Abstract
This research is dedicated to the study of electric power system generation expansion planning considering uncertainty of climate change. Policy makers are increasingly concerned about the effects of climate change and its impact on human systems when making decisions. Electric power generation expansion planning (GEP) problems that determine the optimal expansion capacity and technology under particular technical constraints, given cost and policy assumptions, are undoubtedly among those decisions. The best approach to comprehensively model climate change uncertainties, and to optimize the generation planning under uncertainty needs to be rigorously studied. In this research, a preliminary GEP model is proposed with available input data from various sources. Discrete scenarios of possible climate change outcomes are defined and optimization models are formulated to specifically model uncertainty. Relationships between climate change and GEP parameters are defined for each scenario to consider their effects. The preliminary GEP model is then solved under each scenario to identify the climate change impact on generation expansion planning decisions. Two related optimization models are then presented and solved to find the optimal results under uncertainty: Model 1 is expected total cost minimization, and Model 2 is maximum regret minimization. Both models find compromise solutions that are suitable for all scenarios, which avoid the possible risk associated with a poor decision that is only optimal for one particular scenario, or only for an average climate change forecast.
| Original language | English |
|---|---|
| Pages (from-to) | 401-412 |
| Number of pages | 12 |
| Journal | Electric Power Systems Research |
| Volume | 140 |
| DOIs | |
| Publication status | Published - Nov 1 2016 |
Fingerprint
Keywords
- Climate change
- Generation expansion planning
- Scenarios
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
Cite this
Stochastic optimization for electric power generation expansion planning with discrete climate change scenarios. / Li, Shuya; Coit, David W.; Felder, Frank.
In: Electric Power Systems Research, Vol. 140, 01.11.2016, p. 401-412.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Stochastic optimization for electric power generation expansion planning with discrete climate change scenarios
AU - Li, Shuya
AU - Coit, David W.
AU - Felder, Frank
PY - 2016/11/1
Y1 - 2016/11/1
N2 - This research is dedicated to the study of electric power system generation expansion planning considering uncertainty of climate change. Policy makers are increasingly concerned about the effects of climate change and its impact on human systems when making decisions. Electric power generation expansion planning (GEP) problems that determine the optimal expansion capacity and technology under particular technical constraints, given cost and policy assumptions, are undoubtedly among those decisions. The best approach to comprehensively model climate change uncertainties, and to optimize the generation planning under uncertainty needs to be rigorously studied. In this research, a preliminary GEP model is proposed with available input data from various sources. Discrete scenarios of possible climate change outcomes are defined and optimization models are formulated to specifically model uncertainty. Relationships between climate change and GEP parameters are defined for each scenario to consider their effects. The preliminary GEP model is then solved under each scenario to identify the climate change impact on generation expansion planning decisions. Two related optimization models are then presented and solved to find the optimal results under uncertainty: Model 1 is expected total cost minimization, and Model 2 is maximum regret minimization. Both models find compromise solutions that are suitable for all scenarios, which avoid the possible risk associated with a poor decision that is only optimal for one particular scenario, or only for an average climate change forecast.
AB - This research is dedicated to the study of electric power system generation expansion planning considering uncertainty of climate change. Policy makers are increasingly concerned about the effects of climate change and its impact on human systems when making decisions. Electric power generation expansion planning (GEP) problems that determine the optimal expansion capacity and technology under particular technical constraints, given cost and policy assumptions, are undoubtedly among those decisions. The best approach to comprehensively model climate change uncertainties, and to optimize the generation planning under uncertainty needs to be rigorously studied. In this research, a preliminary GEP model is proposed with available input data from various sources. Discrete scenarios of possible climate change outcomes are defined and optimization models are formulated to specifically model uncertainty. Relationships between climate change and GEP parameters are defined for each scenario to consider their effects. The preliminary GEP model is then solved under each scenario to identify the climate change impact on generation expansion planning decisions. Two related optimization models are then presented and solved to find the optimal results under uncertainty: Model 1 is expected total cost minimization, and Model 2 is maximum regret minimization. Both models find compromise solutions that are suitable for all scenarios, which avoid the possible risk associated with a poor decision that is only optimal for one particular scenario, or only for an average climate change forecast.
KW - Climate change
KW - Generation expansion planning
KW - Scenarios
UR - http://www.scopus.com/inward/record.url?scp=84994673168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84994673168&partnerID=8YFLogxK
U2 - 10.1016/j.epsr.2016.05.037
DO - 10.1016/j.epsr.2016.05.037
M3 - Article
VL - 140
SP - 401
EP - 412
JO - Electric Power Systems Research
JF - Electric Power Systems Research
SN - 0378-7796
ER -