Flexibility of thermal power generation for RES supply in Germany until 2020 View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-12

AUTHORS

Günther Brauner, Stefan Bofinger, Wolfgang Glaunsinger, Ireneusz Pyc, Florian Steinke, Ulrich Schwing, Wendelin Magin

ABSTRACT

EC Directives (EC in SEC 85/3, 2008; EP, EC in COM 19, 2008) give individual targets in emission reduction and renewable energy share to the member states of the European Union. Germany is obligated to reduce its green house gas emissions by 14 % until the year 2020 related to the year 2005 and to increase its share of renewable energy in the final energy consumption to 18 %. For electrical energy—which is the main topic of this paper—the portion of electricity based on renewable energy sources (RES) is projected to increased from 15 % in 2008 to 40 % until 2020 (Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit, 2012). Because of the short period of time, this ambiguous target represents a big challenge in development of adequate renewable generation. The high shares of wind or PV in the supply system necessitates to expand the storage capacities, extend the transmission and distribution grids and improve flexible operation of the entire energy system, starting with generation and ending with demand side. Long term scenarios of electricity generation and demand in Europe until 2040 can be found in Eurel (2012). To maintain the high standard of security of supply in the German grid, a Task Force of VDE has investigated the needs to balance the German energy system under the aspect of high penetration of RES. For the analysis of the consequences for the non-renewable power generation, a simulation model of the German energy system had been elaborated, which considers the development of RES shown in the reference scenario (VDE AT40, 2012), for the following development of RES until the year 2020: wind 60 GW, PV 60 GW, run-of-river hydro 5 GW (constant) and biomass 7 GW. In total the installed power of RES might achieve 130 GW. Considering the coincidence of RES generation, it will touch the grid load many times over the year and to a small extend overshoot the existing demand. The system simulations show, that the thermal fleet will be facing load gradients of up to 15 GW/h over 1 h in the year 2020. These high gradients will need flexible thermal power plants, which will be able to respond to fast changing renewable generation. As the power plants will in future have only a capacity factor of 20 % it will be difficult to find an economic operation scheme within the existing electricity market model. More... »

PAGES

361-365

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00502-014-0234-9

DOI

http://dx.doi.org/10.1007/s00502-014-0234-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052887906


