Seite - 209 - in Emerging Technologies for Electric and Hybrid Vehicles
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Energies 2016,9, 594
2. Whenthe totalpowerPofagroupofhouses isaboveagiventhresholdofXwatts, theEVsare
requestedtodecrease their total charging in thenext time intervalbyΔ=P−X inawaythat
keeps the individual localpowerprofilesasflataspossible for their remainingchargingwindow.
3. When the total power P of a group of houses is below a given threshold of Y watts (e.g.,
PVproductionpeak), the EVs are requested to increase their total charging in the next time
intervalbyΔ=Y−P inawaythatkeeps the individual localpowerprofilesasflataspossible
for their remainingchargingwindow.
This approach,which is detailed below, has several important advantages. By startingwith
flatprofilesonhouse level in thefirst step, the individualpeak loads,andwith it theprobabilityof
overloadingthenetwork,decreases. Furthermore,flatteningthehouseloadincreasesself-consumption
of locallygeneratedelectricity (e.g.,PV),hasapositive impactonthevoltage,avoidsoverloadingthe
grid,anddecreases losses (as isdemonstrated inSection6).
Whenthere is still apeakconsumptionfor thegroupofhouses (e.g., in theevening), thesecond
stepof theproposedapproachcoordinates thechargingwhileavoidingnewlocalpeaks. Toquantify
these localpeaks,weuseacost function Cˆn(δ) thatexpresseshowmuchchangingthepower for the
upcominginterval fromxn,m toxn,m+δ influences theflatnessof theentirepowerprofile forhouse
n (i.e., also considering impact on the future). This functionaidsuswithfinding theEVs that can
contribute toobtaininga totalpowerdifferencewithminimal impact to the localflatness, andthus
preventingproblems in the future.Morespecifically,ourobjective is toobtaina totalpowerdifference
Δ for thegroupofhouses in thenext time interval,while retaining theflatnessof thepowerprofilesof
individualhousesasmuchaspossible. This isexpressedmathematicallyas:
Problem2.
min
δ1,...,δN f( δ)= N
∑
n=1 Cˆn(δn),
subject to g( δ)= N
∑
n=1 δn=Δ,
where f and g are introduced for reference in the later results. To ease the presentation,
maximal charging powers are not considered in this formulation. However, to solve Problem 2
with additionalmaximal chargingpower constraints, we just can solve Problem2 (without these
constraints)andfixanyviolationsusingaPeggingMethod; see [22] foradiscussionofsuchmethods.
Beforewe can solve this problem,wefirst need a formal description of Cˆn(δn). These costs
dependonthecharging level tobeattained in thecharging intervals (i.e., thefill levelZn), and the
uncontrollable loads in theother intervals. Thesubsetof intervalswherecharginguptoZ takesplace
isdenotedbyIn⊆{1,. . . ,M}, i.e.,In contains the intervalswhere pn,m−qn,m<Zn. Let In= |In|
denote thenumberof such intervals (excluding thefirst interval). Using thisnotation, thecosts for
housen canbedeterminedas functionofZn (note thatC(Z)nowreceiveda subscript to indicate
thehouse):
Cn(Zn)= ∑
m∈In Z2n+ ∑
m∈{1,...,M}\In (pn,m−qn,m)2
= InZ2n+ ∑
m∈{1,...,M}\In (pn,m−qn,m)2.
Intuitively, thismeans that the incurred costs are the costs of chargingup toZn in the active
intervalsIn (first term), andthecostsof the intervalswherenocharging takesplace (secondterm).
Wenotice that, forpracticaldata, (see thediscussion in thenextsection) In rarelychangeswithδand
209
Emerging Technologies for Electric and Hybrid Vehicles
- Titel
- Emerging Technologies for Electric and Hybrid Vehicles
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2017
- Sprache
- englisch
- Lizenz
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03897-191-7
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 376
- Schlagwörter
- electric vehicle, plug-in hybrid electric vehicle (PHEV), energy sources, energy management strategy, energy-storage system, charging technologies, control algorithms, battery, operating scenario, wireless power transfer (WPT)
- Kategorie
- Technik