Novel Approaches to Support Energy Systems: Analysis & Learning I – Invited Special Session

B4L-F: Novel Approaches to Support Energy Systems: Analysis & Learning I - Invited Special Session

Session Type: Lecture
Session Code: B4L-F
Location: Room 6
Date & Time: Thursday March 23, 2023 (14:00-15:00)
Chair: Sijia Geng, Yury Dvorkin
Track: 12

Paper No.	Paper Name	Authors	Abstract
3229	Modeling and Analysis of Large Power Systems with High Percentage of IBRs – Delivery of System Services	Deepak Ramasubramanian	-
3158	A Clustering Appraoch to Modeling the Aggregate Flexibility of Large Electric Vehicle Populations	Feras Al Taha{2}, Tyrone Vincent{1}, Eilyan Bitar{2}	The increasing prevalence of plug-in electric vehicles (EVs) in the transportation sector will introduce a large number of highly flexible electric loads that EV aggregators can pool and control to provide energy and ancillary services to the wholesale electricity market. To integrate large populations of EVs into electricity market operations, aggregators must express the aggregate flexibility of the EVs under their control in the form of a small number of energy storage (battery) resources that accurately capture the supply/demand capabilities of the individual EVs as a collective. To this end, we propose a novel multi-battery flexibility model that consists of a weighted aggregation of a small number of base sets (batteries) that represent the differing geometric shapes of the individual EV flexibility sets, allowing a trade-off between model complexity and fidelity. Using this framework, we study the problem of computing an optimal multi-battery flexibility set that has minimum Hausdorff distance to the true aggregate flexibility set, subject to the requirement that the multi-battery flexibility set be contained within the aggregate flexibility set. While this problem is shown to be computationally intractable in general, we provide a conservative approximation in the form of a convex program whose size scales polynomially with the number and dimension of the individual flexibility sets and base sets. We illustrate the performance achievable by our method with numerical experiments.
3109	Decision-Dependent Stochastic Optimization for Competitive Energy Markets	Killian Wood, Ana Ospina, Elisabetta Perotti, Emiliano Dall’Anese	We propose a competitive market in which service providers compete to maximize their expected profit, in the presence of price-dependent demand and operating conditions that evolve over time. In particular, we focus on electric vehicle (EV) charging markets, and incorporate the charging rates and capacity of each provider in their utility. To account for stochasticity in the responsiveness of EV drivers to price fluctuations, we leverage theory on decision-dependent data distributions and formulate our competitive market problem as a stochastic optimization problem with decision-dependent distributions. With this formulation in place, we demonstrate that an online stochastic primal-dual algorithm is capable of tracking desirable prices for strongly-convex-strongly-concave utility functions and demand data that are revealed sequentially in time. These solutions, coined equilibrium points, place the market in an equilibrium state as they are optimal prices for the users’ demand and are shown to amount to repeatedly retraining new price models each time a drift occurs.