Optimising Distributed Energy Operations in Buildings

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Optimising Distributed Energy Operations in Buildings Markus Groissböck Center for Energy and Innovative Technologies Somayeh Heydari University College London Ana Mera Tecnalia

1 Optimising Distributed Energy Operations in Buildings Markus Groissböck Center for Energy and Innovative Technologies Somayeh Heydari University College London Ana Mera Tecnalia Research & Innovation Eugenio Perea Tecnalia Research & Innovation Afzal Siddiqui University College London and Stockholm University Michael Stadler Center for Energy and Innovative Technologies

2 Background F EU policy objectives for the year 2020 include: I Reduction in GHG emissions by at least 20% below 1990 levels I 20% of EU energy consumption to come from renewable resources I 20% reduction in primary energy use relative to projections F Most of these targets will have to be realised by making existing buildings more efficient F However, multiple objectives and manifold combinations of resource-load pairs imply that an optimisation approach would be worthwhile (Hobbs, 1995) I Siddiqui et al. (2005), Siddiqui et al. (2007), King and Morgan (2007), Marnay et al. (2008), Stadler et al. (2011) I Ravn et al. (2005), Fleten et al. (2007), Madlener and Wickart (2007), Maribu and Fleten (2008), Siddiqui and Marnay (2008), Siddiqui and Maribu (2009), Maurovich-Horvat et al. (2012) April of 21

3 Background April of 21

4 Research Objective F Rather than assuming fixed demand, we develop lowerlevel energy-balance constraints that reflect: I Building physics I Thermodynamics of conventional heating and HVAC systems I Solar gains and external temperatures I Internal loads I User preferences for comfort from the perspective of a building operator F Combine models for building physics and thermodynamics of heating systems (Engdahl and Johansson, 2004, Xu et al., 2008, Platt et al., 2010) with EU standards (DIN, 2003) in an optimisation framework (Liang et al., 2011) I Implementation of the approach at two test sites finds reduction in energy demand of 10% I Richer modelling of energy system leads to more flexibility in operations April of 21

5 EnRiMa DSS Schema Strategic DVs Strategic Module EnRiMa DSS Strategic Constraints Upper-Level Operational DVs Upper-Level Energy- Balance Constraints Lower-Level Operational DVs Operational Module Lower-Level Energy- Balance Constraints April of 21

6 Simple Model for Zonal Energy Flow Λ t = Ã γ air ρ air ψ δ 1 + ν α wall! γair ρ air ψ +ν α wall χ t 1 + σ t 1 ² φ α glass +λ t 1 α floor, t T O δ Λ t April of 21

7 Zonal Temperature Update Λ t = Ã! 1 γ air ρ air ψ + ν α δ wall + Ω t vent ρ air γ air γair ρ air ψ Λ t 1 + Ψ t η δ δ + ν α wall χ t 1 +σ t 1 ² φ α glass + λ t 1 t α floor +ρ air γ air Ω t vent Υ, t T O (1) April of 21

8 Zonal Temperature Constraint, Heat Flow Relations, and Heat Demand κ t Λ t κ t, t T O (2) Ψ t = δ η ξ (ζ Γt ) ³ ln ζ Λ t Γ t Λ t 1 % ϕ, t T O (3) Ψ t = δ η Ωt water ρ water γ water ζ Γ t, t T O (4) D t space heat = δ η Ωt water ρ water γ water ζ Γ t 1, t T O (5) April of 21

9 Technical Constraints on Radiator and HVAC Λ t Γ t ζ, t T O (6) D t space heat ι, t T O (7) Υ t = μ water Ω t water μ water, t T O (8) Φ t χ t 1 +(1 Φ t ) Λ t 1 vent only ς ³ cool & χ t 1 < χ ς ς ς + χ t 1 χ cool & χ χ t 1 < χ χ χ ς cool & χ χ t 1 t T O (9) April of 21

10 HVAC s Supply-Air Temperature Function April of 21

11 Miscellaneous Constraints D t cooling = Ω t vent ρ air γ air δ η Φ t χ t Φ t Λ t 1 Υ t, t T O (10) = y t HVAC,electricity ½ ω Ω t vent vent only E HVAC,electricity,cooling Dcooling t cooling t T O (11) τ Φ t τ, t T O (12) μ vent Ω t vent μ vent, t T O (13) April of 21

12 Optimisation Problem F Equations (1) (13) become constraints in an optimisation problem with the following objective function: I Conventional heating system only that operates a boiler running on NG min P t T O CP t NG,RTG D t space heat E boiler,ng,hot water (14) I Purchase of district heating plus an HVAC system running on electricity min P t T O CP t electricity,rte yhvac,electricity t +CPheat,RTH t Dspace t heat (15) April of 21

which belongs to Fundación Asturiana de Atención y Protección a Personas con Discapacidades y/o Dependencias (FASAD)

13 Numerical Examples: Test Sites F We perform a lower-level deterministic optimisation over a representative winter day with hourly decision-making steps at our two test sites I Centro de Adultos La Arboleya (in Siero, Asturias, Spain), which belongs to Fundación Asturiana de Atención y Protección a Personas con Discapacidades y/o Dependencias (FASAD) I Fachhochschul Studiengänge Burgenland s Pinkafeld campus (in Pinkafeld, Burgenland, Austria) April of 21

14 Numerical Examples: Cases and Data F We run the optimisation under the following four assumptions I FMT: Fixed Mean Temperature I FLT: Fixed Lower Temperature I OFP: Optimisation with Fixed Prices I OTT: Optimisation with a TOU Tariff (Pinkafeld only) F We have the following main parameters: I FASAD: χ t (2.2 C, 8.4 C), σ t (0 kw/m 2, 0.19 kw/m 2 ), λ t (0 kw/m 2, 0.01 kw/m 2 ), (κ t, κ t )=(22 C, 25 C) from 8 AM to 9 PM, E boiler,ng,hot water =1.11 kwh/kwh, CP t NG,RTG = EUR/kWh, ψ =41901m 3, α wall =2282m 2, α glass =842m 2 I Pinkafeld: χ t ( 3.6 C, 3.6 C), σ t (0 kw/m 2, 0.24 kw/m 2 ), λ t (0.003 kw/m 2, kw/m 2 ), (κ t, κ t )=(19 C, 22 C) from 7 AM to 6 PM, E HVAC,electricity,cooling = kwh e /kwh, CP t electricity,rte = 0.15 EUR/kWh e, CP t heat,rth = EUR/kWh, ψ =11081m 3, α wall =6143m 2, α glass =426m April of 21

15 Numerical Examples: FASAD Results April of 21

16 Numerical Examples: FASAD Results April of 21

17 Numerical Examples: Pinkafeld Results April of 21

18 Numerical Examples: Pinkafeld Results April of 21

19 Numerical Examples: Pinkafeld Results April of 21

20 Summary F Incorporation of lower-level details about building physics and equipment thermodynamics I A deterministic operational optimisation for hourly decision makingduringrepresentativedaysusingdatafromtwotestsites I Relative to the rigid approach, flexibility over operations provides a reduction in energy consumption of 10% I Surprisingly, an optimisation with temperature ranges outperforms even a case with the temperature fixed at the lower limit I A case with a TOU tariff shiftsheatingtooff-peak periods and may not reduce energy consumption F Limitations and directions for future work I Better data for FASAD I Validation of model via laboratory site I Integration with upper-level operational constraints I Incorporation into a stochastic optimisation that could be used for risk management April of 21

21 Questions Afzal Siddiqui University College London and Stockholm University April of 21

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