Benchmarking of hydroelectric stochastic risk management models using financial indicators

N. A. Iliadis; M. V.F. Pereira; S. Granville; M. Finger; P. A. Haldi; L. A. Barroso

doi:10.1109/pes.2006.1709283

Benchmarking of hydroelectric stochastic risk management models using financial indicators

N. A. Iliadis^*, M. V.F. Pereira, S. Granville, M. Finger, P. A. Haldi, L. A. Barroso

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

The objective of this paper is to present the operating and hedging analysis of a hydroelectric system in a non-hydro dominated market using a specifically-developed tool for operating and contracting decisions. Hydropower companies are likely to face stochastic inflows, spot prices, and forward prices, during their operation. The objective of the tool is to maximize expected revenues from spot and forward market trading, considering suitable indicators of the company risk aversion. We benchmark the implemented risk indicator of required Minimum Revenues in the optimization tool using financial risk indicators, such as Value at Risk, Conditional Value at Risk, and the Risk Premium of a Utility function. This portfolio management problem, which includes physical and financial assets, is formulated as a stochastic revenue maximization problem under a specified risk aversion constraint. The company risk aversion is apprehended by penalizing reservoir operation and derivative instruments contracting decisions policies that lead to financial performances that are violating the required Minimum Revenues at the end of a predefined profit period. A hybrid Stochastic Dynamic Programming (SDP) / Stochastic Dual Dynamic Programming (SDDP) formulation is adopted to solve this large-scale optimization problem.

Original language	English
Title of host publication	2006 IEEE Power Engineering Society General Meeting, PES
Publisher	IEEE Computer Society
ISBN (Print)	1424404932, 9781424404933
DOIs	https://doi.org/10.1109/pes.2006.1709283
Publication status	Published - 2006
Externally published	Yes
Event	2006 IEEE Power Engineering Society General Meeting, PES - Montreal, QC, Canada Duration: 18 Jun 2006 → 22 Jun 2006

Publication series

Name	2006 IEEE Power Engineering Society General Meeting, PES

Conference

Conference	2006 IEEE Power Engineering Society General Meeting, PES
Country/Territory	Canada
City	Montreal, QC
Period	18/06/06 → 22/06/06

Keywords

CvaR
Dynamic programming
Electricity financial risk indicators
Hydropower
Portfolio management
Stochastic optimization
Utility function and VaR

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/pes.2006.1709283

Cite this

Iliadis, N. A., Pereira, M. V. F., Granville, S., Finger, M., Haldi, P. A., & Barroso, L. A. (2006). Benchmarking of hydroelectric stochastic risk management models using financial indicators. In 2006 IEEE Power Engineering Society General Meeting, PES Article 1709283 (2006 IEEE Power Engineering Society General Meeting, PES). IEEE Computer Society. https://doi.org/10.1109/pes.2006.1709283

@inproceedings{202b8de21be9407baa15b324938a614e,

title = "Benchmarking of hydroelectric stochastic risk management models using financial indicators",

abstract = "The objective of this paper is to present the operating and hedging analysis of a hydroelectric system in a non-hydro dominated market using a specifically-developed tool for operating and contracting decisions. Hydropower companies are likely to face stochastic inflows, spot prices, and forward prices, during their operation. The objective of the tool is to maximize expected revenues from spot and forward market trading, considering suitable indicators of the company risk aversion. We benchmark the implemented risk indicator of required Minimum Revenues in the optimization tool using financial risk indicators, such as Value at Risk, Conditional Value at Risk, and the Risk Premium of a Utility function. This portfolio management problem, which includes physical and financial assets, is formulated as a stochastic revenue maximization problem under a specified risk aversion constraint. The company risk aversion is apprehended by penalizing reservoir operation and derivative instruments contracting decisions policies that lead to financial performances that are violating the required Minimum Revenues at the end of a predefined profit period. A hybrid Stochastic Dynamic Programming (SDP) / Stochastic Dual Dynamic Programming (SDDP) formulation is adopted to solve this large-scale optimization problem.",

keywords = "CvaR, Dynamic programming, Electricity financial risk indicators, Hydropower, Portfolio management, Stochastic optimization, Utility function and VaR",

author = "Iliadis, {N. A.} and Pereira, {M. V.F.} and S. Granville and M. Finger and Haldi, {P. A.} and Barroso, {L. A.}",

year = "2006",

doi = "10.1109/pes.2006.1709283",

language = "English",

isbn = "1424404932",

series = "2006 IEEE Power Engineering Society General Meeting, PES",

publisher = "IEEE Computer Society",

booktitle = "2006 IEEE Power Engineering Society General Meeting, PES",

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note = "2006 IEEE Power Engineering Society General Meeting, PES ; Conference date: 18-06-2006 Through 22-06-2006",

