Technical Potential of Buildings in Germany as

DOI: 10.1002/ente.201600655

Technical Potential of Buildings in Germany as Flexible Power-to-Heat Storage for Smart-Grid Operation Peter Kohlhepp* and Veit Hagenmeyer[a] Storage is a key concept to cope with a growing supply of volatile renewable energy. Compared with genuine grid storage (power$X), domestic heating and cooling, operated as flexible loads (power to heat), promises to lower infrastructure costs and offers potential for a short-term demand response. Before designing new service markets, the true technical potential must be assessed. Herein, storage capacity is estimated by exploiting the entire thermally usable building

mass as back-end storage. Balancing power is determined from the steady-state coincidence factor of buildings seen as thermostatically controlled load (TCL) populations. Results are reported for the residential and tertiary sectors in Germany by considering six classes of heating and cooling equipment. One advantage of the method is simplicity, which results in closed-form estimates derived top-down from nationwide and publicly available data on the building stock.

Introduction In the context of the German energy system transition, nuclear and fossil fuel based loads in the grid system are being replaced by increasing levels of volatile renewable energy (RE; mainly wind and photovoltaics (PV)). Hence, much more storage is needed to match demand and supply at all times, to ensure grid security and integrity, and to avoid excessive curtailment of wind or solar power. Capacity market assets, such as combined-cycle gas turbines, as backup plants cause substantial partial loading and standby costs. Genuine power storage, that is, mechanical, thermal, or chemical bulk storage; flywheels; pumped hydro plants; or compressed-air caverns,[1–6] raise concerns about infrastructure and installation costs. Converting surplus electricity into something else (mechanical, potential, or chemical energy, that is, P$X) and back to electricity when needed entails new conversion paths, including grid infrastructure and construction projects.[6, 7] Round-trip efficiency, cycle stability, and ramping rates become serious issues. Herein, we focus on the load management (LM) or demand-side management (DSM) approaches and explore, in particular, domestic heating, ventilation, and air-conditioning (HVAC; in residential, commercial, and public buildings) as thermal storage (TES) and as flexible loads. In this context, in the 1970s, air-conditioning units in the USA were utilized for peak shaving and load shedding. Today numerous municipal energy utilities in Germany offer time-of-use tariffs and critical-peak or off-peak pricing to operate night storage heaters and heat pumps, and they consider lowering the forward temperatures in district heating. In the last ten years, however, domestic HVAC, providing short-term demand response, has been gaining much more attention. The goal is to correct imbalances due to forecasting errors and to respond to frequency events in the case of outages.[8–10]

Energy Technol. 2017, 5, 1084 – 1104

Figure 1 depicts how power to heat (P2H) can provide positive and negative control power alike without feeding electricity back into the grid: the sign refers to some mean or reference load trajectory typical for the application. Positive control power to offset positive residual load[5] is realized by reducing or delaying power consumption, thereby emptying the storage; negative control power is achieved by intensifying or moving the load in time, and thus, filling the storage. How the P2H principle works for air-conditioned buildings is also illustrated in Figure 1. The varying heating or cooling load to maintain an ideal building temperature (center row) is linked to the electric load by an equivalent power factor (EPF, bottom of Figure 1). Relaxing the temperature specification about the reference opens up a tubular region for thermal and electric loads. Power curves inside the tube still meet the relaxed thermal requirements, and deviations from the reference could serve as control power. The dynamic storage temperature follows a heat equation and is driven by the residual thermal load and input power. Together, target flexibility and skillful transient charging and discharging of the thermal mass realize the power flexibility. To exploit the full potential, the timescale of grid services[11] to be provided should match the storage dynamics, that is, the time constant of the building structural mass and the [a] Dr. P. Kohlhepp, Prof. Dr. V. Hagenmeyer Institute for Applied Computer Science (IAI) Karlsruhe Institute of Technology KIT Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen (Germany) E-mail: [email protected] Supporting Information and the ORCID identification number(s) for the author(s) of this article can be found under http://dx.doi.org/10.1002/ ente.201600655. This publication is part of a Special Issue on "Energy Research at Karlsruhe Institute of Technology". To view the complete issue, visit: http://dx.doi.org/10.1002/ente.v5.7.

T 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

1084

Figure 1. The flexibility of P2H applied to buildings. See the text for an explanation and Appendix A-2 in the Supporting Information, Eq. (11), for more details.

flexibility should be planned in advance. On short notice, the scope of flexibility is much smaller. Domestic P2H is attractive, first, because it can respond quickly to service requests, in the order of 1 s.[12] Response times are determined by control interfaces and algorithms, not by the inertia of a TES operated in the background. Second, because heat is an end use and not produced for storage purposes, no round-trip conversion losses are realized, as with grid storage (P$X). Furthermore, unlike battery storage, heat or cold stores in buildings do not suffer from fatigue. Third, domestic TES promises lower infrastructure and investment cost, although such ad hoc assessments are not supported by sufficient quantitative studies. Some optimists voice the opinion that smart meters for billing and market incentives for participating are all that is necessary. In fact, great development efforts are required in the HVAC control and communication sectors. Data privacy and data security must be ensured for all stakeholders when HVAC consumers become part of the grid system. Building automation (BAS) and home energy management systems (HEMS) for buildings of all kinds will need to adopt new roles as intermediaries between the interests of grid operators and building owners. Designers of a new market for grid services with new tariffs for short-term participation of domestic HVAC should be informed of the true size and value of contributions and how Energy Technol. 2017, 5, 1084 – 1104

benefits and burdens are shared. Valuable information is gained from a quantitative assessment of the technical potential by considering the quality of service constraints (thermal safety, thermal comfort) of the appliances and the constraints of HVAC operation.

Assessment Methodology In systems analysis and technology assessment research, a common taxonomy with respect to assessment scope and purpose distinguishes between theoretical, technical, economic, and practical potentials. Taxonomy has been narrowed down and applied by Grein and Pehnt to assess grid storage and LM potentials,[13] and has been extended to the demands on flexibility by Kondziella and Bruckner.[5] The resulting sets, quantities, or amounts shrink by imposing independent sets of constraints and successive intersections. Whereas theoretical potential simply refers to the physical supply of a resource, such as installed electric power (existing or predicted), technical assessment imposes further operational restrictions, for example, on the quality of service delivered by electricity use, on how electric load profiles can be manipulated (delayed, interrupted, modulated, etc.) and on technical limitations of storage devices and their control. Economic assessment,[13–23] in addition, considers grid resources that can be scheduled economically, assuming that all


1085

stakeholders—energy providers, grid operators, and customers—use a cost-effective combination of technologies.[14] The effectively realizable practical potential captures the subset of economic potential that comes from users, who accept load interventions and see clear advantages of participating in a LM program.[20] It seems that the technical assessment, prior to economic assessment, in some cases, comes close to the theoretical potential.[15–17, 19, 20] Determining cost-effectiveness and profitability assumes a regulatory framework, a cost structure, and an optimal planning/scheduling tool, such as REMix,[20] to allocate resources for generation, LM, and storage. The economic potential of a new resource or technology with respect to any goal variable, such as control power supply (in GW), generation cost (in E KWh@1), or CO2 emissions (in ton GWh@1), is then the difference obtained for running the same scheduler on the same input (demand) scenarios, by including the new resource and by excluding it. The time resolution of scheduling is typically 1 h, which does not capture short-term balancing or frequency responses. Important underlying regulatory; market; and, in particular, broader technological and societal framework assumptions[24] used in economic assessments do not always appear to us to be stated explicitly. Sometimes elements from existing electricity markets or tariffs existing in special demand response programs seem to have been scaled up and placed into a different context; for example, see reports by Kintner-Meyer et al.[25] and Kondziella and Bruckner.[5] In the first place, the pricing structure in an economic model for new grid services should account for the investment and installation costs to develop or toughen up domestic HVAC for large-scale grid participation. How high are these costs and which stakeholders will bear them? Equally important is what is the added value offered to the grid by new HVAC customers and who will reap the benefits? The value lies in the renewable electricity integration costs avoided or mitigated owing to new flexibility options from demand response (DR): balancing costs due to limited predictability, demanding a larger operating reserve or more expensive storage options; profile costs due to intermittency, causing inefficient partial loading of backup plants or curtailment costs; and grid costs incurred by longer distance power transmission, grid congestion, or grid expansion.[5, 26] Weighing all development, investment, and operating costs with cost savings, how much could residents charge and how much is a (distribution) grid operator, utility company, or an intermediate aggregator for pooling LM customers (households) willing to pay? Will new players entering the market mix up the price structure of existing products? These uncertainties suggest modifying the order of steps: 1) Assess the technical constraints and potentials more rigorously than that previously achieved, and at different temporal and spatial scales. 2) Perform large field studies for pilot implementation and evaluation to anticipate, in part, the practical potential and obstacles to exploit it. 3) Develop a new market model based on the results of 1) and 2), which may provide regulations to incentivize investment into new Energy Technol. 2017, 5, 1084 – 1104

technologies.[27, 28] Finally, 4) perform economic assessment of the new pricing structure. Therefore, the optimum scheduler, who would calculate economically dispatchable resources, that is, ancillary services, would come last.

Related Work Storage performance criteria Diverse criteria to compare different types of storage greatly help in assessment of the technical potential because P2H applications complement or compete with genuine grid storage P$X,[5, 6] and help to identify ancillary services to which P2H/demand response appear to be tailor-made.[6, 26] A priori criteria for participating in LM programs, such as minimum participation, required response time, interruption period, advance notification, and metering requirements, are discussed in the ONRL study[1] for US markets, which refers to another study by the Brattle Group.[22] Relevant operational performance criteria are stated in the Sandia report[2] and the technology assessment[3] for the US congress. Storage classification schemes focusing on European energy markets are discussed, for example, by Fuchs et al.[4] on behalf of the Smart Energy for Europe Platform (SEFEP) and were investigated in several Ph.D. projects.[15, 23, 29] Tahersima et al.[30] and Roossien[31] discuss flexibility measures for rather general classes of electrical appliances. Roossien defines the difference in consumption that a consumer or load group can hold for a given duration as a more relevant criterion than that of instantaneous power.[31] Totu gives a Bayesian definition similar to ours for the expected (signed, momentary) control power of a population of thermostatically controlled loads (TCLs).[32] Mathieu et al. specify an assessment framework for domestic TCL devices by narrowing down the definition of storage capacity to electric devices; notwithstanding the fact that such a load acts as a front end and a lever to operate a much larger thermal mass behind.[33] On the other part, their measure of power flexibility appears generous or optimistic, coming close to that of the installed power. A related definition for TCL populations based on a generalized battery model is given by Hao et al.[34] De Coninck and Helsen proposed cost curves to quantify the cost of flexibility imposed on the power consumption of HVAC in buildings.[35] Objective and quantitative criteria of technical storage assessment, which we base our work on, have been laid down, for example, in a framework by Oldewurtel et al.[36] The three core criteria are storage capacity (GWh or TWh), power capacity (signed control power (GW), optionally power sustained for a certain period), and the rate of ramping up and down (GW h@1). Further criteria concern response time, readiness (frequency of use, resilience against exhaustion), response granularity (0–100 %), power density (KW m@3), energy density (KWh m@3), conversion efficiency (conversion losses), location constraints, infrastructure needs for communication and control, investment, installation, and operating costs (E KWh@1, E KW@1). The original framework


1086

comprises nine criteria that take discrete values in the set {+ + , + , 0, @} and is applicable to both technical and revenue assessment.[36] A comparative qualitative potential assessment of batteries, electric vehicles (plug-in electric vehicle (PEV)), building HVAC, and TCL in Switzerland was performed.[36] The complexity of the BAS assumed is influential on grid performance, in particular, on the achievable response times.

