Toward Sustainable Electricity Markets: Capacity-Based Pricing for Electric Vehicle Smart Charging

• 成果类型: Article
• 署名作者: Valogianni, Konstantina; Ketter, Wolfgang; Collins, John; Adomavicius, Gediminas
• 署名单位: IE University; University of Cologne; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.2023.0078
• 发表日期: 2026-03
• 关键词: Green IS; IS artifact; electric vehicles; sustainability; intelligent agents; energy informatics; smart markets; design science; demand-side management; REINFORCEMENT LEARNING APPROACH; design science research; information-systems; renewable energy; COORDINATION; POWER; green; time; strategy

摘要

We present a novel information systems (IS)-enabled pricing artifact for variablerate electric vehicle (EV) charging that can facilitate a sustainable EV introduction. EVs can significantly reduce carbon intensity in modern cities and address important sustainability challenges. However, large-scale EV introduction is expected to increase electricity demand peaks, threatening grid stability and reliability. Most proposed methods to coordinate EV charging have shortcomings, such as the inability to guarantee incentive alignment between grid and EV owner objectives, or avalanche effects, which might create new demand peaks as EV owners receive the same price signals and make similar charging decisions. To address this issue, we present a capacity-based pricing artifact that includes a dynamic, charging-rate-based price component and a set of price-setting methods based on analytical or computational heuristics. The proposed approach benefits from a variety of available information in the environment and allows rational EV agents to optimize their own costs through planning and scheduling in the presence of their individual charging needs and constraints, while at the same time rebalancing the total charging demand to mitigate avalanche effects in EV charging. The proposed artifact is highly effective in reducing demand volatility or, alternatively, in achieving a desired match between the output of renewable energy sources and overall charging demand. We demonstrate the benefits of the proposed approach empirically, by comparing it to traditional, currently used pricing benchmarks in several realistic scenarios. Our artifact supports grid operators in their effort to rebalance the overall EV charging demand across time. Furthermore, the proposed approach enables energy providers to maintain the overall revenues (i.e., to achieve rebalancing without changing the total charging cost for the same energy needs), while respecting marketimposed price constraints. Finally, energy market stakeholders can use our pricing scheme to induce demand profiles that follow renewable generation patterns, maximizing renewable usage and reducing inefficiencies in renewable energy utilization by the grid.