}

Iliadis, NA, Pereira, MVF, Granville, S, Finger, M, Haldi, PA & Barroso, LA 2006, Benchmarking of hydroelectric stochastic risk management models using financial indicators. in 2006 IEEE Power Engineering Society General Meeting, PES., 1709283, 2006 IEEE Power Engineering Society General Meeting, PES, IEEE Computer Society, 2006 IEEE Power Engineering Society General Meeting, PES, Montreal, QC, Canada, 18/06/06. https://doi.org/10.1109/pes.2006.1709283

Benchmarking of hydroelectric stochastic risk management models using financial indicators. / Iliadis, N. A.; Pereira, M. V.F.; Granville, S. et al.
2006 IEEE Power Engineering Society General Meeting, PES. IEEE Computer Society, 2006. 1709283 (2006 IEEE Power Engineering Society General Meeting, PES).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Iliadis, N. A.

AU - Pereira, M. V.F.

AU - Granville, S.

AU - Finger, M.

AU - Haldi, P. A.

AU - Barroso, L. A.

PY - 2006

Y1 - 2006

N2 - The objective of this paper is to present the operating and hedging analysis of a hydroelectric system in a non-hydro dominated market using a specifically-developed tool for operating and contracting decisions. Hydropower companies are likely to face stochastic inflows, spot prices, and forward prices, during their operation. The objective of the tool is to maximize expected revenues from spot and forward market trading, considering suitable indicators of the company risk aversion. We benchmark the implemented risk indicator of required Minimum Revenues in the optimization tool using financial risk indicators, such as Value at Risk, Conditional Value at Risk, and the Risk Premium of a Utility function. This portfolio management problem, which includes physical and financial assets, is formulated as a stochastic revenue maximization problem under a specified risk aversion constraint. The company risk aversion is apprehended by penalizing reservoir operation and derivative instruments contracting decisions policies that lead to financial performances that are violating the required Minimum Revenues at the end of a predefined profit period. A hybrid Stochastic Dynamic Programming (SDP) / Stochastic Dual Dynamic Programming (SDDP) formulation is adopted to solve this large-scale optimization problem.

AB - The objective of this paper is to present the operating and hedging analysis of a hydroelectric system in a non-hydro dominated market using a specifically-developed tool for operating and contracting decisions. Hydropower companies are likely to face stochastic inflows, spot prices, and forward prices, during their operation. The objective of the tool is to maximize expected revenues from spot and forward market trading, considering suitable indicators of the company risk aversion. We benchmark the implemented risk indicator of required Minimum Revenues in the optimization tool using financial risk indicators, such as Value at Risk, Conditional Value at Risk, and the Risk Premium of a Utility function. This portfolio management problem, which includes physical and financial assets, is formulated as a stochastic revenue maximization problem under a specified risk aversion constraint. The company risk aversion is apprehended by penalizing reservoir operation and derivative instruments contracting decisions policies that lead to financial performances that are violating the required Minimum Revenues at the end of a predefined profit period. A hybrid Stochastic Dynamic Programming (SDP) / Stochastic Dual Dynamic Programming (SDDP) formulation is adopted to solve this large-scale optimization problem.

KW - CvaR

KW - Dynamic programming

KW - Electricity financial risk indicators

KW - Hydropower

KW - Portfolio management

KW - Stochastic optimization

KW - Utility function and VaR

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SN - 1424404932

SN - 9781424404933

T3 - 2006 IEEE Power Engineering Society General Meeting, PES

BT - 2006 IEEE Power Engineering Society General Meeting, PES

PB - IEEE Computer Society

T2 - 2006 IEEE Power Engineering Society General Meeting, PES

Y2 - 18 June 2006 through 22 June 2006

ER -

Benchmarking of hydroelectric stochastic risk management models using financial indicators

Abstract

Publication series

Conference

Keywords

UN SDGs

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