HPC-Based Probabilistic Analysis of LV Networks With EVs: Impacts and Control

Procopiou, Andreas T.; Quirós-Tortós, Jairo; Ochoa, Luis F.

doi:10.1109/tsg.2016.2604245

Cited by 35 publications

(32 citation statements)

References 14 publications

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“…For reliable operation of the EV charging station, these exercises are required to be accurate and computationally cheap. The accuracy usually 5 gets tainted by the uncertainties involved in SOC, number of EVs, PV output, and grid loads, while the computational burden gets compounded by the governing charging coordination strategy [31] and the need for running the load flows for several times [32]. Though the charging coordination strategy is computationally cheap for a small number of EVs, it can become particularly expensive with the increase in the EV number [31].…”

Section: Operational Planning Of Pv and Bes Based Ev Chargingmentioning

confidence: 99%

“…However, neither PV nor BES has been considered in these papers. Deterministic grid load or grid load ignored [16,17,47,53,57,75,[80][81][82][83][84][85][86][87] Recorded grid load data; probabilistic EV load [88] Constructed worst case scenario with deterministic EV load [89] Probabilistic EV and grid loads [32,90,91] Deterministic PV output [17,83,84] Probabilistic PV output [47,75,86] For probabilistic modelling, the combined EV load is a function of the number of EVs [82,85], their charging characteristics with respect to SOC as discussed in Section 2.2 and various other parameters. Referred to Table 2.7, among these parameters, daily travel distance, arrival time and departure time are frequently used.…”

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

“…Aside from these parameters, many papers have incorporated charging and discharging characteristics of the EV battery, dynamics of SOC of EV battery, purposes of the trips, EV types (e.g., Table 2.5) and their market shares in the EV load models. It is also noted that when the EV loads are not modeled in terms of the parameters above, their probabilistic models are either obtained from the historical data [32] or household survey data [88]. A developed tool to generate the probabilistic EV loads or the parameters above can also be observed in [83].…”

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

“…[54,55,86,89] Line & substation loading [17,18,26,76,84,85,88,90,92] Transformer ageing [84,93] Loading margin [62] GHG emissions [6,78,86,94] Reliability [76] In order to do so, Monte Carlo simulation, high-performance computation, principal component analysis, scenario reduction algorithm, and statistical clustering to find representative feeders as shown in Table 2.12 have been most commonly employed techniques in the literature. High-performance computation [32] Clustering to find representative feeders [76] Principal component analysis [18] 22 Though these studies are comprehensive enough, quite a few numbers of additional aspects can be incorporated to advance the understanding of the impact of the EV charging. These aspects are,  The voltage and SOC dependency of the EV load as discussed in Section 2.2 must be taken into account.…”

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

“… Though the reactive power involved in the EV charging has been incorporated in the impact analysis in [26,32], such analysis must be further reinforced by incorporating the proper model of the reactive power as discussed in Section 2.2, rather than simply assuming a 0.98 inductive power factor as in [32].…”

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

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PV-based EV charging station with vehicle-to-grid services for business premises

