A Probabilistic Approach to Determine Optimal Capacity and Location of Electric Vehicles Parking Lots in Distribution Networks

Mirzaei, Mojtaba; Kazemi, A.; Homaee, Omid

doi:10.1109/tii.2015.2482919

Cited by 101 publications

(34 citation statements)

References 15 publications

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“…Probabilistic methods have been widely used in optimizing integrated electric and traffic systems, such as the two-stage optimization method [91,92], the chance constrained optimization method [93,94], and the point estimate method [95]. For instance, a two-stage optimization method was proposed in [91] to minimize the energy losses in a microgrid with different penetrations of hybrid EVs (HEVs).…”

Section: Probabilistic Methodsmentioning

confidence: 99%

A comprehensive review of Energy Internet: basic concept, operation and planning methods, and research prospects

Cao

Tan

et al. 2017

J. Mod. Power Syst. Clean Energy

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With the intensifying energy crisis and environmental pollution, the Energy Internet and corresponding patterns of energy use have been attracting more and more attention. In this paper, the basic concept and characteristics of the Energy Internet are summarized, and its basic structural framework is analyzed in detail. On this basis, couplings between the electric power system and other systems such as the cooling and heating system, the natural gas system, and the traffic system are analyzed, and the operation and planning of integrated energy systems in both deterministic and uncertain environments are comprehensively reviewed. Finally, the research prospects and main technical challenges of the Energy Internet are discussed.

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Section: Probabilistic Methodsmentioning

confidence: 99%

A comprehensive review of Energy Internet: basic concept, operation and planning methods, and research prospects

Cao

Tan

et al. 2017

J. Mod. Power Syst. Clean Energy

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“…Reference [10] presents charging control schemes for EVs considering transmission congestion. Reference [11] proposes a method to estimate the optimal capacity and location of EV parking lots in a distribution system through a stochastic approach. In Reference [12], the charging preference of the driver is considered through the utility function, and the charge scheduling of a plug-in hybrid electric vehicle is performed by adjusting the real-time charging price based on the charging preference.…”

Section: Introductionmentioning

confidence: 99%

Optimal Charging Strategy Based on Model Predictive Control in Electric Vehicle Parking Lots Considering Voltage Stability

2018

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Abstract:Recently, electric vehicles (EVs) using energy storage have gained attention over conventional vehicles using fossil fuels owing to their advantages such as being eco-friendly and reducing the operation cost. In a power system, an EV, which operates through the energy stored in the battery, can be used as a type of load or energy source; hence, an optimal operation of EV clusters in power systems is being extensively studied. This paper proposes an optimal strategy for charging EVs in parking lots. This strategy is based on the model predictive control (MPC) framework due to the uncertainty of loads, renewable energy sources, and EVs, and considers the voltage stability of the distribution systems. EV chargers in the parking lot charge EVs to minimize the charging cost, which results in a sudden increase in charge load at a certain time. As a result, an excessive voltage drop may occur in the power system at that time. Therefore, we need to minimize the charging cost of EVs while preventing an excessive voltage drop in the power system. The parking lot is stochastically modeled to consider EV uncertainty under the MPC framework. In the MPC framework, the charging schedule of an EV charger in the parking lot is optimized by considering both voltage stability and charging cost minimization in real time. The charging constraints on voltage stability are updated through parameters that change in real time, and thus, errors caused by uncertainty can be reduced. Subsequently, this charging strategy is applied to multiple chargers through Monte Carlo simulation. The proposed charging strategy is verified based on MATLAB/Simulink.

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“…33 bus radial distribution network [97,104] For finding the optimal locations as such, the authors of these papers have resorted to various solution algorithms listed in Table 2.16, including artificial bee colony (ABC) algorithm, chanceconstrained programming, genetic algorithms (GAs), mixed integer linear programming (MILP), 24 modified binary particle swarm optimization (BPSO) based on Taboo mechanism, point estimate method and two-step screening method. Chance-constrained programming [29] Genetic algorithms (GAs) [41,98] Mixed integer linear programming (MILP) [100] Modified binary particle swarm optimization (BPSO) based on the Taboo mechanism [102] Point estimate method [105] Two-step screening method [103] These algorithms have taken into account various factors such as voltage, losses, reliability, costs, location adaptability and land price, service radius, minimum total transportation distance (TTD), and vehicle-miles-travelled (VMT) being electrified. The relevant references have been tabulated in Table 2.17.…”

Section: 33mentioning

confidence: 99%

“…However, none of these algorithms has considered the GHG emissions. Reliability [100] Minimum total transportation distance (TTD) [102] Costs [28,29,41,60,97,100,105] Vehicle-miles-travelled (VMT) being electrified [101] Finally, these algorithms are intended for either HC or BC, or CC as shown in Table 2.18.…”

Section: 33mentioning

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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A Probabilistic Approach to Determine Optimal Capacity and Location of Electric Vehicles Parking Lots in Distribution Networks

Cited by 101 publications

References 15 publications

A comprehensive review of Energy Internet: basic concept, operation and planning methods, and research prospects

A comprehensive review of Energy Internet: basic concept, operation and planning methods, and research prospects

Optimal Charging Strategy Based on Model Predictive Control in Electric Vehicle Parking Lots Considering Voltage Stability

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

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