Analysis of reflectivity & predictability of electricity network tariff structures for household consumers
Distribution network operators charge household consumers with a network tariff, so they can recover their network investment and operational costs. With the transition; towards a sustainable energy system, the household load is changing, through the introduction of photovoltaics and electric vehicl...
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Veröffentlicht in: | Energy policy 2017-10, Vol.109, p.631-641 |
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description | Distribution network operators charge household consumers with a network tariff, so they can recover their network investment and operational costs. With the transition; towards a sustainable energy system, the household load is changing, through the introduction of photovoltaics and electric vehicles. The tariff structures which are currently employed in the EU are either capacity and/or energy consumption based. In light of the changes in the household load the question whether these tariff structures are the most suitable merits renewed attention. In this work, the cost-reflectivity of various tariff structures has been computed based on a distribution network planning approach. Next to this, the predictability of a network tariff, i.e. how much change would a household experience in network charges in two consecutive years has also been computed to gain insight into how well users will be able to react to the tariff. The results show that a peak load based network tariffs score best on the reflectivity while having an acceptable level of predictability. The switch from an energy consumption based network tariff, which is now most often applied, towards a peak load based network tariff should therefore, be considered.
•Network utilisation assessment based characterisation of household load.•Analysis of the reflectivity and predictability of 5 network tariff structures.•Energy usage based network tariffs can be predicted most accurately.•Peak load charges remain most reflective in light of the energy transition. |
doi_str_mv | 10.1016/j.enpol.2017.07.049 |
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•Network utilisation assessment based characterisation of household load.•Analysis of the reflectivity and predictability of 5 network tariff structures.•Energy usage based network tariffs can be predicted most accurately.•Peak load charges remain most reflective in light of the energy transition.</description><identifier>ISSN: 0301-4215</identifier><identifier>EISSN: 1873-6777</identifier><identifier>DOI: 10.1016/j.enpol.2017.07.049</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Alternative fuel vehicles ; Capacity tariffs ; Charge distribution ; Computation ; Consumers ; Cost-reflectivity ; Distribution management ; Distribution network economics ; Distribution network tariff ; Electric vehicles ; Electricity ; Electricity consumption ; Energy consumption ; Energy policy ; Households ; Investments ; Network analysis ; Networks ; Operating costs ; Operators ; Peak load ; Photovoltaic cells ; Photovoltaics ; Planning ; Reflectance ; Solar cells ; Sustainable energy ; Tariffs</subject><ispartof>Energy policy, 2017-10, Vol.109, p.631-641</ispartof><rights>2017 The Author(s)</rights><rights>Copyright Elsevier Science Ltd. Oct 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c467t-b3c56b2feffdbe64d4a14cfdd0d0a9b5583505e838ad097c99228973789095963</citedby><cites>FETCH-LOGICAL-c467t-b3c56b2feffdbe64d4a14cfdd0d0a9b5583505e838ad097c99228973789095963</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.enpol.2017.07.049$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27866,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Nijhuis, M.</creatorcontrib><creatorcontrib>Gibescu, M.</creatorcontrib><creatorcontrib>Cobben, J.F.G.</creatorcontrib><title>Analysis of reflectivity & predictability of electricity network tariff structures for household consumers</title><title>Energy policy</title><description>Distribution network operators charge household consumers with a network tariff, so they can recover their network investment and operational costs. With the transition; towards a sustainable energy system, the household load is changing, through the introduction of photovoltaics and electric vehicles. The tariff structures which are currently employed in the EU are either capacity and/or energy consumption based. In light of the changes in the household load the question whether these tariff structures are the most suitable merits renewed attention. In this work, the cost-reflectivity of various tariff structures has been computed based on a distribution network planning approach. Next to this, the predictability of a network tariff, i.e. how much change would a household experience in network charges in two consecutive years has also been computed to gain insight into how well users will be able to react to the tariff. The results show that a peak load based network tariffs score best on the reflectivity while having an acceptable level of predictability. The switch from an energy consumption based network tariff, which is now most often applied, towards a peak load based network tariff should therefore, be considered.
