Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties
Facing a significant increase in connected distributed energy systems, the optimal coordination strategy among multiple distributed energy systems is vitally important to explore. In this paper, an energy management system is introduced and operated by an energy service company, which is responsible...
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Veröffentlicht in: | Energy (Oxford) 2021-07, Vol.227, p.120460, Article 120460 |
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description | Facing a significant increase in connected distributed energy systems, the optimal coordination strategy among multiple distributed energy systems is vitally important to explore. In this paper, an energy management system is introduced and operated by an energy service company, which is responsible for managing the interaction of multiple distributed energy systems. To optimize the day-ahead scheduling of the distributed energy systems, a coordination scheme with a bilevel framework is proposed. The energy interaction between the energy management system and distributed energy systems contains electricity and heat, which is a Stackelberg problem. Two types of internal price schemes, namely, real-time pricing and time-of-use pricing, are discussed. Moreover, the uncertainties of renewable energy resources, energy demand, and energy prices are considered within both upper- and lower-level problems. The problem is formulated as a nonlinear bilevel robust optimization model and transformed into a single-level mixed-integer linear problem. Numerical cases illustrate how the energy management system coordinates with distributed energy systems and show the effectiveness of the coordination strategy such that all participators benefit from the proposed strategy and create a win-win situation. The model and results can serve as references for the business managers of companies that provide energy services for building clusters.
•The coordination strategy for multiple distributed energy systems is optimized.•A robust bilevel programming model is proposed under a Stackelberg framework.•Real-time pricing and time-of-use pricing schemes for coordination are discussed.•The renewable energy, energy demand, and energy price uncertainties are considered. |
doi_str_mv | 10.1016/j.energy.2021.120460 |
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•The coordination strategy for multiple distributed energy systems is optimized.•A robust bilevel programming model is proposed under a Stackelberg framework.•Real-time pricing and time-of-use pricing schemes for coordination are discussed.•The renewable energy, energy demand, and energy price uncertainties are considered.</description><identifier>ISSN: 0360-5442</identifier><identifier>EISSN: 1873-6785</identifier><identifier>DOI: 10.1016/j.energy.2021.120460</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Coordination ; Coordination strategy ; Distributed energy systems ; Distributed generation ; Electric power distribution ; electricity ; energy ; Energy demand ; Energy management ; Energy management system ; Energy resources ; Energy sources ; Energy trading prices ; heat ; management systems ; Mixed integer ; Optimization ; prices ; Renewable energy ; renewable energy sources ; Robust bilevel programming ; Robustness (mathematics) ; System effectiveness ; Time of use electricity pricing ; Uncertainty</subject><ispartof>Energy (Oxford), 2021-07, Vol.227, p.120460, Article 120460</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright Elsevier BV Jul 15, 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-7e4f5eebd60dc43f32a5d2918e48934afe421202d42a94374a80d71efcbb12803</citedby><cites>FETCH-LOGICAL-c367t-7e4f5eebd60dc43f32a5d2918e48934afe421202d42a94374a80d71efcbb12803</cites><orcidid>0000-0001-7541-7570</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.energy.2021.120460$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids></links><search><creatorcontrib>Li, Longxi</creatorcontrib><creatorcontrib>Cao, Xilin</creatorcontrib><creatorcontrib>Wang, Peng</creatorcontrib><title>Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties</title><title>Energy (Oxford)</title><description>Facing a significant increase in connected distributed energy systems, the optimal coordination strategy among multiple distributed energy systems is vitally important to explore. In this paper, an energy management system is introduced and operated by an energy service company, which is responsible for managing the interaction of multiple distributed energy systems. To optimize the day-ahead scheduling of the distributed energy systems, a coordination scheme with a bilevel framework is proposed. The energy interaction between the energy management system and distributed energy systems contains electricity and heat, which is a Stackelberg problem. Two types of internal price schemes, namely, real-time pricing and time-of-use pricing, are discussed. Moreover, the uncertainties of renewable energy resources, energy demand, and energy prices are considered within both upper- and lower-level problems. The problem is formulated as a nonlinear bilevel robust optimization model and transformed into a single-level mixed-integer linear problem. Numerical cases illustrate how the energy management system coordinates with distributed energy systems and show the effectiveness of the coordination strategy such that all participators benefit from the proposed strategy and create a win-win situation. The model and results can serve as references for the business managers of companies that provide energy services for building clusters.