Islam¹

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The transportation sector is the second major contributor to the global greenhouse gas (GHG) emissions, next to electricity generation sector. GHG emissions are projected to grow in the future due to the over-reliance on fossil fuels for both electricity generation and transportation sectors.However, the growth can be stalled by gradually introducing electric vehicles (EVs) as they produce less GHG compared to their conventional counterparts. Despite that, their proliferation has been stunted by a completely new set of challenges in addition to a very high capital cost. These challenges include low driving ranges, scarcity of electric vehicle charging stations (EVCS), long charging time and inability to provide charging service readily. Nevertheless, some of these challenges can be addressed by installing EVCS at business premises (i.e., offices, universities, etc.) and augmenting EVCS with onsite PV and battery energy storage (BES). In that case, the impacts of EV charging on the grid will be less significant; charging services will be available readily, and GHG emissions growth can be reduced significantly. Moreover, EVCS, under suitable conditions, would be able to provide vehicle-to-grid (V2G) services, which makes EVCS more attractive.Despite such prospective opportunities, offhand planning and operational planning of EVCS would deem them unachievable. In addition, the inherent intermittency of PV, and EV and grids loads will pose considerable hindrance on availing those benefits.Hence, this research focusses on an EVCS with onsite PV and BES, in the perspective of planning and operational planning including V2G services. The cornerstone of the planning and operational planning is an appropriate EV load model that captures the uncertainties of EV charging. In addition, the EV load model reflects the grid voltage and EV battery's state of charge (SOC)-dependency of EV charging. Moreover, it accounts for the diversity of the EV population embodied by market shares, battery sizes, charging levels, and charging voltages, currents, and power factors. Furthermore, it is scalable to facilitate seamless assimilation of a large EV population. Therefore, a new EV load model is first proposed with MATLAB/Simulink validation reflecting the factors above along with the recommendations by the standard BS EN 61851-23:2014. This EV model is then incorporated into the planning activities, which include four chronological exercises. Firstly, the impact (i.e., grid voltage, current, losses, etc.) of the EV charging on the grid at different locations, namely home, office, public charging, is investigated considering the uncertainties. Secondly, the optimal location of the PV and BES based EVCS is divulged regarding the reduction of the impact and costs involved while enhancing the charging quality of service (QoS). Thirdly, a suitability analysis is performed for the obtained optimal location to find the most suitable combination of the grid upgrading, PV deployment, and BES iii deployment to satisfy the QoS, ...

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Section: Operational Planning Of Pv and Bes Based Ev Chargingmentioning

confidence: 99%

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

See 3 more Smart Citations

PV-based EV charging station with vehicle-to-grid services for business premises

Islam¹

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An intelligent decentralized energy management strategy for the optimal electric vehicles' charging in low‐voltage islanded microgrids

Boglou

Karavas

Karlis

et al. 2021

Intl J of Energy Research

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Summary The expected significant growth in global electricity demand, followed by the adoption of green energy sources, led to the modernization of the energy distribution grids, such as the development of microgrids that can improve the reliability of the electric power system. Meanwhile, the increased integration of electric vehicles is expected to have a negative impact on power quality, normal operation, and investment costs of the microgrid. In this paper, a microgrid topology was studied for the islanded operation of a low‐voltage distribution network. The deployment of such energy management systems is essential to guarantee the safe and reliable operation of isolated microgrids. Hence, a decentralized energy management system, based on multi‐agent systems, was developed for the efficient charging of electric vehicles, by expanding a state‐of‐the‐art fuzzy logic controller‐based energy management strategy, in combination with a charging power controller, based on fuzzy cognitive maps. According to the authors' best knowledge, this is the first time that fuzzy cognitive maps theory is introduced in EVs' charging management problems. The performance of the proposed multi‐agent decentralized energy management system presented significant reduction in the investment costs of the microgrid, as compared with the use of a sole fuzzy logic controller‐based energy management strategy. The total cost of the microgrid for a 20‐year investment period is decreased by approximately 8.8%. Furthermore, the incorporation of the fuzzy cognitive maps‐based charging power controller leads to a significant amount of chargeable EVs. The mean chargeable EVs increased by 31%. Finally, the proposed energy management system reduces the peak load and load variances approximately 17% and 29%, respectively, without shifting and delaying the charging of the EVs. Hence, the proposed novel charging management system offers an intelligence approach for islanding distribution grids including the high penetration of electric vehicles by presenting operational and financial benefits.

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Risk Assessment of Voltage Limit Violation Based on Probabilistic Load Flow in Active Distribution Network

Dong

2018

Communications in Computer and Information Science

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HPC-Based Probabilistic Analysis of LV Networks With EVs: Impacts and Control

Cited by 35 publications

References 14 publications

PV-based EV charging station with vehicle-to-grid services for business premises

PV-based EV charging station with vehicle-to-grid services for business premises

An intelligent decentralized energy management strategy for the optimal electric vehicles' charging in low‐voltage islanded microgrids

Risk Assessment of Voltage Limit Violation Based on Probabilistic Load Flow in Active Distribution Network

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