Risk-Constrained Self-Scheduling of a Thermal Power Producer

Conejo, Antonio J.; Nogales, Francisco J.; Arroyo, José M.; García-Bertrand, Raquel

doi:10.1109/tpwrs.2004.831652

Cited by 152 publications

(78 citation statements)

References 26 publications

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“…In [4], the self scheduling of a hydro based Genco is analyzed with an emphasize on various technical constraints of hydro units. The concept of risk minimization along with profit maximization is the inspiration of many self scheduling researches [5], [6]. A fuzzy approach for benefit maximizing while the demand, reserve services, market prices, and probability that reserves are called and generated are uncertain quantities [7].…”

Section: A Motivation and Approachmentioning

confidence: 99%

Smart self-scheduling of Gencos with thermal and energy storage units under price uncertainty

Soroudi

2013

Int. Trans. Electr. Energ. Syst.

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SUMMARY This paper provides a self‐scheduling tool for price taker Gencos. This methodology is based on Robust Optimization (RO) to deal with the uncertainties of market price values in the day‐ahead electricity pool market. The Genco is assumed to be the entity who decides about the operating schedules of its thermal units and Compressed Air Energy Storage units. The benefits of Genco brought by smart grid technology and energy storage systems are investigated in this work. The applicability of the proposed method is analyzed through different scenarios. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: A Motivation and Approachmentioning

confidence: 99%

Smart self-scheduling of Gencos with thermal and energy storage units under price uncertainty

Soroudi

2013

Int. Trans. Electr. Energ. Syst.

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“…Second, since equilibrium prices are the result of the equilibrium between supply and demand of electricity, any new investment has a zero marginal value. Hence, if it is used to compute a NPV as described in problem (9), it will produce a null value. Hence, the coexistence of these two pricing systems still presents methodological problems of overall time consistency.…”

Section: Economic Decision and Risk Management Time Consistencymentioning

confidence: 99%

Long-Term Risk Management for Utility Companies: The Next Challenges

Aïd

2010

Int. J. Theor. Appl. Finan.

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Since the energy markets liberalisation at the beginning of the 1990s in Europe, electricity monopolies have gone through a profound evolution process. From an industrial organisation point of view, they lost their monopoly on their historical business, but gained the capacity to develop in any sector. Companies went public and had to upgrade their financial risk management process to international standards and implement modern risk management concepts and reporting processes (VaR, EaR...). Even though important evolutions have been accomplished, we argue here that the long-term risk management process of utility companies has not yet reached its full maturity and is still facing two main challenges. The first one concerns the time consistency of long-term and mid-term risk management processes. We show that consistencies issues are coming from the different classical financial parameters carrying information on firms' risk aversion (cost of capital and short-term risk limits) and the concepts inherited from the monopoly period, like the loss of load value, that are still involved in the utility company decision-making process. The second challenge concerns the need for quantitative models to assess their business model. With the deregulation, utilities have to address the question of their boundaries. Although intuition can provide insights on the benefits of some firm structures like vertical integration, only sound and tractable quantitative models can bring answers to the optimality of different possible firm structures.

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“…In [15], the optimal offering strategy of the coordinated wind-thermal power plants was proposed and the market risks were modelled and optimized using the CVaR model. In [16,17], a self-scheduling problem was presented, and the risk was modeled by considering the variance of the market clearing prices. However, it is known that by using CVaR and VaR, the risk management can be done more effectively [18,19].…”

Section: Introductionmentioning

confidence: 99%

Risk averse optimal operation of a virtual power plant using two stage stochastic programming

Tajeddini

Rahimi-Kian

Soroudi

2014

Energy

152

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Publication informationEnergy, 73 (2014): 958-967Publisher Elsevier Item record/more information http://hdl.handle.net/10197/6116 Publisher's statementThis is the author's version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy (VOL 73, ISSUE 2014, (2014 AbstractVirtual Power Plant (VPP) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a generation companys (GenCo) expected profit. Furthermore, the Conditional Value at Risk (CVaR) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed.

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Risk-Constrained Self-Scheduling of a Thermal Power Producer

Cited by 152 publications

References 26 publications

Smart self-scheduling of Gencos with thermal and energy storage units under price uncertainty

Smart self-scheduling of Gencos with thermal and energy storage units under price uncertainty

Long-Term Risk Management for Utility Companies: The Next Challenges

Risk averse optimal operation of a virtual power plant using two stage stochastic programming

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