Hydro-electric unit commitment subject to uncertain demand

Philpott, Andy; Craddock, Mark; Waterer, Hamish

doi:10.1016/s0377-2217(99)00172-1

Cited by 55 publications

(30 citation statements)

References 11 publications

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“…An intuitive way to reduce the number of scenarios is to cluster them in specified periods according to their parameters, and represent scenarios in the same cluster by their expected values [19][20][21][22]. Sampling a subset of scenarios is another common approach [23].…”

Section: Literature Reviewmentioning

confidence: 99%

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Solution sensitivity-based scenario reduction for stochastic unit commitment

Feng

Ryan

2014

Comput Manag Sci

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A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if many scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the US, FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial. Abstract A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment (SRUC) are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if a lot of scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the U.S., FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial.

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Section: Literature Reviewmentioning

confidence: 99%

“…Formula (20) requires that at least one scenario is selected from every nonempty category, which avoids ignoring categories with small probabilities, and in a certain way takes into account extreme scenarios. This strategy can be applied to customize the Cluster step in FSRC.…”

Section: Pre-categorization Of Scenarios In Clustering (Pc)mentioning

confidence: 99%

Solution sensitivity-based scenario reduction for stochastic unit commitment

Feng

Ryan

2014

Comput Manag Sci

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“…This function associates to each admissible value of the water storage a value in Euros. It is a strictly increasing function which is quadratic in [5] and linear in [17]. We assume this function is concave and piecewise affine.…”

Section: Hydroelectric Power Stationsmentioning

confidence: 99%

“…Production management problems (and particularly power management problems) have been widely studied both concerning the modelling and the solution methods ( [5], [7], [8], [17], [12] [1], [11], [19], [16], [18]). Generally, the evolution of the uncertain parameters over the management period is modeled by a scenario tree and the goal is to minimize the expected production cost over this set of scenarios.…”

Section: Introductionmentioning

confidence: 99%

A stabilized model and an efficient solution method for the yearly optimal power management

Guigues

2011

Optimization Methods and Software

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“…A stochastic hydropower planning model, which takes into account uncertainty related to the real-time market prices is provided in [5]. A hydro-electric unit commitment model subject to uncertain demand is developed in [6]. A twostage stochastic optimization problem to study joint optimization of a wind farm and a pumped-storage unit in a market environment is presented [7].…”

Section: Introductionmentioning

confidence: 99%

A sensitivity analysis of short-term hydropower planning using stochastic programming

Vardanyan

Amelin

2012

2012 IEEE Power and Energy Society General Meeting

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Abstract-Huge amount of uncertainties are being introduced to the power market because of the ongoing growth in the renewable energy sources like wind and solar power. The intermittent nature of these power sources increases the volatility of the day-ahead market prices. Therefore, improving planning tools and constructing an optimal bidding strategy to the day-ahead market is an essential task for the price-taker hydropower producer. This paper applies an optimal bidding model under the uncertainties of the day-ahead market prices and the water inflow level. Specifically, the model is built using a two-stage stochastic mixed integer linear programming approach. The uncertainties are handled by generating scenarios based on historical data. The model is tested by studying three reservoir test system. Profound sensitivity analysis is provided, in terms of volatility in day-ahead market prices and water inflow level as well as in terms of water opportunity cost and initial volume of the reservoir. Based on the comparison of the stochastic and corresponding deterministic problems, the result aims to show the impact of modeling the uncertainties explicitly.

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Hydro-electric unit commitment subject to uncertain demand

Cited by 55 publications

References 11 publications

Solution sensitivity-based scenario reduction for stochastic unit commitment

Solution sensitivity-based scenario reduction for stochastic unit commitment

A stabilized model and an efficient solution method for the yearly optimal power management

A sensitivity analysis of short-term hydropower planning using stochastic programming

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