Solution of the mixed integer large scale unit commitment problem by means of a continuous Stochastic linear programming model

Siface, Dario; Vespucci, Maria Teresa; Gelmini, Alberto

doi:10.1007/s12667-013-0107-z

Cited by 18 publications

(4 citation statements)

References 8 publications

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“…The TIMES_RSE model only considers 12 time slices within a year, a time resolution not sufficient to describe the operability of the power system and the production variability of renewable sources over days and seasons. Therefore, this work followed a two-step approach: first, the scenario analysis for the overall Italian energy system developed with the TIMES_RSE national energy model set out the total electricity demand and indicative generation mix; then, outputs and constraints from the national model were used as inputs for a detailed study of the impact on the Italian power system, and its specific requirements were carried out with a dedicated simulation model, the sMTSIM [20][21][22].…”

Section: Energy and Power System Model Usedmentioning

confidence: 99%

Achieving Net Zero Emissions in Italy by 2050: Challenges and Opportunities

2021

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This paper contributes to the climate policy discussion by focusing on the challenges and opportunities of reaching net zero emissions by 2050 in Italy. To support Italian energy planning, we developed energy roadmaps towards national climate neutrality, consistent with the Paris Agreement objectives and the IPCC goal of limiting the increase in global surface temperature to 1.5 °C. Starting from the Italian framework, these scenarios identify the correlations among the main pillars for the change of the energy paradigm towards net emissions by 2050. The energy scenarios were developed using TIMES-RSE, a partial equilibrium and technology-rich optimization model of the entire Italian energy system. Subsequently, an in-depth analysis was developed with the sMTISIM, a long-term simulator of power system and electricity markets. The results show that, to achieve climate neutrality by 2050, the Italian energy system will have to experience profound transformations on multiple and strongly related dimensions. A predominantly renewable-based energy mix (at least 80–90% by 2050) is essential to decarbonize most of the final energy consumption. However, the strong increase of non-programmable renewable sources requires particular attention to new flexibility resources needed for the power system, such as Power-to-X. The green fuels produced from renewables via Power-to-X will be a vital energy source for those sectors where electrification faces technical and economic barriers. The paper’s findings also confirm that the European “energy efficiency first” principle represents the very first step on the road to climate neutrality.

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Section: Energy and Power System Model Usedmentioning

confidence: 99%

Achieving Net Zero Emissions in Italy by 2050: Challenges and Opportunities

2021

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“…Previous SUC research has focused on improving the mathematical formulation, developing solution approaches to decrease the optimality gap, and devising various scenario reduction techniques to decrease the solution time. Various alternative formulations for unit commitment under uncertainty have been proposed to reduce the computation times [32][33][34]. Moreover, scenario reduction techniques that are specified to SUC are proposed to decrease the computational demands to a degree [35,36].…”

Section: Literature Reviewmentioning

confidence: 99%

Statistical reliability of wind power scenarios and stochastic unit commitment cost

Sari

Ryan

2017

Energy Syst

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Probabilistic wind power scenarios constitute a crucial input for stochastic day-ahead unit commitment in power systems with deep penetration of wind generation. To minimize the expected cost, the scenario time series of wind power amounts available should accurately represent the stochastic process for available wind power as it is estimated on the day ahead. The high computational demands of stochastic programming motivate a search for ways to evaluate scenarios without extensively simulating the stochastic unit commitment procedure. Reliability of wind power scenario sets can be assessed by statistical verification approaches. In this study, we examine the relationship between the statistical evaluation metrics and the results of stochastic unit commitment. Lack of uniformity in a mass transportation distance rank histogram can eliminate scenario sets that might lead to either excessive no-load costs of committed units or high penalty costs for violating energy balance. Event-based metrics can help to predict the cost performance of the remaining scenario sets.

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“…In contrast, stochastic unit commitment procedures [16,24,25] assume the availability of a number of forecast scenarios, each representing a distinct potential time series of the forecasted quantities. 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.…”

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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Solution of the mixed integer large scale unit commitment problem by means of a continuous Stochastic linear programming model

Cited by 18 publications

References 8 publications

Achieving Net Zero Emissions in Italy by 2050: Challenges and Opportunities

Achieving Net Zero Emissions in Italy by 2050: Challenges and Opportunities

Statistical reliability of wind power scenarios and stochastic unit commitment cost

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

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