Journal
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 126, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2020.106588
Keywords
Capacity expansion; Linear decision rules; Multi-stage planning; Renewable energy; Stochastic programming
Categories
Funding
- Ministry of Science and Innovation of Spain [PID2019-111211RBI00/AEI/10.13039/501100011033]
- US National Science Foundation [1808169]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1808169] Funding Source: National Science Foundation
Ask authors/readers for more resources
The article investigates the capacity expansion problem of renewable sources as a long-term multistage decision-making issue, comparing four different approaches. Through case studies and computational models, the impact of long-term uncertainty factors is explored, analyzing the pros and cons of each method.
The capacity expansion problem of renewable sources faced by a central planner is essentially a long-term multistage decision-making problem under uncertainty. However, the size of the optimization problems describing multi-stage decision-making processes may lead to computational intractability even if a small number of stages is considered. We tackle this problem considering an explicit characterization of the long-term uncertainty and compare the outcomes of four different approaches for such problem, namely: (i) multi-stage stochastic -programming; (ii) linear decision rule (LDR); (iii) two-stage stochastic-programming under a rolling window procedure; (iv) and deterministic. The impact of considering an increasingly accurate representation of the evolution over time of the uncertain parameters is studied by solving the previous models for different planning schemes. The pros and cons of each approach are analyzed quantitatively and qualitatively using a case study based on the IEEE 24-node reliability test system (RTS). Finally, the performance of the stochastic programming and the LDR approaches is assessed by performing out-of-sample analyses.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available