Integrating Aggregated Electric Vehicle Flexibilities in Unit Commitment Models using Submodular Optimization

TL;DR

This paper presents a novel exact optimization method leveraging submodular optimization to efficiently incorporate aggregated EV flexibilities into the unit commitment problem, enabling scalable solutions for large-scale power systems.

Contribution

It introduces a new approach that exploits the polymatroid structure of EV flexibilities for exact and scalable unit commitment optimization.

Findings

Exact solution with linear (up to log) scaling in EV count

Effective integration of EV flexibility in real-world UC problem

Demonstrated scalability on European grid data

Abstract

The Unit Commitment (UC) problem consists in controlling a large fleet of heterogeneous electricity production units in order to minimize the total production cost while satisfying consumer demand. Electric Vehicles (EVs) are used as a source of flexibility and are often aggregated for problem tractability. We develop a new approach to integrate EV flexibilities in the UC problem and exploit the generalized polymatroid structure of aggregated flexibilities of a large population of users to develop an exact optimization algorithm, combining a cutting-plane approach and submodular optimization. We show in particular that the UC can be solved exactly in a time which scales linearly, up to a logarithmic factor, in the number of EV users when each production unit is subject to convex constraints. We illustrate our approach by solving a real instance of a long-term UC problem, combining open-source data of the European grid (European Resource Adequacy Assessment project) and data originating from a survey of user behavior of the French EV fleet.