SOCRATES: A system for scheduling hydroelectric generation under uncertainty

Jacobs, Jonathan M.; Freeman, Gary E.; Grygier, Jan C.; Morton, David P.; Schultz, Gary L.; Staschus, Konstantin; Stedinger, Jery R.

doi:10.1007/bf02031745

Cited by 108 publications

(55 citation statements)

References 29 publications

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“…Early explicit WRM approaches were based on conventional optimisation methods, such as Stochastic Linear Programming (Jacobs et al, 1995) and Stochastic Dynamic Programming (Tejada-Guibert et al, 1995). More recently, different EAs were used (e.g.…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

523

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

523

317

View full text Add to dashboard Cite

show abstract

“…A separate modeling and algorithmic strategy within the stochastic programming community uses the concept of scenario trees, where sample paths of exogenous information are generated, retaining the entire history at each node in the tree. Jacobs et al (1995) andDe Ladurantaye et al (2007) give nice illustrations of this strategy in a hydroelectric setting. Heuristic approaches to reservoir management are described in Johnson et al (1991), Edmunds &Bard (1990b) andEdmunds &Bard (1990a).…”

Section: Review Of Energy Modelsmentioning

confidence: 99%

SMART: A Stochastic Multiscale Model for the Analysis of Energy Resources, Technology, and Policy

Powell

George

Simão

et al. 2012

INFORMS Journal on Computing

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We address the problem of modeling long-term energy policy and investment decisions while retaining the important ability to capture fine-grained variations in intermittent energy and demand, as well as storage. In addition, we wish to capture sources of uncertainty such as future energy policies, climate, and technological advances, in addition to the variability as well as uncertainty in wind energy, demands, prices and rainfall. Accurately modeling the value of all investments such as wind and solar requires handling fine-grained temporal variability and uncertainty in wind and solar, as well as the use of storage. We propose a modeling and algorithmic strategy based on the framework of approximate dynamic programming (ADP) that can model these problems at hourly time increments over a multidecade horizon, while still capturing different types of uncertainty. This paper describes initial proof of concept experiments for an ADP-based model, called SMART, by describing the modeling and algorithmic strategy, and providing comparisons against a deterministic benchmark as well as initial experiments on stochastic datasets.

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“…[9]) using scenario trees. Stochastic Dual Dynamic Programming (SDDP) [15] was developed as a response to the problem of dealing with a rapidly growing scenario tree.…”

Section: Introductionmentioning

confidence: 99%

Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion

Philpott

Matos

2012

European Journal of Operational Research

177

115

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We consider the incorporation of a time-consistent coherent risk measure into a multi-stage stochastic programming model, so that the model can be solved using a SDDP-type algorithm. We describe the implementation of this algorithm, and study the solutions it gives for an application of hydro-thermal scheduling in the New Zealand electricity system. The performance of policies using this risk measure at di¤erent levels of risk aversion is compared with the risk-neutral policy.

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SOCRATES: A system for scheduling hydroelectric generation under uncertainty

Cited by 108 publications

References 29 publications

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

SMART: A Stochastic Multiscale Model for the Analysis of Energy Resources, Technology, and Policy

Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion

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