Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment

被引:7
|
作者
Yan, Bing [1 ]
Fan, Haipei [2 ]
Luh, Peter B. [1 ]
Moslehi, Khosrow [3 ]
Feng, Xiaoming [4 ]
Yu, Chien Ning [3 ]
Bragin, Mikhail A. [1 ]
Yu, Yaowen [5 ]
机构
[1] Univ Connecticut, Dept Elect & Comp Engn, Storrs, CT 06269 USA
[2] Univ Connecticut, Storrs, CT 06269 USA
[3] ABB Inc, San Jose, CA 95134 USA
[4] ABB Inc, Raleigh, NC 27606 USA
[5] ABB Enterprise Software, San Jose, CA 95134 USA
基金
美国国家科学基金会;
关键词
Branch-and-cut; interval optimization; Markov decision process; remote wind farms; surrogate Lagrangian relaxation (SLR); unit commitment; OPTIMIZATION; POWER; SECURITY;
D O I
10.1109/JAS.2017.7510505
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Grid integration of wind power is essential to reduce fossil fuel usage but challenging in view of the intermittent nature of wind. Recently, we developed a hybrid Markovian and interval approach for the unit commitment and economic dispatch problem where power generation of conventional units is linked to local wind states to dampen the effects of wind uncertainties. Also, to reduce complexity, extreme and expected states are considered as interval modeling. Although this approach is effective, the fact that major wind farms are often located in remote locations and not accompanied by conventional units leads to conservative results. Furthermore, weights of extreme and expected states in the objective function are difficult to tune, resulting in significant differences between optimization and simulation costs. In this paper, each remote wind farm is paired with a conventional unit to dampen the effects of wind uncertainties without using expensive utility-scaled battery storage, and extra constraints are innovatively established to model pairing. Additionally, proper weights are derived through a novel quadratic fit of cost functions. The problem is solved by using a creative integration of our recent surrogate Lagrangian relaxation and branch-and-cut. Results demonstrate modeling accuracy, computational efficiency, and significant reduction of conservativeness of the previous approach.
引用
收藏
页码:205 / 215
页数:11
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