electric-vehicle-chargingThis post delves into a somewhat arcane subject, which is the usage and re-charging strategies and decisions users of electric vehicles EVs will need to become adept at as penetration of these all electric vehicles increases and the many factors that influence price of power for example need to be well integrated into an individuals individual usage pattern.

by Dr. Sarvapali D. Ramchurn, lecturer at the School of Electronics and Computer Science, University of Southampton. Read his blog, Agents in the Smart Grid. Connect with Sarvapali on Linkedin.

Electric vehicles (EV)s are likely to make significant demands on how the grid operates but also on how people will use them most efficiently while minimising their impact on their lifestyle. Indeed, across the spectrum of potential users of EVs, users’ mobility demands varies significantly. For example, while companies operating fleets of EVs (e.g., light-weight goods vans or cars) may need to serve specific routes all the time, professionals commuting to work will only do so on specific days (work week) and will drive on other, possibly longer distance, routes at other times. Also, families may need to have two cars to serve different sets of itineraries on different days (e.g., dropping off the kids, shopping trips, visits to parents, etc..). These usage scenarios exemplify the variety in energy demand that can be put onto the batteries of such EVs. Thus, while in the simplest case, fleets may only need enough energy for short routes and can afford to keep a stock of charged batteries, in the more complex and general setting (which we focus on here), individuals may only be able to purchase one or two electric vehicles and only charge them individually overnight or at specific charge points along their routes (e.g., shopping centre car parks or stadium). Moreover, with the deployment of charge points (e.g., at home, en-route, or at service stations) at different locations and with different prices, users will be faced with a range of options as to where to charge their batteries, each with a price to pay. To this end, it is important to design information systems that will help them do so, while taking into account their travel needs.

To understand what types of systems will need to be designed, we consider some of the key features of EV usage in the smart grid as outlined by [1,2]. Key ideas include the concept of ‘mobility-on-demand‘ whereby users will hire vehicles for only those trips they need and that of leaving it to those engineering the infrastructure to make sure users’ traditional mobility demands are served efficiently. Both approaches will require the design of decision support systems to enable a user to find the cheapest and most efficient way to get to her destination at all times.

Within the mobility-on-demand vision, EVs would be stacked at various locations in a region and charged, ready to be rented out to users. This scenario requires that the user (or users) be informed about where these stacks are, how long it will take to get there, and where to deposit the vehicle and how long it will take to get from depositing the vehicle to her final destination. This assumes that each vehicle will have enough charge to take the user to the destination given the route taken and road conditions (e.g, blockages, hills). While existing satellite navigation systems can help estimate travel distances and hence the amount of energy (hence travel costs) required for a trip, users will need to be provided with real-time information about where EVs are available and an estimate of how much it is going to cost them (given the predicted route she will take) and how long it is going to take to get to their destination. Presenting that information to all users for them to choose their cheapest and fastest option would need to factor in the different time/destination preferences (and trade-offs between time and cost) of each user to minimise delays without overloading users with too much information. Hence, the challenge here is to minimise the complexity of providing overall routing cost and duration (pickup+travel+time from drop-off) information while ensuring that users make an informed decision that will minimise their costs and allow them to reach their destinations in time. This will be made more complex if different stacks are priced differently by the system, in an attempt to balance demand and supply of EVs across the region [2]. If users’ mobility demands are optimised accordingly, more predictable behaviours in the system would result (as users act rationally), thus allowing for better system planning.

To read more on how one day become part of a network of rechargeable batteries that can help smooth out the intermittent nature of wind and solar power see our related post: “Rising Hopes that Electric Cars Can Play a Key Role on the Grid“.

Now, if users instead own the EVs they use, they mainly need to worry about where and when to charge their vehicles to ensure they can make the trips they intend to do by the time they reach the next charge point/destination. To this end, users will be faced with a number of optimisation problems. First, similar to a vehicle routing problem, users will need to plan their trips around charge points in an attempt to minimise the overall cost of energy by either choosing the shortest routes or the cheapest charge points. The routing problem is further complicated by the fact that each charge points may have different power ratings, hence charging time, which contribute to the delays the user incurs in charging her vehicle. Second, if the user only travels short distances and charges her car at home, she would need to consider the costs to charge her EV at different times of the day (assuming a spot price for electricity), which means she would need to optimise the EV charging cycle with respect to her travel needs in order to minimise her costs in the long term (over days or months). This may require predicting spot prices for a number of days ahead and, more importantly, the user’s travel needs. While spot prices are to some extent more easily predictable (various models exist for this [5]), travel needs may be harder to predict and could either be based on historical data or user input. Then, by combining such information with road condition predictions and, possibly, prices at other charge points on predicted routes, the charge cycle could be further optimised. Third, given the demand-side management and Vehicle-To-Grid (V2G) programmes that user could have access to, she will need to optimise the types of price plans and V2G sessions she participates in order to maximise her profits. This may eventually mean trading-off certain trips to be able to sell back electricity to the grid.

Some of the above decision making processes could be automated but would need to ensure that users understand what they commit to as the impact of wrong charging cycles could negatively impact their general lifestyle and safety (e.g., if they need a car to go the hospital and the battery is flat). Initial work in the area of EV usage optimisation such as [3,4] do provide novel optimisation algorithms but these are limited to the interaction mechanisms required to inform and receive input from users.

To read more on the subject of electric vehicles see our related post: “Can Electric Vehicles Take Off? A Roadmap to Find the Answer“.

[1] UK Royal Academy of Engineering (2010) Electric Vehicles: Charged With Potential.

[2] Mitchell, W., Borroni-Bird, C. and Burns L. (2010) Reinventing the Automobile.

[3] Gerding, E., Robu, V., Stein, S., Parkes, D., Rogers, A. and Jennings, N. (2011) Online Mechanism Design for Electric Vehicle Charging. In: The Tenth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2011), 2-6 May 2011, Taipei, Taiwan. (In Press)

[4] Hutson, C., Venayagamoorthy, G. and Corzine, K. (2008) Intelligent scheduling of hybrid and electric vehicle storage capacity in a parking lot for profit maximisation in grid power transactions. In Energy 2030 Conference, IEEE, pages 1-8.

[5] R. Weron (2006) Modeling and forecasting electricity loads and prices: A statistical approach.

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© 2011, Sarvapali Ramchurn. All rights reserved. Do not republish.

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Author: Sarvapali Ramchurn (1 Articles)

Dr. Sarvapali D. Ramchurn is a lecturer at the School of Electronics and Computer Science, University of Southampton. He obtained his PhD in Multi-Agent Systems under the supervision of Prof. Nick Jennings (a pioneer of multi-agent systems). Prior to his appointment as lecturer, he was a lead research fellow on the ALADDIN and IDEAS projects. Dr. Ramchurn has published over 35 peer-reviewed articles in prestigious journals (AIJ, JAIR) and conferences (AAMAS, IJCAI, AAAI) on the topics of multi-agent coordination, coalition formation, and more recently, on agent-based energy management applications. With his colleagues, he won the best paper award at AAMAS 2010 (the premier international conference) for their work on agent-based micro-storage in the smart grid. His work on agent-based demand-side management was also nominated for the best innovative agents application award at AAMAS 2011. Previously Dr. Ramchurn also led teams that won the Prisoner’s dilemma competition (2004&2005), and the RobocupRescue Infrastructure competition (2007). Read his blog, Agents in the Smart Grid