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Navarro, A.; Favereau, M.; Lorca, A.; Olivares, D.; Negrete-Pincetic, M. |
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Title |
Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks |
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2024 |
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Applied Energy |
Abbreviated Journal |
Appl. Energy |
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358 |
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122554 |
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Keywords |
OR in energy; Hydrothermal scheduling; Optimization under uncertainty; Renewable energy; Stochastic dual dynamic programming |
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Abstract |
The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium -term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed shortterm operational model to be correctly linked to the proposed medium -term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium -term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations. |
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0306-2619 |
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WOS:001154000300001 |
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UAI @ alexi.delcanto @ |
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1946 |
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Author |
Reus, L.; Pagnoncelli, B.; Armstrong, M. |
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Title |
Better management of production incidents in mining using multistage stochastic optimization |
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Year |
2019 |
Publication |
Resources Policy |
Abbreviated Journal |
Resour. Policy |
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63 |
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13 pp |
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Keywords |
Mining incidents; Optimal policy; Stochastic dual dynamic programming; Risk-aversion; CVaR; Julia language |
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Abstract |
Among the many sources of uncertainty in mining are production incidents: these can be strikes, environmental issues, accidents, or any kind of event that disrupts production. In this work, we present a strategic mine planning model that takes into account these types of incidents, as well as random prices. When confronted by production difficulties, mines which have contracts to supply customers have a range of flexibility options including buying on the spot market, or taking material from a stockpile if they have one. Earlier work on this subject was limited in that the optimization could only be carried out for a few stages (up to 5 years) and in that it only analyzed the risk-neutral case. By using decomposition schemes, we are now able to solve large-scale versions of the model efficiently, with a horizon of up to 15 years. We consider decision trees with up to 615 scenarios and implement risk aversion using Conditional Value-at-Risk, thereby detecting its effect on the optimal policy. The results provide a “roadmap” for mine management as to optimal decisions, taking future possibilities into account. We present extensive numerical results using the new sddp.jl library, written in the Julia language, and discuss policy implications of our findings. |
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[Reus, Lorenzo] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile, Email: lorenzo.reus@uai.cl; |
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Elsevier Sci Ltd |
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English |
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0301-4207 |
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WOS:000488888100004 |
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UAI @ eduardo.moreno @ |
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1165 |
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