Loading…
Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets with Dynamic Storage Management
The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers can be aggregated in virtual power plants to participate in energy markets. In this paper, a reconfigurable hierarchical multi-time scale framework is developed by combining the...
Saved in:
Published in: | IEEE access 2022-01, Vol.10, p.1-1 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c408t-7067cec004ed32b3e4fd451bd2a36a4cea5965366e1cd7a4e7e0709151d553113 |
---|---|
cites | cdi_FETCH-LOGICAL-c408t-7067cec004ed32b3e4fd451bd2a36a4cea5965366e1cd7a4e7e0709151d553113 |
container_end_page | 1 |
container_issue | |
container_start_page | 1 |
container_title | IEEE access |
container_volume | 10 |
creator | Aguilar, J. Bordons, C. Arce, A. Galan, R. |
description | The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers can be aggregated in virtual power plants to participate in energy markets. In this paper, a reconfigurable hierarchical multi-time scale framework is developed by combining the concepts of dynamic storage virtualization and intent profiling with model predictive control. The combined implementation of these concepts allows the simultaneous weighted participation in different energy markets, not only according to some aggregators' criteria, but also to several risk factors. In a first stage, the framework optimizes the strategy for bidding in day-ahead market whereas the second one consists of a control stage to mitigate deviations and potential penalties. The smart management of individual storage virtualization enables the participation in the demand-response program, which improves the forecasted economical profit related to the day-ahead participation. The changes in the schedule are performed considering new potential penalties. The framework is reconfigurable at every sample time at control stage. This enables to make dynamic participations depending on node availability or system peaks. The proposed case studies cover day-ahead and demand-response participations, but the framework is open to other multi-service configurations. The results have been assessed with satisfactory conclusions. |
doi_str_mv | 10.1109/ACCESS.2022.3155170 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_ACCESS_2022_3155170</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9722885</ieee_id><doaj_id>oai_doaj_org_article_cc7362a18fa944aa9a1632529c70a6db</doaj_id><sourcerecordid>2635711981</sourcerecordid><originalsourceid>FETCH-LOGICAL-c408t-7067cec004ed32b3e4fd451bd2a36a4cea5965366e1cd7a4e7e0709151d553113</originalsourceid><addsrcrecordid>eNpNUU1rGzEQXUoDDUl-QS6Cnu3qYyWtjsF1W0NCDG56FWPtrCt3LblaLcHQHx85G0LnMsPMvPdmeFV1y-icMWq-3C0Wy81mzinnc8GkZJp-qC45U2YmpFAf_6s_VTfDsKclmtKS-rL6twoZQybrFDvfI9nkBBl3J9LFRH75lEfoyTo-YyLrHs6LkLJ3_gjZx0B8IBt_GPsMAeM4kGXAVMAPkP5gHsizz7_J11OAg3eFOibYYRmGkg5F9bq66KAf8OYtX1VP35Y_Fz9m94_fV4u7-5mraZNnmirt0FFaYyv4VmDdtbVk25aDUFA7BGlU-U4hc62GGjVSTQ2TrJVSMCauqtXE20bY22PyB0gnG8Hb10ZMO_v6VY_WOS0UB9Z0YOoawABTgktunKag2m3h-jxxHVP8O-KQ7T6OKZTzLVdCasZMc1YU05ZLcRgSdu-qjNqza3ZyzZ5ds2-uFdTthPKI-I4wmvOmkeIF6uuUTA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2635711981</pqid></control><display><type>article</type><title>Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets with Dynamic Storage Management</title><source>IEEE Xplore Open Access Journals</source><creator>Aguilar, J. ; Bordons, C. ; Arce, A. ; Galan, R.</creator><creatorcontrib>Aguilar, J. ; Bordons, C. ; Arce, A. ; Galan, R.</creatorcontrib><description>The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers can be aggregated in virtual power plants to participate in energy markets. In this paper, a reconfigurable hierarchical multi-time scale framework is developed by combining the concepts of dynamic storage virtualization and intent profiling with model predictive control. The combined implementation of these concepts allows the simultaneous weighted participation in different energy markets, not only according to some aggregators' criteria, but also to several risk factors. In a first stage, the framework optimizes the strategy for bidding in day-ahead market whereas the second one consists of a control stage to mitigate deviations and potential penalties. The smart management of individual storage virtualization enables the participation in the demand-response program, which improves the forecasted economical profit related to the day-ahead participation. The changes in the schedule are performed considering new potential penalties. The framework is reconfigurable at every sample time at control stage. This enables to make dynamic participations depending on node availability or system peaks. The proposed case studies cover day-ahead and demand-response participations, but the framework is open to other multi-service configurations. The results have been assessed with satisfactory conclusions.