Distributionally robust day-ahead combined heat and power plants scheduling with Wasserstein Metric

作     者：Skalyga, Mikhail Amelin, Mikael Wu, Qiuwei Soder, Lennart 

作者机构：Tech Univ Denmark DTU Ctr Elect Power & Energy Dept Elect Engn DK-2800 Lyngby Denmark KTH Royal Inst Technol Sch Elect Engn Div Elect Power & Energy Syst Teknikringen 33 S-10044 Stockholm Sweden Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China 

出 版 物：《ENERGY》 (能源杂志)

年 卷 期：2023年第269卷第1期

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：EUDP programme through the SMART MLA project [64018-0302] DTU, Denmark through the Alliance PhD scholarship 

主　　题：Stochastic programming Combined heat and power District heating Distributionally robust optimization Electricity markets 

摘      要：Combined heat and power (CHP) plants are main generation units in district heating systems that produce both heat and electric power simultaneously. Moreover, CHP plants can participate in electricity markets, selling and buying the extra power when profitable. However, operational decisions have to be made with unknown electricity prices. The distribution of unknown electricity prices is also not known exactly and uncertain in practice. Therefore, the need of tools to schedule CHP units production under distributional uncertainty is necessary for CHP producers. On top of that, a heating network could serve as a heat storage and an additional source of flexibility for CHP plants. In this paper, a distributionally robust short-term operational model of CHP plants in the day-ahead electricity market is developed. The model accounts for the heating network and considers temperature dynamics in the pipes. The problem is formulated in a data-driven manner, where the production decisions explicitly depend on the historical data for the uncertain day-ahead electricity prices. A case study is performed, and the resulting profit of the CHP producer is analyzed. The proposed operational strategy shows high reliability in the out-of-sample performance and a profit gain of the CHP producer, who is aware of the temperature dynamics in the heating network.