Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot

Ioakimidis, Christos S.; Thomas, Dimitrios; Rycerski, Pawel

doi:10.1016/j.energy.2018.01.128

Cited by 199 publications

(64 citation statements)

References 27 publications

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“…In this paper, in order to improve the performance of the EVCS and its positive impact on the operation of the network, EV's charging/discharging planning has been discussed from the perspectives of the DSO and the EV's owners [38]. From the DSO's point of view, peak shaving, valley filling and flattening the load curve [38][39][40] are the main goals according to which the following objective function is introduced: As mentioned, factors such as charge and discharge rate ( rate ch and rate disch ) and charging/discharging hours play a major role in correcting the load characteristic of the grid. Therefore, in the above formulation, the objective is to optimize the above parameters.…”

Section: Proposed Methodology For Smart Evs Charging/dischargingmentioning

confidence: 99%

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

et al. 2020

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Modeling and allocation of the Electric vehicles Charging Stations (EVCS) within the distribution network, as per the growing use of Electric vehicles (EVs) is a challenging task. In this paper, to manage the EVCS, first, with the aim of peak shaving, valley filling, and flattening the load curve of the network, the optimal planning of EV's charging/discharging is devised. In this regard, after modeling involving random variables, a novel hybrid method, based on the Multi Objective Particle Swarm Optimization (MOPSO) optimization algorithm and sequential Monte Carlo simulation is presented. The purpose of the presented optimal charge/discharge schedule is to control the rate and time of charging/ discharging of EVs. In the proposed model, various battery operation strategies, including Uncontrolled Charging Mode (UCM), Controlled Charging Mode (CCM), and Smart Charge/Discharge Mode (SCDM) are also considered. In the next step, in order to implement the charge/discharge schedule of the EVCS profitably, a new formulation is presented for the allocation of two EVCSs (administrative and residential EVCS). In the proposed formulation, various objective functions such as power loss reduction, reducing power purchases from the upstream network, reducing voltage deviation in buses, improving reliability is addressed. Moreover, to incentivize the owner to construct the EVCS with adopted charging/discharging schedule, the optimal profits sharing between Distribution System Operator (DSO) and EVCS owner is also performed. The proposed formulation is applied to a standard network (IEEE 69 buses) and encouraging results are achieved.

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Section: Proposed Methodology For Smart Evs Charging/dischargingmentioning

confidence: 99%

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

et al. 2020

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“…By introducing an SA-CMS in PED it is possible to operate in Smart Charging mode, i.e., the aggregate charging power, which is given by Equation 2, is controlled to follow the daily power profile of the internal generation sources. This can result in an EV's load shifting during the hours of more internal generation or may involve a decrease in each EV's charging power to smooth the EV's load profile during peak hours [24][25][26][27]. Of course, this operating mode aims to minimize the power that the system absorbs from the grid, by prioritizing the internal and non-programmable RESs, which present a lower energy price.…”

Section: The Charging Management System: Logic and Aimsmentioning

confidence: 99%

“…Finally, it is an indisputable fact that EVs, storage systems, and distributed RES should be coordinated intelligently with the rest of the grid in order to maximize the use of renewable sources [19] and provide flexibility to the whole electrical system. In recent years, research interest in these topics, as well as the deployment of pilot projects, have increased [20][21][22][23][24]. Regarding the integration of EVs into electric systems, the reference literature, apart from the classical charging mode, the so-called grid-to-vehicle (G2V), distinguishes two principal operating modes.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization

et al. 2020

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In the context of electric vehicle (EV) development and positive energy districts with the growing penetration of non-programmable sources, this paper provides a method to predict and manage the aggregate power flows of charging stations to optimize the self-consumption and load profiles. The prediction method analyzes each charging event belonging to the EV population, and it considers the main factors that influence a charging process, namely the EV’s characteristics, charging ratings, and driver behavior. EV’s characteristics and charging ratings are obtained from the EV model’s and charging stations’ specifications, respectively. The statistical analysis of driver behavior is performed to calculate the daily consumptions and the charging energy request. Then, a model to estimate the parking time of each vehicle is extrapolated from the real collected data of the arrival and departure times in parking lots. A case study was carried out to evaluate the proposed method. This consisted of an industrial area with renewable sources and electrical loads. The obtained results show how EV charging can negatively impact system power flows, causing load peaks and high energy demand. Therefore, a charging management system (CMS) able to operate in the smart charging mode was introduced. Finally, it was demonstrated that the proposed method provides better EV integration and improved performance.

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“…Ref. [11,16,17] showed that the charging stations had a load which represented a mixture of charging profiles. In other words, the charging stations are used by multiple users during the day, and these users had different charging behaviors.…”

Section: P Vmentioning

confidence: 99%

Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data

Shepero

2018

Applied Energy

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In the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the electricity grid especially if high charging powers and opportunistic charging are used. Several models have been proposed to quantify this impact. Multiple papers have observed that the charging stations are used by multiple users during the day. However, this observation was not assumed in any previous model. Moreover, none of the previous models relied on geospatial maps to extract information about the parking lots-where charging stations are installed-and the charging profiles of the potential users of these charging stations. In this paper, a spatial Markov chain model is developed to model the charging load of EVs in cities. The model assumes three distinct charging profiles: Work, Home, and Other. Geospatial maps were used to estimate the charging profile, or mixture of profiles, of the charging stations based on the nearby building types. A case study was made on the city of Uppsala, Sweden-a city with approximately 44,000 cars. The results of the case study indicated that the aggregate load of the EVs in the city reduced the charging impact. For example when using 22 kW chargers, the peak load in the city per EV was estimated to be 1.29 kW/car in case of spatio-temporally opportunistic charging, and 1.47 kW/car in case of residential only opportunistic charging. This is to say that the Swedish grid operators can expect that every EV in the city will increase the peak load by at most 1.47 kW due to aggregation; this is assuming that 22 kW chargers were used. In addition, we showed that the minute-minute variability of the charging load in cities might cause some future challenges. In our case, up to 3% of the EVs in the city simultaneously started charging. This caused a one-minute-ramp in the charging load of 1.1 MW-if charging using 3.7 kW. Charging with higher powers will exacerbate these ramps, e.g., charging with 22 kW will cause sudden one-minute-increases as high as 6.7 MW in the charging load. Such a finding indicates that using high charging powers might cause high variability in the charging load of EVs in cities. This high variability might limit the synergy potentials between EVs and variable renewable energy sources (RES). The proposed model can potentially be used along with RES models to estimate the spatio-temporal synergy potentials between the two technologies. Evaluating the synergy potentials might be of value to grid operators, policy makers, market traders, etc.

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Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot

Cited by 199 publications

References 27 publications

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization

Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data

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