Acquilino, Alberto

Bracco, Stefano

Gabrielli, Paolo

Mazzotti, Marco

Sansavini, Giovanni

Siri, Silvia

English

Open Access Repository

Journal Pre-proofsOptimization of low-carbon multi-energy systems with seasonal geothermalenergy storage: The Anergy Grid of ETH ZurichPaolo Gabrielli, Alberto Acquilino, Silvia Siri, Stefano Bracco, GiovanniSansavini, Marco MazzottiPII: S2590-1745(20)30024-6DOI: https://doi.org/10.1016/j.ecmx.2020.100052Reference: ECMX 100052To appear in: Energy Conversion and Management: XReceived Date: 29 May 2020Accepted Date: 12 July 2020Please cite this article as: P. Gabrielli, A. Acquilino, S. Siri, S. Bracco, G. Sansavini, M. Mazzotti, Optimizationof low-carbon multi-energy systems with seasonal geothermal energy storage: The Anergy Grid of ETH Zurich,Energy Conversion and Management: X (2020), doi: https://doi.org/10.1016/j.ecmx.2020.100052This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a coverpage and metadata, and formatting for readability, but it is not yet the definitive version of record. This versionwill undergo additional copyediting, typesetting and review before it is published in its final form, but we areproviding this version to give early visibility of the article. Please note that, during the production process, errorsmay be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.© 2020 Published by Elsevier Ltd.1.5 ◦C8 ◦C 22 ◦CHEZHPLHPZHCIHWNHEZelectricity gridNG gridelectricityNG heatcoolingboilerchillerHPL substationfrom thermal networkto thermal networkelectricity heatcooling LTcooling HTHWO geothermal field (GF)HC GFHPL GFundergroundLTHEHTHEHPT1T2T3 T4HP LTHEenergy end-users (cluster buildings)central boiler(HEZ)central chiller(HEZ)from thermal network or geothermal fieldto thermal network or geothermal fieldHTHEHWTSElectricity from gridcentral heat central coldHeat LT cold HT coldElectricity from gridNatural gas from griddemand clusterminx,y(c1Tx + c2Ty)s.t.f(x, y) = bx ≥ ∈ RX , y ∈ {0, 1}Yc1 c2 xy f x yb XY x yC DG IO D G IB MMD RN NT ambDi,j ∀i ∈ D, ∀j ∈ Cuj vj ∀j ∈ Cǫj ∀j ∈ CSi,k ∀i ∈ O ∀k ∈ Mxi,k ∈ {0, 1}N ∀i ∈ D ∀k ∈ MDmi,l ∀i ∈ O ∀l ∈ BT ini,k Touti,k ∀i ∈ D ∀k ∈ MDT inj Toutj ∀j ∈ GT Fj ∀j ∈ GT l ∀l ∈ BF i,k,j ∀i ∈ D ∀k ∈ M, ∀j ∈ CP i,k,j ∀i ∈ D ∀k ∈ M, ∀j ∈ CEi ∀i ∈ Ddl ∈ {0, 1}N ∀l ∈ Bt ∈ {1, ..., N}Pt FtPt FtPt = ηFtnηηTLT◦CTHT◦Cηη ◦CηββδΛΠηητcmmT ◦Cλα m2/s 5.1 · 10−7γRLT ◦CT ◦C0 ≤ Ft ≤ Sη Si ∈ DPt,i Ft,imt,i Tint,i Toutt,iPt,i = ηt,iFt,iPt,i = Ft,i + cmi,t(T int,i − Toutt,i)xt,iδSixt,i ≤ Ft,i ≤ Sixt,ixt,i i tδSi cηt,iηt,i =T condT cond − T evat,iξξ T cond40 ◦C T evat,iT outt,i = Tint,i −(T int,i − Tevat,i)[1 − exp(−UAcmt,i)]xt,iU APt,i Tint,iPt,i = cmt,i(T outt,i − Tint,i)xt,i0 ≤ Pt,i ≤ Sixt,iT cT int,i ≤ TcTcg ggb(r, t) = log(4αtr2)− γr t alphaγgggj∀j ∈ GTt,j = T0 +12πλLnjt∑k=1(Qk,j − Qk−1,j) g (rj , t − k)T 0 λ Lnj rjQ P − FQt,j = cmt,j(Tt,j − Tt,j)m T int,j Toutt,jQt,jLnj=1R(Tt,j − Tt,j)R Tt,jT ≤ Tt,j ≤ TT0,j = TN,ji ∈ D Et,iEt,i = Et−1,i (1 − Λ∆t) −(ΠSiht + ηinFt,i −1ηoutPt,i)∆tE0,i = EN,iht =T min − T ambtT max − T min0 ≤ Et,i ≤ Si−Siτ≤ Ft,i, Pt,i ≤SiτΛ Π htηin ηout∆t tt − 1 t τT max 55 ◦C T min 40 ◦CB = {1, 2, 3}i ∀i ∈ I3∑l=1dt,i,lmt,i,l =3∑l=1(1 − dt,i,l) mt,i,lmt,i,l il t dt,i,ld = 1 d = 0i3∑l=1dt,i,lmt,i,lTt,i,l =3∑l=1(1 − dt,i,l) mt,i,lTt,i,lyt,i[dt,i,lTt,i,l + (1 − dt,i,l) Tt,i,l]= γt,i, l = {1, 2, 3}yt,i = 2 −3∑l=1dt,i,lyt,iyt,i = 0yt,i = 1ij ∈ C∑k∈M(Pt,i,k,j − Ft,i,k,j) − Dt,i,j = 0Pt,i,k,j Ft,i,k,j j ki t Dt,i,j∑k∈M(Pt,i,k,j − Ft,i,k,j) − Dt,i,j ≥ 0N∑t=1∑i∈D∑j∈C[∑k∈M(Pt,i,k,j − Ft,i,k,j) − Dt,i,j]≤ φN∑t=1∑i∈D∑j∈CDt,i,jφ∑Nt=1∑i∈D∑j∈C Dt,i,j∆tee =∑j∈Cǫj∑i∈I∑k∈MN∑t=1Ft,i,k,j∆tǫj jǫe = 30 CO2ǫg = 237 CO2Input data: weather data, energy demands, energy prices, grid carbon intensities, technology parameters and sizesLower bound of objective function: assessment of minimum value of CO2emissionsProfile of mass flow rate, m0Original optimization problemMINLP: bilinearity between water temperature, T, and mass flow rates, m, within energy balancesRelaxed optimization problemMILP: linearization of the bilinearity mT through relaxation of energy balances (McCormick relaxation)Reduced optimization problemMILP: linearization of the bilinearity mT by fixing the mass flow rate, m, and solving for temperature, TOptimal solution• Scheduling and generated power of HPs and HEs• Temperature and mass flow profiles for all network branches• Temperature and heat profiles for all geothermal fields• Minimum value of CO2 emissionsInput dataFrom MINLP to MILPSolution of MILPComputation of flow directions and mass flow rates, m, based on m0Pt,i = η1Ft,i +(η2Tt,i + η3)xt,iTt,i = Tt,i − (Tt,i − Tt,i )(β1mt,i + β2)xi,tmTm̃ = mTmT m̃m0m̃mm0m0 ν mt = ν, ∀t ∈ {1, ..., N}m0 ζ mt = m0t ζ, ∀t ∈ {1, ..., N}m0 κ mt = m0t + κ, ∀t ∈ {1, ..., N}µm m0µ =∑Nt=1 mt∑Nt=1 m0tµ(i) µ =Nν∑Nt=1 m0t(ii) µ = ζ(iii) µ =Nκ∑Nt=1 m0t+ 1ν ζ κ µm T1T2 ≤ T1T3 ≥ T2T4 ≥ T3µ δµµ δµ = 2.18δ δδδδ = 0δ = 0.1H sH sµ = 2.18 δ = 0.1µ = 2.18 δ = 0.114 ◦Cµ φµHPZHCHCPHCOHWOHWNHPL HPLDemand clustersGeothermal fieldsfrom HEZ to HEZµ φµ = 1.423.6 ◦Cφ = 130/120 = 1.1φ = 0φ = 0φ = 0µφ = 0.15φφ = 1φ = 0 φ = 1φ = 0µ = 1.42δ = 0.1 φ = 08 ◦C 22 ◦Cφ = 0gA m2cDdEe CO2FgHhLmm̃ ◦CnPQRrSsT ◦CU m2uvxyα m2/sβγδǫ CO2ζηκΛλµνξΠτφBCDGIMMDODtotggg◦C◦C

Optimization of low-carbon multi-energy systems with seasonal geothermal energy storage: The Anergy Grid of ETH Zurich

https://www.openaccessrepository.it/record/33706/files/fulltext.pdf

Optimization of low-carbon multi-energy systems with seasonal geothermal energy storage: The Anergy Grid of ETH Zurich

Abstract

Similar works

Full text

Available Versions

Open Access Repository