study for Maldives

Cited by 23 publications

References 42 publications

A critical review on contemporary power electronics interface topologies to vehicle‐to‐grid technology: Prospects, challenges, and directions

A critical review on contemporary power electronics interface topologies to vehicle‐to‐grid technology: Prospects, challenges, and directions

Analysis of Long-Term Variable Renewable Energy Heavy Capacity Plans Including Electric Vehicle and Hydrogen Scenarios: Methodology and Illustrative Case Study for Turkey

Optimal electric vehicle scheduling in unbalanced distribution system by spatial and temporal data mining and bi‐level particle swarm optimisation

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