Uncertainty Management in the Unit Commitment Problem

Ruiz, Pablo A.; Philbrick, C. Russell; Zak, Eugene; Cheung, Kwok W.; Sauer, Peter W.

doi:10.1109/tpwrs.2008.2012180

Cited by 353 publications

(224 citation statements)

References 19 publications

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“…The following studies use MonteCarlo simulation to evaluate UC schedules: Ruiz et al [16] report on an evaluation of deterministic and two-stage SUC under load and generator failure uncertainty, using the IEEE reliability test system [17]. Papavasiliou and Oren [18] apply Lagrangian relaxation and Benders decomposition to solve two-stage stochastic problems with uncertain wind production and security constrained problems with contingency scenarios.…”

Section: Previous Evaluationsmentioning

confidence: 99%

“…After switching on the first pump, the others are switched on in order from lowest to highest (15) to avoid symmetric solutions. The pumping level is decided by the number of active pumps, since they can only run at full capacity (16). The binaries for each pump, δ qits , could be replaced by a single integer variableδ qts indicating the number of active pumps.…”

Section: Transmission Boundary Limits For Allmentioning

confidence: 99%

“…However, not all variables appearing in these constraints require explicit non-anticipativity constraints if that property can be deduced from other variables linked with them. For pump level variables p pum qts , non-anticipativity follows directly from constraints (16) and (29). Then for reservoir levels it follows from constraints (18), (32) and the fact that initial reservoir levels are fixed.…”

Section: Transmission Boundary Limits For Allmentioning

confidence: 99%

See 2 more Smart Citations

The value of stochastic programming in day-ahead and intra-day generation unit commitment

Schulze

McKinnon

2016

Energy

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The recent expansion of renewable energy supplies has prompted the development of a variety of efficient stochastic optimization models and solution techniques for hydro-thermal scheduling. However, little has been published about the added value of stochastic models over deterministic ones. In the context of day-ahead and intra-day unit commitment under wind uncertainty, we compare two-stage and multi-stage stochastic models to deterministic ones and quantify their added value. We present a modification of the WILMAR scenario generation technique designed to match the properties of the errors in our wind forecasts, and show that this is needed to make the stochastic approach worthwhile. Our evaluation is done in a rolling horizon fashion over the course of two years, using a 2020 central scheduling model based on the British power system, with transmission constraints and a detailed model of pump storage operation and system-wide reserve and response provision. We show that in day-ahead scheduling the stochastic approach saves 0.3% of generation costs compared to the best deterministic approach, but the savings are less in intra-day scheduling.

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Section: Previous Evaluationsmentioning

confidence: 99%

Section: Transmission Boundary Limits For Allmentioning

confidence: 99%

Section: Transmission Boundary Limits For Allmentioning

confidence: 99%

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The value of stochastic programming in day-ahead and intra-day generation unit commitment

Schulze

McKinnon

2016

Energy

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“…Ruiz et al [18] combine stochastic programming methods with increased reserve to examine the impact of wind on the day-ahead UC. It is shown that using stochastic methods combined with a proper amount of reserve reduces wind curtailment and increases the robustness of the day-ahead solutions.…”

Section: Uc In Day-ahead Onlymentioning

confidence: 99%

Impact of wind power on the unit commitment, operating reserves, and market design

Kiviluoma

O’Malley

Tuohy

et al. 2011

2011 IEEE Power and Energy Society General Meeting

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This article highlights and demonstrates the new requirements variable and partly unpredictable wind power will bring to unit commitment and power system operations. Current practice is described and contrasted against the new requirements. Literature specifically addressing questions about wind power and unit commitment related power system operations is surveyed. The scope includes forecast errors, operating reserves, intra-day markets, and sharing reserves across interconnections. The discussion covers the critical issues arising from the research

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“…Throughout, we use the term scenario in a narrow sense, representing a full specification of all random data required to instantiate a unit commitment problem, with associated probability of occurrence. In aggregate, the set of scenarios should represent the range of possible behaviors on day d. By explicitly representing forecast uncertainty through sets of scenarios, it should be possible to significantly decrease generation reserve margins and consequently reduce overall system operation costs [21]. However, the need for multiple scenarios imposes fundamentally novel requirements on forecasting technologies, which have yet to be adequately addressed.…”

Section: Introductionmentioning

confidence: 99%

Toward scalable stochastic unit commitment. Part 1: load scenario generation

et al. 2015

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Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Tra-ditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load sce-narios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessi-tates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation method-ology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit com-mitment solvers, as described in the companion paper. The scenarios generated by our method are available as an online supplement to this paper, as part of a novel, publicly available large-scale stochastic unit commitment benchmark. Abstract Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Traditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load scenarios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessitates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation methodology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO-NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit commitment solvers, as described in the companion paper. The scenarios generated Disciplines Industrial Engineering | Systems Engineering

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Uncertainty Management in the Unit Commitment Problem

Cited by 353 publications

References 19 publications

The value of stochastic programming in day-ahead and intra-day generation unit commitment

The value of stochastic programming in day-ahead and intra-day generation unit commitment

Impact of wind power on the unit commitment, operating reserves, and market design

Toward scalable stochastic unit commitment. Part 1: load scenario generation

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