Impacts of electric vehicles' charging strategies in the electricity prices

Camus, Cristina; Farias, Tiago L.

doi:10.1109/eem.2011.5953125

Cited by 6 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both these authors consider the user profiles to be of primary impact on recharging times. In a much more recent study, Camus et al [25] Mody and Steffen [13] presented a study in 2013 describing the need to use automated control for 'optimal' charging of EVs. They observed that the increasing EV population will lead to electricity grid problem if it is treated like a normal appliance.…”

Section: Charging Solutionsmentioning

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.

View full text Add to dashboard Cite

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.

show abstract

Section: Charging Solutionsmentioning

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.

View full text Add to dashboard Cite

show abstract

“…Both these authors consider the user profiles to be of primary impact on recharging times. In a much more recent study, Camus et al [13] simulate a 2020 scenario of 2 million EVs in the Portuguese spot electricity market, considering different mixes of renewable power generation. They conclude that with low renewables and high cost, charging of EVs during peak times can lead to electricity prices of 17 Euro cents/KWh.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Charging of EVs in a Real Time Pricing Electricity Market

Mody¹,

Steffen²

2013

SAE Int. J. Alt. Power.

View full text Add to dashboard Cite

The idea of grid friendly charging is to use electricity from the grid to charge batteries when electricity is available in surplus and cheap. There are several ways of achieving this, for example using droop control, using night time electricity tariffs, or using smart metering. The goal is twofold: to avoid putting additional load on the electricity grid and power generation, and to reduce the cost to the consumer. This paper looks at the saving potential when charging an electric car using real time tariffs provided by a smart meter, using the Ameren tariffs in Illinois as an example. If prices are known in advance (day-ahead pricing), the optimization only requires picking the cheapest time slots for charging the battery. Further savings can be made by using real time prices that are not known in advance, but the optimization problem then depends on price prediction models, and it becomes much more difficult to solve. This paper presents a simple suboptimal approach, and it quantifies the potential improvements that could be made using more sophisticated price predictions.The result is that cost savings in the order of about 50 USD (1/3 of the electricity costs) are feasible if a fast charger is used using real time pricing. The scale of the savings is such that complex optimization strategies are not worthwhile, and for the foreseeable future simple solutions are expected to be more cost effective.

show abstract

Electric vehicles as a mean to reduce, energy, emissions and electricity costs

Camus

Farias

2012

2012 9th International Conference on the European Energy Market

View full text Add to dashboard Cite

Impacts of electric vehicles' charging strategies in the electricity prices

Cited by 6 publications

References 1 publication

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

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

Optimal Charging of EVs in a Real Time Pricing Electricity Market

Electric vehicles as a mean to reduce, energy, emissions and electricity costs

Contact Info

Product

Resources

About