A Day-Ahead Forecasting Model for Probabilistic EV Charging Loads at Business Premises

Islam, Sameer; Mithulananthan, Nadarajah; Hung, Duong Quoc

doi:10.1109/tste.2017.2759781

Cited by 63 publications

(22 citation statements)

References 32 publications

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“…(2).33 (5).38 (10).21 (4).50 (10).66 (10) (2).38 (10).21 (2).50 (9).66 (9) (12).10(15).11 (15).12 (10).13 (11).14 (15).23 (8).24 (12).25 (11).26 (11).27 (14).28 (15).29 ( (15).9 (15). 10(15).11 (12).12 (7).13 (13).14 (15).23 (8).24 (12).25 (1).27 (7).28 (12 (14).9 (14). 10(14).11 (7).12 (13).13 (14).14 (14).23 (2).24 (6).25 (7).26 (3).27 (8).28 (3).29 (7…”

Section: Performance Comparison Of Algorithmsunclassified

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Liu

Yan

et al. 2020

Int Trans Electr Energ Syst

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To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.

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Section: Performance Comparison Of Algorithmsunclassified

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Liu

Yan

et al. 2020

Int Trans Electr Energ Syst

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“…For a large number of EV units, Islam et al, developed a combined SOC-based methodology to calculate day-ahead combined probabilistic charging loads [94]. Instead of managing the charging rate of every EV separately, their proposed model charges EVs with a lower SOC level at a higher rate, and vice versa.…”

Section: Ev Demand Modelingmentioning

confidence: 99%

A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)

et al. 2020

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The deployment of solar photovoltaics (PV) and electric vehicles (EVs) is continuously increasing during urban energy transition. With the increasing deployment of energy storage, the development of the energy sharing concept and the associated advanced controls, the conventional solar mobility model (i.e., solar-to-vehicles (S2V), using solar energy in a different location) and context are becoming less compatible and limited for future scenarios. For instance, energy sharing within a building cluster enables buildings to share surplus PV power generation with other buildings of insufficient PV power generation, thereby improving the overall PV power utilization and reducing the grid power dependence. However, such energy sharing techniques are not considered in the conventional solar mobility models, which limits the potential for performance improvements. Therefore, this study conducts a systematic review of solar mobility-related studies as well as the newly developed energy concepts and techniques. Based on the review, this study extends the conventional solar mobility scope from S2V to solar-to-buildings, vehicles and storage (S2BVS). A detailed modeling of each sub-system in the S2BVS model and related advanced controls are presented, and the research gaps that need future investigation for promoting solar mobility are identified. The aim is to provide an up-to-date review of the existing studies related to solar mobility to decision makers, so as to help enhance solar power utilization, reduce buildings’ and EVs’ dependence and impacts on the power grid, as well as carbon emissions.

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“…These two categories have been adopted previously (Mullan et al 2011) because they are potentially representative of two extreme cases of carbon emissions resulting from EV deployment. A third scenario, which lies between these two extremes, is one where charging occurs at random times (Islam, Mithulananthan, and Hung 2018).…”

Section: Charging and Impact On Loadmentioning

confidence: 99%

Electric Vehicle Deployment and Carbon Emissions in Saudi Arabia: A Power System Perspective

Elshurafa¹,

Peerbocus²

2019

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The King Abdullah Petroleum Studies and Research Center (KAPSARC) is a non-profit global institution dedicated to independent research into energy economics, policy, technology and the environment across all types of energy. KAPSARC's mandate is to advance the understanding of energy challenges and opportunities facing the world today and tomorrow, through unbiased, independent, and high-caliber research for the benefit of society. KAPSARC is located in Riyadh, Saudi Arabia.

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A Day-Ahead Forecasting Model for Probabilistic EV Charging Loads at Business Premises

Cited by 63 publications

References 32 publications

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)

Electric Vehicle Deployment and Carbon Emissions in Saudi Arabia: A Power System Perspective

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