In the bustling heart of Shanghai, a team of researchers led by Aijia Ding from the School of Mechatronic Engineering and Automation at Shanghai University is tackling a pressing challenge in the energy sector: the growing disparity between peak and off-peak loads. This issue, which threatens the economic operation of power systems and the integration of renewable energy, is the focus of their recent study published in the journal *Energy Conversion and Management: X* (translated as *Energy Conversion and Management: Cross-disciplinary Research and Applications*).
The team’s innovative approach centers on managing the coupled loads of electric vehicle (EV) charging and air conditioning (AC) systems in commercial buildings. “We saw an opportunity to optimize these loads collaboratively, rather than treating them as separate entities,” Ding explains. This collaborative framework is designed to maintain human comfort while significantly improving the efficiency of energy use.
The strategy unfolds in two stages. First, the researchers predict thermal sensation votes using a sophisticated hybrid method, allowing them to dynamically adjust the temperature set-point of AC units and evaluate their load control capacity. “This predictive approach ensures that we can maintain a comfortable environment for building occupants while also optimizing energy use,” Ding notes.
In the second stage, the team develops a multi-objective intra-day control strategy to optimize the operational parameters of EV charging and AC load regulation. A modified heuristic-based approach is employed to solve this complex problem. The results are impressive: the method reduces EV battery degradation costs by 15% when AC systems are integrated and achieves a 43.2% reduction in peak-valley difference and a 20.01% improvement in valley filling compared to the baseline.
The implications for the energy sector are substantial. As commercial buildings increasingly adopt EVs and advanced AC systems, the ability to manage these loads collaboratively could transform energy consumption patterns. “This research paves the way for more intelligent and efficient energy management in commercial buildings,” Ding says. “It’s a step towards a more sustainable and economically viable energy future.”
The study’s findings are particularly relevant to the growing interest in vehicle-to-building systems, load management, and flexible load strategies. As the energy sector continues to evolve, the integration of renewable energy sources and the optimization of load management will be critical. This research offers a promising approach to addressing these challenges, potentially shaping future developments in the field.
With the validation of real-world distribution system data, the team’s method demonstrates a practical and effective solution for the energy sector. As the world moves towards smarter and more sustainable energy use, the insights from this study could play a pivotal role in reshaping the landscape of commercial building energy management.