Storage demand To become meaningful, absolute figures of supply potential should be compared with corresponding demands for balancing power or frequency reserve,[5, 23, 29, 37] or should be related to the grid size, such as by specifying a percentage of peak load.[38] Crucial influencing variables on storage demand are the penetration of RE and the curtailment of excess RE supply.[29, 33, 37] Hartmann,[29] in particular, estimated demands on storage capacity and control power for different levels of RE penetration and curtailment in Germany; this provided valuable reference figures. Teng et al.,[39, 40] in addition, considered explicitly the reduced rotational inertia in grids with large shares of PV and wind energy. Formulae based on the swing equation are given to derive the demand of control power backwards from national requirements for how to stabilize the grid frequency after an outage or contingency, such as maximum deviation of frequency (nadir) and the time needed to return to acceptable levels.

Storage supply Relatively few studies elaborate on the surveying methodology, that is, how large-scale quantity structures of buildings and HVAC equipment are acquired efficiently and reasonably accurately.[13, 19, 29, 41] In the collaborative study by RWTH Aachen and E.ON[41] on dual-demand side management, a modeling region around the city of Bottrop was surveyed in detail with the help of semantic data models and geographical information systems (GIS) maps. The European Institute for Energy Research (EIFER) took a slightly different avenue by using building typologies.[42] Mathieu et al. assessed six major Californian cities to specify the required data on energy consumption and equipment.[33] Assessments of domestic storage and HVAC in the literature are often combinations of theoretical and economic potential, according to the classification discussed above,[13] for example, reports by the groups of Stadler,[16, 17] Klobasa,[15] Grein,[13] Gils,[19, 20] Hartmann,[29] Aunedi,[39, 40, 43] and Patteeuw.[44] Potentials, both theoretical[15–17, 19, 29] and technical,[33, 45–47] are often specified explicitly, depending on ambient temperature or season. Concrete assessment projects reviewed are summarized in Table A4 in Appendix A-1 in the Supporting Information. Energy Technol. 2017, 5, 1084 – 1104

Contributions, scope, and restrictions Before establishing domestic HVAC to provide short-term grid services through LM at a large scale, a thorough assessment of technical potentials is necessary. Conditioned buildings for P2H storage should be compared with genuine grid storage P$X by common quantitative criteria. As the initial new steps, we present an estimation method for storage capacity that accounts for the entire building mass as back-end storage and the individual temperature ranges of different storage types. To estimate the control power available from large storage aggregations under quality-of-service constraints, their coincidence factor must be derived. We make the simplifying approximation of air-conditioned buildings as TCLs, regardless of the complexity of their control, and derive new estimates on the expected sustained control power of TCL populations. The main goal of these first steps is to derive quick estimates by using closed formulae at spreadsheet calculator level (back-of-the-envelope assessment[32]). Neither resource scheduling nor dynamic simulation is required. Consistent with simplicity, quantity structures are determined top-down from publicly available, nationwide data on the building stock by using morphological development parameters and HVAC technology data. In contrast, a more common bottom-up alternative[41, 48, 49] to the present purely desk research, which is much more accurate at the building or district level, is to survey selected model regions in detail by using GIS data. Extrapolating them to larger regions may be more time-consuming and entails some generalization risks. To assess the plausibility of these results, they could be compared with our top-down results. Nationwide results are reported for the residential and tertiary sectors in Germany by considering six classes of HVAC equipment: conventional (combustion-based, central or district) heating comprising parasitic electrical equipment; electric storage heaters; separate electric water heaters; heat pumps; air-conditioning; and goods cooling in fridges, freezers, and cold warehouses. An essential working assumption for LM potential assessment is that any HVAC technology has a well-defined and known EPF factor that relates thermal and electric power. It may depend on ambient conditions, but not on the internal storage state. Restrictions exist in both the scope of practical assessment and methodology, as discussed later. Domestic power generation (combined heat and power (CHP)[49] and PV prosumers) in this work has not been assessed explicitly. The impact on structures and amounts of power generation seems to be limited at present in Germany. It is assumed that HVAC electricity works to continuously control thermal processes. Tasks with a specific starting time and deadline, for example, washing machine, clothes dryer, or dishwasher programs, and the further constraints imposed by these factors are not our focus, although they do include aspects of TES. Appliances with limited or no flexibility, that is, lighting, consumer electronics, and computers, are exclud-


1087

ed. For the classification of electric loads by their flexibility properties, readers are referred, for example, to reports by Roossien,[31] Bitar et al.,[10] or Petersen et al.[50] Contributions from the mobility sector, especially electric vehicle battery charging, are ignored because they are seen as P$X storage and candidates for the comparison of results. TES in production processes and LM potential from the industrial sector in general has been excluded. For technical potential assessment, the operational constraints matter, but not how we actually interact with the HVAC loads, such as by directly controlling them,[8–10, 30–32] by incentivizing through real-time price signals, or by forward contracts rewarding some degree of flexibility flat rate.[10, 30, 40, 44, 51, 52]

Results and Discussion Storage capacity Storage capacity is defined herein as the electrical work corresponding to the thermal energy to completely fill or empty the storage within the bounds that the primary service permits. Although storage mass is often constant, as applies to solid building materials or water tanks in which withdrawn amounts are refilled, the admissible temperature range defines the filling level and the useful heat amount of a TES. Regardless of the active process, heating up or cooling down, and nature providing the opposite passive process, temperatures usually have both lower and upper boundaries. The (heat-equivalent electrical) capacity is estimated by the general formula given by Equation (1): Qcap el

XE C ¼ V j 1j j mj Cj DT j h@1 j fj j

ð1Þ

in which index j runs over all TESs (buildings); Qcap el (in J) is the electrical work; Vj (in m3) and 1j (in kg m@3) are the volume and specific weight, respectively; mj (in kg) is the mass. Alternatives in Equation (1) are denoted by j ; Cj (in J kg@1 K@1) is the specific heat capacity of materials; DTj (in K) is the operational temperature range of the storage; hj is the EPF, that is, units of thermal power per unit of auxiliary electrical power spent; the inverse, h@1 j , indicates the power share of the thermal technology; the utilization rate is fj2{0, 1} for LM.

Explanations max Capacity refers to a maximal temperature range [T min ] j , Tj within which flexibility is permitted. The variable DTj measures the distance between the actual storage temperature, Tj, and, depending on the type of application, the upper or lower temperature limit [filling level; Eq. (1 a)].

Energy Technol. 2017, 5, 1084 – 1104

8 > T @ T min if T min , T j , T max ðheatingÞ j ; j j > < j max min max DT j :¼ T j @ T j ; if T j , T j , T j ðcoolingÞ > > : 0; else

ð1aÞ

Because actual temperatures are unknown and assessment interest lies more in the expected filling level at a random instant than in the maximum, we assume that the storage temperatures are uniformly distributed random variables and take the expectation E(DTj) = (T max @T min j j )/2. Only mass fractions within the thermal range, for which DTj + 0 [Eq. (1 a)], contribute to capacity. All building materials, including masonry, steel, glass, and soapstone, as heat storage media, as well as zone air, ice, or water in tanks or ducts, are relevant. Phase change materials (PCM) and special refrigerants exist in rather marginal quantities that are not significant for rough assessment and have therefore been omitted from Equation (1).[89] The storage material also has electrical properties defined by the heating or cooling technology, such as an electrical resistance heater, heat pump, compressor chiller, or combustion-based heating by using auxiliary electric power. The equivalence factor for power h@1 is used, although Equation (1) states equality of energy, that is, power integrated over a time interval [t0, t1]. If the factor h varies over time, a time-averaged EPF relates electrical and thermal energy [Eq. (1 b)]: ( :¼ h

1 t1 @ t0

Z t0

t1

cap (@1 hðtÞdt ) Qcap el & Qth h

ð1bÞ

Furthermore, for capacity estimates of any HVAC type, we average EPF over all possible operating conditions. For example, heat pumps (air source, ground water) in Germany have a mean annual performance of h¯ & 3.7.[53] Thus, energy capacity depends on the energy conversion technology used (HVAC), but, unlike the retrievable control power, not on ambient conditions. The utilization rate f indicates whether the TES is suitable and disposable for LM in the sense of a priori criteria of the ONRL study.[1] An individual unit has nonzero f if its use for grid services is technically and economically viable, if building owners or tenants are ready to participate, and if the control system interface is prepared to respond. For storage collections of type j, 0 , fj , 1 indicates the fraction of accessible components and the likelihood of a random component contributing to the potential. Small values for f model low practical potential; how to use and choose f is discussed in the section on numerical results. The utilization rate does not indicate whether heating or cooling is actually operating. Operating conditions influence the power of charging or discharging in the shape of heating intensity or active power, which is rated heating power modulated by a seasonal or ambient temperature parameter (see Appendix A-5 in the Supporting Information) and implicitly makes a statement about whether heating is likely to be on.


1088

Equation (1) defines energy capacity as an instantaneous property, that is, as the maximum or expected value at a random instant, and is completely different from the total amount of work that the storage performs in a certain period of time, for example, by multiple activations in a heating season.[15] Furthermore, the operational context of the storage, and the purpose and timescale of LM, such as load-shifting or real-time balancing, have an influence (through the temperature range) on the storage capacity determined. For example, a night storage heater used for diurnal load shifting (off-peak charging, peak-time discharging) has a capacity that corresponds to the work required to fully charge it, for example, 40 kWh, if charging takes 8 h at a rated power of 5 kW. During that time, the heating core is fully heated from, for example, 50 to 500 8C, which is equivalent to a thermal range of DToff$on = 450 K. On the other hand, an electrical heater transferring heat continuously through an air convector (fan) to the building during a cold winterQs day maintains its core operating temperature (bricks) over a rather narrow band, for example, 450 to 500 8C, or DTon = 50 K. When responding to a frequency event, there is no option—not even time—to cool it down to 50 8C. Therefore, its core capacity is rather small (e.g., 200 kg bricks, C = 1000 J kg@1 K@1, DTon = 50 K)Qcap th < 2.78 kWh). However, the heat stored in the building mass is still available and could be tapped for short-term demand response by switching off or bypassing the fan (convector), interrupting heat withdrawal from the core, and keeping it warm. Consequently, the more relevant capacity is that of the building mass, not the heater core. Although the thermal comfort range of the building is even smaller (DTBM & 5 K), the proportional building mass dominates the heater core by roughly a factor 103 (100–200 tons). Generally, the total capacity of a storage comprising interconnected cascaded heat-exchanging units (higher-order system) with individual capacities is dominated, and can be expediently approximated, by the unit with largest mass and longest time constant. Grouping and clustering To obtain top-down estimates, the total building stock (residential and tertiary sector) is grouped into disjointed use cases to keep variations within each group small. Furthermore, the total capacity is factored into independent influencing variables with corresponding averages. For example, the distribution of building sizes, masses, and thermal capacities is assumed to be independent of how buildings are heated, the electricity shares of heating systems (EPF), and storage temperature ranges. Nonzero correlation coefficients exist, but are neglected. HVAC use cases are then combined into portfolios by specifying a vector of utilization rates. A division into six use cases is displayed in Table 1. We also make simple proportionality assumptions. When a use case is known from data sources as some share or perPU centage, ak, of a whole, k¼1 ak ¼ 1, the properties specific to that use case, k, apply to a proportional part of the total Energy Technol. 2017, 5, 1084 – 1104

¯. mass, MB, of all buildings with average specific capacity, C The shares ak are optionally available as fractions of installed power, conditioned floor space, total number of households, or installed HVAC units. Total capacity in each case is approximated by the weighted average of U classes or use cases with appropriate class means of temperature spans, DT, and power shares, h¯ @1 [Eq. (2)]: cap el

Q

( & MB C

U X k¼1

@1 k

( DT k h

U X ak f k ( f :¼ ak f k (f k¼1

! ð2Þ

The total building mass, MB, is decomposed further below into core, envelope, and air fractions, independently for residential and tertiary sectors. Several use cases not related to building masses directly, such as separate electric hot water (consumable), internal buffer storage in the heat distribution loops (nonconsumable), and the masses stored in separate tanks or buffers (as ice in fridges and freezer cabinets) are determined separately (please see the Experimental Section for information on data surveying).

