A stochastic market-clearing model using semidefinite relaxation

Erik F. Alvarez, Juan C. López, Pedro P. Vergara, Jefferson J. Chavez, Marcos J. Rider

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)


This paper proposes a two-stage stochastic market clearing (SMC) model based on a semidefinite programming (SDP) relaxation. The SMC model aims at determining the day-ahead schedule (DA) and the real-time (RT) balance settlement that minimize the total expected production cost. The network capacity constraints are considered in the proposed model through an AC power flow formulation, while the uncertainty in the renewable-based generation is taking into account using a set of stochastic scenarios. In order to solve the proposed non-linear programming model, a SDP relaxation is used. An illustrative example (3-bus test system) and the IEEE Reliability 24-bus test system are used to show the effectiveness and accuracy of the proposed model. Results shown that the proposed SDP relaxation introduce a negligible error, when compared with the solution after solving the original non-linear model.

Original languageEnglish
Title of host publication2019 IEEE Milan PowerTech, PowerTech 2019
ISBN (Electronic)9781538647226
Publication statusPublished - 2019
Externally publishedYes
Event2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy
Duration: 23 Jun 201927 Jun 2019

Publication series

Name2019 IEEE Milan PowerTech, PowerTech 2019


Conference2019 IEEE Milan PowerTech, PowerTech 2019


  • AC optimal power flow
  • Semidefinite relaxation
  • Stochastic market-clearing


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