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Hydropower Producer Day-ahead Market StrategicOffering Using Stochastic Bi-level Optimization
KTH, School of Electrical Engineering (EES), Electric Power Systems.
UCL - Université catholique de Louvain. (Center for Operations Research and Econometrics)
KTH, School of Electrical Engineering (EES), Electric Power Systems.
2015 (English)In: Proceedings of the International MultiConference of Engineers and Computer Scientists,Hong Kong, 18-20 March, 2015, IAENG , 2015, Vol. 2Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a bi-level stochastic optimization problem (a Stackelberg game) to generate optimal bids for a profit maximizing hydropower producer and presents a mathematical approach to solve it. The first level represents the strategically acting hydropower producer also called a Stackelberg leader, while the second level represents the transmission system operator (TSO) also called a Stackelberg follower. To solve the bi-level stochastic optimization problem, the second level is replaced by its KKT (Karush-Kuhn-Tucker) optimality conditions, which results in a stochastic MPEC (mathematical program with equilibrium constraints). Finally, the stochastic MPEC is reformulated as a stochastic MILP (mixed integer linear program) using linearization and SOS1 variables (special ordered sets of type 1). Results are reported studying a small case, which point out the impact on the market outcomes when a hydropower producer behaves strategically.

Place, publisher, year, edition, pages
IAENG , 2015. Vol. 2
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-165033ISBN: 978-988-19253-2-9 (print)OAI: oai:DiVA.org:kth-165033DiVA: diva2:806782
Conference
International MultiConference of Engineers and Computer Scientists,Hong Kong, 18-20 March, 2015
Note

QC 20150522

Available from: 2015-04-21 Created: 2015-04-21 Last updated: 2016-09-22Bibliographically approved
In thesis
1. Optimal bidding of a hydropower producer insequential power markets with riskassessment: Stochastic programming approach
Open this publication in new window or tab >>Optimal bidding of a hydropower producer insequential power markets with riskassessment: Stochastic programming approach
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Short-term hydropower planning and bidding under uncertainty is a complicated task. The problem became more challenging with the liberalized market environment within the last two decades. Apart from this new reform taking place in the electricity market, the electricity market participants including hydropower producers experienced the second change in the form of intermittent wind power integration into power systems. Thus, previous decision support tools are not capable of fulfilling market participants’ expectations in the new competitive and highly uncertain environment. Intermittent power sources, namely wind power, increase the imbalances in the power system, which in turn increases the need of the regulating power sources. Being a flexible energy source, hydropower can provide regulating power. For this purpose, new hydropower planning and bidding models must be developed, capable of addressing uncertainties and the dynamics existing within market places.

In this dissertation, a set of new short-term hydropower planning and bidding models are developed for sequential electricity markets under price uncertainties. Developed stochastic coordinated hydropower planning and bidding tools can be classified into two classes, as models with exogenousand endogenous prices.

In the first class, developed coordinated bidding tools address the price uncertainties using scenario trees, which are built based on the distribution function of the unknown variables. Thus, the proposed coordinated bidding and planning tools consider all possible future prices and market outcomes together with the likelihood of these market outcomes. To reflect the continuously clearing nature of intra-day and real-time markets rolling planning is applied. In addition, models apply risk measures as another way to hedge against uncertain prices.

In the second class, hydropower stochastic strategic bidding models are developed using stochastic bi-level optimization methodology. Here market prices are calculated internally as dual variables of the load balance constraints in the lower level ED problems. To be able to solve the stochastic bilevel optimization problem, KKT optimality conditions are applied. By this transformation the problem is converted to a single-level stochastic program, which is simplified further using a corresponding discretization technique.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2016
Series
TRITA-EE, ISSN 1653-5146 ; 2016:039
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-192628 (URN)978-91-7595-890-3 (ISBN)
Public defence
2016-10-14, K1, Teknikringen 56, Stockholm, 11:00 (English)
Opponent
Supervisors
Note

QC 20160922

Available from: 2016-09-22 Created: 2016-09-16 Last updated: 2016-09-22Bibliographically approved

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