Building mass The procedure used for capacity assessment and data dependencies is shown in Figure 2 as a data flow diagram. The main features are as follows: 1) Basic morphological development parameters, such as specific gross building volume (GBV) and specific surface areas of building envelopes per unit of floor area, are applied to the total floor area of residential and nonresidential buildings. 2) The building masses (envelopes and cores) and the mass of conditioned zone air are roughly estimated on a national scale. 3) By using a hypothetical standard building, an average composition of solid materials of buildings is defined; this yields the specific heat capacity. 4) After estimating the thermally usable fractions of masses, especially for building envelopes, and their temperature ranges, the heat quantities in envelopes, cores, and zone air are determined. 5) Finally, portfolios combining several types of building HVAC are defined. The quantity frameworks, that is, number of units or shares per type and utilization rates for LM, determine the mass fractions according to Equation (2). Storable heat amounts in building cores, envelopes, and zone air shown in Figure 2 and determined by using Equation (1) are combined in Equation (3):


1089

Energy Technol. 2017, 5, 1084 – 1104 & 11.2el

& 0.98

fridges, freezers goods store (gastronomy, retail, …) water tank, boiler

& 2–5 h¯ = 2.5

& 16el

whole building[c]/ warehouse

& 2–12 h¯ = 2.5

& 9.6th R: & 2.2th NR: & 9.8th R + NR: & 0.9el

HVAC loops (buffer)

whole building[c]

E-heater

whole building[c]

When used heating period

heating period

all year (mostly)

cooling period or all year all year

all year

Storage medium volume [m3] standard building air zones PCM heating water 13–60 [m3 GW@1] standard building air zones firebricks PCM standard building air zones heating water

standard building air zones ice storage cooled goods water & 7.5 W 105 m3

60 to 70

1 to 8 @24 to @15

Top[d] : DTctrl[e] Top : 35 to 50 DTctrl : 2–3 18 to 25

Top[d] : DTctrl[e] Top : 300 to 450 DTctrl : 10 to 30 18 to 25

Top[d] : DTctrl[e] Top : 65 to 90 DTctrl : 2 to 3 18 to 25

18 to 25

Usable thermal range [8C]

[a] Residential buildings (R) and nonresidential buildings (NR) in the tertiary sector. Subscripts denote electrical (el) or thermal (th) end energy or heating/cooling power. [b] COP/EPF = coefficient of performance/ equivalence factor for electric power. [c] Structural thermal mass of building. [d] Top is the operating temperature of the storage medium, such as a heating loop or stone, which may vary on a case-by-case basis. [e] DTctrlis a tolerance band of temperature control and determines the thermal range usable for power demand flexibility.

–

?

R: & 40 W 106 fridges NR:? & 5 W 106

goods cooling

electric water heating

R: 900 W 106 NR: 634 W 106

2 066 000 (?)

space cooling

R + NR: & 8–10el R: 0.19th NR: 20.8th R + NR: & 5el

& 2–12 h¯ = 3.7

6.56th

3.9–5.2el 11.8th

R: 81 W 106

& 460 000 (?)

heat pumps

& 0.95

?

21.4el

R: 155 W 106

& 1.6 W 106

electric heaters

HVAC loops (buffer)

whole building[c]

10–25

R: 3.65 W 109 NR: 1.4 W 109

& 40.77 W 106

default heating

design load R: 4.0el W m@2 NR: 22.6el W m@2

TES

600th–780thNR: 43.4el–57el R: 23.4el–30.7el NR: 43.4el–57el

COP[b]/EPF

installed power [GW]

Structure data energy demand [TWh a@1]

area [m2]

no. households or units

Use case

Table 1. Domestic TES in Germany partitioned into six use cases.[a]

( DT env (f h (@1 QelH ¼ mH C (f h ( (@1 Qel ¼ mK CDT K

el Air

Q

(@1 ¼ mA Cair DT (f h

(f h (@1 ¼ U X k¼1


(@1 ak f k h k

ð3Þ

in which mH, mK, and mA denote usable envelope, core, and air mass fractions, respectively, of the total building mass, and the common factor given by Equation (4) symbolizes a weighted (by utilization rates f) inverse portfolio EPF of HVAC technologies collected in one portfolio; ak are their shares.

ð4Þ

More details on the determination of the remaining unknown parameters in Equations (3) and (4) from the data sources are given in the Experimental Section. In particular, only a fraction, m ¯ Tenv, of building envelope mass with a useful temperature range, DTenv, is actually usable as a TES; an example calculation is outlined in the Experimental Section. An essential feature, which is described in the Experimental Section, is the composition of solid materials ¯ taken from Doka,[54] who anato find C lyzed the mass fractions of different construction materials in a hypothetical average Swiss building.

Control power—General context

The main purpose of TES is to maintain thermal constraints given by standards of thermal comfort in buildings or by safety (hygiene) regulations for domestic hot water (DHW) or frozen foods. The technical potential to serve further goals, that is, to provide flexible power consumption to the grid (P2H), is derived backwards from the quality-of-service demands. These are taken as hard constraints, although some might be negotiable on a case-by-case basis with the flexibility interests of a utility or grid operator (e.g., see Ref. [10]). Counteracting unwanted heat losses or gains to or from the environment is the core purpose of storage control. Varying thermal load is out cancelled by corresponding heating or cooling power (HVAC), which goes hand in hand with electrical power demand through the EPFh (direct P2H conversion factor or share of

1090

Figure 2. Data flow for estimating the heat capacity of the building stock.

auxiliary electricity) and defines the reference power, Pref(t). The actual power consumption, Pel(t), of a passive P2H storage, which, unlike CHP, never feeds electricity into the grid, is in the range of 0 to Peff, in which Peff is rated, or maximal, power consumption. Control power can be any deviation from the reference, Pref(t), for which 0 , Pref(t) , Peff must also hold to ensure quality of service (see Figure 1). Power flexibility, F + and F @ denoting positive and negative control power, respectively, are related to the difference DP(t): = Pref(t)@Pel(t) through Equation (5):

F þ ðtÞ ¼ maxðDPðtÞ,0Þ + 08t F @ ðtÞ ¼ minðDPðtÞ,0Þ , 08t

Control power—TCL abstraction Simply adding HVAC power ratings inside a balancing area provides no meaningful estimates of control power, since the statistical simultaneity factor is essential. On the other hand, detailed simulation models to determine the precise reference power are not necessary for a quick assessment. In this section, simple statistical formulae are given for the steady state. Based on the classification by Roossien,[31] the flexibility potential of TES is understood to be the maximum decrease or increase of power consumption that could be offered instantaneously at time t and be held for a certain period t. Consistent with the signs of residual load, it is denoted by F + (t, t) + 0 and F @(t, t) , 0, respectively:

ð5Þ

Step › ,HVAC uses DP = F + (t, t) less power for time t ,A battery feeds DP into the grid for time t. Step ﬂ ,HVAC uses DP = @F @(t, t) more power for time t ,A battery stores DP from the grid for time t.

Thermal losses affect the location of reference power Pref2[0, Peff]. With more losses or gains, the reference power to counteract them rises. Because the TES has a heavier loading, there is greater scope to decrease power consumption (higher F + ). Conversely, with lower Pref, there is more room to increase power (higher F @). Making use of that larger potential, however, risks exhausting the storage capacity even faster, that is, limits are reached more quickly. Notably, the filling level and capacity given by Equations (1) and (1 a) equates to the time-integrated power flexibility [Eq. (5)]; in other words, capacity is accumulated potential in the storage to deviate from the reference power curve given by ambient heat gains or losses. Capacity is defined by the thermal flexibility and by process and material parameters in Equation (1) and is not intrinsically degraded by environmental losses.

For a quick assessment, all domestic TES are approximated as TCL units and the entire HVAC stock as TCL populations.[8, 33, 34] In other words, storage temperatures are kept inside a dead band [T@, T + ], which now represents the thermal flexibility, and two-point control switches the power state (on$off) as soon as either end point is reached. TCL abstraction may seem obvious, because thermal bounds of comfort or hygiene define the quality-of-service constraints of HVAC, and it serves our purpose to estimate the simultaneity factor; however, thermostats do not reflect the complexity of modern HVAC controls. Goal variables of temperature, pressure, and flow rates of air, water, and compressor fluids are regulated together in nested control loops by varying continuously the speed of pumps or compressors from the power or voltage.[45, 46] The flexibility of electricity consumption measured under realistic conditions could differ

) F þ ðtÞ þ F @ ðtÞ ¼ DPðtÞ8t F þ ðtÞ þ jF @ ðtÞj , Peff el 8t

Energy Technol. 2017, 5, 1084 – 1104


1091

temperatures T(t) and operating states on(t), off(t), in particular, denote deterministic quantities. Now let N storage devices operate independently with mean and total rated power of P¯eff and Peff = NP¯eff, respectively. To assess their aggregated flexibility, simple probabilistic reasoning is used, notwithstanding RoossienQs deterministic approach;[31] this is more similar to the approach reported by Totu.[32] T(t) and on(t) denote the states of a random unit, equivalently, of a unit the initial temperature states of which may range over an entire probability density function, which has two parts, fon(T,t) and fon(T,t). In other words, T(t) ~ fon j off(T,t) is a continuous random variable (r.v.) and on(t) a BooR lean r.v. with Pr(on(t)) = 1 @1 fon(T,t)dT. The load flexibility is then expressed as a dependent r.v. with expectation [Eq. (6)]. E C EðF þ ðt; tÞÞ ¼ Peff Pr onðtÞ; T @ , Foff t ðT ðt ÞÞ , T þ E C EðF @ ðt; tÞÞ ¼ @Peff Pr :onðtÞ; T @ , Fon t ðT ðt ÞÞ , T þ

Figure 3. Control power of a single TCL sustained for a period t. Top: Thermostat-controlled heating application oscillating between T@ and T + and approaching asymptotic temperatures Ton in the active state, respectively, Toff in the passive state. Bottom: Requesting positive control power by switching off and negative control power by switching on. Control power is zero if thermal constraints would be violated during the requested period (highlighted in red). The active cooling case is similar except that temperature decreases during active power phases (shaded blue) and Ton < T@ < T + < Toff holds.

significantly from that under thermostatic abstraction, even for rough and large-scale assessment. A direct consequence of thermostats is that a single TCL unit provides either zero or power Peff in absolute values (see Figure 3). For a single TCL, for example, a heated building, power is given by Equation (5 a): ( 8 > < Peff ; if on ðtÞ ^ Foff ðT ðtÞÞ + T @ ðheatingÞ t F þ ðt; tÞ ¼ , T þ ðcoolingÞ > : 0; else ( 8 > < @Peff ; if off ðtÞ ^ Fon ðT ðtÞÞ , T þ ðheatingÞ t F @ ðt; tÞ ¼ + T @ ðcoolingÞ > : 0; else

off in which Fon t ðT ðt ÞÞ and Ft ðT ðt ÞÞ denote the transition functions of storage temperature under full and under zero power, respectively, which yield the temperature T(t + t) starting from T(t), assuming no stochastic disturbance. All variables and functions in Equation (5) are deterministic;

