Sustainable Electric Vehicle Charging using Adaptive Pricing

• 成果类型: Article
• 署名作者: Valogianni, Konstantina; Ketter, Wolfgang; Collins, John; Zhdanov, Dmitry
• 署名单位: IE University; University of Cologne; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam; University of Minnesota System; University of Minnesota Twin Cities; University System of Georgia; Georgia State University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13179
• 发表日期: 2020
• 页码: 1550-1572
• 关键词: adaptive pricing; electric vehicles; electricity markets; smart grid; sustainability

摘要

A transition to electric vehicles (EVs) is widely assumed to be an important step along the road to environmental sustainability. However, large-scale adoption of EVs may put electricity grids under critical strain, since peaks in electricity demand are likely to increase radically. Efforts to manage demand peaks through pricing schemes may create new peaks at low-price periods, if large numbers of EV owners use smart charging to benefit from low prices. This effect is expected to be amplified when EV owners adopt smart decision support to assist them with optimal charging decisions. Therefore, energy policymakers are interested in advanced pricing schemes that can smooth demand or induce demand that comes as close as possible to a desired profile. We show, through simulations calibrated with real-world data, that current approaches to electricity pricing are limited in their ability to induce desired demand profiles. To address this challenge, we present adaptive pricing, a method to learn from EV owner reactions to prices and adjust announced prices accordingly. Our method draws on the Green Information Systems principles and can assist grid operators in ensuring the reliable operation of the grid. We evaluate our results in simulations, where we find that adaptive pricing outperforms current electricity pricing schemes, yielding results close to the theoretically optimal ones. We test our method in inducing both flat and extremely volatile demand profiles, and we see that in both cases it manages to induce EV charging close to the ideal scenario under perfect information.