Risk-Limiting Unit Commitment in Smart Grid with Intelligent Periphery

作     者：Peng, Chaoyi Hou, Yunhe Yu, Nanpeng Wang, Weisheng 

作者机构：Department of Electrical and Electronic Engineering University of HongKong Pokfulam Hong Kong HKU Shenzhen Institute of Research and Innovation Shenzhen518057 China Department of Electrical and Computer Engineering University of California Riverside RiversideCA92521 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing100085 China 

出 版 物：《IEEE Transactions on Power Systems》 (IEEE Trans Power Syst)

年 卷 期：2017年第32卷第6期

页      面：4696-4707页

核心收录：

学科分类：0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 0701[理学-数学] 

基　　金：Manuscript received August 4, 2016 revised December 6, 2016 and February 8, 2017 accepted February 10, 2017. Date of publication February 22, 2017 date of current version October 18, 2017. This work was supported by Research funding from State Grid Corporation of China (SGLNSY00FZJS1601337). Paper no. TPWRS-01183-2016 

主　　题：Smart power grids 

摘      要：This paper proposes the risk-limiting unit commitment (RLUC) as the operational method to address the uncertainties in the smart grid with intelligent periphery (GRIP). Three key requirements are identified for the RLUC in GRIP. The first one requires the RLUC to be modeled as a multistage multiperiod unit commitment problem considering power trades, operational constraints, and operational risks. The second one requires the RLUC considering the conditional prediction to achieve a globally optimal solution. It is addressed by using conditional probability in a scenario-based form. The last one requires the risk index in the RLUC to be both valid and computationally friendly, and it is tackled by the utilization of a coherent risk index and the mathematical proof of a risk chain theorem. Finally, the comprehensive RLUC in GRIP satisfying all the three requirements is solved by an equivalent transformation into a mixed integer piecewise linear programming problem. Case studies on a nine-bus system, a realistic provincial power system, and a regional power grid in China demonstrate the advantages of the proposed RLUC in GRIP. © 2017 IEEE.