Energy Technol. 2017, 5, 1084 – 1104

Notably, in Equation (6), thermal conditions apply over the whole time interval, [t, t + t], but are sufficiently formulated for the end point because they are assumed to hold at the time t of request, and the undisturbed thermal processes are monotonic. Also, the total probabilities in Equation (6) can be factored because the thermal processes, Fon and Foff, with known input in the future, [t, t + t], are determined by the initial temperature, T(t), but are statistically independent of the power state on immediately before (Markov property). Because device temperatures and probabilities are continuous variables, large TCL populations can statistically provide flexibility to any desired granularity in the sense of that reported by Oldewurtel et al.[36]

Simplifying assumptions 1) Exponential heating and cooling with corresponding logarithmic (in t) temperature probabilities are replaced by piecewise linear gradients for rough estimates. The constant drift rates (r for power on and q for off) and the corresponding expected durations t¯ on and t¯ off, respectively, to traverse the temperature band with DT: = T + @T@ are denoted by Equation (7): q :¼ DT=(toff ,

ð5aÞ

ð6Þ

r :¼ DT=(ton

ð7Þ

2) A homogeneous TCL population with identical heating and cooling rates is assumed to be in steady state prior to a request to reduce or increase power consumption. The devices operate independently and freely, that is, neither temperature thresholds T@ and T + nor operating states have been manipulated externally before. Device temperatures are uniformly distributed in [T@, T + ], and the fractions of devices in both power states are in equilibrium. Assumptions 1 and 2 allow a particularly simple calculation of probabilities in Equation (6). The more important and critical assumption 2 relates to the undisturbed steady


1092

state of a TCL population because it will be destroyed just by the intended LM operations, at least temporarily. Under assumptions 1 and 2, the probabilities to find a random load in the alternating on and off states [Eq. (7)] are given by Equation (8): (ton q ¼ (ton þ (toff q þ r (toff r Prðoff ðtÞÞ ¼ ¼ (ton þ (toff q þ r

PrðonðtÞÞ ¼

ð8Þ

From Equations (6) and (8), the expected power flexibility of N independent loads together in steady-state is given by Equation (9): E C (ton Pr T @ , Foff ðT ðtÞÞ , T þ t (ton þ (toff E C (toff EðF @ ðt; tÞÞ ¼ @Peff Pr T @ , Fon t ðT ðt ÞÞ , T þ (ton þ (toff EðF þ ðt; tÞÞ ¼ Peff

ð9Þ

The conditions on the right-hand side of Equation (9) that no temperature violations occur through switching the device on or off for duration t, under assumption 1 (linear heating/ cooling), are equivalent to a timing condition that an interval of length t with a random start point in an on- or off-duty interval of length t¯ on j off fits completely inside: . t Pr T @ , F ðT ðtÞÞ , T þ ¼ max 1 @ ;0 (toff . E C t Pr T @ , Fon ð T ð t Þ Þ , T ¼ max 1 @ ; 0 þ t (ton E

off t

C

ð9aÞ

Dropping assumption 1, for exponential heating/cooling, the probabilities have been calculated in Appendix A-3 in the Supporting Information [see Eq. (17) therein], and the corresponding formulae for a cooling application are given by Equation (9 b)] E C Pr T @ , Foff t ðT ðt ÞÞ , T þ ¼ 1 T þ @ T on ln a (ton T off þ eat ðT @ @ T off Þ @ T on

ðt , (toff Þ ð9bÞ

E C Pr T @ , Fon t ð T ðt ÞÞ , T þ ¼ 1 T @ @ T off ln a (toff T on þ eat ðT þ @ T on Þ @ T off

ðt , (ton Þ

For the heating case, T@ and T + in Equation (9 b) are interchanged, see Appendix A-3 in the Supporting Information; a denotes a thermodynamic parameter in the heat equation in standard form, see Appendix A-2 in the Supporting Information and Equation (12) therein. In practice, the HVAC stock is modeled by use cases that include very large device classes, such as heat pumps, electric storage heaters, water heaters, and freezers in warehouses, with diverse physical parameters (Table 1). Consequently, Equation (9) is replaced by a sum of class-specific parameters. Energy Technol. 2017, 5, 1084 – 1104

Furthermore, Equation (9) does not yield the true control power sustainable for periods longer than the on- or off-duty cycles, since our simplistic model assumes that all storage devices are continuously involved in providing the service, that is, holding the power. Because each device supplies its rated power or zero, and none can hold the power state longer without violating temperature limits, together the devices cannot hold the power state either. This assumption is not needed, of course, and simple extensions across multiple cycles are possible, but are not discussed herein. Although the state probabilities in Equation (9), with the first factor based on Equation (8), are determined by the ratio R of on- to off-duty cycle lengths, the second factor, which is the probability of being able to hold the control power [Eq. (9 a) and (9 b)], depends on the holding duration normalized according to the cycle length (t/t¯ on j off , 1). Ratio R can be coarsely approximated from the locations of the asymptotic temperature relative to the thermostat dead band illustrated in Figure 3 [’ + ’ holds for heating, ’@’ for cooling; Eq. (10)]: ton=off :¼ R :¼

1@R 2 ½@1; 1A maxð1; RÞ

(ton T þj@ @ T off & 2 ½0; 1A (toff T on @ T @jþ

ð10Þ

The cycle ratio, ton/off, in Equation (10) yields a convenient symmetric parameterization of signed control power for the cases t¯ on , t¯ on (,ton/off2[0,1]) and t¯ on + t¯ off (,ton/off2[@1,0]). See Appendix A-2 in the Supporting Information for information on how to populate large aggregate models and set the parameters from thermodynamic properties of the building stock that are only roughly known. Quantity structures and data collection Storage capacity and control power estimates for Germany were generated from the quantity structures summarized in the Experimental Section, which comprise the conditioned building stock and the distribution of HVAC types characterized in Table 1 for six use cases. These data, together with Equations (1)–(4) for capacity assessment and Equations (6)– (10) for control power assessment, were used to obtain all numerical results reported in the next two sections. Numerical results—Capacity In Figure 4, the estimated storage capacity in all buildings in Germany is shown, as distinguished by use, such as heated or cooled, and by sectors, private households and tertiary sector. Capacity in the heating portfolio is plotted as a function of the unknown and rather low LM utilization rate f, which characterizes the dominant default heating use case in Table 1. Fixed and high utilization rates, however, were adopted for power-affine HVAC types, such as heat pumps (f = 0.8) and electric storage heaters (f = 1); similarly for compressor-based cooling and DHW. Capacity will not drop


1093

Figure 4. Storage capacity depending on the LM utilization rate, as distinguished by HVAC use cases and indicated by uncertainty bands. Top row: residential buildings. Bottom row: buildings from the tertiary sector. Left column: heating, varying the utilization f of default heating. Right column: cooling, varying utilization f of air-conditioning units.

to zero when f = 0 because the fixed contribution from electric heaters and heat pumps remains as a constant offset. Function plots are curved to the right because of the offset (both axes have logarithmic scales) and because heat capacity is indeed slightly nonlinear as the product of average inverse portfolio EPF, which depends on f, and the storage mass accessible by LM, which is proportional to f. In the cooling portfolio (Figure 4, right), the only use case from Table 1 is space cooling, with a variable utilization rate (f = 0.1 or 0.8). The mean outside temperature is assumed to be 30 8C in cooling mode, which is closer to the target band (18–25 8C) than the average 6 8C in the heating season. Therefore, the thermally usable portion of building enveEnergy Technol. 2017, 5, 1084 – 1104

lopes is larger (38 %) than that in heating mode (21 %). Hence, the energy shares are slightly closer together. The major difference between the residential (R, top row) and nonresidential (NR, bottom row) sectors is due to space cooling. After the Umweltbundesamt (UBA) study,[55] only about 2 % residential floor area, but 33 % floor area in the tertiary sector, in Germany are air-conditioned. The share of floor area serves as a key reference and determines accessible storage mass. Minor differences between R and NR also exist in the proportion of building mass per unit floor area. The same material composition (standard building) and distribution of heating equipment types were adopted for both sectors as a first assumption.


1094

Shaded surface stripes in Figure 4 show uncertainty regions of storage capacity due to the combined influence of several uncertain input parameters together, such as hours of space heating at full load, allowable temperature ranges, EPF ranges, and process parameters, especially internal buffer sizes. The last parameter makes the uncertainty band particularly large for the storage category of heating buffers, which are shaded in light green in Figure 4. Not all parameters are treated as uncertain; number of households, stock of HVAC equipment, building mass fractions, and specific heat capacity of buildings are assumed to be known and fixed. Hot-water capacity from five million electric water heaters in Germany (f = 1) is indicated by two solid lines (min/max) for comparison with the building structures. The logarithmic diagrams highlight the orders of magnitude. Air zones and heating buffers together relate to the building envelopes and cores roughly as 1 to 10–100 to 1000. By far the largest electrically effective heat is in the structural thermal mass of building cores (floors, slabs) due to their sheer mass and despite water in tanks having fourfold higher specific heat capacity and larger temperature ranges. The shares of separate water heating, buffers in heating circuits, and zone air together are below 10 %; zone air is negligible.

In the four pie charts in Figure 5, the percentages of total capacity are indicated for different storage types; the share of separate electric water heating (residential, maximal LM utilization f = 1) represents the same absolute quantity in all four diagrams and is shown as a common reference. Figure 6 summarizes the absolute contributions to capacity from different use cases and heat carriers by assuming average values for the uncertain parameters and maximal LM utilization. The maximal heat capacity exploitable was, at most, 209 GWh (177 R + 32 NR) during heating and 46 GWh (12 R + 34 NR) during cooling, and less than 5 GWh for separate electric water heating. Assuming low utilization (f = 0.001) for default heating and moderate f = 0.1 for cooling, the potential drops to 81 GWh (69 R + 12 NR) for heating and about 6.6 GWh (2.3 R + 4.3 NR) for cooling (not shown in Figure 6). For the foreseeable future, space heating will remain much more important than air-conditioning (20:1) in the residential sector. Numerical results—Control power Figures 7 and 8 illustrate the steady-state control power parameterized by the symmetric ratio ton/off [Eq. (10)] and the

Figure 5. Shares of different heat storage types in terms of total capacity. Top row: residential buildings. Bottom row: buildings from the tertiary sector. Left column: utilization rate f = 0.1; right column: f = 0.8.

