Electric Vehicles' Managed Home-Charging Programs
55 Pages Posted: 13 Apr 2022 Last revised: 16 May 2022
Date Written: March 18, 2022
Experts estimate 20 million electric vehicles (EVs) will be on U.S. roads by 2030, and the majority (around 80%) of EV owners will perform home charging. Many utilities are designing managed charging programs (currently at the design/pilot stages) to effectively integrate and manage the total EV load. Motivated by several managed charging pilots, in this paper, we study a general case of an active managed home-charging (AMH) program, which is executed as follows. An AMH participant plugs in her EV at some time and indicates her required load and charging window. This information is transmitted to the utility. The utility is obligated to satisfy her load requirement within her charging window. The utility minimizes the total energy cost by dynamically managing the load supply to each individual participant. We consider a large number of non-homogeneous participants. These participants have different arrival times, charging windows, load requirements, and charging speed limits. We present a large-scale non-linear optimization model for executing these AMH programs. Our model has an infinite horizon, and its variables and constraints grow in the number of AMH participants, making it unsolvable in practical applications. We present an effective solution method that consists of three approximations: aggregation, truncation, and linearization. These approximations transform our model to a mixed-integer linear program, with a reasonable size that does not grow in the number of participants. We present thorough theoretical and numerical analyses of our approximation and provide worst-case bounds for its error components. Our theoretical results indicate the relative error of our approximation is very small and that it reduces in the number of participants, making it suitable for practical instances. Our model and solution can effectively be used as a decision support tool to answer a variety of managerial questions related to the design and promotion of managed charging programs. We present managerial insights on how utilities should promote participation in these programs and whether utilities should promote charging more frequently. First, we show increasing participation in managed charging programs may not be a cost-saving strategy. Second, we show higher charging frequency may increase the total cost. Interestingly, these insights are contrary to the utilities' common beliefs.
Keywords: electric vehicles, managed charging programs, home charging, large-scale optimization, approximation, error analysis
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