Risk management of energy system for identifying optimal power mix with financial-cost minimization and environmental-impact mitigation under uncertainty
John Liu () and
Charley Z. Huang
Energy Economics, 2017, vol. 61, issue C, 313-329
An interval-stochastic risk management (ISRM) method is launched to control the variability of the recourse cost as well as to capture the notion of risk in stochastic programming. The ISRM method can examine various policy scenarios that are associated with economic penalties under uncertainties presented as probability distributions and interval values. An ISRM model is then formulated to identify the optimal power mix for the Beijing's energy system. Tradeoffs between risk and cost are evaluated, indicating any change in targeted cost and risk level would yield different expected costs. Results reveal that the inherent uncertainty of system components and risk attitude of decision makers have significant effects on the city's energy-supply and electricity-generation schemes as well as system cost and probabilistic penalty. Results also disclose that import electricity as a recourse action to compensate the local shortage would be enforced. The import electricity would increase with a reduced risk level; under every risk level, more electricity would be imported with an increased demand. The findings can facilitate the local authority in identifying desired strategies for the city's energy planning and management in association with financial-cost minimization and environmental-impact mitigation.
Keywords: Decision making; Energy planning; Financing; Power mix; Risk management; Stochastic programming (search for similar items in EconPapers)
JEL-codes: C61 G1 O2 Q4 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:61:y:2017:i:c:p:313-329
Access Statistics for this article
Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant
More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().