In the rapidly evolving energy sector, the quest for low-carbon, cost-effective solutions has led to innovative research that could reshape how virtual power plants operate. A recent study published in *Power Construction*, led by WANG Jiayi from Hebei Electric Power Trading Center Co., Ltd., and HE Shuaijia from Sichuan University, introduces a groundbreaking model that integrates emerging distributed resources and electricity-carbon trading to enhance the economic and environmental performance of virtual power plants.
The study addresses a critical challenge in the energy sector: improving the low-carbon economic performance of scheduling strategies for virtual power plants. By incorporating emerging distributed resources like electric hydrogen production systems and carbon capture systems, alongside traditional resources such as energy storage, wind power, and photovoltaics, the researchers have developed a distributionally robust low-carbon scheduling model. This model not only minimizes costs but also accounts for the impact of electricity-carbon trading, a burgeoning market mechanism that incentivizes low-carbon operations.
One of the standout findings of the research is the significant cost reduction achieved through electricity-carbon trading. “Electricity-carbon trading reduced costs by approximately 24.7% compared to no electricity-carbon trading,” noted WANG Jiayi, lead author of the study. This highlights the potential for virtual power plants to leverage carbon markets to enhance their financial viability while reducing their environmental footprint.
The integration of electric hydrogen production systems and carbon capture systems further amplifies these benefits. The study found that considering both systems reduced abandoned electricity and operating costs by about 34.7% and 28.1%, respectively, compared to scenarios where only carbon capture systems were considered. “The total profit error of the proposed distributionally robust optimization method was approximately 1.7%, and the solving speed improved by approximately 40%,” added HE Shuaijia, emphasizing the model’s accuracy and efficiency.
The research also introduces a novel approach to handling the uncertainty inherent in renewable energy sources. By constructing an uncertainty set of probability distributions using the 1-norm and infinite-norm, the study avoids the complex iterations required in traditional multiple discrete-scenario distributionally robust optimization methods. This innovation not only improves decision-making accuracy but also significantly enhances computational efficiency.
The implications of this research are far-reaching for the energy sector. As virtual power plants become increasingly integral to the grid, the ability to optimize their scheduling strategies while minimizing costs and carbon emissions will be crucial. The study’s findings suggest that electricity-carbon trading and the integration of emerging distributed resources can jointly reduce scheduling costs, abandoned electricity, and carbon emissions, paving the way for a more sustainable and economically viable energy future.
For energy professionals, this research offers a compelling case for adopting distributionally robust optimization methods and exploring the synergies between emerging technologies and market mechanisms. As the energy transition accelerates, such innovations will be key to achieving a low-carbon economy.
Published in *Power Construction*, the study provides a robust framework for future developments in virtual power plant management, offering a blueprint for integrating cutting-edge technologies and market strategies to achieve optimal performance.