Developing a Test Data Set for Electric Vehicle Applications in Smart Grid Research

Akhavan-Hejazi, Hossein; Mohsenian‐Rad, Hamed; Nejat, Ali

doi:10.1109/vtcfall.2014.6965934

Cited by 34 publications

(17 citation statements)

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“…Here, φ[t] serves as a reference on whether the price µ[t] is low enough to schedule the load, or if we should rather wait for a future time slot. The value of φ[t] depends on the probability mass function of the user departure time, e.g., see [4]. …”

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confidence: 99%

Optimal Charging of Electric Vehicles With Uncertain Departure Times: A Closed-Form Solution

Mohsenian‐Rad

Ghamkhari

2015

IEEE Trans. Smart Grid

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Abstract-In this paper, we show that an uncertain departure time significantly changes the analysis in optimizing the charging schedule of electric vehicles. We also obtain a closed-form solution for the stochastic optimization problem that is formulated to schedule charging of electric vehicles with uncertain departure times in presence of hourly time-of-use pricing tariffs.Keywords: Electric vehicles, uncertain departure time, timeshiftable loads, stochastic optimization, closed-form solution. I. BACKGROUNDAssume that time is divided into T time slots. Consider an electric vehicle (EV) charging station with multiple charging outlets, c.f. [1], [2]. At each charging outlet, let α ≥ 1 denote the time slot at which an electric vehicle plugs in to the charging outlet. Also let e denote the total energy needed to charge the electric vehicle. In this section, we assume that the driver indicates her departure time β ≤ T . As an example, if a Chevrolet Volt with a depleted battery plugs in to the charging outlet at 10:00 AM and the driver indicates that she will depart at 2:00 PM and she expects a fully charged battery, then we have α = 10, β = 14, and e = 16 kWh. Let x[t] denote the scheduled energy usage for charging at time slot t. To finish charging before the departure time β, it is required that(1)Let µ[t] denote the price of electricity at time slot t. The cost to finish charging is. If all parameters are known, then the cost is minimized if we choose x[τ ] = e, where τ = arg min α≤t≤β µ[t]; and we set x[t] = 0 for t = τ . Here, τ is the time slot between α and β with the lowest price. Note that, the above analysis works at each charging outlet, regardless of the number of charging outlets at the charging station. An exception is when the charging station is very large and thus price-maker, as we point out in Section IV. II. ANALYSIS UNDER UNCERTAIN DEADLINENext, assume that the deadline β is not known. Once an EV plugs in, its target charge level e and start time α are identified. However, the charging station may not know when the EV will depart. Nevertheless, the charging station needs to operate in a way that it minimizes the cost of electricity.The system setup is as follows. At the beginning of each time slot t ≥ α, if the user indicates that the EV will departThe authors are with the Department of Electrical Engineering, University of California, Riverside, CA, USA, e-mail: {hamed, ghamkhari}@ee.ucr.edu. This work was supported by NSF grants ECCS 1253516, ECCS 1307756, and CNS 1319798. The corresponding author is H. Mohsenian-Rad.at the end of the current time slot, then we must setbecause we must assure finishing charging before the user's departure as required by (1). Otherwise, i.e., if the user does not indicate that it will depart at the end of the current time slot t, then the charging station faces the uncertainty that the EV may depart at any of the future time slots t + 1, . . . , T . At each time slot t ≥ α, let π k|t P r{β = k | β > t} denote the conditional probability that the user will depa...

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confidence: 99%

Optimal Charging of Electric Vehicles With Uncertain Departure Times: A Closed-Form Solution

Mohsenian‐Rad

Ghamkhari

2015

IEEE Trans. Smart Grid

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“…On the contrary, Regions 2 and 3 are both located at rural areas, where renewables are installed at most of the residences. Our load data and renewables power generation profiles were obtained from http:// data-archive.ethz.ch/delivery/ DeliveryManagerServlet?dps_pid=IE594964 and https://transparency.entsoe.eu/generation/r2/ dayAheadGenerationForecastWindAndSolar/show, respectively, and the data of EVs and battery storage systems were, respectively, collected from Akhavan-Hejazi et al 32 to run the proposed scheduling program. Tables 1 and 2 list the parameters used in our simulations.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A load-balancing power scheduling system for virtual power plant considering emission reduction and charging demand of moving electric vehicles