•Network utilisation assessment based characterisation of household load.•Analysis of the reflectivity and predictability of 5 network tariff structures.•Energy usage based network tariffs can be predicted most accurately.•Peak load charges remain most reflective in light of the energy transition.</description><subject>Alternative fuel vehicles</subject><subject>Capacity tariffs</subject><subject>Charge distribution</subject><subject>Computation</subject><subject>Consumers</subject><subject>Cost-reflectivity</subject><subject>Distribution management</subject><subject>Distribution network economics</subject><subject>Distribution network tariff</subject><subject>Electric vehicles</subject><subject>Electricity</subject><subject>Electricity consumption</subject><subject>Energy consumption</subject><subject>Energy policy</subject><subject>Households</subject><subject>Investments</subject><subject>Network analysis</subject><subject>Networks</subject><subject>Operating costs</subject><subject>Operators</subject><subject>Peak load</subject><subject>Photovoltaic cells</subject><subject>Photovoltaics</subject><subject>Planning</subject><subject>Reflectance</subject><subject>Solar cells</subject><subject>Sustainable energy</subject><subject>Tariffs</subject><issn>0301-4215</issn><issn>1873-6777</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>7TQ</sourceid><recordid>eNp9kEFr3DAQhUVpoNs0vyAXQaA3OyNLtqxDDyE0TWChl_YsbGnEynGsrSSn7L-P3O05MDDMzHsD7yPkmkHNgHW3U43LMcx1A0zWUEqoD2THesmrTkr5keyAA6tEw9pP5HNKEwCIXokdme6WYT4ln2hwNKKb0WT_6vOJfqXHiNabPIx-3hZFgNs5erONC-a_IT7TPETvHE05riavERN1IdJDWBMewmypCUtaXzCmL-TCDXPCq__9kvx--P7r_rHa__zxdH-3r4zoZK5GbtpubBw6Z0fshBUDE8ZZCxYGNbZtz1tosef9YEFJo1TT9Epy2StQrer4Jbk5_z3G8GfFlPUU1lhiJs3KWXDom6ao-FllYkipJNfH6F-GeNIM9AZVT_ofVL1B1VBKqOL6dnZhCfDqMepkPC6mgIoFjbbBv-t_AzJPg7c</recordid><startdate>20171001</startdate><enddate>20171001</enddate><creator>Nijhuis, M.</creator><creator>Gibescu, M.</creator><creator>Cobben, J.F.G.</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TA</scope><scope>7TB</scope><scope>7TQ</scope><scope>8BJ</scope><scope>8FD</scope><scope>DHY</scope><scope>DON</scope><scope>F28</scope><scope>FQK</scope><scope>FR3</scope><scope>H8D</scope><scope>JBE</scope><scope>JG9</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20171001</creationdate><title>Analysis of reflectivity & predictability of electricity network tariff structures for household consumers</title><author>Nijhuis, M. ; Gibescu, M. ; Cobben, J.F.G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c467t-b3c56b2feffdbe64d4a14cfdd0d0a9b5583505e838ad097c99228973789095963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Alternative fuel vehicles</topic><topic>Capacity tariffs</topic><topic>Charge distribution</topic><topic>Computation</topic><topic>Consumers</topic><topic>Cost-reflectivity</topic><topic>Distribution management</topic><topic>Distribution network economics</topic><topic>Distribution network tariff</topic><topic>Electric vehicles</topic><topic>Electricity</topic><topic>Electricity consumption</topic><topic>Energy consumption</topic><topic>Energy policy</topic><topic>Households</topic><topic>Investments</topic><topic>Network analysis</topic><topic>Networks</topic><topic>Operating costs</topic><topic>Operators</topic><topic>Peak load</topic><topic>Photovoltaic cells</topic><topic>Photovoltaics</topic><topic>Planning</topic><topic>Reflectance</topic><topic>Solar cells</topic><topic>Sustainable energy</topic><topic>Tariffs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nijhuis, M.</creatorcontrib><creatorcontrib>Gibescu, M.</creatorcontrib><creatorcontrib>Cobben, J.F.G.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>PAIS Index</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>PAIS International</collection><collection>PAIS International (Ovid)</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>International Bibliography of the Social Sciences</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Energy policy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nijhuis, M.</au><au>Gibescu, M.</au><au>Cobben, J.F.G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of reflectivity & predictability of electricity network tariff structures for household consumers</atitle><jtitle>Energy policy</jtitle><date>2017-10-01</date><risdate>2017</risdate><volume>109</volume><spage>631</spage><epage>641</epage><pages>631-641</pages><issn>0301-4215</issn><eissn>1873-6777</eissn><abstract>Distribution network operators charge household consumers with a network tariff, so they can recover their network investment and operational costs. With the transition; towards a sustainable energy system, the household load is changing, through the introduction of photovoltaics and electric vehicles. The tariff structures which are currently employed in the EU are either capacity and/or energy consumption based. In light of the changes in the household load the question whether these tariff structures are the most suitable merits renewed attention. In this work, the cost-reflectivity of various tariff structures has been computed based on a distribution network planning approach. Next to this, the predictability of a network tariff, i.e. how much change would a household experience in network charges in two consecutive years has also been computed to gain insight into how well users will be able to react to the tariff. The results show that a peak load based network tariffs score best on the reflectivity while having an acceptable level of predictability. The switch from an energy consumption based network tariff, which is now most often applied, towards a peak load based network tariff should therefore, be considered.
•Network utilisation assessment based characterisation of household load.•Analysis of the reflectivity and predictability of 5 network tariff structures.•Energy usage based network tariffs can be predicted most accurately.•Peak load charges remain most reflective in light of the energy transition.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.enpol.2017.07.049</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Alternative fuel vehicles Capacity tariffs Charge distribution Computation Consumers Cost-reflectivity Distribution management Distribution network economics Distribution network tariff Electric vehicles Electricity Electricity consumption Energy consumption Energy policy Households Investments Network analysis Networks Operating costs Operators Peak load Photovoltaic cells Photovoltaics Planning Reflectance Solar cells Sustainable energy Tariffs |
title | Analysis of reflectivity & predictability of electricity network tariff structures for household consumers |
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