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Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0002-4210-8672
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0003-3014-5609
2018 (English)In: 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 338-343, article id 8340694Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, end-users can participate in demand response (DR) programs, and even slight load reductions from many houses can add up to major load shifts in the power system. Aggregators, which act as mediators between end-users and distribution system operator (DSO), play an important role here. The aggregator contracts the end-users for DR programs, plans ahead for times when customers should shift their load, and controls the load shifts in the running time. In this paper, our main focus is on planning the end-users for load shifting. Here, we first define and formulate the flexibilities (e.g., Stamina, repetition, and capacity) related to the dynamic loads such as space heating systems (SHSs) in detached houses. Assuming some end-users being contracted for DR program, based on estimation of their house characteristics and load flexibilities, an algorithm is then proposed to plan the SHSs for load shifting. In this algorithm the states in which a flexible load can be planned, kept in backup, or unavailable are considered by the aggregator. Another algorithm has been proposed here to deal with the different sources of uncertainties (which cause some of the planned SHSs to become unavailable). Numerical results are presented at the end, which discuss performance of the proposed strategy in terms of load flexibilities, load shifts in response to DR signals, and sensitivity analysis. Here, how to estimate the houses characteristics is a difficult issue, and we approximate them based on available models in the literature.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 338-343, article id 8340694
National Category
Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-226678DOI: 10.1109/SmartGridComm.2017.8340694Scopus ID: 2-s2.0-85051029519ISBN: 9781538640555 (print)OAI: oai:DiVA.org:kth-226678DiVA, id: diva2:1201147
Conference
2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, Dresden, Germany, 23 October 2017 through 26 October 2017
Note

QC 20180515

Available from: 2018-04-24 Created: 2018-04-24 Last updated: 2018-09-05Bibliographically approved

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Paridari, KavehNordström, Lars

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