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2022.3155170</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Batteries ; Distributed generation ; Energy ; Energy industry ; Energy sources ; Fines & penalties ; IP networks ; Mathematical Programming ; Optimization ; Participation ; Peer-to-peer computing ; Power consumption ; Predictive control ; Reconfiguration ; Risk analysis ; Smart grid ; Strategy ; Virtual Battery ; Virtual Power Plant ; Virtual power plants ; Virtualization</subject><ispartof>IEEE access, 2022-01, Vol.10, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-7067cec004ed32b3e4fd451bd2a36a4cea5965366e1cd7a4e7e0709151d553113</citedby><cites>FETCH-LOGICAL-c408t-7067cec004ed32b3e4fd451bd2a36a4cea5965366e1cd7a4e7e0709151d553113</cites><orcidid>0000-0002-3315-2428 ; 0000-0001-5060-7888</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9722885$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,27614,27905,27906,54914</link.rule.ids></links><search><creatorcontrib>Aguilar, J.</creatorcontrib><creatorcontrib>Bordons, C.</creatorcontrib><creatorcontrib>Arce, A.</creatorcontrib><creatorcontrib>Galan, R.</creatorcontrib><title>Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets with Dynamic Storage Management</title><title>IEEE access</title><addtitle>Access</addtitle><description>The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers can be aggregated in virtual power plants to participate in energy markets. In this paper, a reconfigurable hierarchical multi-time scale framework is developed by combining the concepts of dynamic storage virtualization and intent profiling with model predictive control. The combined implementation of these concepts allows the simultaneous weighted participation in different energy markets, not only according to some aggregators' criteria, but also to several risk factors. In a first stage, the framework optimizes the strategy for bidding in day-ahead market whereas the second one consists of a control stage to mitigate deviations and potential penalties. The smart management of individual storage virtualization enables the participation in the demand-response program, which improves the forecasted economical profit related to the day-ahead participation. The changes in the schedule are performed considering new potential penalties. The framework is reconfigurable at every sample time at control stage. This enables to make dynamic participations depending on node availability or system peaks. The proposed case studies cover day-ahead and demand-response participations, but the framework is open to other multi-service configurations. The results have been assessed with satisfactory conclusions.</description><subject>Batteries</subject><subject>Distributed generation</subject><subject>Energy</subject><subject>Energy industry</subject><subject>Energy sources</subject><subject>Fines & penalties</subject><subject>IP networks</subject><subject>Mathematical Programming</subject><subject>Optimization</subject><subject>Participation</subject><subject>Peer-to-peer computing</subject><subject>Power consumption</subject><subject>Predictive control</subject><subject>Reconfiguration</subject><subject>Risk analysis</subject><subject>Smart grid</subject><subject>Strategy</subject><subject>Virtual Battery</subject><subject>Virtual Power Plant</subject><subject>Virtual power plants</subject><subject>Virtualization</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1rGzEQXUoDDUl-QS6Cnu3qYyWtjsF1W0NCDG56FWPtrCt3LblaLcHQHx85G0LnMsPMvPdmeFV1y-icMWq-3C0Wy81mzinnc8GkZJp-qC45U2YmpFAf_6s_VTfDsKclmtKS-rL6twoZQybrFDvfI9nkBBl3J9LFRH75lEfoyTo-YyLrHs6LkLJ3_gjZx0B8IBt_GPsMAeM4kGXAVMAPkP5gHsizz7_J11OAg3eFOibYYRmGkg5F9bq66KAf8OYtX1VP35Y_Fz9m94_fV4u7-5mraZNnmirt0FFaYyv4VmDdtbVk25aDUFA7BGlU-U4hc62GGjVSTQ2TrJVSMCauqtXE20bY22PyB0gnG8Hb10ZMO_v6VY_WOS0UB9Z0YOoawABTgktunKag2m3h-jxxHVP8O-KQ7T6OKZTzLVdCasZMc1YU05ZLcRgSdu-qjNqza3ZyzZ5ds2-uFdTthPKI-I4wmvOmkeIF6uuUTA</recordid><startdate>20220101</startdate><enddate>20220101</enddate><creator>Aguilar, J.</creator><creator>Bordons, C.</creator><creator>Arce, A.</creator><creator>Galan, R.