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The emergence of prosumers in distribution systems has enabled competitive electricity markets to transition from traditional hierarchical structures to more decentralized models such as peer-to-peer (P2P) and community-based (CB) energy transaction markets. However, the network usage charge (NUC) that prosumers pay to the electric power utility for network services is not adjusted to suit these energy transactions, which causes a reduction in revenue streams of the utility. In this study, we propose an NUC calculation method for P2P and CB transactions to address holistically economic and technical issues in transactive energy markets and distribution system operations, respectively. Based on the Nash bargaining (NB) theory, we formulate an NB problem for P2P and CB transactions to solve the conflicts of interest among prosumers, where the problem is further decomposed into two convex subproblems of social welfare maximization and payment bargaining. We then build the NUC calculation model by coupling the NB model and AC optimal power flow model. We also employ the Shapley value to allocate the NUC to consumers fairly for the NUC model of CB transactions. Finally, numerical studies on IEEE 15-bus and 123-bus distribution systems demonstrate the effectiveness of the proposed NUC calculation method for P2P and CB transactions.

Disruptive technologies have been employed in various fields with a strategic priority in several countries as the core driving force of the fourth industrial revolution, significantly impacting the development of new power systems. It is a kernel to effectively identify the future potential of disruptive technologies. To overcome the subjectivity and limitations of existing disruptive technology identification methods, we propose a disruptive technology identification method based on patent evolution analysis. Firstly, the evolution matrix of the patent data is calculated. Afterward, we dig into the characteristics of disruptive technologies to build a more targeted identification index system. Finally, the fields of electric power communication and energy generation are selected as typical cases to build the patent data sets. The future development of the identified technologies, including the identified quantum technologies and controlled fusion, is analyzed. The results demonstrate that the proposed model can identify the key technologies of new power systems accurately and contribute to completing the industrial upgrading and transformation more rapidly.

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