Energy Technol. 2017, 5, 1084 – 1104


1095

Figure 6. Absolute heat capacity of different heat carriers during heating and cooling by assuming average values of uncertain parameters and a high utilization rate (f = 0.8). Results for R buildings are shown at the top and those for the NR sector at the bottom.

normalized holding duration [see Eq. (9 a)]; the left-hand plots show positive control power and the right-hand plots

negative control power. The surface values are actually fractions of a constant total rated power, Peff, of the selected HVAC portfolio. The portfolio chosen for illustration includes three types of heating systems with a total power of roughly 20 GW, which is close to the achievable maximum. Reference values for further portfolios are listed in the bar chart in Figure 9. The expected power that accounts for simultaneity decreases linearly with the holding time, t, to zero under the assumptions made, in particular, constant drift/linear cooling in Figure 7 [Eq. (9 a)] and linear drift/exponential cooling in Figure 8 [Eq. (9 b)]. Positive and negative peak powers for t = 0 are complementary and, in absolute values, add up to the rated power. If toff ! ton (ton/off !@1), the probability of finding loads switched on is maximal and so is the positive power. Conversely, if toff @ ton (ton/off !1), the positive power tends to zero and the negative power becomes maximal. In practice, when HVAC systems are undersized with respect to demands, for example, under harsh ambient conditions or with poorly insulated buildings, the systems are heavily loaded and chances are highest of finding devices to shut off. Conversely, when systems are oversized for the job and are heating well-insulated homes, the number of idle devices that can be switched on is highest. The results confirmed our initial remarks on control power. So far, power potentials are based on the constant rated power. The level of power actually activated for heating or cooling, depending on ambient temperature, further influences the control power potential. The seasonal active heating power level is a more meaningful reference value than those of installed or rated power. In Figure 10, the active power function of ambient temperature is plotted relative to some yearly average power; see Appendix A-4 in the Supporting Information for details. Above the lower heating threshold temperature (e.g., 15 8C) and below the upper cooling thresh-

Figure 7. Expected positive (left) and negative (right) control powers of TCL aggregation, as a function of the cycle ratio, ton/off2[@1,1], by using Equation (10) and the normalized holding time t/t¯on j off2[0,1]. Flexibility is estimated by using Equation (9 a), assuming constant drift (constant temperature derivative), that is, linear zigzagging of temperatures within the (thermostat) bounds, for simplicity.

Energy Technol. 2017, 5, 1084 – 1104


1096

Figure 8. Expected positive (left) and negative (right) control powers of TCL aggregation, as a function of the cycle ratio, ton/off, and the normalized holding time. Flexibility is estimated by using Equation (9 b), assuming linear drift (temperature follows the linear first-order ODE in Figure 1), that is, exponential heating and cooling within the (thermostat) bounds.

Figure 9. Different scenarios and utilization rates for LM, and the resulting electric power ratings of HVAC that determine the maximum or base value of the available control power (installed electric power of domestic P2H in Germany).

old temperature (e.g., 25 8C), the active power of HVAC almost vanishes. We conclude that for a large part of the year in Germany no control power would be available, except from separate water heating, stationary cooling (fridges, freezers, cold warehouses, hospitals, etc.) and ventilation; the bulk of these exist in public or commercial buildings. This finding seems to contradict a result by Kouzelis et al.,[56] who observed most residential flexibility in the transition periods between summer and winter. However, their heat pumps were assumed to operate all year, independent Energy Technol. 2017, 5, 1084 – 1104

Figure 10. Active HVAC power parameterized by outside temperature as a more meaningful reference for power flexibility than rated power; see Appendix A-4 in the Supporting Information for details. Three curves for different heating threshold temperatures (HTT) are depicted, assuming air-source heat pumps for which both the heat load and the power conversion factor COP/EPF depend on ambient temperature.

of heating or cooling demands. Control power, which is not distinguished by sign, would, according to our terms, be the minimum positive and negative powers, which would indeed become maximal under moderate loading of the heat pumps; this would most likely occur in spring and autumn. It is illustrative to compare the offered storage capacity of maximally 200 GWh and control power of 20 GW with demands. For increasing levels of RE penetration in Germany (in three scenarios: 50, 80, or 100 % PV and wind), Hartmann estimated the required control power to be 27, 78, and 139 GW,[29] respectively, when all RE power was fed into the grid; energy capacity demands amounted to 245 GWh,


1097

6.3 TWh, and 83 TWh, respectively. For the targeted higher levels of 80 % (or even 100 %) RES coverage, the demand would grow at an exponential rate to numbers not realizable by LM.[29] The degree or percentage of curtailment correlates with the amount of storage deployed. If just 1 % of intermittent RE were curtailed, the demands would decrease noticeably. Some authors, for example, Brouwer et al.,[26] take large PV and wind curtailment as a reasonably economically viable option. Discussion of TCL abstraction Detailed simulation models at the building system level with a focus on P2H, in contrast to our quick characterization of an entire building stock, show that uncertain but influential parameters exist well beyond control (TCL) abstraction, for instance, in the heat distribution system.[48, 49, 57–59] Schneider et al. from ORNL investigated a load model for a reversible heat pump based on a four-state diagram driven by ambient operating conditions, instead of a two-state TCL model.[58] Patteeuw and Helsen compared several simplified building models with one detailed reference model that contained a heat pump with variable COP.[48] With the same power and energy demand, indoor temperatures deviated in the order of : 1 K.[48] To be cautious with thermal comfort, we may shrink the thermal bandwidth by 2 K and note that control power, in general, is not very sensitive to the thermal bandwidth parameter (Appendix A-2 in the Supporting Information mentions a simplified estimate independent of the bandwidth). Estimating the power input flexibility of general HVAC control systems (continuous, multivariate, and nonlinear because of bounded power input) backwards from their task specification, which is to keep target variables in a bounded region, is highly relevant for DSM. Flexibility from relaxed but permanently valid control invariants, unlike the more common batch tasks specified by power or energy invariants, could be stated concisely as an inverse robust control problem in the trajectory space. However, closed formulae that provide sufficient (conservative) but nontrivial bounds seem to be lacking. Timing constraints and planning horizons are crucial parts of the problem. Model-predictive control[23, 35, 44] or optimum planning, based on objectives and (in-)equality constraints,[11] are standard but complex forward methodologies that may yield power flexibility as a byproduct of an optimal schedule (the deviations of power consumption might be triggered by artificial cost spikes). Such flexibility estimate then applies to one problem instance, that is, holds for the specific initial and boundary values.

Conclusions In smart grids with a high penetration of wind and PV power, the short-term demand response from domestic appliances operating as P2H can make significant buffering contributions. The statistical flexibility found in load profiles aggregated from many individually controllable consumers Energy Technol. 2017, 5, 1084 – 1104

seems to form a custom-fit and long-awaited counterpart to the volatility of many small producers of renewable electricity. Herein, a top-down and fast method for the technical assessment of HVAC LM potentials at a large scale was developed and applied to the building and HVAC stock in Germany. The attribute technical refers to the thermal and operational constraints obeyed, but the analysis requires no simulation or scheduling methods in the time domain. The two core criteria are storage capacity and control power. Capacity is linked to building mass, which is roughly known per unit area of floor space. Capacity assessment therefore relates to the proportional floor areas heated or cooled by each HVAC type. Control power is linked to the control goals of HVAC devices, their simultaneity factor, and ultimately to the shares of installed total electricity. Capacity assessment differs from previous work because the entire building mass contributes to capacity, however, only to the extent of usable temperature ranges. The offered storage capacity results in the order of 200 GWh. Control power held for a given period was derived under steady-state conditions by simplified TCL modeling. At most 20 GW storage control power, positive and negative combined, are thus obtained. Validation of the roughly calculated potentials by running detailed and already validated simulation models or by computing resource schedules, as well as critical review and comparison with established grid storage systems, such as batteries, pumped hydro, and compressed air-cavern storage, are still required. However, a first look into relevant work and studies on grid storage, such as that by Hartmann,[29] reveals that the offered storage capacity in the order of 200 GWh, at most, and the storage power of 20 GW, at most, are in the range of the needs of 245 GWh capacity and 27 GW storage power in Germany, when 50 % of electricity production stems from renewables. On the other hand, one has to consider that HVAC contributions are small within the band of ambient temperature (15–25 8C) that requires neither heating nor cooling. In this range, only separate electric water heaters, freezers, permanently cooled buildings or warehouses, and mechanical ventilation contribute to balancing power, which might explain why several US researchers, notably Hao et al.,[45, 46] focus on fans in public buildings to provide short-term demand response. There are several avenues for future work. Spatial distribution of potentials could be provided easily from a resolved map of population density in Germany, since all potentials derived herein are spatially distributed proportionally to the domestic electricity demand density. The ramp rates of HVAC aggregations and the time needed to fully recover after spending control power may be quantified by using envelope bounds recently developed by Tindemans et al.[60] and that already used by Teng.[40] A possible conflict in goals between demanding steep ramp rates and fast recovery times should be investigated further. The input flexibility possible from flexibly bounded goal variables, as mentioned previously, should be characterized


1098

systematically for important classes of continuous control systems to establish conservative, but nontrivial bounds. For practical potential assessment, transition paths of HVAC becoming smart-grid ready must be better understood. Will a new generation of HEMS evolve soon, prevail, and replace the old HVAC control systems/BAS within a short time? These new HEMS and the information architecture to integrate them into the grid must solve many problems at once: providing thermal comfort and saving electricity cost for residents, supplying flexibility to the grid, and ensuring information privacy and security. Or, more likely, will we see coexistence of these systems for a long time, leading to work around (or on top of) many legacy heating control systems, especially in residential homes, or will only the most rewarding P2H examples, such as district heating or cooling networks, be developed? The course and progress of that transition process will greatly influence the realizable potential of storage capacity and power. Within the Energy Lab 2.0 test-bed at KIT,[61] we will have combined simulation and power hardware-in-the-loop facilities to perform realistic experiments with P2H applications, including real HVAC control hardware and software.

Experimental Section HVAC use cases Six use cases and their underlying information basis were analyzed in detail: default heating, electric heaters, heat pumps, space cooling, goods cooling, and electric water heating. Due to the variety of data sources, the reference years were in the range 2010–2015, but not identical. In some cases, complementary or even inconsistent information from different sources on the same feature was found, and sometimes we could not assure that the semantics of data found (if any) complied with our intention. Key parameters for assessment are summarized in Table 1.

Default heating Default heating subsumes all combustion-based heating, in which heat is generated in a boiler and transferred to a heating circuit and electricity is required mainly for transport. Distinction by energy carriers is not important (oil, gas, coal, wood pellets, biomass, or biogenic fuels), neither is the spatial organization, so that heat is generated locally by central heating or distributed by district heating, nor is the process principle, that is, if fuel is used for heating directly or if waste heat from another process is used, which primarily or incidentally generates mechanical or electrical power (e.g., cogeneration). For LM potential assessment, we throw much in the “default pot” that is differentiated cleanly in energy statistics or sustainability studies. Heat transfer requires pumps for heating water circulation and for liquid or gaseous fuels, and furthermore, fans for ventilation. In principle, almost any HVAC system requires some of these. In combustion-based heating, only these power-consuming components are usable for LM. In the US study by Westphalen and Koszalinski,[62] they are referred to somewhat misleadingly as parasitic equipment, although heat distribution is mandatory and not intrinsically wasted energy. Because no correspondingly detailed European or German study was found, design load intensiEnergy Technol. 2017, 5, 1084 – 1104