Huang

Liu

et al. 2019

Measurement and Control

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With the rapid development of the emerging technologies and significant cost reduction of the deployment for solar energy and wind power, the replacement of traditional power generation by renewable energy becomes feasible in the future. However, different from currently deployed centralized power sources, renewables are categorized as one kind of intermittent energy sources, and the scale of renewables is small and scattered. In the recent literature, the architecture of virtual power plant was proposed to replace the current smart grid in the future. However, the energy sharing concept and the uncertainties of intermittent energy sources will cause the short-term energy management for the virtual power plant much more complicated than the current centralized control energy management for traditional power generation system. We thus propose a hierarchical day-ahead power scheduling system for virtual power plant in this work to tackle the complex short-term energy management problems. We first collect electricity consumption data from smart appliances used in households and predict power-generating capacity of renewable energy sources at the prosumer level. Then, the proposed hierarchical power scheduling system is employed to schedule the usage of electricity for the customers by considering the efficiency of the use of distributed renewables. Notably, charging management of a moving electric vehicle is also considered in the proposed power scheduling mechanism. In addition, a real-time power tracking mechanism is presented to deal with the forecast errors of volatile renewable power generation, electricity load, and moving electric vehicle charging, and the maximal usage of renewables and reduction of the burden on community virtual power plants during time period of peak load can be achieved accordingly. The experimental results show that the proposed day-ahead power scheduling system can mitigate the dependency on traditional power generation effectively, and balance peak and off-peak period load of electricity market.

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“…The former is a linear combination of ESS injection decision variables while the latter is a linear combination of power draw from random variables at different buses. Given the expressions in (5), (6), and (7), at each time slot t, we can define the Cumulative Distribution Functions (CDFs) for the distribution line active power flows as…”

Section: B Stochastic Representation Of Key Operational Parametersmentioning

confidence: 99%

“…The baseload is synthesized by aggregating the metered . The hourly load of EV charging stations are from [6]. Given the focus of this paper, we take the PDFs of the random variables, e.g., solar generation, baseload, EV charging, etc.…”

Section: Case Studiesmentioning

confidence: 99%

“…For example, it might be reasonable to assume statistical independence and/or Gaussian distributions for the generation outputs of wind and/or solar farms that are scattered across a large transmission network [4]. However, these assumptions may not hold in a distribution grid with renewable DGs and EV charging stations that are confined to a relatively small geographical location due to the dependency in solar irradiance in proximate system buses [5] and the non-standard distribution of EV charging loads [6]. Also, the impact of some fluctuating elements may dominate the overall uncertainty in a distribution grid, making the typical use of the central limit theorem less practical.…”

Section: Introduction a Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Energy Storage Planning in Active Distribution Grids: A Chance-Constrained Optimization with Non-Parametric Probability Functions

Akhavan-Hejazi

Mohsenian‐Rad

2016

IEEE Trans. Smart Grid

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Abstract-By considering the specific characteristics of random variables in active distribution grids, such as their statistical dependencies and often irregularly-shaped probability distributions, we propose a non-parametric chance-constrained optimization approach to operate and plan energy storage units in power distribution girds. In particular, we develop new closedform stochastic models for the key operational parameters in the system. Our approach is analytical and allows formulating tractable optimization problems. Yet, it does not involve any restricting assumption on the distribution of random parameters, hence, it results in accurate modeling of uncertainties. Different case studies are presented to compare the proposed approach with the conventional deterministic and parametric stochastic approaches, where the latter is based on approximating random variables with Gaussian probability distributions.

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Developing a Test Data Set for Electric Vehicle Applications in Smart Grid Research

Cited by 34 publications

References 7 publications

Optimal Charging of Electric Vehicles With Uncertain Departure Times: A Closed-Form Solution

Optimal Charging of Electric Vehicles With Uncertain Departure Times: A Closed-Form Solution

A load-balancing power scheduling system for virtual power plant considering emission reduction and charging demand of moving electric vehicles

Energy Storage Planning in Active Distribution Grids: A Chance-Constrained Optimization with Non-Parametric Probability Functions

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