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0906", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Electrical and Electronic Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "TU Wien", 
          "id": "https://www.grid.ac/institutes/grid.5329.d", 
          "name": [
            "Institut f\u00fcr Energiesysteme und Elektrische Antriebe, Technische Universit\u00e4t Wien, Gu\u00dfhausstra\u00dfe 25\u201329, 1040, Wien, Austria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brauner", 
        "givenName": "G\u00fcnther", 
        "id": "sg:person.010363033025.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010363033025.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Fraunhofer Institute for Wind Energy and Energy System Technology", 
          "id": "https://www.grid.ac/institutes/grid.8440.8", 
          "name": [
            "IWES-Fraunhofer, Kassel, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bofinger", 
        "givenName": "Stefan", 
        "id": "sg:person.015112635257.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015112635257.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "VDE, Frankfurt am Main, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Glaunsinger", 
        "givenName": "Wolfgang", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Siemens (Germany)", 
          "id": "https://www.grid.ac/institutes/grid.5406.7", 
          "name": [
            "Siemens, Erlangen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pyc", 
        "givenName": "Ireneusz", 
        "id": "sg:person.07410273637.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07410273637.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Siemens (Germany)", 
          "id": "https://www.grid.ac/institutes/grid.5406.7", 
          "name": [
            "Siemens, Erlangen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Steinke", 
        "givenName": "Florian", 
        "id": "sg:person.015454754743.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015454754743.73"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Energie Baden-W\u00fcrttemberg (Germany)", 
          "id": "https://www.grid.ac/institutes/grid.424459.a", 
          "name": [
            "EnBW, Erlangen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schwing", 
        "givenName": "Ulrich", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "ABB (Germany)", 
          "id": "https://www.grid.ac/institutes/grid.423531.2", 
          "name": [
            "ABB, Mannheim, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Magin", 
        "givenName": "Wendelin", 
        "type": "Person"
      }
    ], 
    "datePublished": "2014-12", 
    "datePublishedReg": "2014-12-01", 
    "description": "EC Directives (EC in SEC 85/3, 2008; EP, EC in COM 19, 2008) give individual targets in emission reduction and renewable energy share to the member states of the European Union. Germany is obligated to reduce its green house gas emissions by 14 % until the year 2020 related to the year 2005 and to increase its share of renewable energy in the final energy consumption to 18 %. For electrical energy\u2014which is the main topic of this paper\u2014the portion of electricity based on renewable energy sources (RES) is projected to increased from 15 % in 2008 to 40 % until 2020 (Bundesministerium f\u00fcr Umwelt, Naturschutz und Reaktorsicherheit, 2012). Because of the short period of time, this ambiguous target represents a big challenge in development of adequate renewable generation. The high shares of wind or PV in the supply system necessitates to expand the storage capacities, extend the transmission and distribution grids and improve flexible operation of the entire energy system, starting with generation and ending with demand side. Long term scenarios of electricity generation and demand in Europe until 2040 can be found in Eurel (2012). To maintain the high standard of security of supply in the German grid, a Task Force of VDE has investigated the needs to balance the German energy system under the aspect of high penetration of RES. For the analysis of the consequences for the non-renewable power generation, a simulation model of the German energy system had been elaborated, which considers the development of RES shown in the reference scenario (VDE AT40, 2012), for the following development of RES until the year 2020: wind 60 GW, PV 60 GW, run-of-river hydro 5 GW (constant) and biomass 7 GW. In total the installed power of RES might achieve 130 GW. Considering the coincidence of RES generation, it will touch the grid load many times over the year and to a small extend overshoot the existing demand. The system simulations show, that the thermal fleet will be facing load gradients of up to 15 GW/h over 1 h in the year 2020. These high gradients will need flexible thermal power plants, which will be able to respond to fast changing renewable generation. As the power plants will in future have only a capacity factor of 20 % it will be difficult to find an economic operation scheme within the existing electricity market model.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00502-014-0234-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136004", 
        "issn": [
          "0932-383X", 
          "1613-7620"
        ], 
        "name": "e & i Elektrotechnik und Informationstechnik", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "131"
      }
    ], 
    "name": "Flexibility of thermal power generation for RES supply in Germany until 2020", 
    "pagination": "361-365", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "fbebc06cf44e717d8cbecdcca54e366883849a422228202047b3f60886056a18"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00502-014-0234-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1052887906"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00502-014-0234-9", 
      "https://app.dimensions.ai/details/publication/pub.1052887906"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T19:04", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8678_00000492.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00502-014-0234-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00502-014-0234-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00502-014-0234-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00502-014-0234-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00502-014-0234-9'


 

This table displays all metadata directly associated to this object as RDF triples.

