Mid Sweden University

miun.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Impact of the Measurement Time Resolution on Energy Key Performance Indicators for Distributed Energy Resources: An Experimental Analysis
Show others and affiliations
2021 (English)In: AMPS 2021 - 2021 11th IEEE International Workshop on Applied Measurements for Power Systems, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2021Conference paper, Published paper (Refereed)
Abstract [en]

The growing presence of Renewable Energy Sources (RESs) is heavily affecting the control and management of distribution grids. Battery Energy Storage Systems (BESSs) are increasingly adopted to mitigate the adverse impacts caused by the uncertainty of RES generators. Most of the existing works in the literature aims at using BESSs to maximize the self-consumption of energy provided by RES generators or focuses on the mitigation of voltage and/or frequency drift effects. More recently, some research works also studied the effects that BESSs have on the active power flows at the Point of Common Coupling (PCC) of prosumers. All these studies are based on the definition of proper energy Key Performance Indicators (KPIs) based on heterogeneous information collected from several measurement devices deployed in the field. This also means that the results of such studies could be significantly affected by the quality of data, particularly concerning the time resolution of the measurements. To better highlight this concern, this paper proposes an analysis of the impact of the time resolution of measurements on the evaluation of the absolute power ramp reduction at the PCC of prosumers in presence of BESS installations. The analysis was performed on real data collected from the eLUX lab of the University of Brescia, Italy, by varying the time resolution from 5 s to 15 min. The results of the analysis demonstrated that the time resolution has a great impact on the evaluation of the considered KPI, by leading to completely different performance assessments. © 2021 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021.
Keywords [en]
Battery Energy Storage Systems, Distribution Grid, Energy Key Performance Indicators, Renewable Energy Sources, Smart Grid, Time resolution, Battery storage, Benchmarking, Digital storage, Electric batteries, Electric fault currents, Electric power transmission networks, Natural resources, Smart power grids, Energy, Energy key performance indicator, Key performance indicators, Measurement time, Point of common coupling, Renewable energy source, Time-resolution, Renewable energy resources
Identifiers
URN: urn:nbn:se:miun:diva-43834DOI: 10.1109/AMPS50177.2021.9586020Scopus ID: 2-s2.0-85119077305ISBN: 9781728169231 (print)OAI: oai:DiVA.org:miun-43834DiVA, id: diva2:1613987
Conference
11th IEEE International Workshop on Applied Measurements for Power Systems, AMPS 2021, 29 September 2021 through 1 October 2021
Note

