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Electricity markets operation planning with risk-averse agents: stochastic decomposition and equilibium
KTH, School of Electrical Engineering and Computer Science (EECS). Research Technology Institute, Comillas Pontifical University, Spain; Delft University of Technology, the Netherlands..
2019 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

The growing penetration of renewable energy sources in electricity systems requires adapting operation models to face the inherent variability and uncertainty of wind or solar generation. In addition, the volatility of fuel prices (such as natural gas) or the uncertainty of the hydraulic natural inflows requires to take into account all these sources of uncertainty within the operation planning of the generation system. Thus, stochastic optimization techniques have been widely used in this context. From the point of view of the system operation, the introduction of wind and solar generation in the mix has forced conventional generators to be subject to more demanding schedules from the technical point of view, increasing for example the number of start-up and shutdown decisions during the week, or having to face more pronounced ramps. From the point of view of the market, all these technical issues are transferred to the market prices that are subject to greater volatility. This thesis focuses on the problem of risk management using the Conditional Value at Risk (CVaR) as a coherent risk measure. The thesis presents a novel iterative method that can be used by a market agent to optimize its operating decisions in the short term when the uncertainty is characterized by a set of random variable scenarios. The thesis analyses how it is possible to decompose the problem of risk management by means of Lagrangian Relaxation techniques and Benders decomposition, and shows that the proposed iterative algorithm (Iterative-CVaR) converges to the same solution as under the direct optimization setting. The algorithm is applied to two typical problems faced by agents: 1) optimization of the operation of a combined cycle power plant (CCGT) that has to cope with the volatility in the spot market price to build the supply curve for the futures market, and 2) strategic unit-commitment model. In a second part of the thesis the problem of market equilibrium is studied to model the interaction between several generating companies with mixed generation portfolios (thermal, hydraulic and renewable). The thesis analyses how the Nash equilibrium solution is modified at different risk-aversion level of the risk of the agents. In particular, the thesis studies how the management of hydroelectric reservoirs ismodified along the annual horizon when agents are risk-averse, and it is compared with the risk-neutral solution that coincides with a centralized planning when the objective is the minimization expected operational cost.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. , p. 140
Series
TRITA-EECS-AVL ; 2019:71
Keywords [en]
decomposition techniques, market equilibrium, risk-averse agents, stochastic optimization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-261626ISBN: 978-84-09-13208-9 (print)OAI: oai:DiVA.org:kth-261626DiVA, id: diva2:1359288
Public defence
2019-11-05, 12:00
Available from: 2019-10-09 Created: 2019-10-08 Last updated: 2019-10-11Bibliographically approved

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910111213141512 of 18
CiteExportLink to record
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Cite
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  • apa
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  • ieee
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  • en-US
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Output format
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