Market-clearing with stochastic security-part I: formulation

By François Bouffard, Francisco D. Galiana, Antonio J. Conejo. IEEE Transactions on Power Systems, 2005

480 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/de1db688bd211943cba050b0f3364549674d3bdb

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Citations in the corpus

A Review of Power System Flexibility With High Penetration of Renewables (2019, methodology): “However, tuning the penalty function is a challenging task, since improper choice of the penalty weight may result in unnecessary load shedding [63].”
Demand Response Scheduling by Stochastic SCUC (2010, background): “In [17], the market-clearing problem with security is formulated as a stochastic programming problem with uncertainty affecting only the objective function.”
Optimizing the Spinning Reserve Requirements Using a Cost/Benefit Analysis (2007, background): “References [17] and [18] discuss the difficulties associated with the selection of appropriate ELNS ceilings and propose to penalize the ELNS inside the objective function to avoid choice of arbitrary ELNS and/or LOLP ceilings.”
Spinning Reserve Estimation in Microgrids (2009, background): “One way to limit computational complexity is to replace the higher order terms in the product expansions of these probabilities by upper bounds [8], [21].”
Cost of Reliability Analysis Based on Stochastic Unit Commitment (2008, background): “References [13] and [14] introduced the costs of normal state operation and contingencies into the objective function.”
Pricing Electricity in Pools With Wind Producers (2012, methodology): “Furthermore, reserve capacity costs can be easily incorporated into market-clearing model (1) in the form of reserve capacity bids, as in [6] and [11].”
Multi-Area Energy and Reserve Dispatch Under Wind Uncertainty and Equipment Failures (2013, background): “Section III demonstrates and discusses the results obtained from a two-area three-bus test system and the three-area IEEE-RTS.”
Umbrella contingencies in security-constrained optimal power flow (2005, methodology): “Unlike its deterministic counterpart, by accounting for the post-contingency expected social welfare, the stochastic SCOPF is well suited to balance the relative benefits and costs of reserve scheduling and deployment versus those of load shedding [20,21].”
Tight and Compact MILP Formulation for the Thermal Unit Commitment Problem (2013, background): “Nevertheless, improving an MILP formulation can dramatically reduce its computational burden and thus allow the implementation of more advanced and computationally demanding problems, such as stochastic formulations [7], accurate modelling of different types of (online and offline) reserves [8], or ”
Towards Full Integration of Demand-Side Resources in Joint Forward Energy/Reserve Electricity Markets (2012, methodology): “We consider scheduling of power and spinning reserve in a day-ahead electricity market with stochastic security by adapting the formulation introduced in [12] and [13].”
Market-clearing with stochastic security-part II: case studies (2005, methodology): “For a listing of the symbols used here, we refer the reader to the companion paper [1].”
Stochastic Multiobjective Market Clearing of Joint Energy and Reserves Auctions Ensuring Power System Security (2009, methodology): “In addition to the above comparison between deterministic and stochastic frameworks, it is noted that in the deterministic framework, the interruption cost is not considered [7], [8].”
Optimal Participation of an Electric Vehicle Aggregator in Day-Ahead Energy and Reserve Markets (2016, unknown): “[9].”
Benefits and challenges of electrical demand response: A critical review (2014, background): “This is predominantly employed in studies considering the impact of demand response on the system capacity requirements and the need for generation reserves [48,50,60].”
Wind power day-ahead uncertainty management through stochastic unit commitment policies (2009, methodology): “Subsequently, the modeling has been extended to incorporate the uncertainty in the market parameters and outcomes [10], fuel cost [11], and network aspects [12], [13].”