Congestion Management in a Stochastic Dispatch Model for Electricity Markets

Endre Bjørndal (), Mette Bjørndal (), Kjetil Midthun () and Golbon Zakeri ()
Additional contact information
Endre Bjørndal: Dept. of Business and Management Science, Norwegian School of Economics, Postal: NHH , Department of Business and Management Science, Helleveien 30, N-5045 Bergen, Norway
Mette Bjørndal: Dept. of Business and Management Science, Norwegian School of Economics, Postal: NHH , Department of Business and Management Science, Helleveien 30, N-5045 Bergen, Norway, https://www.nhh.no/en/employees/faculty/mette-helene-bjorndal/
Kjetil Midthun: SINTEF Technology and Society, Postal: SINTEF Technology and Society, P.O. Box 4760 Sluppen, NO-7465 Trondheim, Norway, http://www.sintef.no/en/all-employees/employee/?EmpID=2322
Golbon Zakeri: Dept. of Engineering Science, University of Auckland, Postal: The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand, https://unidirectory.auckland.ac.nz/profile/g-zakeri
Authors registered in the RePEc Author Service: Endre Jostein Bjørndal

No 2016/12, Discussion Papers from Norwegian School of Economics, Department of Business and Management Science

Abstract: We consider an electricity market organized with two settlements: one for a pre-delivery (day-ahead) market and one for real time, where uncertainty regarding production from non-dispatchable energy sources as well as variable load is resolved in the latter stage. We formulate two models to study the efficiency of this market design. In the myopic model, the day-ahead market is cleared independently of the real-time market, while in the integrated stochastic dispatch model the possible outcomes of the real-time market clearing are considered when the day-ahead market is cleared. We focus on how changes in the design of the electricity market influence the efficiency of the dispatch, measured by expected total cost or social welfare. In particular, we examine how relaxing network flow constraints and, for the stochastic dispatch model, even the balancing constraints in the day-ahead part of the dispatch models affects the overall efficiency of the system. This allows the dispatch to be infeasible day-ahead, while these infeasibilities will be handled in the real-time market. For the stochastic dispatch model we find that relaxing the network flows and balancing constraints in the dayahead part of the market provides additional flexibility that can be valuable to the system. In our examples with high up-regulation cost we find a value of "overbooking" that lead to lower total costs. In the myopic model the results are more ambiguous, however, leaving too many constraints to be resolved in the real-time market only, can lead to infeasibilities or high regulation cost.

Keywords: OR in Energy; Stochastic Programming; Electricity Markets; Market Design (search for similar items in EconPapers)
JEL-codes: C60 L10 L94 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2016-08-25
New Economics Papers: this item is included in nep-ene
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://hdl.handle.net/11250/2401598 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://hdl.handle.net/11250/2401598 [302 Found]--> https://www.unit.no/brage-denne-lenken-er-ikke-lenger-gyldig [301 Moved Permanently]--> https://sikt.no/brage-denne-lenken-er-ikke-lenger-gyldig)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hhs:nhhfms:2016_012

Access Statistics for this paper

More papers in Discussion Papers from Norwegian School of Economics, Department of Business and Management Science NHH, Department of Business and Management Science, Helleveien 30, N-5045 Bergen, Norway. Contact information at EDIRC.
Bibliographic data for series maintained by Stein Fossen ().