Asynchronous Smart Electricity Strategy: Residential Energy Trading and an MPC with Flexible Load

Abstract

Retail electricity management is inherently affected by two major sources of uncertainty: demand variability and electricity market conditions. To address these challenges, this paper proposes an asynchronous smart electricity strategy for the coordinated operation of residential demand and market participation. The proposed framework is structured as a two-layer, non-cooperative architecture. The upper layer represents an external trading agent, while the lower layer governs the control of flexible residential demand. In the upper layer, an energy trading stochastic programming model determines the optimal day-ahead procurement strategy by minimizing the expected cost under uncertainty. In the lower layer, a Model Predictive Control (MPC) scheme dynamically adjusts flexible Heating, Ventilation, Air Conditioning (HVAC) based residential loads to track the energy procured in the day-ahead market. The results demonstrate that the proposed approach maintains indoor thermal comfort within acceptable bounds while reducing the mean absolute tracking error (MAE) from 10.7 kWh to 7.6–8.5 kWh per 15-minute interval.