114 TRIPLES      20 PREDICATES      27 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00502-014-0234-9 schema:about anzsrc-for:09
2 anzsrc-for:0906
3 schema:author Nc4437bc5a73e4d78b8e28fe7362f7afe
4 schema:datePublished 2014-12
5 schema:datePublishedReg 2014-12-01
6 schema:description EC Directives (EC in SEC 85/3, 2008; EP, EC in COM 19, 2008) give individual targets in emission reduction and renewable energy share to the member states of the European Union. Germany is obligated to reduce its green house gas emissions by 14 % until the year 2020 related to the year 2005 and to increase its share of renewable energy in the final energy consumption to 18 %. For electrical energy—which is the main topic of this paper—the portion of electricity based on renewable energy sources (RES) is projected to increased from 15 % in 2008 to 40 % until 2020 (Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit, 2012). Because of the short period of time, this ambiguous target represents a big challenge in development of adequate renewable generation. The high shares of wind or PV in the supply system necessitates to expand the storage capacities, extend the transmission and distribution grids and improve flexible operation of the entire energy system, starting with generation and ending with demand side. Long term scenarios of electricity generation and demand in Europe until 2040 can be found in Eurel (2012). To maintain the high standard of security of supply in the German grid, a Task Force of VDE has investigated the needs to balance the German energy system under the aspect of high penetration of RES. For the analysis of the consequences for the non-renewable power generation, a simulation model of the German energy system had been elaborated, which considers the development of RES shown in the reference scenario (VDE AT40, 2012), for the following development of RES until the year 2020: wind 60 GW, PV 60 GW, run-of-river hydro 5 GW (constant) and biomass 7 GW. In total the installed power of RES might achieve 130 GW. Considering the coincidence of RES generation, it will touch the grid load many times over the year and to a small extend overshoot the existing demand. The system simulations show, that the thermal fleet will be facing load gradients of up to 15 GW/h over 1 h in the year 2020. These high gradients will need flexible thermal power plants, which will be able to respond to fast changing renewable generation. As the power plants will in future have only a capacity factor of 20 % it will be difficult to find an economic operation scheme within the existing electricity market model.
7 schema:genre research_article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N854f5e9cd80f48c885d09e2ac87c6c3c
11 Nb609a1d11e3b42d6b22a4f6f85ef7011
12 sg:journal.1136004
13 schema:name Flexibility of thermal power generation for RES supply in Germany until 2020
14 schema:pagination 361-365
15 schema:productId N33be5b47130648fc87f913634b7c7151
16 N9c3e896758274448a3b3cc2f79db6fb9
17 Nc9a980139b964d7f9c0bd8a3840e4e99
18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052887906
19 https://doi.org/10.1007/s00502-014-0234-9
20 schema:sdDatePublished 2019-04-10T19:04
21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
22 schema:sdPublisher Ne1326e2d1d354b0ba5e3b507159c389b
23 schema:url http://link.springer.com/10.1007/s00502-014-0234-9
24 sgo:license sg:explorer/license/
25 sgo:sdDataset articles
26 rdf:type schema:ScholarlyArticle
27 N229df0edeb354bbe904aaa87843608cf rdf:first sg:person.015112635257.76
28 rdf:rest Nc753e8e2293f4d1b9de5f4a06045c551
29 N22bbd0e81eb7477fb479962635e56bb4 rdf:first sg:person.015454754743.73
30 rdf:rest N3ca2c722102549f1a8b96cdd510d1430
31 N2b3179a24fed49deb843cac3b0fa90ec rdf:first sg:person.07410273637.34
32 rdf:rest N22bbd0e81eb7477fb479962635e56bb4
33 N2cb8614eb34f4fe3b1d326de4b74737c rdf:first N8c96f18401ea40dc80d711f03facbc90
34 rdf:rest rdf:nil
35 N33be5b47130648fc87f913634b7c7151 schema:name doi
36 schema:value 10.1007/s00502-014-0234-9
