Market-clearing with stochastic security - part I: formulation

Bouffard, François; Galiana, F.D.; Conejo, Antonio J.

doi:10.1109/pes.2006.1708879

Cited by 47 publications

(82 citation statements)

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“…3 surplus (cost in case of inelastic demands) in intra-day operations. Examples include the works of Takriti et al (1996), Wang et al (2008), Constantinescu et al (2011), Jin et al (2014), Ruiz et al (2009), Bouffard et al (2005), Papavasiliou and Oren (2013). These studies have demonstrated significant improvements in reliability over deterministic formulations.…”

Section: Previous Workmentioning

confidence: 99%

A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties

Zavala

Kim

Anitescu

et al. 2017

Operations Research

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We argue that deterministic market clearing formulations introduce arbitrary distortions between day-ahead and expected real-time prices that bias economic incentives and block diversification. We extend and analyze the stochastic clearing formulation proposed by Pritchard et al. (2010) in which the social surplus function induces penalties between day-ahead and real-time quantities. We prove that the formulation yields price distortions that are bounded by the bid prices, and we show that adding a similar penalty term to transmission flows and phase angles ensures boundedness throughout the network. We prove that when the price distortions are zero, day-ahead quantities converge to the quantile of real-time counterparts. The undesired effects of price distortions suggest that stochastic settings provide significant benefits over deterministic ones that go beyond social surplus improvements. We propose additional metrics to evaluate these benefits.

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Section: Previous Workmentioning

confidence: 99%

A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties

Zavala

Kim

Anitescu

et al. 2017

Operations Research

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“…Depending on the relative size of the available dataset {l d | d ∈ D T } and the number of coefficients in A T , it may be necessary to introduce additional constraints and regularization terms to formulation (2). Another potential issue is that the resulting A T may be ill conditioned, causing difficulties in the calculation A −1 T .…”

Section: Data Transformationmentioning

confidence: 99%

“…The procedures described below extend to wind and solar plant production, which we will address in a future contribution. While stochastic bidding or trading [23] and market clearing formulations [2,15] for the day ahead wholesale electricity market have also been proposed, we consider the reliability unit commitment (also called resource adequacy assessment) process conducted by system operators. Consequently, we generate scenarios specifying zonal or systemwide load rather than demand bids by load-serving entities.…”

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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“…For instance, Bouffard et al formulated a two-stage stochastic program for securityconstrained unit commitment to address market-clearing in [8], and Bouffard and Galiana [9] analyzed the impact of wind energy penetration on reserve requirements in a small-scale model. Morales et al used stochastic programming to co-optimize energy and reserve in an electricity pool with significant wind penetration [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…an hour is the basic time period in this paper. Formula (8) states that for each bus b, a shortage α + bts will result if the sum of energy amounts provided by each unit at that bus, p gts , and net energy transmitted to that bus on each line, ω ts , is less than load d bts in period t in scenario s; or excess, α − bts , will occur if the sum is greater than the load. In this paper, the scenario-specific parameters d bts represent net load computed by subtracting nondispatchable variable energy generation from load.…”

Section: Objective Function Minmentioning

confidence: 99%

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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Market-clearing with stochastic security - part I: formulation

Cited by 47 publications

References 0 publications

A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties

A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties

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

Solution sensitivity-based scenario reduction for stochastic unit commitment

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