•The coordination strategy for multiple distributed energy systems is optimized.•A robust bilevel programming model is proposed under a Stackelberg framework.•Real-time pricing and time-of-use pricing schemes for coordination are discussed.•The renewable energy, energy demand, and energy price uncertainties are considered.</description><subject>Coordination</subject><subject>Coordination strategy</subject><subject>Distributed energy systems</subject><subject>Distributed generation</subject><subject>Electric power distribution</subject><subject>electricity</subject><subject>energy</subject><subject>Energy demand</subject><subject>Energy management</subject><subject>Energy management system</subject><subject>Energy resources</subject><subject>Energy sources</subject><subject>Energy trading prices</subject><subject>heat</subject><subject>management systems</subject><subject>Mixed integer</subject><subject>Optimization</subject><subject>prices</subject><subject>Renewable energy</subject><subject>renewable energy sources</subject><subject>Robust bilevel programming</subject><subject>Robustness (mathematics)</subject><subject>System effectiveness</subject><subject>Time of use electricity pricing</subject><subject>Uncertainty</subject><issn>0360-5442</issn><issn>1873-6785</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kctKBDEQRYMoOI7-gYuAGxf2mFe_NoIMvkBwo-uQSaqHDN1Jm6SF_nsj7cqFmxSEc6vq1kXokpINJbS6PWzAQdjPG0YY3VBGREWO0Io2NS-quimP0YrwihSlEOwUncV4IISUTduu0Pw2JjuoHmvvg7FOJesdjimoBPsZdz7gYeqTHXvAxuZ_u5sSGLwMxHGOCYaY1S5aA8G6PY7TOPbzDTYwKGducH7wGKwGPDkNISnrkoV4jk461Ue4-K1r9PH48L59Ll7fnl6296-F5lWdihpEVwLsTEWMFrzjTJWGtbQB0bRcqA4Ey46ZEUy1gtdCNcTUFDq921HWEL5G10vfMfjPCWKSg40a-l458FOUrKxoybioWUav_qAHPwWXt8uUIKVoayYyJRZKBx9jgE5md4MKs6RE_uQhD3I5j_zJQy55ZNndIoNs9stCkFFbyBcxNoBO0nj7f4NvzxWYAw</recordid><startdate>20210715</startdate><enddate>20210715</enddate><creator>Li, Longxi</creator><creator>Cao, Xilin</creator><creator>Wang, Peng</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0001-7541-7570</orcidid></search><sort><creationdate>20210715</creationdate><title>Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties</title><author>Li, Longxi ; Cao, Xilin ; Wang, Peng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-7e4f5eebd60dc43f32a5d2918e48934afe421202d42a94374a80d71efcbb12803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Coordination</topic><topic>Coordination strategy</topic><topic>Distributed energy systems</topic><topic>Distributed generation</topic><topic>Electric power distribution</topic><topic>electricity</topic><topic>energy</topic><topic>Energy demand</topic><topic>Energy management</topic><topic>Energy management system</topic><topic>Energy resources</topic><topic>Energy sources</topic><topic>Energy trading prices</topic><topic>heat</topic><topic>management systems</topic><topic>Mixed integer</topic><topic>Optimization</topic><topic>prices</topic><topic>Renewable energy</topic><topic>renewable energy sources</topic><topic>Robust bilevel programming</topic><topic>Robustness (mathematics)</topic><topic>System effectiveness</topic><topic>Time of use electricity pricing</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Longxi</creatorcontrib><creatorcontrib>Cao, Xilin</creatorcontrib><creatorcontrib>Wang, Peng</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Longxi</au><au>Cao, Xilin</au><au>Wang, Peng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties</atitle><jtitle>Energy (Oxford)</jtitle><date>2021-07-15</date><risdate>2021</risdate><volume>227</volume><spage>120460</spage><pages>120460-</pages><artnum>120460</artnum><issn>0360-5442</issn><eissn>1873-6785</eissn><abstract>Facing a significant increase in connected distributed energy systems, the optimal coordination strategy among multiple distributed energy systems is vitally important to explore. In this paper, an energy management system is introduced and operated by an energy service company, which is responsible for managing the interaction of multiple distributed energy systems. To optimize the day-ahead scheduling of the distributed energy systems, a coordination scheme with a bilevel framework is proposed. The energy interaction between the energy management system and distributed energy systems contains electricity and heat, which is a Stackelberg problem. Two types of internal price schemes, namely, real-time pricing and time-of-use pricing, are discussed. Moreover, the uncertainties of renewable energy resources, energy demand, and energy prices are considered within both upper- and lower-level problems. The problem is formulated as a nonlinear bilevel robust optimization model and transformed into a single-level mixed-integer linear problem. Numerical cases illustrate how the energy management system coordinates with distributed energy systems and show the effectiveness of the coordination strategy such that all participators benefit from the proposed strategy and create a win-win situation. The model and results can serve as references for the business managers of companies that provide energy services for building clusters.
•The coordination strategy for multiple distributed energy systems is optimized.•A robust bilevel programming model is proposed under a Stackelberg framework.•Real-time pricing and time-of-use pricing schemes for coordination are discussed.•The renewable energy, energy demand, and energy price uncertainties are considered.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2021.120460</doi><orcidid>https://orcid.org/0000-0001-7541-7570</orcidid></addata></record> |
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subjects | Coordination Coordination strategy Distributed energy systems Distributed generation Electric power distribution electricity energy Energy demand Energy management Energy management system Energy resources Energy sources Energy trading prices heat management systems Mixed integer Optimization prices Renewable energy renewable energy sources Robust bilevel programming Robustness (mathematics) System effectiveness Time of use electricity pricing Uncertainty |
title | Optimal coordination strategy for multiple distributed energy systems considering supply, demand, and price uncertainties |
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