In the ever-evolving landscape of energy systems, the integration of diverse energy sources and technologies is becoming increasingly crucial. Jin Gao, a researcher from the Key Laboratory of Energy Digitalization at Fuzhou University, China, has made significant strides in this area with a groundbreaking study published in Energies. The research introduces a novel energy trading strategy for integrated energy systems (IESs) that not only addresses the uncertainties of renewable energy sources but also ensures a fair distribution of benefits among multiple IESs.
The study, which focuses on the optimization of IESs, highlights the importance of incorporating power-to-gas (P2G) and carbon capture systems (CCS) to reduce carbon emissions. Gao explains, “By integrating these technologies, we can create a more sustainable and efficient energy system that is better equipped to handle the fluctuations of renewable energy sources.” This integration is a significant step towards achieving a low-carbon energy future, a goal that is increasingly important as traditional fossil fuel resources deplete and environmental concerns grow.
One of the key innovations in Gao’s research is the development of a four-level robust optimization model. This model considers the probability distribution scenarios of renewable energy and the uncertainty of its output, ensuring that the scheduling results are closer to actual conditions. Gao elaborates, “Our model employs multiple interval uncertainty sets, which help in reducing the conservatism of the optimization results, making the system more adaptable and efficient.”
The study also introduces a column-and-constraint generation (C&CG) algorithm based on an alternating iteration strategy (AIS). This algorithm addresses the issue of excessive iterations due to nonlinear terms in the subproblems, effectively reducing the number of iterations and ensuring efficient problem-solving. This advancement is particularly important for the energy sector, as it can lead to significant cost savings and improved operational efficiency.
To tackle the issue of benefit allocation among multiple IESs, Gao and his team employed the Nash–Harsanyi bargaining method. This method considers the bargaining power of each IES, encouraging active participation in energy cooperation. “By quantifying the bargaining power of each IES using marginal contribution and energy contribution, we can achieve a fair allocation of benefits,” Gao explains. This approach not only enhances the overall operational efficiency of the system but also ensures that each IES feels valued and motivated to participate in energy trading.
The research also highlights the potential commercial impacts of these advancements. By improving the stability and efficiency of IESs, energy providers can reduce operational costs and enhance their competitive edge in the market. Moreover, the fair distribution of benefits can foster greater collaboration among energy systems, leading to a more resilient and sustainable energy infrastructure.
The study, published in Energies, underscores the importance of addressing the uncertainties of renewable energy sources and ensuring a fair distribution of benefits among multiple IESs. As the energy sector continues to evolve, Gao’s research provides a roadmap for developing more stable and efficient energy trading strategies. This work is poised to shape future developments in the field, paving the way for a more sustainable and integrated energy future.
