Robust Reserve Capacity Provision and Peak Load Reduction from Buildings in Smart Grids

TL;DR

This paper introduces a robust model predictive control algorithm for HVAC systems in smart grids, optimizing costs and capacity participation while managing uncertainties, demonstrated through simulations in Singapore's electricity market.

Contribution

It develops a robust MPC scheme that accounts for thermal load uncertainties, enhancing demand response strategies for HVAC systems in smart grids.

Findings

Effective demand-side control under uncertainties

Cost savings and peak load reduction demonstrated

Potential for utility adoption in real markets

Abstract

This paper proposes a robust demand-side control algorithm in a smart grid environment for heating, ventilation and air conditioning (HVAC) systems. A robust model predictive control (RMPC) scheme in a receding horizon fashion is deployed, which optimizes electricity cost and capacity market participation of the HVAC system, while satisfying comfort and operational constraints of the building and utility, respectively. Thermal load uncertainties experienced by the HVAC system are included to perform a realistic assessment of the developed controller. The National Electricity Market of Singapore (NEMS) is used as a case study and the developed RMPC scheme is tested for various price signals and scenarios. Numerical simulation results show the effectiveness of the developed framework to be readily adopted by utilities -- interested in realizing a grid-friendly and economicaly eficient demand response (DR) strategy.