Distributed robust optimization for low-carbon dispatch of wind-thermal power under uncertainties

Faced with the requirement of carbon emission reduction in power industry, low-carbon power dispatch involving various low-carbon approaches has been recognized as one of effective ways. Concentrate on several important approaches: wind power integration and carbon reduction cooperation, it is necessary to deal with the uncertainties of wind power and carbon reduction modes for thermal power encountered in low-carbon power dispatch. For this purpose, this paper firstly presents a distributed robust optimization model synthetically considering robustness, economy, and environment. Next, wind power characterizations, scenario division and compression methods, and allocation algorithms of initial carbon emission rights are fully discussed for the convenience of model solution. Finally, empirical analysis shows that (1) the proposed model proves to be effective not only in coping with wind power uncertainties and reducing operating costs, (2) but also in dealing with the uncertainties of carbon reduction modes and reducing carbon emissions, and (3) low-carbon power dispatching strategies combining robustness, economy, and environment could be achieved through the proposed model and method, which are especially helpful to minimize interference from these two types of uncertainty more scientifically and reasonably.

Automated summary

By considering robustness, economy, and environment, this paper presents a distributed robust optimization model to address the uncertainties of wind power and carbon reduction modes in low-carbon power dispatch. Empirical analysis demonstrates the effectiveness and practicality of the proposed model.