In a groundbreaking development for the energy sector, researchers have proposed a novel approach to optimize the planning of integrated energy systems, potentially revolutionizing how we manage and distribute energy. The study, led by ZHANG Wenxuan from the College of Electrical and Power Engineering at Taiyuan University of Technology, introduces a data-driven two-stage distributed robust collaborative planning model for electric-hydrogen-gas integrated energy systems. This model aims to fully exploit the coupling flexibility of these systems, incorporating demand response mechanisms to enhance efficiency and reduce costs.
The research, published in *Power Construction* (Dianli jianshe), addresses critical challenges in existing equipment modeling methods, which often suffer from inaccuracies and low solving efficiency. The team developed a refined modeling approach that considers distributed power supply, energy coupling equipment, hybrid energy storage, and demand response mechanisms. “Our goal was to create a more accurate and efficient model that could better integrate the various components of an energy system,” explained ZHANG Wenxuan. “By doing so, we can significantly improve the overall performance and cost-effectiveness of the system.”
One of the key innovations in this study is the development of a demand response incentive mechanism that accounts for baseline uncertainty. Using MATLAB simulations, the researchers demonstrated that their baseline load prediction model, based on Gaussian process regression, could calculate baseline loads more accurately and rapidly while considering response uncertainty. This refinement led to substantial reductions in various costs, including operation, planning, carbon trading, and demand response costs, by 2.55%, 10.78%, 1.08%, and 2.55%, respectively.
The study also highlights the benefits of collaborative optimization of carbon trading and demand response mechanisms. By leveraging flexible loads and distributed power sources, the system can achieve low-carbon and stable operation, reducing the power purchased from the upper power grid. “This approach not only cuts costs but also promotes sustainable energy practices,” noted ZHANG Wenxuan. “It’s a win-win situation for both the environment and the energy sector.”
Compared to traditional methods like Stochastic Optimization (SO) and Robust Optimization (RO), the proposed Distributed Robust Optimization (DRO) planning method showed superior balance between economy and robustness. This makes it particularly applicable for integrated energy system planning, offering a more reliable and cost-effective solution.
The implications of this research are far-reaching for the energy sector. By significantly reducing the annual comprehensive cost of integrated energy systems and improving the utilization of renewable energy, this model provides a blueprint for future developments. It offers valuable insights for subsequent research on the planning of electric-hydrogen-gas integrated energy systems, paving the way for more efficient and sustainable energy management.
As the energy sector continues to evolve, innovations like this are crucial for meeting the growing demand for clean and reliable energy. The work of ZHANG Wenxuan and their team represents a significant step forward in this direction, offering a promising solution that could shape the future of energy planning and management.