ties (DLIs), specified as installed power per unit floor area, for auxiliary equipment were transferred from the US study.[62] The study was made mainly for commercial building applications, but many components do apply to R heating. Others belong to central air-handling systems typically found in large NR buildings, and still others are typical of space cooling. DLI figures for equipment found in both R and NR buildings, in central or district heating and cooling systems, were adopted as the universally valid ones. DLI values below 0.01 W ft@2 were neglected and ranges replaced by their mean, low, or high values, whichever seemed more appropriate for central European conditions; the resulting specific DLI values are shown in column 5 in Table 1. The installed power of 14.6 (R) and 27.1 GW (NR) for 1600 to 2100 full load hours per heating period[88] yields 23.4– 30.7 (R) and 43.4–57.0 TWh (NR), respectively, for power demand (column 4 in Table 1), which is close to the value of 17.5 TWh for circulation pumps alone and surveyed by the German Energy Agency (dena). Relating the total yearly heating demand of 600 to 780 TWhth to the alleged parasitic electricity demand TWhel, results in EPF > 10 for R and EPF < 10 for NR buildings. Another source, the FfE study,[63] cites a range of 10 to 25, that is, 4 to 10 % electricity share of default heating in column 6 in Table 1. A largely open question remains as to how many default heating systems would be available for LM. In the case of R systems, it depends on whether and how feed pumps for heating water are controllable to jointly regulate power consumption and thermal comfort, for example, by modulating the speed or flow rate. The focus herein is on district heating networks[64] with concentrated electrical equipment. In contrast to liquid fluid pumps, mechanical air ventilation (fans) can be well managed through the air flow rate to jointly satisfy building users and the electricity grid, as studied in numerous works, deemed feasible, and even assessed economically,[46] mostly for public buildings. Default heating systems that still account for the bulk of HVAC systems in Germany therefore have a low utilization rate for LM that should reflect roughly the percentage of households connected to district heating. These households should be available (indirectly) as back-end storages for LM from the source electrical facilities to generate and distribute district heat. Although herein building and HVAC equipment stocks are accepted “as is”, more promising new scenarios could be built by upgrading combustion heating, for example, by adding a direct P2H facility, such as an electric boiler, or by enlarging the water buffer storage. In district heating grids fired by CHP plants, the heating demand in some periods could thus be covered by surplus electricity from the grid.[65] Even small gas or oil condensing boiler systems in single- or two-family households could be equipped with an extra 500–1000 L hot water tank and an electric heating rod (about 6 kW, see Ehrlich et al.[66]) At a larger scale, new configurations similar to this would affect our assessment results as follows: The heating rod absorbing excess electricity, rather than generating it (CHP), increases the power share of default heating from , 10 to + 95 % at times, and thus, delivers much more negative control power. A higher storage tank volume, compared with our dimensioning assumptions, together with a larger allowable temperature spread of : 20 K, boosts the power-equivalent storage capacity of the building mass.


1099

Because we lack nationwide figures on the market development potential of such scenarios, we forgo including them in our calculations.

with one scenario discussed in Ref. [69] (see Figure 5-10 therein). The increase may, however, be offset partly by technological innovation scenarios that increase energy efficiency.[75]

Electric heaters

We note that auxiliary electric equipment mentioned in the section on default heating applies to space cooling with an even higher specific DLI of 29 W m@2, but is included in the figures on space cooling already, most of which is due to the compressors.

Electric heaters comprise direct and (night) storage heaters. The surprisingly high number of 1.6 million installed units in Germany (column 2 in Table 1), according to an ifeu study,[67] is even surpassed by figures from BUND Germany. Heated floor area is taken from a Prognos study,[68] and an electricity demand of 21.4 TWhel/a (for 2015) is reported in GobmaierQs PhD thesis published by FfE.[69]

Heat pumps Heat pumps for space conditioning, often in combination with PV self-generation and integrated storage tanks, are especially attractive for low-energy homes, and their potential for LM and smart-grid integration has been analyzed in detailed buildinglevel simulation models.[33, 35, 41, 44, 45, 48, 57–59, 70, 71] With regard to LM, uses such as diurnal load shifting prevail, that is, flexibility is planned ahead and focuses on customer bills.[44, 48, 71] Technical (e.g., forward temperature design) and weather influences (on COP/EPF),[57, 58] as well as the heat distribution system (radiators, underfloor, etc.),[57, 59] have an effect on both the thermal comfort and power flexibility. For our assessment, inventory data of air-source and groundwater heat pumps or possibly wastewater-driven ones were found in the geothermal study by GZB and ZSW,[53] in the Ecofys–Prognos study (2014),[68] and in the energy reference scenario by Prognos, EWI, and GWS (2014).[72] Numbers of installed units differed, ranging from 345 000[68] to 460 000 (EurObservQER 2013[73]) to about 788 000.[53] The total floor area conditioned by means of heat pumps (column 3 in Table 1) also comes from the Prognos study.[72] Yearly electricity demand of 3.9 (in 2010) to 5.2 TWhel (in 2015) is supported by the results reported by Gobmaier.[69] The installed heating power and heating demand are estimated to be 6.56 GWth and 11.8 TWhth/a,[53, 74] respectively, and are consistent with a heating period of 1600 to 2000 full load hours. One common weakness of surveying results is in failing to account for the large cooling contribution of reversible heat pumps in Germany. The EPF estimate of h¯ = 3.7 is a weighted average over all types and working conditions of heat pumps in Germany.[53] Values of up to h¯ = 12 are found in heat pumps without compressors that use ground water directly for cooling (concrete core activation), but their number is insignificant.

Space cooling Figures on installed air-conditioning units, air-conditioned floor space, energy demand, and installed cooling capacity were found in studies by the German Umweltbundesamt (UBA),[74, 75] FfE,[63, 69] and the European Union.[73] Slight differences are observed. The NR sector consistently accounts for the vast majority (90 %) in terms of installed units and cooling power, but the R sector is catching up. Due to urbanization, growing demands for thermal comfort, and climate change, the R and NR sectors together may experience a linear increase of annual electricity demand by 300 GWh/a in the next 20–30 years, in accordance Energy Technol. 2017, 5, 1084 – 1104

Goods cooling Goods cooling comprises small fridge and freezer storage units in households and in the NR sector, as well as chilled warehouses (only stationary ones connected to the grid are considered). The LM potential from goods cooling has been studied in depth by Klobasa,[15] Kremers et al.,[9] and by UBA,[74] and is not our main focus. According to the UBA study,[74] cooling in warehouses, food retail, and gastronomy in Germany result in 900 MWel maximum load of all cooling compressors, among them about 180 MWel in cold stores. Thermal capacity in cold stores, mainly from 1.5 million tons of cooled goods, is estimated to be close to 4200 MWhth. By assuming a COP of 2.5 (h@1 = 0.4), this makes 1.68 GWhel under our terms. Thus, the total capacity amounts to about 1 % of our building capacities, with a rated power of 4 % of the total HVAC power and 10 % of the overall space-cooling power installed. Still, year-round operation with 5000 to 6000 full load hours per year[15, 74] makes goods cooling attractive for supplying control power in seasons without any heating and cooling demands.

Electric water heating DHW is one of the earliest uses of P2H for demand response.[76–78] Water heating is often integrated into the heating system and mixed with cold water. With regard to control power, it is already included in the appropriate use case, such as default heating or heat pumps. In addition, about five million households have separate electric boilers and storage tanks in Germany, according to a study by BEI and IWU.[79] To meet a demand of 50 L of heated water per person per day assumed at 60 8C, a yearly power demand of at least 11.2 TWh is inferred. Publicly available daily tapping profiles, which show a recurring pattern, indicate that the peak-hours demand is roughly 12 times the daily average demand (depending on the actual number of people sharing one DHW storage tank, that is, the simultaneity factor). To cover such peak demand, we estimate an installed power of about 16 GW. Regarding storage capacity, after sizing guidelines from heating sector associations, the storage tank volume should cover approximately 1.5 to 2 times the daily demand of all people who receive their hot water from the same system. The guideline explains the medium storage volume in column 8 in Table 1, which is based on 5 million households with separate electric DHW. Storage tanks of integrated DHW, the size of which we do not know, are missing and the storage capacity is therefore underestimated.

Building parameters Number of households and total floor space of R and NR buildings are found in the BPIE study (2011)[80] on Europe and Euro-


1100

pean countries; more up-to-date figures for Germany are from the National Statistics Agency.[81] The floor area of heated NR buildings was grossly estimated to be 1.4 billion m2, combining and extrapolating data from a typology of the NR building stock in Germany by BMVBS (German Federal Ministry of Traffic, Construction, and Urban Development;[82] see Tables 33 and 34 therein). NR buildings include office space, public education and health services, commerce, warehouses, hotels, restaurants, and culture buildings, but exclude industrial production and the agricultural sector. Average specific gross volume, rV/A (in m3 m@2), per m2 floor area for R and NR buildings was defined by Aksçzen,[83] who investigated 154 buildings from different cities and city districts and divided them into 8 classes. Similar statistics on urban development parameters with slightly different results are also found in other sources. The ratio rS/V (in m2 m@3), another morphological development parameter, specifies mean surface area of the building envelope per m3 of building volume, for R and NR buildings. By relating envelope area to building volume, the ratio rS/V is useful to estimate heat gains and losses, and therefore, control power, and it serves to estimate the mass fractions in capacity assessment. The shape feature rS/V was analyzed on a European scale in the study by Kemna and Acedo[84] for all buildings that formed topologically separate built entities, including block development. They were classified as R versus NR by using a building typology. Average values of rS/V = 0.51 for R and rS/V = 0.32 for NR buildings were determined and used in our estimates.[84] Two further ratios, the specific mass, rMB/V (in kg m@3), and specific volume, rVB/V (in m3 m@3), of solid materials per m3 of GBV yield the overall mass of solid building materials. Several EU studies[42, ,80, 84] contain detailed information on R and NR buildings by typologies. The connection to building material depends on the building purpose and its construction year and type; in particular, wall and floor slab thicknesses. For a specific volume of building materials, u (in m3 of solid materials per m3 enclosed space), the relation-

ship n / GBV@1/3 and the formula n / l@1, in which l is the virtual edge length of the building regarded as a cube, were determined.[85] With a known material composition, the specific mass of building materials can be determined. Values depend on the wall thickness, which is also proportional to u, and the compactness of the design shape; mean values for both variables are unknown. The recommendation we followed was to consider the dependence of the building material (construction waste quantity) up to GBV = 6000 m3 and above 6000 m3 by setting rMB/V to a constant amount of 0.4 t m@3 for R and 0.3 t m@3 for NR.[85] More details reported by Arendt for the specific volume and mass of building materials painted a broadly consistent picture.[86] Thus, the following average values for R buildings can be used: n = 0.334 m3 m@3, or 0.528 t m@3, or 2.55 t @2 floor space. The essential composition of solid construction materials was analyzed by Doka for a hypothetical average Swiss building from the perspective of life cycle assessment and, specifically, disposal.[54] An advantage of this approach is to reveal the actual construction materials, but no other lifecycle components, such as worn and replaced parts, which are not targeted for estimating heat capacity. Because we are only aware of such statistics for Switzerland, we transfer these results to German conditions, ignoring thermally insignificant exterior plaster, as well as mass fractions less than 1 % and normalizing the rest to 100 % (column 3 in Table 2). The resulting heat capacity of the building material mixture has been verified to be relatively insensitive to the exact material composition. From building materials databas¯ of the Swiss standard building is es, the specific heat capacity C estimated.

Calculation of mass fractions GBV encloses the solid and static building structure and its complement, the zone air, and ignores the fact that not all building cavities or voids are conditioned zones. In the literature on material flow analysis, relatively stable numerical relationships be-

Table 2. Composition of a Swiss standard building according to reports by Mueller[85] and Doka[54] (Table 40 therein).

Building material[a]

Mass fractions

Significant fractions

concrete enforced concrete not enforced interior plaster exterior plaster brick gypsum glass steel PVC PE EPS PUR PVC sealing PE sealing colors wood treated wood not treated electric cables

0.468 0.241 0.0257 0.0128 0.181 0.0156 0.00137 0.0154 0.000806 0.000483 0.000129 0.000103 0.0000327 0.0000463 0.00036 0.00807 0.0289 0.0000693

0.468 0.241 0.0257

2800 2100 849

0.75 0.96 0.95

0.181 0.0156 0.00137 0.0154

2000 2780 2580 7800

0.9 1.09 0.84 0.49

0.00807 0.0289

850 850

2.4 2.4

S = 0.98504

Specific density [kg m@3]

1¯ = 2398.8

Specific heat capacity [kJ kg@1 K@1]

C¯ = 0.8841/8 = 0.8975

[a] PVC = polyvinyl chloride, PE = polyethylene, EPS = expanded polystyrene, PUR = polyurethane.