37 rdf:type schema:PropertyValue
38 N3ca2c722102549f1a8b96cdd510d1430 rdf:first Nff7d624968bf4f0f8b5f4e590384b222
39 rdf:rest N2cb8614eb34f4fe3b1d326de4b74737c
40 N432ca52e85b047ef857f499f590b26e7 schema:affiliation Nc64ce9c2735344f38fab97c5c9248888
41 schema:familyName Glaunsinger
42 schema:givenName Wolfgang
43 rdf:type schema:Person
44 N854f5e9cd80f48c885d09e2ac87c6c3c schema:issueNumber 8
45 rdf:type schema:PublicationIssue
46 N8c96f18401ea40dc80d711f03facbc90 schema:affiliation https://www.grid.ac/institutes/grid.423531.2
47 schema:familyName Magin
48 schema:givenName Wendelin
49 rdf:type schema:Person
50 N9c3e896758274448a3b3cc2f79db6fb9 schema:name readcube_id
51 schema:value fbebc06cf44e717d8cbecdcca54e366883849a422228202047b3f60886056a18
52 rdf:type schema:PropertyValue
53 Nb609a1d11e3b42d6b22a4f6f85ef7011 schema:volumeNumber 131
54 rdf:type schema:PublicationVolume
55 Nc4437bc5a73e4d78b8e28fe7362f7afe rdf:first sg:person.010363033025.85
56 rdf:rest N229df0edeb354bbe904aaa87843608cf
57 Nc64ce9c2735344f38fab97c5c9248888 schema:name VDE, Frankfurt am Main, Germany
58 rdf:type schema:Organization
59 Nc753e8e2293f4d1b9de5f4a06045c551 rdf:first N432ca52e85b047ef857f499f590b26e7
60 rdf:rest N2b3179a24fed49deb843cac3b0fa90ec
61 Nc9a980139b964d7f9c0bd8a3840e4e99 schema:name dimensions_id
62 schema:value pub.1052887906
63 rdf:type schema:PropertyValue
64 Ne1326e2d1d354b0ba5e3b507159c389b schema:name Springer Nature - SN SciGraph project
65 rdf:type schema:Organization
66 Nff7d624968bf4f0f8b5f4e590384b222 schema:affiliation https://www.grid.ac/institutes/grid.424459.a
67 schema:familyName Schwing
68 schema:givenName Ulrich
69 rdf:type schema:Person
70 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
71 schema:name Engineering
72 rdf:type schema:DefinedTerm
73 anzsrc-for:0906 schema:inDefinedTermSet anzsrc-for:
74 schema:name Electrical and Electronic Engineering
75 rdf:type schema:DefinedTerm
76 sg:journal.1136004 schema:issn 0932-383X
77 1613-7620
78 schema:name e & i Elektrotechnik und Informationstechnik
79 rdf:type schema:Periodical
80 sg:person.010363033025.85 schema:affiliation https://www.grid.ac/institutes/grid.5329.d
81 schema:familyName Brauner
82 schema:givenName Günther
83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010363033025.85
84 rdf:type schema:Person
85 sg:person.015112635257.76 schema:affiliation https://www.grid.ac/institutes/grid.8440.8
86 schema:familyName Bofinger
87 schema:givenName Stefan
88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015112635257.76
89 rdf:type schema:Person
90 sg:person.015454754743.73 schema:affiliation https://www.grid.ac/institutes/grid.5406.7
91 schema:familyName Steinke
92 schema:givenName Florian
93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015454754743.73
94 rdf:type schema:Person
95 sg:person.07410273637.34 schema:affiliation https://www.grid.ac/institutes/grid.5406.7
96 schema:familyName Pyc
97 schema:givenName Ireneusz
98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07410273637.34
99 rdf:type schema:Person
100 https://www.grid.ac/institutes/grid.423531.2 schema:alternateName ABB (Germany)
101 schema:name ABB, Mannheim, Germany
102 rdf:type schema:Organization
103 https://www.grid.ac/institutes/grid.424459.a schema:alternateName Energie Baden-Württemberg (Germany)
104 schema:name EnBW, Erlangen, Germany
105 rdf:type schema:Organization
106 https://www.grid.ac/institutes/grid.5329.d schema:alternateName TU Wien
107 schema:name Institut für Energiesysteme und Elektrische Antriebe, Technische Universität Wien, Gußhausstraße 25–29, 1040, Wien, Austria
108 rdf:type schema:Organization
109 https://www.grid.ac/institutes/grid.5406.7 schema:alternateName Siemens (Germany)
110 schema:name Siemens, Erlangen, Germany
111 rdf:type schema:Organization
112 https://www.grid.ac/institutes/grid.8440.8 schema:alternateName Fraunhofer Institute for Wind Energy and Energy System Technology
113 schema:name IWES-Fraunhofer, Kassel, Germany
114 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...