Robust optimisation scheduling of CCHP systems with multi‐energy based on minimax regret criterion. Issue 9 (1st June 2016)

• Record Type: Journal Article
• Authors: Wang, Luhao; Li, Qiqiang; Sun, Mingshun; Wang, Guirong
• Is Part Of: IET generation, transmission & distribution. Volume 10:Issue 9(2016)
• Page Start: 2194
• Page End: 2201
• Publication Date: 2016-06-01
• Subjects: cogeneration -- power generation scheduling -- iterative methods -- optimisation -- entropy; robust optimisation scheduling -- CCHP systems -- minimax regret criterion -- combined cooling heating and power system -- renewable energy -- load uncertainty -- day‐ahead scheduling decision -- electrical load tracking -- thermal load tracking -- hybrid solution method -- two‐stage Lagrangian relaxation iterative algorithm -- cross entropy algorithm; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2015.1344
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The intermittency of renewable energy and load uncertainty in the combined cooling, heating, and power (CCHP) system operation is considered in this study. The authors propose a robust optimisation scheduling method to attenuate the disturbance of uncertainty, and derive the day‐ahead scheduling decision under two strategies including electrical load tracking and thermal load tracking. Also, the budget of uncertainty is introduced to alleviate the conservatism of robust optimisation. Moreover, a minimax regret non‐linear formulation is constructed to describe the performance of the system and different realisations of uncertainty. To solve the problem, the authors develop a hybrid solution method, which is composed of a two‐stage Lagrangian relaxation iterative algorithm and an improved cross entropy algorithm. Simulation results demonstrate that the minimum of the maximum regret can be obtained, and the conservatism of robust optimisation is significantly reduced by properly setting the level of budget of uncertainty. As a result, the validity and effectiveness of the proposed robust optimisation model and algorithm are confirmed.