Heydaryan Manesh, Behzad (Technical University of Kaiserslautern), Al Khatib, Mohammad (Technical University of Kaiserslautern), Bajcinca, Naim (University of Kaiserslautern)

Hierarchical Energy Management for Load Shifting in Bidirectional Charging Hubs

Scheduled for presentation during the Regular Session "Optimisation" (WeCD), Wednesday, June 11, 2025, 18:10−18:30,

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

The increasing integration of electric vehicles (EVs) into power systems presents both challenges and opportunities for energy management. In this paper, we propose a two-level hierarchical model predictive control (MPC) framework for optimizing energy transactions in bidirectional charging hubs, focusing on load shifting as a long-term demand response (DR) strategy. At the higher level, an optimization problem based on portfolio optimization is solved every 15 minutes to determine the optimal energy packets allocated to EVs, the energy buffer, and grid interactions, considering energy prices and grid demands. This problem can be relaxed to a formulation as a mixed-integer linear program (MILP). At the lower level, a quadratic optimization problem with linear constraints determines the charging and discharging power of each EV with a higher time resolution to ensure that the assigned energy packets are accurately delivered while adhering to operational constraints. The proposed approach enables scalable and flexible load shifting, allowing bidirectional charging hubs to manage large-scale EV fleets while contributing to DR programs efficiently. Unlike traditional short-term optimization methods, our framework considers a long-term planning horizon, exploiting the flexibility provided by EVs and energy buffers to optimize energy trading decisions dynamically. Simulation results demonstrate the effectiveness of the proposed hierarchical control strategy.