Energy Technol. 2017, 5, 1084 – 1104


1101

tween floor area and GBV (rV/A) and between GBV and the envelope surface area (rS/V) can be found for existing buildings in Germany, for both R and various types of NR buildings. It is expedient to subdivide the solid mass into cores, consisting of slabs, pillars, and inner walls, and envelopes, comprising exterior walls, faÅades, glazing, roofs, and floor plates.

envelope mass follows by integration over Tair2[Tmin, Tmax] [Eq. (3 e)]:

It is further assumed that the material in the building core per m2 of floor space will correspond approximately to the material of the envelope per m2 of envelope surface, and indeed for all buildings, regardless of their size, whether they tend more towards tents, tin boxes, or bunkers. It is sufficient if a stable relationship between the thickness of walls and roof structure and the thickness of slabs exists; this leads to an adjusted ratio of specific surface area per volume, rS/V, and further yields the proportions by weight for building cores and envelopes given by Equation (3 a), which augments Equation (3) (see also Figure 2):

and the expected usable temperature range is given by Equation (3 f):

rK ¼

rS=V ? rV=A 1 ðcoreÞ, rK ¼ ðenvelopeÞ 1 þ rS=V ? rV=A 1 þ rS=V ? rV=A ð3aÞ

The mass fractions of envelopes, cores, and zone air are given by Equation (3 b), (3 c), and (3 d), respectively:

MB ¼ AD rV=A rMB=V ,

ð3bÞ

mK ¼ MB rK ,

ð3cÞ

mH ¼ MB rH mT env ,

mA ¼ AD rV=A ð1@rMB=V Þ1air

ð3dÞ

mT env ¼ 1 þ

DT env &

T amb @ T min T max @ T amb ln 2 ð0; 1Þ T max @ T min T min @ T amb

T max @ T min 4

ð3eÞ

ð3fÞ

For example, a mean ambient temperature during a heating season in Germany of Tamb = 6 8C and a band of comfort of 18– 25 8C results in mTenv & 0.21 and T¯env & 1.75. Thus, Equation (3 e) yields only about 20 % of the building envelopes as usable TES at a mean temperature of 20 8C, which is barely 2 K above the acceptable minimum.

Acknowledgements We thank Prof. Ralf Mikut and Simon Waczowicz for their helpful discussions and comments on an earlier version of this work. We further thank Jens Buchgeister from the Institute for Technology Assessment and Systems Analysis (KIT-ITAS) for careful reading of the manuscript and insightful suggestions on the assessment methodology. Last, but not least, we thank the reviewers for thorough proofreading and for valuable hints to improve the presentation.

in which AD denotes a total floor area of 3.645 X 109 (R) and 1.4 X 109 m2 (NR) and other parameters are summarized in Table 3.

Keywords: computational chemistry · energy storage · renewable resources · smart grids · thermal storage

Table 3. Morphological building parameters. Sector

rV/A

rS/V

rMB/V

rVB/V

rK

rH

C¯

R NR

2.73 4.33

0.51 0.32

528 400

0.334 0.334

0.418 0.420

0.582 0.580

0.8975

Thermally usable building envelope The thermal mass of the building envelope is adjacent to both the environment at air temperature, Tamb and the thermal zones inside. If the inner zone temperature, Tair, lies within the thermal comfort band [Tmin, Tmax], which is also the usable temperature range, only a fraction of the envelope mass contains usable heat energy above Tmin. Estimation of the usable mass fractions and temperatures completes Equation (3); the dataflow is summarized in Figure 2. Assuming the case of heating here (Tamb < Tmin) and a linear temperature curve (steady state) proceeding in the normal direction of the building envelope from the outside (Tamb) inward (Tair), and assuming that the volume elements are evenly distributed along the normal axis, the temperatures of unit volume elements are uniformly distributed in [Tamb, Tair]. The usable volume or mass fraction for each value of the storage target temperature is roughly (Tair@Tmin)/(Tair@Tamb), and the mean temperature of the usable part amounts to (Tair + Tmin)/2. Finally, by assuming finally a random storage and zone temperature uniformly distributed inside the building [Tmin, Tmax], the expected fraction of usable Energy Technol. 2017, 5, 1084 – 1104

[1] M. Starke, O. Onar, B. Devault, Report ONRL/TM-2011/143, Oak Ridge National Laboratory, USA, 2011, http://info.ornl.gov/sites/publications/files/Pub30408.pdf. [2] J. Eyer, G. Corey, Report SAND2010-0815, Sandia National Laboratories, USA, 2010, http://www.sandia.gov/ess/publications/ SAND2010-0815.pdf [3] P. W. Parfomak, Energy Storage for Power Grids and Electric Transportation: A Technology Assessment, Congressional Research Service, USA, 2012, http://www.fas.org/sgp/crs/misc/R42455.pdf. [4] G. Fuchs, B. Lunz, M. Leuthold, D. U. Sauer, Smart Energy for Europe Platform GmbH (SEFEP), RWTH Aachen, Germany, 2012, http://sei.info.yorku.ca/files/2013/03/Sauer2.pdf. [5] H. Kondziella, T. Bruckner, Renewable Sustainable Energy Rev. 2016, 53, 10 – 22. [6] P. Komarnicki, Arch. Elektrotech. 2016, 65, 495 – 511. [7] T. Bocklisch, J. Energy Storage 2016, 8, 311 – 319. [8] D. S. Callaway, I. A. Hiskens, Proc. IEEE 2011, 99, 184 – 199. [9] E. Kremers, J. M. G. de Durana, O. Barambones, Appl. Energy 2013, 101, 709 – 717. [10] E. Bitar, K. Poolla, P. Varaiya, PSERC Publication 14-12, Cornell University, USA, 2014, http://pserc.wisc.edu/documents/publications/ reports/2014_reports/S-52_Final-Report_ExecSum < ? < Jan2015.pdf. [11] A. Barbato, A. Capone, Energies 2014, 7, 5787 – 5824. [12] Z. Xu, J. Østergaard, M. Togeby, IEEE Trans. Power Syst. 2011, 26, 1062 – 1071. [13] A. Grein, M. Pehnt, Energy Policy 2011, 39, 5598 – 5608. [14] A. Faruqui, R. Hledik (Brattle Group), Draft Consultant Report Prepared for the California Energy Commission, CEC-200-2007-007-D,


1102

[15] [16] [17] [18]

[19] [20] [21] [22]

[23]

[24]

[25]

[26] [27] [28] [29] [30] [31]

[32]

[33] [34] [35] [36]

[37]

[38]

[39] [40]

2007, http://listserver.energy.ca.gov/2007publications/CEC-200-2007007/CEC-200-2007-007-F.PDF. M. Klobasa, PhD thesis, ETH Zgrich (Switzerland) 2007, http://e-collection.library.ethz.ch/view/eth:29926. I. Stadler, PhD thesis, University of Kassel (Germany), 2005. I. Stadler, Util. Policy 2008, 16, 90 – 98. VaasaETT, Oxford University Environmental Change Institute, and Pçyry, Final Report funded by the European Commission Directorate General Information Society and Media, 2012, http://www.eci.ox. ac.uk/research/energy/downloads/darby12-eusmartgrids.pdf. H. C. Gils, 8th Int. Conference on Energy Economics, Vienna, IEWT, 2013, http://elib.dlr.de/83717/. H. C. Gils, PhD thesis, University of Stuttgart (Germany), 2015, http://elib.uni-stuttgart.de/opus/volltexte/2016/10398/. P. D. Lund, J. Lindgren, J. Mikkola, J. Salpakari, Renewable Sustainable Energy Rev. 2015, 45, 785 – 807. Brattle Group, Federal Energy Regulatory Commission Staff Report, 2009, http://www.ferc.gov/legal/staff-reports/06-09-demand-response.pdf. S. Koch, PhD thesis, ETH Zgrich (Switzerland), 2012, http://citeseerx. ist.psu.edu/viewdoc/download?doi = 10.1.1.700.3144&rep = rep1& type = pdf. W. Weimer-Jehle, J. Buchgeister, W. Hauser, H. Kosow, T. Naegler, W.-R. Poganietz, T. Pregger, S. Prehofer, A. von Recklinghausen, J. Schippl, S. Vçgele, Energy 2016, 111, 956 – 970. M. C. Kintner-Meyer, J. C. Molburg, K. Subbarao, J. Wang, N. Prakash Kumar, F. Zhao, G. Bandyopadhyay, L. Brackney, C. Finley, A. R. Florita, V. S. Koritarov, Report PNNL-19853, Pacific Northwest National Laboratory, Richland, WA, USA 2010, http://www.pnnl.gov/ main/publications/external/technical_reports/PNNL-19853.pdf. A. S. Brouwer, M. van den Broek, W. Zappa, W. C. Turkenburg, A. Faaij, Appl. Energy 2016, 161, 48 – 74. H. Auer, R. Haas, Energy 2016, 115, 1592 – 1601. B. Zakeri, PhD thesis, Aalto University (Finland), 2016. N. Hartmann, PhD thesis, University of Stuttgart (Germany), 2013, http://elib.uni-stuttgart.de/handle/11682/2167. F. Tahersima, P. P. Madsen, P. Andersen, IEEE Int. Conf. on Control Applications (CCA) 2013, 521 – 526. B. Roossien, Mathematical Quantification of Near Real-Time Flexibility for Smart Grids, Flexines Project Deliverable D8.1, Energy Research Centre of the Netherlands (ECN), 2010, http://www.flexines. org/publicaties/eindrapport/BIJLAGE14a.pdf. L. C. Totu, PhD thesis, Aalborg University (Denmark), 2015, http:// vbn.aau.dk/en/publications/large-scale-demand-response-of-thermostatic-loads(7a3f31e0-8942-4fe6-bb58-f59ffc288f7f).html. J. L. Mathieu, M. E. Dyson, D. S. Callaway, Energy Policy 2015, 80, 76 – 87. H. Hao, B. M. Sanandaji, K. Poolla, T. L. Vincent, Energy Policy 2015, 79, 115 – 126. R. De Coninck, L. Helsen, Appl. Energy 2016, 162, 653 – 665. F. Oldewurtel, T. Borsche, M. Bucher, P. Fortenbacher, M. Gonz#lez Vay#, T. Haring, J. L. Mathieu, O. M8gel, E. Vrettos, G. Andersson, IREP Symposium Bulk Power System Dynamics and Control—IX Optimization, Security and Control of the Emerging Power Grid (IREP), 2013, 1 – 24, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp = &arnumber = 6629419&isnumber = 6629341. FfE Forschungsstelle fgr Energiewirtschaft e.V., Final Report EnEff:Stadt—Chancen und Risiken von KWK im Rahmen des IEKP, 2012, https://www.ffe.de/download/article/269/20120820_Endbericht_DEA_ Verbund-dezentraler-Anlagen.pdf. M. Kintner-Meyer, P. Balducci, W. Colella, M. Elizondo, C. Jin, T. Nguyen, V. Viswanathan, Y. Zhang, National Assessment of Energy Storage for Grid Balancing and Arbitrage: Phase 1, WECC, Pacific Northwest National Laboratory, PNNL-21388, 2012, http:// www.pnnl.gov/main/publications/external/technical_reports/PNNL21388.pdf. F. Teng, M. Aunedi, D. Pudjianto, G. Strbac, Front. Energy Res. 2015, 3, DOI: 10.3389/fenrg.2015.00036. F. Teng, PhD thesis, Imperial College London (UK), 2015, https://spiral.imperial.ac.uk/bitstream/10044/1/25265/3/Teng-F-2015-PhD Thesis.pdf.

