A Robust Optimization Approach for Demand Response Participation of Fixed-Frequency Air Conditioners

TL;DR

This paper introduces a robust optimization model for fixed frequency air conditioners to participate effectively in demand response programs, considering temperature uncertainties and maximizing profit while ensuring user comfort.

Contribution

It develops a probabilistic response model for FFACs and formulates a robust optimization framework reformulated as a MILP for efficient solution.

Findings

The model effectively maximizes aggregator profit during DR events.

Simulation validates the robustness and practicality of the approach.

Temperature uncertainty is explicitly incorporated to protect user comfort.

Abstract

With the continuous increase in the penetration of renewable energy in the emerging power systems, the pressure on system peak regulation has been significantly intensified. Against this backdrop, demand side resources particularly air conditioning loads have garnered considerable attention for their substantial regulation potential and fast response capabilities, making them promising candidates for providing auxiliary peak shaving services. This study focuses on fixed frequency air conditioners (FFACs) and proposes an optimization model and solution method for their participation in demand response (DR) programs. First, a probabilistic response model for FFACs is developed based on the Markov assumption. Second, by sampling this probabilistic model, the aggregate power consumption of an FFAC cluster under decentralized control is obtained. Subsequently, a robust optimization model is formulated to maximize the profit of an aggregator managing the FFAC cluster during DR events, taking into account the aggregated response power. The model explicitly considers temperature uncertainty to ensure user comfort in a robust sense. Finally, leveraging the structure of the proposed model, it is reformulated as a mixed-integer linear programming (MILP) problem and solved using a commercial optimization solver. Simulation results validate the effectiveness of the proposed model and solution approach.