A regional integrated energy system is an important carrier of the energy Internet. It is a major challenge for the operation of a regional integrated energy system to deal with the uncertainty of distributed energy and multiple loads by using the coupling characteristics of equipment in a regional integrated energy system. In this paper, a two-stage robust economic dispatch model of a regional integrated energy system is proposed considering the source-load uncertainty. Firstly, the basic architecture of the regional integrated energy system is introduced. Based on the extreme scenario of uncertain power supply and load, the uncertainty set was established, the two-stage robust optimization model of regional integrated energy system was constructed and the column-and-constraint generation algorithm was used to solve the model. The effectiveness of the two-stage robust optimization model in improving the economy and robustness of the system was analyzed.
With the development of the Energy Internet and the Internet of Things, diversified social production activities are making the interactions between energy, business, and information flow among physical, social, and information systems increasingly complex. As the carrier of information and the hub between physical and social systems, the effective management of energy big data has attracted the attention of scholars. This work indicates that China’s energy companies have carried out a series of activities that are centered on energy big data collection, as well as development and exchange, and that the energy big data ecosystem has begun to take shape. However, the research on and the application of energy big data are mainly limited to micro-level fields, and the development of energy big data in China remains disordered because the corresponding macro-level instructive governance frameworks are lacking. In this work, to facilitate the sustainable development of the energy big data ecosystem and to solve existing problems, such as the difficult-to-determine governance boundaries and the difficult-to-coordinate interests, and to analyze the structure and mechanism of the energy big data ecosystem, data curation is introduced into energy big data governance, and a paradigm is constructed for sustainable energy big data curation that encompasses its full life cycle, including the planning, integration, application, and maintenance stages. Key paradigmatic issues are analyzed in-depth, including data rights, fusion, security, and transactions.
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