Energy Technol. 2017, 5, 1084 – 1104

[41] H. Harb, P. Matthes, C. Molitor, I. Stoyanova, H. Wolisz, A. Monti, D. Mgller, Technical Report: Dual Demand Site Management, E.ON Energy Research Center Series, 2014, 7, RWTH Aachen, Germany, http://www.rwth-aachen.de/global/show_document.asp?id = aaaaaaaaaamcxzu&download = 1. [42] London School of Economics (LSE), European Institute for Energy Research (EIFER), Cities and Energy—Urban Morphology and Heat Energy Demand, Final Report, London, 2014, http://epb.sagepub. com/content/41/1/138.short. [43] M. Aunedi, P.-A. Kountouriotis, J. E. Calderon, D. Angeli, G. Strbac, IEEE Trans. Smart Grid 2013, 4, 2036 – 2048. [44] D. Patteeuw, G. P. Henze, L. Helsen, Appl. Energy 2016, 167, 80 – 92. [45] H. Hao, A. Kowli, Y. Lin, P. Barooah, S. Meyn, American Control Conference (ACC), Washington (DC), USA, 2013, http://ieeexplore. ieee.org/stamp/stamp.jsp?tp = &arnumber = 6579881. [46] H. Hao, Y. Lin, A. Kowli, P. Barooah, S. Meyn, IEEE Trans. Smart Grid 2014, 5, http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber = 6839081. [47] M. Maasoumy, J. Ortiz, D. Culler, A. Sangiovanni-Vincentelli, IEEE Green Energy and Systems Conference (IGESC), 2013, http://arxiv. org/pdf/1311.6094v1. [48] D. Patteeuw, L. Helsen, 9th Int. Conf. on System Simulation in Buildings, Liege, 2014, https://lirias.kuleuven.be/bitstream/123456789/ 473921/1/P015v3.pdf. [49] S. Stinner, K. Huchtemann, D. Mgller, Appl. Energy 2016, 181, 140 – 154. [50] M. K. Petersen, K. Edlund, L. H. Hansen, J. Bendtsen, J. Stoustrup, 2013 American Control Conference, 2013, http://ieeexplore.ieee.org/ stamp/stamp.jsp?tp = &arnumber = 6579991&isnumber = 6579790. [51] S. Waczowicz, M. Reischl, V. Hagenmeyer, S. Klaiber, P. Bretschneider, I. Konotop, D. Westermann, R. Mikut, Proc. IEEE Powertech, 2015, 1 – 6. [52] S. Waczowicz, M. Reischl, S. Klaiber, P. Bretschneider, I. Konotop, D. Westermann, V. Hagenmeyer, R. Mikut, Energy Technol. 2016, 4, 163 – 176. [53] Internationales Geothermie-Zentrum Bochum, GZB-ZSW-Study, Bochum, Germany, 2014, http://www.geothermie-zentrum.de/fileadmin/media/geothermiezentrum/GeothermieCampus_Bochum/Forschung_und_Projekte/Analyse_des_deutschen_Waermepumpenmarktes/WP-StudieII_GZB_2014.pdf. [54] G. Doka, :koinventar der Entsorgungsprozesse von Baumaterialien—Grundlagen zur Integration der Entsorgung in :kobilanzen von Geb-uden, Schweizerisches Bundesamt fgr Energie, Zgrich, 2000, http://www.bfe.admin.ch/php/modules/enet/streamfile.php?file = 000000006962.pdf&name = 2003 [55] Umweltbundesamt, UBA-FB 001467, Dessau, Germany, 2011, http:// www.uba.de/uba-info-medien/3979.html. [56] K. Kouzelis, I. Diaz de Cerio Mendaza, B. Bak-Jensen, IEEE PES General Meeting, Conference & Exposition, National Harbor, MD, 2014. [57] A. Arteconi, N. J. Hewitt, F. Polonara, Appl. Therm. Eng. 2013, 51, 155 – 165. [58] K. Schneider, J. Fuller, D. Chassin, IEEE Trans. Power Syst. 2011, 26, 2425 – 2433. [59] G. Reynders, T. Nuytten, D. Saelens, Build. Environ. 2013, 64, 187 – 199. [60] S. H. Tindemans, V. Trovato, G. Strbac, IEEE Trans. Control Syst. Technol. 2015, 23, 1685 – 1700. [61] V. Hagenmeyer, H. K. C¸akmak, C. Dgpmeier, T. Faulwasser, J. Isele, H. B. Keller, P. Kohlhepp, U. Kghnapfel, U. Stucky, S. Waczowicz, R. Mikut, Energy Technol. 2016, 4, 145 – 162. [62] D. Westphalen, S. Koszalinski, Energy Consumption Characteristics of Commercial Building HVAC Systems, Vol. II: Thermal Distribution, Auxiliary Equipment, and Ventilation, U.S. Department of Energy, 1999, http://apps1.eere.energy.gov/buildings/publications/ pdfs/commercial_initiative/hvac_volume2_final_report.pdf. [63] FfE Forschungsstelle fgr Energiewirtschaft, Energy Future 2050, Part 1, Munich, 2009, https://www.ffe.de/download/berichte/Endbericht_Energiezukunft_2050_Teil_I.pdf. [64] H. Lund, S. Werner, R. Wiltshire, S. Svendsen, J. E. Thorsen, F. Hvelplund, B. V. Mathiesen, Energy 2014, 68, 1 – 11.


1103

[65] D. Bçttger, M. Gçtz, N. Lehr, H. Kondziella, T. Bruckner, Energy Procedia 2014, 46, 246 – 253. [66] L. G. Ehrlich, J. Klamka, A. Wolf, Energy Policy 2015, 87, 417 – 428. [67] ifeu—Institut fgr Energie- und Umweltforschung GmbH, Strategy and discussion paper commissioned by the UBA, Heidelberg, 2012, http://www.ifeu.de. [68] Ecofys Germany GmbH, Prognos AG, Ecofys-Prognos Heat Pump Study for the German Federal Ministry of Economics, BMWi Project 50/10, 2011, http://www.ecofys.com/files/files/ecofys_2011_potenziale_ der_waermepumpe.pdf. [69] T. Gobmaier, PhD thesis, Technical University of Munich (Germany), 2013, https://mediatum.ub.tum.de/doc/1166299/1166299.pdf. [70] R. S. Adhikari, M. Buzzetti, S. Magelli, 2011 Int. Conf. on Clean Electrical Power (ICCEP), 2011, 461 – 465, http://ieeexplore.ieee.org/abstract/document/6036292/. [71] D. Vanhoudt, D. Geysen, B. Claessens, F. Leemans, L. Jespers, J. Van Bael, Renewable Energy 2014, 63, 531 – 543. [72] Prognos, EWI, GWS, Final Report Project Nr. 57/12, 2014, http:// www.bmwi.de/DE/Mediathek/publikationen,did = 644920.html. [73] P. RiviHre, J. Adnot, L. Grignon-Masse, S. Legendre, D. Marchio, G. Nermond, S. Rahim, P. Andre, L. Detroux, J. Lebrun, J. LQHoest, V. Teodorose, J. L. Alexandre, E. Sa, Georg Benke, T. Bogner, A. Conroy, R. Hitchin, C. Pout, W. Thorpe, S. Karatasou, Preparatory Study on the Environmental Performance of Residential Room Conditioning Appliances (Airco and Ventilation), Final report, 2008, https:// www.ebpg.bam.de/de/ebpg_medien/tren10/010_studyf_08-07_airco_ part2.pdf. [74] Umweltbundesamt, UBA-FB 001550, Dessau, Germany, 2011, ISSN 1862-4359,http://www.uba.de/uba-info-medien/3979.html. [75] C. Heinrich, S. Wittig, P. Albring, L. Richter, M. Safarik, U. Bçhm, A. Hantsch, UBA Study 2014, 2014, Dresden, Germany http:// www.umweltbundesamt.de/publikationen/nachhaltigekaelteversorgung – in-deutschland-an-den. [76] A. Gomes, G. Martins, R. Figueiredo, Int. J. Energy Res. 1999, 23, 169 – 181. [77] K. Elgazzar, H. Li, L. Chang, Proc. of Canadian Conference on Electrical and Computer Engineering (CCECE), 2009, 1141 – 1146, http:// ieeexplore.ieee.org/document/5090304/. [78] T. Ericson, Energy Policy 2009, 37, 3502 – 3512. [79] N. Diefenbach, H. Cischinsky, M. Rodenfels, K.-D. Clausnitzer, Datenbasis Geb-udebestand: Datenerhebung zur energetischen Qualit-t und zu den Modernisierungstrends im deutschen Wohngeb-udebestand, Wohnen und Umwelt, Darmstadt, 2010, ISBN: 978-3-94114016-5.

Energy Technol. 2017, 5, 1084 – 1104

[80] European Buildings under the Microscope, Buildings Performance Institute Europe, 2011, ISBN: 978-9491143014. [81] Statistisches Bundesamt, Bestand an Wohnungen—Fachserie 5 Reihe 3, 2014, https://www.destatis.de/DE/Publikationen/Thematisch/ Bauen/Wohnsituation/BestandWohnungen2050300147004.pdf;jsessionid = 77F1F051FA54D35867D958528C98F66F.cae4?__blob = publicationFile. [82] BMVBS (Ed.), Online publication 16/2011, 2011, http:// www.bbsr.bund.de/BBSR/DE/Veroeffentlichungen/BMVBS/Online/ 2011/DL ON162011.pdf;jsessionid = F162201E982E5EB633DB79E77CA592D3.live21304?__blob = publicationFile&v = 2. [83] M. H. Aksçzen, PhD thesis, Karlsruhe Institute of Technology (Germany), 2012, http://docplayer.net/26674457-Adaptive-cycle-analysisof-urban-fragments.html. [84] R. Kemna, J. Moreno Acedo, Average EU building heat load for HVAC equipment (Final Report to Contract No. ENER/C3/412-2010/ 15/FV2014-558/SI2.680138), Delft, NL, 2014, https://ec.europa.eu/ energy/sites/ener/files/documents/2014_final_report_eu_building_ heat_demand.pdf. [85] “Tools for Management of Construction and Demolition Waste”: A. Mueller, EURASIA 2014 Waste Management Symposium, Istanbul, 2014. [86] M. Arendt, PhD thesis, Forschungszentrum Karlsruhe (Germany), 2001, p. 115, http://archiv.ub.uni-heidelberg.de/volltextserver/1795/1/ Dissertation.pdf. [87] K. Jagnow, D. Wolff, S. Horschler, 2012, Die neue Energieeinsparungsverordnung 2002: Kosten- und verbrauchsoptimierte Gesamtlçsungen, Fachverlag Deutscher Wirtschaftsdienst, ISBN 3-87156-499-0. [88] https://de.wikipedia.org/wiki/Heizlast. [89] If the storage contains PCM, such as paraffin, or thermochemical materials, such as zeolite, and the temperature band contains a point TL of phase change, then the latent heat L (in J kg@1) is added once, and the thermal energy factor, Qth = mjCjDTj, in Equation (1) is replaced by mjCjDTj + Lj. If both temperatures, storage and phase change, are seen as independent random variables in the thermal band, the expectation would become mjCjDTj/2 + Lj/4.

Manuscript received: October 25, 2016 Revised manuscript received: January 17, 2017 Accepted manuscript online: January 20, 2017 Version of record online: April 5, 2017


1104