A research team from Tsinghua University has proposed a novel framework called “ergonomics in energy use” to address the growing challenge of balancing energy system demands with end-user service quality. Published in Engineering, the study establishes a systematic approach to quantifying demand-side flexibility while explicitly accounting for human factors such as comfort, productivity, and convenience.

The research emerges against the backdrop of accelerating renewable energy deployment. In China, grid-connected solar and wind capacity reached 1.48 billion kW in the first quarter of 2025, representing 43.2% of total installed capacity—the first time renewables have surpassed thermal power generation. However, the intermittent nature of these sources creates supply-demand imbalances that require flexible demand-side resources to manage.

Traditional approaches to demand flexibility have operated under the assumption of “maintaining human-desirable services” or “without sacrificing end-user interests.” The researchers identify this as problematic because users would only adjust their energy usage if services were completely unaffected—a logical impossibility. “This definition poses a paradox,” the authors note, “as users would initially align their energy usage with the energy system’s objectives... if services were completely unaffected.”

The proposed framework characterizes each flexibility resource through three components: a physical machine model, a target parameter, and a human evaluation model. The physical machine model describes energy transfer processes with mathematical physics equations, while the human evaluation model assesses human responses using probability theory and statistical methods. Target parameters serve as the bridge—quantifiable states that influence humans, such as indoor air temperature for HVAC systems, state-of-charge for electric vehicles, and illuminance for lighting systems.

To demonstrate the framework, the researchers examined an office building with three flexibility resources: an air-conditioning system with building thermal mass, electric vehicles with smart chargers, and a lighting system. They evaluated performance across two demand response programs: real-time load shedding for peak shaving and day-ahead scheduling with time-of-use tariffs.

The findings reveal distinct operational patterns. For real-time load shedding with a 20% peak power reduction, electric vehicles served as the predominant resource when available, contributing over 60% of power reduction in short-term programs. The air-conditioning system gradually took precedence as response periods extended, particularly at lower load shedding rates. The lighting system only became significant in prolonged, high-reduction scenarios due to its greater sensitivity to power changes.

In day-ahead scheduling, the resources demonstrated different economic flexibility. Electric vehicles provided the primary cost savings at reduction rates between 0% and 10% through strategic charging and discharging. The air-conditioning system became dominant from 10% to 20% reduction through energy-efficient on-off control. Beyond 20%, the lighting system assumed larger roles, initially cutting nighttime usage before reducing daytime illumination.

The study introduces a service quality-based quantification method defining demand flexibility as “the maximum change in energy use for a given increment in service quality impact.” This transforms flexibility from a purely engineering concept into a social engineering concept, using indices such as predicted percentage dissatisfied to measure human impact.

The researchers emphasize that their framework enables optimal segmentation and allocation of power regulation tasks across multiple resources. Compared to conventional rule-based strategies, the service quality-optimal approach significantly enhanced the building’s energy flexibility by consistently engaging resources with the highest marginal flexibility—those providing maximum power change per unit of service quality impact.

The work identifies several directions for future development, including standardized quantification methods for incentive design, machine learning integration for model characterization, and dynamic human evaluation models that better reflect real-world scenarios. The authors call for interdisciplinary collaboration to advance this new research field, noting that “human-system interaction concepts in ergonomics are essential for future” energy system development.

The paper “Ergonomics in Energy Use: Bridging Energy System-Oriented Flexibility and Human-Oriented Service Quality,” is authored by Xiaochen Liu, Ziyi Luo, Tao Zhang, Xiaohua Liu, Yi Jiang. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.12.002. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.