Global solution method for decentralised multi‐area SCUC and savings allocation based on MILP value functions. Issue 16 (1st July 2020)

• Record Type: Journal Article
• Authors: Zheng, Xiaodong; Chen, Haoyong; Xu, Yan; Shen, Feifan; Liang, Zipeng
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 16(2020)
• Page Start: 3230
• Page End: 3240
• Publication Date: 2020-07-01
• Subjects: integer programming -- power generation dispatch -- power system security -- power markets -- game theory -- power generation scheduling -- linear programming -- power generation planning; MILP value function -- Lagrangian dual‐function‐based algorithms -- mixed‐integer linear programming value functions -- regional system operator -- tie‐line power injections -- finite algorithm -- generation cost -- globally optimal interchange plan -- fair savings allocation method -- Shapley value -- value‐function‐based method -- locational marginal cost‐based method -- decentralised M‐SCUC problem -- decentralised multiarea security constrained unit commitment problems -- cooperative game theory; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2020.0438
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : To address the issue that Lagrangian dual‐function‐based algorithms cannot guarantee convergence and global optimality for decentralised multi‐area security constrained unit commitment (M‐SCUC) problems, a novel decomposition and coordination method using mixed‐integer linear programming (MILP) value functions is proposed. In the proposed solution method, first, each regional system operator sets the tie‐line power injections as variational parameters in its regional SCUC model, and utilises a finite algorithm to generate an MILP value function, which returns the optimal generation cost for any given interchange plan. Then, with the value functions available from all system operators, theoretically, a coordinator is able to devise a globally optimal interchange plan. After the problem is solved, considering that power exchanges may alter the financial position of each area considerably from what it would have been via scheduling independently, the authors then propose a fair savings allocation method using the value functions derived above and the Shapley value in cooperative game theory. Numerical experiments on a two‐area 12‐bus system and a three‐area 457‐bus system were carried out. The validity of the value‐function‐based method was verified for the decentralised M‐SCUC problem. The outcome of savings allocation was compared with that of the locational marginal cost‐based method.