Intelligent Energy Management of Electric Vehicles in Distribution Systems
Author
Palomar Lozano, Alberto
Term
4. term
Education
Publication year
2012
Submitted on
2012-12-31
Pages
76
Abstract
Denne kandidatafhandling udvikler en algoritme, der planlægger elbilers opladning og afladning optimalt, og kontrollerer derefter resultaterne i et elektrisk distributionsnet. Først etableres en reference uden elbiler for at vurdere de normale omkostninger og belastninger i nettet. Derefter indgår elbilerne, og de samlede systemomkostninger minimeres efter økonomiske kriterier. Begge algoritmer er implementeret i GAMS (optimeringssoftware). Tre cases undersøges, og timepriser fra tre forskellige dage anvendes i simuleringerne. Metoden giver 24-timers planer for opladning og afladning, der overholder elbilernes tekniske begrænsninger. Planerne valideres i DIgSILENT PowerFactory (et elsystems-simuleringsværktøj) ved at simulere fire forskellige fordelinger af elbiler i nettet for at kontrollere de tekniske begrænsninger. En økonomisk analyse vurderer, om koordineret energistyring og vehicle-to-grid (V2G, hvor elbiler kan levere strøm tilbage til nettet) kan sænke de samlede systemomkostninger.
This master's thesis develops an algorithm to optimally schedule when electric vehicles (EVs) charge and discharge, and then checks the results in an electricity distribution network. First, a baseline without EVs is used to estimate typical costs and loading in the network. Then, EVs are included and the total system cost is minimized under economic criteria. Both algorithms are implemented in GAMS (optimization software). Three scenarios are studied, using hourly electricity prices from three different days. The approach produces 24-hour charging and discharging plans that satisfy the EVs' technical limits. These plans are validated in DIgSILENT PowerFactory (a power system simulation tool) by simulating four different ways of distributing EVs across the network to check technical constraints. An economic analysis examines whether coordinated energy management and vehicle-to-grid (V2G, where EVs can feed power back to the grid) can lower overall system cost.
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