Bilevel approach to wind-CSP day-ahead scheduling with spinning reserve under controllable degree of trust

This paper proposes a day-ahead schedule harmonization between wind power plants and concentrating solar thermal power plants having thermal energy storage. The negative correlation between wind power and solar power is computed and an artificial neural network method estimates the power. The schedu...

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Veröffentlicht in:Renewable energy Jg. 85; S. 917 - 927
Hauptverfasser: Pousinho, H.M.I., Esteves, J., Mendes, V.M.F., Collares-Pereira, M., Pereira Cabrita, C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.01.2016
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ISSN:0960-1481, 1879-0682
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Abstract This paper proposes a day-ahead schedule harmonization between wind power plants and concentrating solar thermal power plants having thermal energy storage. The negative correlation between wind power and solar power is computed and an artificial neural network method estimates the power. The schedule is carried out by a bilevel mathematical programming approach. The upper-level determines energy and spinning reserve schedule by the maximization of profit subject to all lower-level problems. Lower-level problems minimize the post-contingency power output. A controllable degree of trust on the schedule is introduced based on n – K security criterion for worst-case contingency. The approach uses duality theory and problem approximations for a conversion into an equivalent mixed-integer linear programming problem. A case study is presented to illustrate the effectiveness of the approach for power producers not only with transmission constraints, but also valuing safekeeping on the day-ahead schedule to ensure a degree trust on the satisfaction of compromises within electricity markets. •Optimal scheduling of wind power with CSP plants having TES.•Impact assessment of transmission constraints.•Economic impact of n – K security criterion.•Evaluation of the benefit of wind power with CSP plants having TES.
AbstractList This paper proposes a day-ahead schedule harmonization between wind power plants and concentrating solar thermal power plants having thermal energy storage. The negative correlation between wind power and solar power is computed and an artificial neural network method estimates the power. The schedule is carried out by a bilevel mathematical programming approach. The upper-level determines energy and spinning reserve schedule by the maximization of profit subject to all lower-level problems. Lower-level problems minimize the post-contingency power output. A controllable degree of trust on the schedule is introduced based on n – K security criterion for worst-case contingency. The approach uses duality theory and problem approximations for a conversion into an equivalent mixed-integer linear programming problem. A case study is presented to illustrate the effectiveness of the approach for power producers not only with transmission constraints, but also valuing safekeeping on the day-ahead schedule to ensure a degree trust on the satisfaction of compromises within electricity markets.
This paper proposes a day-ahead schedule harmonization between wind power plants and concentrating solar thermal power plants having thermal energy storage. The negative correlation between wind power and solar power is computed and an artificial neural network method estimates the power. The schedule is carried out by a bilevel mathematical programming approach. The upper-level determines energy and spinning reserve schedule by the maximization of profit subject to all lower-level problems. Lower-level problems minimize the post-contingency power output. A controllable degree of trust on the schedule is introduced based on n – K security criterion for worst-case contingency. The approach uses duality theory and problem approximations for a conversion into an equivalent mixed-integer linear programming problem. A case study is presented to illustrate the effectiveness of the approach for power producers not only with transmission constraints, but also valuing safekeeping on the day-ahead schedule to ensure a degree trust on the satisfaction of compromises within electricity markets. •Optimal scheduling of wind power with CSP plants having TES.•Impact assessment of transmission constraints.•Economic impact of n – K security criterion.•Evaluation of the benefit of wind power with CSP plants having TES.
Author Pousinho, H.M.I.
Mendes, V.M.F.
Esteves, J.
Pereira Cabrita, C.
Collares-Pereira, M.
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– sequence: 2
  givenname: J.
  surname: Esteves
  fullname: Esteves, J.
  organization: Department of Electrical Engineering and Automation, Institute Superior of Engineering of Lisbon, R. Conselheiro Emídio Navarro, Lisbon 1950-062, Portugal
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  givenname: V.M.F.
  surname: Mendes
  fullname: Mendes, V.M.F.
  organization: Department of Physics, University of Évora, R. Romão Ramalho, Évora 7002-554, Portugal
– sequence: 4
  givenname: M.
  surname: Collares-Pereira
  fullname: Collares-Pereira, M.
  organization: Department of Physics, University of Évora, R. Romão Ramalho, Évora 7002-554, Portugal
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  givenname: C.
  surname: Pereira Cabrita
  fullname: Pereira Cabrita, C.
  organization: CISE, University of Beira Interior, Convento de Santo António, Covilhã 6200-001, Portugal
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Keywords Bilevel programming
Wind-CSP day-ahead schedule
Spinning reserve
Transmission constraints
n – K security criterion
Language English
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Snippet This paper proposes a day-ahead schedule harmonization between wind power plants and concentrating solar thermal power plants having thermal energy storage....
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StartPage 917
SubjectTerms Bilevel programming
case studies
electricity
energy
linear programming
markets
n – K security criterion
neural networks
power plants
profit maximization
solar thermal energy
spinning
Spinning reserve
Transmission constraints
wind power
Wind-CSP day-ahead schedule
Title Bilevel approach to wind-CSP day-ahead scheduling with spinning reserve under controllable degree of trust
URI https://dx.doi.org/10.1016/j.renene.2015.07.022
https://www.proquest.com/docview/2000518124
Volume 85
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