Optimal siting and sizing of distribution system operator owned EV parking lots

Kazemi, Mohammad Amin; Sedighizadeh, Mostafa; Mirzaei, Mojtaba; Homaee, Omid

doi:10.1016/j.apenergy.2016.06.125

Cited by 84 publications

(29 citation statements)

References 19 publications

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“…However, extensive penetration of DGs greatly increases the risks of safe and economic operation of distribution networks since renewable DGs have inherently intermittent nature [5,6], which makes the planning more challenging than ever before. The traditional distribution network planning options, such as the addition or expansion of substations and lines, are unable to meet the needs of modern complex ADNs facing all alternatives together with generation and load uncertainties [6,8,9]. Therefore, it is necessary to deal with such key challenges in the issue of optimal DG placement.…”

Section: Nomenclature αImentioning

confidence: 99%

Optimal distributed generation planning in active distribution networks considering integration of energy storage

et al. 2018

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A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the 'best' compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG&E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60% which is novel. Index Terms--active distribution network; distributed generation planning; two-stage optimization; energy storage; chance-constrained programming; multi-objective ant lion optimizer.

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Section: Nomenclature αImentioning

confidence: 99%

Optimal distributed generation planning in active distribution networks considering integration of energy storage

et al. 2018

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“…Modelling the charging process itself is a complicated task and is influenced by different variables such as battery type, charging power, single-or multi-phase charging, and user behaviour. A variety of different stochastic approaches and techniques have been published: probabilistic approaches [27][28][29][30], Monte-Carlo [11,31,32], and Markov Chain [11,33,34].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach

Vopava¹,

Koczwara²,

Traupmann³

et al. 2019

Energies

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To achieve climate goals, it is necessary to decarbonise the transport sector, which requires an immediate changeover to alternative power sources (e.g., battery powered vehicles). This change will lead to an increase in the demand for electrical energy, which will cause additional stress on power grids. It is therefore necessary to evaluate energy and power requirements of a future society using e-mobility. Therefore, we present a new approach to investigate the influence of increasing e-mobility on a distribution grid level. This includes the development of a power grid model based on a cellular approach, reducing computation efforts, and allowing time and spatially resolved grid stress analysis based on different load and renewable energy source scenarios. The results show that by using the simplified grid model at least seven times, more scenarios can be calculated in the same time. In addition, we demonstrate the capability of this novel approach by analysing the influence of different penetrations of e-mobility on the grid load using a case study, which is calculated using synthetic charging load profiles based on a real-life mobility data. The results from this case study show an increase on line utilisations with increasing e-mobility and the influence of producers at the same connection point as e-mobility.

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“…Variables such as the distance that a PEV travels, PEV arriving/departure time to/from the charging location, and the initial SOC of PEV battery are considered input variables of the probabilistic model [50,51]. For example, the distance travelled can be represented by a long-normal distribution function [50].…”

Section: Stochastic Planning Modelmentioning

confidence: 99%

“…Based on these scenarios, a planner can distribute the PEVs among power distribution network buses to achieve the planning objectives. One way of estimating the number of PEVs in the zones of the distribution system is to use k-mean clustering method [51,52]. This method classifies the distribution system into zones, and determines the borders of zones.…”

Section: Stochastic Planning Modelmentioning

confidence: 99%

“…Nodal PEV density model [39][40][41][42] Estimation of the stationary demand density at system nodes Traffic flow model [12,[43][44][45][46][47] Estimation of the spatial demand and mobility of PEVs Real data-based model [48,49] Estimation of the spatial-temporal PEV demand Stochastic model [50][51][52] Considering the uncertainty of PEVs 4.1.1. Nodal PEV Density-Based Model…”

Section: Demand Coverage Model Main Featurementioning

confidence: 99%

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A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Abdalrahman

Zhuang

2017

Energies

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Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to electricity demand. Due to PEV mobility, this demand has a random distribution in space and time among distribution system nodes. Therefore, short term forecast of PEV charging demand is more challenging than that for conventional loads. Assessment of PEV impacts on the power system is essential to mitigate the impairments from PEV loads. Optimal planning of PEV charging infrastructure will promote the penetration rate of PEVs and minimize the negative impacts of PEVs on the electric power distribution system and transportation road network. Design of charging facilities with integrated distributed energy resources (DER) is considered a solution to alleviate strain on the grid, reduce the integration cost with the distribution network and the charging cost. In this paper, we present a comprehensive literature survey on modelling of PEV charging demand, impact assessment approaches and tools, and charging infrastructure planning. Moreover, an overview on charging facility design with integrated DER is given. Some future research directions are identified.

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Optimal siting and sizing of distribution system operator owned EV parking lots

Cited by 84 publications

References 19 publications

Optimal distributed generation planning in active distribution networks considering integration of energy storage

Optimal distributed generation planning in active distribution networks considering integration of energy storage

Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

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