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- Publisher Website: 10.1109/TPDS.2016.2615936
- Scopus: eid_2-s2.0-85018160781
- WOS: WOS:000399394200016
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Article: Renewable energy pricing driven scheduling in distributed smart community systems
Title | Renewable energy pricing driven scheduling in distributed smart community systems |
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Authors | |
Keywords | cross entropy optimization pricing scheme renewable energy smart community Smart home |
Issue Date | 2017 |
Citation | IEEE Transactions on Parallel and Distributed Systems, 2017, v. 28, n. 5, p. 1445-1456 How to Cite? |
Abstract | A smart community is a distributed system consisting of a set of smart homes which utilize the smart home scheduling techniques to enable customers to automatically schedule their energy loads targeting various purposes such as electricity bill reduction. Smart home scheduling is usually implemented in a decentralized fashion inside a smart community, where customers compete for the community level renewable energy due to their relatively low prices. Typically there exists an aggregator as a community wide electricity policy maker aiming to minimize the total electricity bill among all customers. This paper develops a new renewable energy aware pricing scheme to achieve this target. We establish the proof that under certain assumptions the optimal solution of decentralized smart home scheduling is equivalent to that of the centralized technique, reaching the theoretical lower bound of the community wide total electricity bill. In addition, an advanced cross entropy optimization technique is proposed to compute the pricing scheme of renewable energy, which is then integrated in smart home scheduling. The simulation results demonstrate that our pricing scheme facilitates the reduction of both the community wide electricity bill and individual electricity bills compared to the uniform pricing. In particular, the community wide electricity bill can be reduced to only 0.06 percent above the theoretic lower bound. |
Persistent Identifier | http://hdl.handle.net/10722/336175 |
ISSN | 2023 Impact Factor: 5.6 2023 SCImago Journal Rankings: 2.340 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, Yang | - |
dc.contributor.author | Hu, Shiyan | - |
dc.date.accessioned | 2024-01-15T08:24:11Z | - |
dc.date.available | 2024-01-15T08:24:11Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Parallel and Distributed Systems, 2017, v. 28, n. 5, p. 1445-1456 | - |
dc.identifier.issn | 1045-9219 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336175 | - |
dc.description.abstract | A smart community is a distributed system consisting of a set of smart homes which utilize the smart home scheduling techniques to enable customers to automatically schedule their energy loads targeting various purposes such as electricity bill reduction. Smart home scheduling is usually implemented in a decentralized fashion inside a smart community, where customers compete for the community level renewable energy due to their relatively low prices. Typically there exists an aggregator as a community wide electricity policy maker aiming to minimize the total electricity bill among all customers. This paper develops a new renewable energy aware pricing scheme to achieve this target. We establish the proof that under certain assumptions the optimal solution of decentralized smart home scheduling is equivalent to that of the centralized technique, reaching the theoretical lower bound of the community wide total electricity bill. In addition, an advanced cross entropy optimization technique is proposed to compute the pricing scheme of renewable energy, which is then integrated in smart home scheduling. The simulation results demonstrate that our pricing scheme facilitates the reduction of both the community wide electricity bill and individual electricity bills compared to the uniform pricing. In particular, the community wide electricity bill can be reduced to only 0.06 percent above the theoretic lower bound. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Parallel and Distributed Systems | - |
dc.subject | cross entropy optimization | - |
dc.subject | pricing scheme | - |
dc.subject | renewable energy | - |
dc.subject | smart community | - |
dc.subject | Smart home | - |
dc.title | Renewable energy pricing driven scheduling in distributed smart community systems | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TPDS.2016.2615936 | - |
dc.identifier.scopus | eid_2-s2.0-85018160781 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 1445 | - |
dc.identifier.epage | 1456 | - |
dc.identifier.isi | WOS:000399394200016 | - |