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Conference Paper: PV- Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer with Distributed PV System
Title | PV- Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer with Distributed PV System |
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Authors | |
Keywords | Customer baseline load distributed PV systems demand response net load PV-Load decoupling |
Issue Date | 2019 |
Publisher | IEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome/1002994/all-proceedings |
Citation | Proceedings of 2019 IEEE Industry Applications Society Annual Meeting, Baltimore, MD, USA, 29 September-3 October 2019, p. 1-8 How to Cite? |
Abstract | Due to increasing installation of distributed photovoltaic systems (DPVSs), load patterns of residential customers become more random, which makes customer baseline load (CBL) estimation harder. This paper proposes a PV-load decoupling approach to improve the CBL estimation accuracy in the presence of DPVSs. Firstly, K-means algorithm is used to divide the customers in control group into k clusters. Secondly, after calculating curve similarity index, each DR participant is matched to the most similar cluster based on the similarity between its load curve and cluster centroids during periods when the distributed photovoltaic (DPV) output power is equal to zero. Then the DPV output power in DR period can be obtained through the estimation model established based on DPV output power of non-DR periods in historical non-DR days and DR event day. Finally, CBL is estimated by the difference between actual load power and DPV output power. Four well-known averaging methods are compared with the proposed approach by using a real dataset of 300 customers in Sydney, Australia. The comparison result indicates the proposed approach shows better accuracy performance. |
Persistent Identifier | http://hdl.handle.net/10722/289414 |
ISSN | 2023 SCImago Journal Rankings: 0.422 |
DC Field | Value | Language |
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dc.contributor.author | Wang, F | - |
dc.contributor.author | Gao, X | - |
dc.contributor.author | Li, K | - |
dc.contributor.author | Ge, X | - |
dc.contributor.author | Hou, Y | - |
dc.date.accessioned | 2020-10-22T08:12:19Z | - |
dc.date.available | 2020-10-22T08:12:19Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of 2019 IEEE Industry Applications Society Annual Meeting, Baltimore, MD, USA, 29 September-3 October 2019, p. 1-8 | - |
dc.identifier.issn | 0197-2618 | - |
dc.identifier.uri | http://hdl.handle.net/10722/289414 | - |
dc.description.abstract | Due to increasing installation of distributed photovoltaic systems (DPVSs), load patterns of residential customers become more random, which makes customer baseline load (CBL) estimation harder. This paper proposes a PV-load decoupling approach to improve the CBL estimation accuracy in the presence of DPVSs. Firstly, K-means algorithm is used to divide the customers in control group into k clusters. Secondly, after calculating curve similarity index, each DR participant is matched to the most similar cluster based on the similarity between its load curve and cluster centroids during periods when the distributed photovoltaic (DPV) output power is equal to zero. Then the DPV output power in DR period can be obtained through the estimation model established based on DPV output power of non-DR periods in historical non-DR days and DR event day. Finally, CBL is estimated by the difference between actual load power and DPV output power. Four well-known averaging methods are compared with the proposed approach by using a real dataset of 300 customers in Sydney, Australia. The comparison result indicates the proposed approach shows better accuracy performance. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome/1002994/all-proceedings | - |
dc.relation.ispartof | 2019 IEEE Industry Applications Society Annual Meeting | - |
dc.rights | IEEE Industry Applications Society (IAS) Annual Meeting. Copyright © IEEE. | - |
dc.rights | ©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Customer baseline load | - |
dc.subject | distributed PV systems | - |
dc.subject | demand response | - |
dc.subject | net load | - |
dc.subject | PV-Load decoupling | - |
dc.title | PV- Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer with Distributed PV System | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Hou, Y: yhhou@hku.hk | - |
dc.identifier.authority | Hou, Y=rp00069 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/IAS.2019.8911969 | - |
dc.identifier.scopus | eid_2-s2.0-85076750220 | - |
dc.identifier.hkuros | 316710 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 8 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0197-2618 | - |