Classifying and modelling demand response in power systems

Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR m...

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Veröffentlicht in:Energy (Oxford) Jg. 242; S. 122544
Hauptverfasser: Morales-España, Germán, Martínez-Gordón, Rafael, Sijm, Jos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.03.2022
Elsevier BV
Schlagworte:
assets
Classification
Computer applications
Demand response
Demand side management
Electric power demand
Electrical loads
Energy
Energy management
Exact solutions
Integer programming
Integrated energy system models
Integrated energy systems
Linear programming
Linearity
Load shifting
Mathematical analysis
Mathematical models
Mixed integer
Mixed-integer programming
Power consumption
Power systems models
Recovery
Renewable energy
renewable energy sources
Scale models
solutions
Linear programming
Demand side management
Demand response
Integrated energy system models
Mixed-integer programming
Power systems models
ISSN:0360-5442, 1873-6785
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Abstract Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models. •We present a collection of linear formulations for demand response (DR) models.•The DR models go from curtailment and ideal shifting to shifting with saturation and load recovery.•We propose an MIP shifting model that guarantees immediate load recovery, and.•Its LP relaxation better approximates the exact solution compared with previous models.•Different categories, benefits and challenges of demand response are identified.
AbstractList Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models.
Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models. •We present a collection of linear formulations for demand response (DR) models.•The DR models go from curtailment and ideal shifting to shifting with saturation and load recovery.•We propose an MIP shifting model that guarantees immediate load recovery, and.•Its LP relaxation better approximates the exact solution compared with previous models.•Different categories, benefits and challenges of demand response are identified.
ArticleNumber 122544
Author Morales-España, Germán
Sijm, Jos
Martínez-Gordón, Rafael
Author_xml – sequence: 1
  givenname: Germán
  orcidid: 0000-0002-6372-6197
  surname: Morales-España
  fullname: Morales-España, Germán
  email: german.morales@tno.nl
  organization: TNO Energy Transition, the Netherlands
– sequence: 2
  givenname: Rafael
  orcidid: 0000-0002-7505-712X
  surname: Martínez-Gordón
  fullname: Martínez-Gordón, Rafael
  organization: Faculty of Science and Engineering, University of Groningen, the Netherlands
– sequence: 3
  givenname: Jos
  orcidid: 0000-0003-3765-3497
  surname: Sijm
  fullname: Sijm, Jos
  organization: TNO Energy Transition, the Netherlands
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Keywords Linear programming
Demand side management
Demand response
Integrated energy system models
Mixed-integer programming
Power systems models
Language English
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Snippet Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR...
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SubjectTerms assets
Classification
Computer applications
Demand response
Demand side management
Electric power demand
Electrical loads
Energy
Energy management
Exact solutions
Integer programming
Integrated energy system models
Integrated energy systems
Linear programming
Linearity
Load shifting
Mathematical analysis
Mathematical models
Mixed integer
Mixed-integer programming
Power consumption
Power systems models
Recovery
Renewable energy
renewable energy sources
Scale models
solutions
Title Classifying and modelling demand response in power systems
URI https://dx.doi.org/10.1016/j.energy.2021.122544
https://www.proquest.com/docview/2638291116
https://www.proquest.com/docview/2636506366
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