prikaz prve stranice dokumenta Stochastic model predictive control of a set of electric vehicle charging stations for demand response service provision
  Download
PDF 5.22 MB
doctoral thesis
Stochastic model predictive control of a set of electric vehicle charging stations for demand response service provision
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:872422
Kovačević, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Kovačević, M. (2023). Stochastic model predictive control of a set of electric vehicle charging stations for demand response service provision (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:872422

Kovačević, Marko. "Stochastic model predictive control of a set of electric vehicle charging stations for demand response service provision." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:872422

Kovačević, Marko. "Stochastic model predictive control of a set of electric vehicle charging stations for demand response service provision." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:872422

Kovačević, M. (2023). 'Stochastic model predictive control of a set of electric vehicle charging stations for demand response service provision', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 02 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:872422

Kovačević M. Stochastic model predictive control of a set of electric vehicle charging stations for demand response service provision [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 02] Available at: https://urn.nsk.hr/urn:nbn:hr:168:872422

M. Kovačević, "Stochastic model predictive control of a set of electric vehicle charging stations for demand response service provision", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:872422

Metadata
TitleStochastic model predictive control of a set of electric vehicle charging stations for demand response service provision
Title (english)Stohastičko modelsko prediktivno upravljanje skupom punionica električnih vozila za pružanje usluge odziva potrošnje
AuthorMarko Kovačević
MentorMario Vašak (mentor)
Committee memberMato Baotić (predsjednik povjerenstva)
Committee memberTomislav Capuder (član povjerenstva)
Committee memberJoško Deur (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Universal decimal classification
(UDC)		621.3 - Electrical engineering
Abstract
Charging electric vehicles (EVs), whose number is increasing, is a great challenge for the power grid due to the charging load variability. Coordinated charging and schedule optimisation are well-known conceptual solutions to that. Still, there are two challenges for the implementation. The first is to adequately predict availability and parameters of electric vehicles which is crucial for determining the charging schedule and the demand response potential. The second challenge is charging schedule optimisation that addresses the uncertainty ensuring the feasibility and economic benefits. This thesis proposes a method to represent a population of electric vehicles that on the one hand enables prediction via machine learning and on the other it enables an accurate optimisation of the charging schedule and demand response ability. The method essence is to use five discrete-time signals spanned over a prediction horizon period which are related to envelopes of feasible charging power and charging states for the EV population on that horizon. It enables to pose and solve the optimisation problem of charging scheduling with included demand response for a predicted population. The aggregated representation is foundation of the proposed charging scheduling framework that includes day-ahead flexibility analysis, short-term forecasting of EV arrivals and finally stochastic model predictive control of EV charging. Day-ahead flexibility analysis based on chance-constraints enables the aggregator to influence the certainty of fulfilling contracted flexibility.
Abstract (english)
Punjenje električnih vozila (EV), čiji je broj u porastu, veliki je izazov za elektroenergetsku mrežu zbog varijabilnosti opterećenja punjenja. Koordinirano punjenje i optimizacija rasporeda dobro su poznata konceptualna rješenja za to. Ipak, postoje dva izazova za provedbu. Prvi je adekvatno predviđanje dostupnosti i parametara električnih vozila što je ključno za određivanje rasporeda punjenja i potencijala odgovora potražnje. Drugi izazov je optimizacija rasporeda punjenja koja se uzima u obzir neizvjesnost osiguravajući izvedivost i ekonomsku korist. U radu se predlaže metoda za prikaz populacije električnih vozila koja s jedne strane omogućuje predviđanje putem strojnog učenja, a s druge strane omogućuje točnu optimizaciju rasporeda punjenja i sposobnosti odziva potražnje. Bit metode je korištenje pet diskretnih vremenskih signala duž horizonta predviđanja koji su povezani s envelopama moguće snage punjenja i stanja punjenja za populaciju električnih vozila na tom horizontu. Omogućuje se postavljanje i rješavanje problema optimizacije rasporeda punjenja s uključenim odgovorom potražnje za predviđenu populaciju. Agregirani prikaz je temelj predloženog okvira optimizacije rasporeda punjenja koji uključuje analizu fleksibilnosti dan unaprijed, kratkoročno predviđanje dolazaka električnih vozila i konačno stohastičko modelsko prediktivno upravljanje punjenja električnih vozila. Analiza fleksibilnosti dan unaprijed temeljena na šansnim ograničenjima omogućuje agregatoru da utječe na sigurnost ispunjenja ugovorene fleksibilnosti.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:872422
Promotion2024
Study programmeTitle: Doctoral study programme "Electrical Engineering and Computing"
Study programme type: university
Study level: postgraduate
Academic / professional title: doktor/doktorica znanosti, po-dručje tehničkih znanosti (doktor/doktorica znanosti, po-dručje tehničkih znanosti)
Type of resourceText
Extent83 str. ; 30 cm
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-05-07 12:01:30