Jin Jingliang, Wen Qinglan, Qiu Yaru, Cheng Siqi, Guo Xiaojun
College of Science, Nantong University, 9 Seyuan Road, Nantong, China.
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Nanjing, China.
Environ Sci Pollut Res Int. 2023 Feb;30(8):20980-20994. doi: 10.1007/s11356-022-23591-8. Epub 2022 Oct 20.
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.
面对电力行业的碳排放减少要求，涉及各种低碳方法的低碳电力调度已被视为有效途径之一。聚焦于几个重要方法：风电整合与碳减排合作，有必要应对低碳电力调度中遇到的风电不确定性和火电碳减排模式的不确定性。为此，本文首先提出一个综合考虑鲁棒性、经济性和环境性的分布式鲁棒优化模型。接下来，为便于模型求解，对风电特性、场景划分与压缩方法以及初始碳排放权分配算法进行了充分讨论。最后，实证分析表明：（1）所提出的模型不仅在应对风电不确定性和降低运营成本方面有效，（2）而且在处理碳减排模式的不确定性和减少碳排放方面有效，（3）通过所提出的模型和方法可以实现结合鲁棒性、经济性和环境性的低碳电力调度策略，这尤其有助于更科学合理地最小化这两种不确定性的干扰。
