In a significant stride towards revolutionizing energy distribution and trading, researchers have developed a novel framework that optimizes peer-to-peer (P2P) energy trading among multiple microgrids (MMGs) and active distribution networks (ADNs). This innovative approach, detailed in a recent study published in the English-language journal *Global Energy Interconnection*, promises to enhance the efficiency and cost-effectiveness of energy management in modern power systems.
The research, led by Peishuai Li from Nanjing University of Science & Technology and Nari Technology Co. Ltd., introduces a distributed co-optimization method that integrates P2P energy trading with network operations. This method is designed to minimize operational and trading costs while ensuring the safe and reliable operation of distribution networks. “Our framework not only incentivizes stakeholders to participate in P2P energy trading but also ensures that the distribution network operates within safe limits,” Li explained.
The study employs a cooperative game model based on Nash bargaining theory to facilitate fair and efficient energy trading among MMGs. This model is coupled with a distributed solution method using the alternating direction method of multipliers (ADMM), which allows for decentralized decision-making and coordination. Additionally, the researchers propose an algorithm that dynamically adjusts the energy trading amount between the ADN and MG, further enhancing the stability and efficiency of the network.
The implications of this research are far-reaching for the energy sector. By optimizing P2P energy trading and network operations, the proposed framework can lead to significant cost savings and improved resource utilization. This is particularly relevant as the penetration of distributed energy resources (DERs) continues to grow, necessitating more sophisticated and adaptive management strategies.
“As the energy landscape evolves, the ability to efficiently manage and trade energy among multiple microgrids and distribution networks becomes increasingly important,” Li noted. “Our method provides a robust solution that can adapt to these changes and support the transition towards a more decentralized and resilient energy system.”
The study’s findings were validated through numerical simulations, which demonstrated the accuracy and effectiveness of the proposed method. These results underscore the potential of the framework to shape future developments in energy distribution and trading, paving the way for more efficient and sustainable energy management practices.
As the energy sector continues to evolve, the integration of advanced technologies and innovative strategies will be crucial in meeting the growing demand for reliable and cost-effective energy solutions. The research by Li and his team represents a significant step forward in this direction, offering valuable insights and tools for the future of energy management.