Optimal Charging of EVs in a Real Time Pricing Electricity Market

Mody, Sagar M.; Steffen, Thomas

doi:10.4271/2013-01-1445

Cited by 4 publications

(13 citation statements)

References 17 publications

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“…The first explains the problem and a deterministic (nonoptimal) solution to it [13]. A case study for the deterministic controller is also explained in the paper.…”

Section: Overview and Contribution Of This Researchmentioning

confidence: 99%

“…It also covers a more in depth literature study of similar approaches, driver charging behavior and explanations of why the charging profile used to prove the controller has been chosen. In this study, The stochastic nature of the problem is realized and a better and optimal solution is presented compared to the sub-optimal controller presented in part 1 [13]. The prediction ability of the controller has been tested using simulation of a 14 hour period of a set of arbitrary prices.…”

Section: Overview and Contribution Of This Researchmentioning

confidence: 99%

“…It can be simply visualized as shown in figure 3. The problem is explained in depth in a previous study [13].…”

Section: Summary Of the Stochastic Dynamic Programming Controller (Sdpm)mentioning

confidence: 99%

“…The vehicle is then used at 07:00 hours and brought back and plugged in at 17:00 on the next day. No daytime charging is assumed in this study, and a review of user charging profiles justifying using this case is covered in [12], [24]. The use of the strategy successfully avoids the high prices right up to the moment they drop enough but also considering how much time is required to give sufficient charge to the vehicle.…”

Section: Proving Sdpm Over a Single Daymentioning

confidence: 99%

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Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging

Mody¹,

Steffen²

2017

SAE Technical Paper Series

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The goal of grid friendly charging is to avoid putting additional load on the electricity grid when it is heavily loaded already, and to reduce the cost of charging to the consumer. In a smart metering system, Day Ahead tariff (DA) prices are announced in advance for the next day. This information can be used for a simple optimization control, to select to charge at cheapest times. However, the balance of supply and demand is not fully known in advance and the Real-Time Prices (RTP) are therefore likely to be different at times. There is always a risk of a sudden price change, hence adding a stochastic element to the optimization in turn requiring dynamic control to achieve optimal time selection. A stochastic dynamic program (SDP) controller which takes this problem into account has been made and proven by simulation in a previous paper.Since there are differences between the DA and the RTP tariff, this paper proposes a (1) predictor to create an unbiased estimate of the RTP tariff based on available data. It uses a regression on historical data to find the best prediction of the expected price. Finally, a (2) case study based on data from the Illinois Electricity Grid prices is presented to validate the SDP controller over several years of data. The stochastic optimization uses the RTP prices effectively, getting very close to the globally optimal charging price. However, the predictor achieves only a slight reduction in prediction uncertainty with this data sate, and it has a negligible effect on cost. This means that DA prices can be used as a fair prediction of RTP charging cost here. The SDPM successfully reacts in the case study and leads to savings on charging costs over the years presented.

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“…The first explains the problem and a deterministic (nonoptimal) solution to it [13]. A case study for the deterministic controller is also explained in the paper.…”

Section: Overview and Contribution Of This Researchmentioning

confidence: 99%

Section: Overview and Contribution Of This Researchmentioning

confidence: 99%

“…It can be simply visualized as shown in figure 3. The problem is explained in depth in a previous study [13].…”

Section: Summary Of the Stochastic Dynamic Programming Controller (Sdpm)mentioning

confidence: 99%

Section: Proving Sdpm Over a Single Daymentioning

confidence: 99%

See 2 more Smart Citations

Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging

Mody¹,

Steffen²

2017

SAE Technical Paper Series

Self Cite

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“…The following paper progresses from the idea in [13] taking into consideration the need of more complex prediction methods to provide an optimal solution to the charging problem. The idea is to automatically manage the charging time once the vehicle is plugged in to provide a full charge when required but at the lowest cost (hence at low demand hours).…”

Section: Introductionmentioning

confidence: 99%

Optimal Charging of Electric Vehicles using a Stochastic Dynamic Programming Model and Price Prediction

Mody

Steffen

2015

SAE Int. J. Passeng. Cars – Electron. Electr. Syst.

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The idea of grid friendly charging is to use electricity from the grid to charge batteries when electricity is available in surplus and cheap. The goal is twofold: to avoid putting additional load on the electricity grid and to reduce the cost to the consumer. To achieve this, a smart meter and a tariff with variable electricity prices has to be in place. In Day Ahead tariff (DA), prices are announced in advance for the next day, and this information can be used to select the cheapest times to charge the battery by the required amount. The optimization method is very simple, and it only has to be run once per day. However, the balance of supply and demand is not fully known in advance. Therefore Real Time Pricing (RTP) tariff supplies electricity at spot market rate depending on the current balance. This makes the charging process less predictable because it adds a stochastic element, but it does offer the potential of higher savings if future prices can be predicted with a reasonable degree of accuracy. This paper proposes an optimal controller based on a stochastic dynamic program (SDP), which predicts future price changes from available data. The controller takes into account price variability via a simple grid model that allows of unexpected price rises and a gradual return to a normal grid price. The DP algorithm has two variables, the state of charge (SoC) and the current electricity cost. It traces the expected total cost based on the stochastic model and makes a decision 'to charge or not' to minimize the expected (average) total cost. The results show that in case of a positive probability of price rises, the time to charge is chosen slightly before the lowest expected cost during the night. This is a rational solution, because waiting longer does increase the risk of an unexpected price spike. In the trivial case of a zero probability of unexpected price rises, the solution converges to the one found by the previous deterministic optimization algorithm.

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Energy Management in a Prosumer Installation Using Hybrid Systems Combining EV and Stationary Storages and Renewable Power Sources

2021

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Modern consumer installations can be equipped with renewable power sources (RESs) and stationary energy storage systems (ESSs). In addition, electric vehicles (EVs) are expected to become part of such installations in the not-too-distant future. The paper presents the control strategy that allows for efficient energy management and the option of EV “fast-home” charging. The novelty of this approach includes the use of the “time-dependent energy storage” (EV battery) together with ESS and PV sources with the focus on prosumer benefits. All goals can be achieved without the need for extensive expenses in the home electric infrastructure. To enable the synergy effect, it was necessary to develop a controller algorithm that uses the operating status of the prosumer infrastructure (current power generation and consumption), the state of charge of both the stationary storage and the EV battery, and the possibility to control the EV drive inverter during the parking state. The paper presents a developed simulator built in the PSCAD environment and the simulation results.

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Optimal Charging of EVs in a Real Time Pricing Electricity Market

Cited by 4 publications

References 17 publications

Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging

Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging

Optimal Charging of Electric Vehicles using a Stochastic Dynamic Programming Model and Price Prediction

Energy Management in a Prosumer Installation Using Hybrid Systems Combining EV and Stationary Storages and Renewable Power Sources

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