The economics of planning electricity transmission to accommodate renewables: Using two-stage optimisation to evaluate flexibility and the cost of disregarding uncertainty

被引:123
作者
van der Weijde, Adriaan Hendrik [1 ]
Hobbs, Benjamin F. [1 ,2 ,3 ]
机构
[1] Univ Cambridge, Fac Econ, Elect Policy Res Grp, Cambridge CB3 9DD, England
[2] Johns Hopkins Univ, Dept Geog & Environm Engn, Baltimore, MD 21218 USA
[3] Johns Hopkins Univ, Environm Energy Sustainabil & Hlth Inst, Baltimore, MD 21218 USA
基金
美国国家科学基金会; 英国工程与自然科学研究理事会;
关键词
Decision making; Electricity; Transmission; Planning; Uncertainty; REGRET;
D O I
10.1016/j.eneco.2012.02.015
中图分类号
F [经济];
学科分类号
02 ;
摘要
Aggressive development of renewable electricity sources will require significant expansions in transmission infrastructure. We present a stochastic two-stage optimisation model that captures the multistage nature of transmission planning under uncertainty and use it to evaluate interregional grid reinforcements in Great Britain (GB). In our model, a proactive transmission planner makes investment decisions in two time periods, each time followed by a market response. Uncertainty is represented by economic, technology, and regulatory scenarios, and first-stage investments must be made before it is known which scenario will occur. The model allows us to identify expected cost-minimising first-stage investments, as well as estimate the value of information, the cost of ignoring uncertainty, and the value of flexibility. Our results show that ignoring risk in planning transmission for renewables has quantifiable economic consequences, and that considering uncertainty can yield decisions that have lower expected costs than traditional deterministic planning methods. In the GB case, the value of information and cost of disregarding uncertainty in transmission planning were of the same order of magnitude (approximately E100 M, in present worth terms). Further, the best plan under a risk-neutral decision criterion can differ from the best under risk-aversion. Finally, a traditional sensitivity analysis-based robustness analysis also yields different results than the stochastic model, although the former's expected cost is not much higher. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:2089 / 2101
页数:13
相关论文
共 65 条
[1]  
[Anonymous], 1997, Introduction to stochastic programming
[2]  
[Anonymous], RESTRUCTURED ELECT P
[3]  
[Anonymous], 2005, CARBON DIOXIDE CAPTU
[4]  
[Anonymous], 2001, Making hard decisions with decision tools
[5]   Planning of the grid integration of wind energy in Germany onshore and offshore up to the year 2020 [J].
Bartels, Michael ;
Gatzen, Christoph ;
Peek, Markus ;
Schulz, Walter ;
Wissen, Ralf ;
Jansen, Andreas ;
Molly, Jens Peter ;
Neddermann, Bernd ;
Gerch, Hans-Paul ;
Grebe, Eckehard ;
Saeenick, Yvonne ;
Winter, Wilhelm .
INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES, 2006, 25 (3-4) :257-275
[6]   REGRET IN DECISION-MAKING UNDER UNCERTAINTY [J].
BELL, DE .
OPERATIONS RESEARCH, 1982, 30 (05) :961-981
[8]  
Bunn D.W., 1984, Applied decision analysis
[9]  
Buygi MO, 2004, 2004 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS, P563
[10]  
California ISO, 2010, REN EN TRAN IN PRESS