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-3315-2428</orcidid><orcidid>https://orcid.org/0000-0001-5060-7888</orcidid></search><sort><creationdate>20220101</creationdate><title>Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets with Dynamic Storage Management</title><author>Aguilar, J. ; Bordons, C. ; Arce, A. ; Galan, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-7067cec004ed32b3e4fd451bd2a36a4cea5965366e1cd7a4e7e0709151d553113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Batteries</topic><topic>Distributed generation</topic><topic>Energy</topic><topic>Energy industry</topic><topic>Energy sources</topic><topic>Fines & penalties</topic><topic>IP networks</topic><topic>Mathematical Programming</topic><topic>Optimization</topic><topic>Participation</topic><topic>Peer-to-peer computing</topic><topic>Power consumption</topic><topic>Predictive control</topic><topic>Reconfiguration</topic><topic>Risk analysis</topic><topic>Smart grid</topic><topic>Strategy</topic><topic>Virtual Battery</topic><topic>Virtual Power Plant</topic><topic>Virtual power plants</topic><topic>Virtualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aguilar, J.</creatorcontrib><creatorcontrib>Bordons, C.</creatorcontrib><creatorcontrib>Arce, A.</creatorcontrib><creatorcontrib>Galan, R.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Xplore Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aguilar, J.</au><au>Bordons, C.</au><au>Arce, A.</au><au>Galan, R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets with Dynamic Storage Management</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2022-01-01</date><risdate>2022</risdate><volume>10</volume><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers can be aggregated in virtual power plants to participate in energy markets. In this paper, a reconfigurable hierarchical multi-time scale framework is developed by combining the concepts of dynamic storage virtualization and intent profiling with model predictive control. The combined implementation of these concepts allows the simultaneous weighted participation in different energy markets, not only according to some aggregators' criteria, but also to several risk factors. In a first stage, the framework optimizes the strategy for bidding in day-ahead market whereas the second one consists of a control stage to mitigate deviations and potential penalties. The smart management of individual storage virtualization enables the participation in the demand-response program, which improves the forecasted economical profit related to the day-ahead participation. The changes in the schedule are performed considering new potential penalties. The framework is reconfigurable at every sample time at control stage. This enables to make dynamic participations depending on node availability or system peaks. The proposed case studies cover day-ahead and demand-response participations, but the framework is open to other multi-service configurations. The results have been assessed with satisfactory conclusions.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2022.3155170</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-3315-2428</orcidid><orcidid>https://orcid.org/0000-0001-5060-7888</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2169-3536 |
ispartof | IEEE access, 2022-01, Vol.10, p.1-1 |
issn | 2169-3536 2169-3536 |
language | eng |
recordid | cdi_crossref_primary_10_1109_ACCESS_2022_3155170 |
source | IEEE Xplore Open Access Journals |
subjects | Batteries Distributed generation Energy Energy industry Energy sources Fines & penalties IP networks Mathematical Programming Optimization Participation Peer-to-peer computing Power consumption Predictive control Reconfiguration Risk analysis Smart grid Strategy Virtual Battery Virtual Power Plant Virtual power plants Virtualization |
title | Intent Profile Strategy for Virtual Power Plant Participation in Simultaneous Energy Markets with Dynamic Storage Management |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T17%3A03%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Intent%20Profile%20Strategy%20for%20Virtual%20Power%20Plant%20Participation%20in%20Simultaneous%20Energy%20Markets%20with%20Dynamic%20Storage%20Management&rft.jtitle=IEEE%20access&rft.au=Aguilar,%20J.&rft.date=2022-01-01&rft.volume=10&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2022.3155170&rft_dat=%3Cproquest_cross%3E2635711981%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c408t-7067cec004ed32b3e4fd451bd2a36a4cea5965366e1cd7a4e7e0709151d553113%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2635711981&rft_id=info:pmid/&rft_ieee_id=9722885&rfr_iscdi=true |