Chance-constrained regulation capacity offering for HVAC systems under non-Gaussian uncertainties with mixture-model-based convexification

TL;DR

This paper introduces a novel convexification approach for chance-constrained regulation capacity offering in HVAC systems, effectively handling non-Gaussian uncertainties and thermodynamic constraints to improve computational efficiency and solution optimality.

Contribution

It develops a mixture-model-based convexification method and a temporal compression approach to efficiently solve regulation capacity problems under non-Gaussian uncertainties.

Findings

The proposed model accurately captures non-Gaussian uncertainties.

It significantly reduces computational complexity.

Numerical results validate the method's effectiveness.

Abstract

Heating, ventilation, and air-conditioning (HVAC) systems are ideal demand-side flexible resources to provide regulation services. However, finding the best hourly regulation capacity offers for HVAC systems in a power market ahead of time is challenging because they are affected by non-Gaussian uncertainties from regulation signals. Moreover, since HVAC systems need to frequently regulate their power according to regulation signals, numerous thermodynamic constraints are introduced, leading to a huge computational burden. This paper proposes a tractable chance-constrained model to address these challenges. It first develops a temporal compression approach, in which the extreme indoor temperatures in the operating hour are estimated and restricted in the comfortable range so that the numerous thermodynamic constraints can be compressed into only a few ones. Then, a novel convexification method is proposed to handle the non-Gaussian uncertainties. This method leverages the Gaussian mixture model to reformulate the chance constraints with non-Gaussian uncertainties on the left-hand side into deterministic non-convex forms. We further prove that these non-convex forms can be approximately convexified by second-order cone constraints with marginal optimality loss. Therefore, the proposed model can be efficiently solved with guaranteed optimality. Numerical experiments are conducted to validate the superiority of the proposed method.