Extracting demand-side flexibility is crucial for improving stability and efficiency in power grids integrated with renewable energy. Thermostatically controlled loads (TCLs) are among the most promising resources for providing such flexibility. However, conventional aggregation methods typically regulate TCL operations collectively to meet occupants' preferences, often resulting in suboptimal system performance or compromised thermal comfort. To address this issue, this study proposes a multi-priority coordination approach to aggregate flexibility from TCLs. The proposed aggregation framework employs linear programming to optimize TCL operation while ensuring occupant comfort requirements are consistently met. Results from on-site experiments demonstrate that the proposed method significantly enhances flexibility provision without compromising end-user comfort. Additionally, the experiments reveal that open public spaces provide greater flexibility compared to private offices, achieving a flexibility generation rate of up to 101% of the required flexibility. © 2025 Copyright held by the owner/author(s).

This paper presents an integrated framework for PV ramp rate management combining adaptive WT-LSTM forecasting with cooling-based control. The forecasting method, enhanced by explanatory variables, mRMR feature selection, and dynamic wavelet selection, achieved up to 63.6% MAPE reduction in demand forecasting and 9.20% MAPE for 30-second-ahead irradiance prediction. The cooling-based control reduced average and maximum ramp rates by 43.5% and 76.2% compared to battery-only methods. Integrated evaluation showed up to 20% fewer ramp rate violations, demonstrating the framework's potential to enhance grid stability and reduce system costs. © 2025 Copyright held by the owner/author(s).

This paper proposes a scalable HVAC hierarchical aggregation method that integrates Deep Reinforcement Learning (DRL) and Linear Programming (LP) to adaptively balance demand-side flexibility and thermal comfort. In the proposal, DRL determines the flexibility requirement commands issued to aggregators, while LP optimizes HVAC control commands to generate flexibility while maintaining thermal comfort. Our approach ensures scalability and adaptability in large-scale demand-side management. The simulation results demonstrate that the proposed method successfully manages HVAC operations by up to 600 rooms, achieving the required flexibility without violating comfort. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

The increasing integration of photovoltaic (PV) systems presents challenges due to output fluctuations caused by variable solar irradiance. Rapid changes in PV output, known as ramp rates, can destabilize power grids. This study introduces a novel ramp rate control method utilizing high-resolution solar irradiance forecasting based on a hybrid wavelet transform and Long Short-Term Memory (LSTM) network model. Wavelet transform decomposes volatile irradiance data, enhancing LSTM prediction accuracy. Predicted solar irradiance anticipated ramp rate violations, and proactive adjustment of PV output through optimization-based control reduced the number of violations by up to 20%. © 2025 Copyright held by the owner/author(s).

This paper proposes a residential energy management system that integrates multi-timescale optimization with electric water heater control. The proposed approach coordinates energy resources to minimize operational costs and maximize the use of renewable energy. Simulation experiments compared our proposal with an existing optimization methods. The results showed that our integrated optimization reduced purchased electricity by 2.1% and energy waste by 20%. It also maintains a more stable water heater temperature and volume. The findings offer insights into effective strategies for sustainable and home energy management. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Electric heat pump water heating systems help reduce environmental impact, but traditional controls struggle with unpredictable demand changes and PhotoVoltaic (PV) power fluctuations, leading to higher energy use. This work applies Model Predictive Control (MPC) to the scheduling of a water heating system by utilizing predicted PV availability. Simulations show the proposal reduces energy use while keeping user-specified water levels and temperatures. © 2024 Copyright held by the owner/author(s).

Extracting demand-side flexibility is important to utilize renewable energy as much as possible with keeping the grid stable. Heating, ventilation, and air conditioning (HVAC) system takes an important role in our comfort life and can be regards as residential thermostatically controlled loads (TCLs). TCLs have inherent thermal storage, so their electricity consumption can be modulated while still meeting the desired temperature requirements of the end user. This paper proposes a comfort-aware HVAC aggregation method based on Linear Programming to optimize HVAC operation while satisfy occupants’ comfort. Our on-site experiments demonstrated that the proposed HVAC aggregation approach can significantly generates flexibility while maintaining end-user comfort. © 2024 Copyright held by the owner/author(s).

In this paper, we construct a temperature prediction model using only indoor temperature, HVAC setting, and outdoor temperature, without considering the HVAC energy consumption. This approach eliminates the need for additional sensing devices for energy measurements, as required by existing methods, and significantly contributes to energy savings in household-level HVAC control by relying solely on smart remote controllers. To build the prediction model, we employed a regression method known as symbolic regression. Our constructed model is able to predict changes in indoor temperature with considerable accuracy, using only the values of indoor temperature, HVAC setting, and outdoor temperature as input variables. © 2024 Copyright held by the owner/author(s).