Conference code: 173323; Export Date: 24 November 2021; Conference Paper; Funding details: Università degli Studi di Brescia; Funding details: Regione Lombardia, POR FESR 2014-2020; Funding text 1: This research activity has been partially funded by University of Brescia as part of the joint research activities of the laboratories “energy Laboratory as University eXpo - eLUX” and by Region Lombardia under POR FESR 2014-2020 Hub Ricerca e Innovazione grant (“Infrastructure and Services for a Sustainable and Resilient Mobility–MoSoRe@UniBS”).; References: Howell, S., Rezgui, Y., Hippolyte, J.L., Jayan, B., Li, H., Towards the next generation of smart grids: Semantic and holonic multi-Agent management of distributed energy resources (2017) Renewable and Sustainable Energy Reviews, 77 (2017), pp. 193-214. , September; Pasetti, M., Ferrari, P., Silva, D.R.C., Silva, I., Sisinni, E., On the use of lorawan for the monitoring and control of distributed energy resources in a smart Campus (2020) Applied Sciences, 10 (320), pp. 1-27; Sharma, V., Aziz, S.M., Haque, M.H., Kauschke, T., Effects of high solar photovoltaic penetration on distribution feeders and the economic impact (2020) Renewable and Sustainable Energy Reviews, 131. , June 110021; Figgener, J., Haberschusz, D., Kaires, K.-P., Wessels, O., Zurmöhlen, S., Sauer, D.U., (2019) Scientific Monitoring and Evaluation Program within the National Market Introduction Programm for PV-Home-Storage Systems. Wissenschaftliches Mess-und Evaluierungsprogramm Solarstromspeicher 2.0. Speichermonitoring BW-Jahresbericht 2019, , Aachen, Germany; Namor, E., Sossan, F., Cherkaoui, R., Paolone, M., Control of battery storage systems for the simultaneous provision of multiple services (2019) IEEE Transactions on Smart Grid, 10 (3), pp. 2799-2808; Kucevic, D., Standard battery energy storage system profiles: Analysis of various applications for stationary energy storage systems using a holistic simulation framework (2019) Journal of Energy Storage, 28 (2020), p. 101077. , November; Macmackin, N., Miller, L., Carriveau, R., Investigating distribution systems impacts with clustered technology penetration and customer load patterns (2020) International Journal of Electrical Power & Energy Systems, 128 (2021), p. 106758. , December; Marchi, B., Zanoni, S., Pasetti, M., A techno-economic analysis of Li-ion battery energy storage systems in support of PV distributed generation (2016) 21st Summer School F. Turco of Industrial Systems Engineering, pp. 145-149. , Sep; Liu, S., Liu, P.X., Wang, X., Wang, Z., Meng, W., Effects of correlated photovoltaic power and load uncertainties on grid-connected microgrid day-Ahead scheduling (2017) IET Generation, Transmission and Distribution, 11 (14), pp. 3620-3627; Della Giustina, D., Rinaldi, S., Robustelli, S., Angioni, A., Massive generation of customer load profiles for large scale state estimation deployment: An Approach to Exploit AMI Limited Data (2021) Energies, 14 (5), p. 1277; Dedé, A., Della Giustina, D., Rinaldi, S., Ferrari, P., Flammini, A., Vezzoli, A., (2015) Smart Meters As Part of A Sensor Network for Monitoring the Low Voltage Grid, , Apr; Castello, P., Muscas, C., Pegoraro, P.A., Sulis, S., Low-cost energy meter with power quality functionalities (2020) 24th IMEKO TC4 International Symposium and 22nd International Workshop on ADC and DAC Modelling and Testing (IMEKO TC-4 2020), pp. 297-302. , Sep; Nakutis, Z., Rinaldi, S., Kuzas, P., Lukocius, R., A method for noninvasive remote monitoring of energy meter error using power consumption profile (2020) IEEE Transactions on Instrumentation and Measurement, 69 (9), pp. 6677-6685; Artale, G., Pq and harmonic assessment issues on low-cost smart metering platforms: A case study (2020) Sensors, 20 (21), pp. 1-27. , Switzerland; Pasetti, M., Assessing the effectiveness of the energy storage rule-based control in reducing the power flow Uncertainties Caused by Distributed Photovoltaic Systems (2021) Energies, 14 (8), p. 2312; Papadopoulos, V., Knockaert, J., Develder, C., Desmet, J., Investigating the need for real time measurements in industrial wind power systems combined with battery storage (2019) Applied Energy, 247, pp. 559-571. , no. January; Weitzel, T., Glock, C.H., Energy management for stationary electric energy storage systems: A systematic literature review (2018) European Journal of Operational Research, 264 (2), pp. 582-606; Baumann, M., Weil, M., Peters, J.F., Chibeles-Martins, N., Moniz, A.B., A review of multi-criteria decision making approaches for evaluating energy storage systems for grid applications (2019) Renewable and Sustainable Energy Reviews, 107, pp. 516-534. , January; Bartecka, M., Barchi, G., Paska, J., Time-series PV hosting capacity assessment with storage deployment (2020) Energies, 13 (10), pp. 1-20; Sharma, R., Karimi-Ghartemani, M., Addressing abrupt PV disturbances, and mitigating net load profile?s ramp and peak demands, using distributed storage devices (2020) Energies, 13 (5), pp. 8-10; Ayodele, T.R., Ogunjuyigbe, A.S.O., Akpeji, K.O., Akinola, O.O., Prioritized rule based load management technique for residential building powered by PV/battery system (2017) Engineering Science and Technology, An International Journal, 20 (3), pp. 859-873; Jankowiak, C., Zacharopoulos, A., Brandoni, C., Keatley, P., Macartain, P., Hewitt, N., Assessing the benefits of decentralised residential batteries for load peak shaving (2020) Journal of Energy Storage, 32, p. 101779; Pasetti, M., Rinaldi, S., Manerba, D., A virtual power plant architecture for the demand-side management of smart prosumers (2018) Applied Sciences, 8 (3), p. 432

Available from: 2021-11-24 Created: 2021-11-24 Last updated: 2021-11-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Sisinni, E.

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 35 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf