Synthetic load profile generation and default sites

Energy system design projects for sites that demand several different forms of energy require profound knowledge of loads to effectively optimize the design. The hot water, heating, cooling and water demand should at least be known in hourly time resolution over the span of an entire year. The electrical energy demand profile should ideally even have a higher time resolution (i.e., minutes or seconds). For the analysis of energy flows, however, and due to the limited availability of minutely data, for this study an hourly resolution of all loads was sufficient. With this assumption, however, dynamic aspects of electric power systems could not be investigated.

Even hourly load profiles were either not available (because it was not measured in this granularity) or confidential (and therefore not published). Thus, a procedure for synthetically generating load profiles (LPG) depending on site-specific parameters (e.g., the ambient temperature) was developed. The LPG model for airport energy systems is exemplarily developed below. For the other two sites, business parks and university

 Electricity and water usage efficiency;

 Existence of travel seasons;

 Permission of night flights.

The LPG model was designed to output hourly profiles of the electric, hot water, heating, cooling and water demand. The parameters for the airport LPG model were mainly determined based on information published by Cardona et al. in a two-parted paper analyzing the Malpensa airport in Milan, Italy (Köppen climate Cfb) (Cardona, Piacentino, et al., 2006; Cardona, Sannino, et al., 2006).

In a first step, the air-conditioned terminal floor areas were related to their annual number of passengers (see Figure 4.3). Values for both were readily available for many airports around the world. Figure 4.3 shows that airports require a certain minimum floor

area (approximately 10,000 m²) up to approximately 10 million passengers per year.

Airport floor areas handling passenger numbers exceeding 10 million per annum could be fitted with a linear fit (note the semi-log plot in Figure 4.3).

Furthermore, heating degree days ( ) and cooling degree days ( ) were defined as follows (ASHRAE, 2013a):

_h,ref

(4.1)

_c,ref

_(4.2)

The numeric values of the degree days depend on the number of days ( ) that are considered (e.g., for an entire year). Hitchin & Knight (2016) determined daily energy consumption signatures for air-conditioned buildings and showed that the cooling energy demand could be related to the cooling degree days of the respective period.

Therefore, the heating energy consumption and the cooling energy consumption were related to their respective degree days. The heating reference temperature ( _h,ref C) and the cooling reference temperature ( _c,ref C) were chosen to achieve the best fit of thermal energy consumption and degree days (see also ASHRAE (2013a)).

Figure 4.3: Airports’ floor areas vs. annual number of passengers (adapted from Thiem, Danov, et al. (2017)).¹⁴

14 Data for the following airports was included in this plot: Beijing Capital International Airport (Wikipedia, 2015a), Dibrugarh Airport (Wikipedia, 2015b), Dubai International Airport (Wikipedia, 2015c), Hong Kong International Airport (Airport Authority Hong Kong, 2015a), Kansai International

88 4.1 Description of the use cases

The specific daily energy consumptions for heating and cooling (per unit floor area and day) were related to the average daily heating and cooling degree days (within a respective month) based on the monthly figures for the Milano-Malpensa airport published in Cardona, Piacentino, et al. (2006) (see Figure 4.4). Note that an average building insulation quality for the Malpensa airport was assumed. The specific heating and cooling energy demands, when the terminal buildings are equipped with good or poor insulation qualities, were estimated according to Eicker (2003). The part of the thermal load that was independent of the degree days (at and , respectively) was assumed to be also independent of the building insulation quality.

Furthermore, it was assumed that the specific heating energy demand at was equal to the hot water demand and the remaining portion of the heating energy demand at was the actual heating load.

Figure 4.4: Specific daily thermal energy consumption vs. average daily degree days: (a) Heating and hot water, (b) Cooling (with data from Cardona, Piacentino, et al. (2006)) (adapted from Thiem, Danov, et al. (2017)).

Airport (Kansai Airports, 2015), Milan Malpensa Airport, Rome Leonardo da Vinci Fiumicino Airport (Cardona, Piacentino, et al., 2006) and Munich Airport (Flight Hub Reviews, 2015; K + P Architekten und Stadtplaner GmbH, 2015; Munich Airport, 2015).

Cardona, Sannino, et al. (2006) depicted for the Malpensa airport that the electric load did not vary remarkably from day to day. An electric load profile relative to peak load was determined based on the information from this paper. Night flight restrictions were further incorporated into this profile.

Hourly terminal building occupancy profiles were created based on passenger occupancy data that was recorded for the Birmingham Airport with almost 10 million passengers per year (Parker, Cropper, & Shao, 2012). If distinct travel seasons existed for a particular airport, the number of passengers traveling during high travel seasons was approximately twice as much as the number during low seasons (Cardona, Piacentino, et al., 2006; Parker et al., 2012). The recorded passenger occupancy profiles were also adjusted for night flight permissions. The annual water consumption was determined to 15 to 30 L per passenger.¹⁵

Based on the presented data and fits, electrical and thermal loads could be calculated reversely. The water consumption was directly related to the current occupancy of airport terminal buildings. Both heating and cooling loads were computed based on the fits shown in Figure 4.4. Heat gains from the instantaneous passenger occupancy were added to the obtained thermal loads. 40% of the passengers were assumed sitting (100 W), 50% standing at rest or walking slowly (130 W), and 10% walking fast (300 W) (The Engineering Toolbox, 2015). Gaussian-distributed noise (standard deviation corresponding to 5% of the base value) was added to all results. Last but not least, the load profiles were multiplied by a factor so that their annual sums equaled those that were specified before.

Note that this Thesis only considered the airport terminal buildings but did not take the jet fuel demand into consideration. In 2014, the Frankfurt airport in Germany demanded 14.7 million liter jet fuel (kerosene) on average each day for approximately 59.6 million passengers in that year (Fraport AG Frankfurt Airport Services Worldwide, 2015). With the International Air Transportation Association specifications for Jet A Kerosene (min. 42.8 MJ/kg at 775 – 840 kg/m³ density), the average jet fuel demand equals to 5.6 – 6.2 GW, two orders of magnitude more than the energy demand for heating, cooling and electrical equipment of the terminal buildings.

Business parks

The method employed for synthetically generating load profiles for business parks was similar to the one described for airports above. However, different sources of data were used. Two of the DOE commercial reference office buildings (large- and medium-sized) were simulated in nergyPlus™ for two locations, Miami (Köppen climate Af) and l Paso (BSk) (Deru et al., 2011). The simulation results yielded the required profiles of office building occupancy, as well as the electrical load, the heating and cooling energy demand and the water consumption profiles. By analyzing different combinations of large- and medium-sized office buildings, a correlation between the air-conditioned floor area and the number of employees was derived. With this data, specific thermal energy

15 The range was determined based on data obtained for the following airports: Copenhagen Airport (Københavns Lufthavne A/S, 2015), Hong Kong International Airport (Airport Authority Hong Kong, 2015b) and Sydney Airport (Sydney Airport, 2009).

90 4.1 Description of the use cases

demands could be related to their respective degree days (see the airport LPG method for reference). The business park load profiles were then computed based on a similar reverse calculation approach.

University campuses

The floor areas of different universities (with both focus on either teaching or research) were fitted to students and campus staff figures. The hourly occupancy profiles of students and staff on campuses were assumed to be similar to the occupancy figures of business parks. Data from Newcomb, Anderson, & McCormick (2011) was processed for obtaining typical university campus electric load profiles. The specific water consumption range was predicted based on data obtained for three university campuses¹⁶. The specific heating and cooling loads were related to their respective degree days based on a similar approach as described for the airport LPG model. A university campus located in Hwaseong, Korea (Köppen climate Dwa) was used as the reference (Choi, Lee, Cho, Jeon, & An, 2014).

Concluding remarks

The fit functions and parameters for the three different sites are summarized in Table F.1 in the appendix. The heating and cooling degree days were calculated based on common reference temperatures. However, as indicated in the table, different reference temperatures were used for the three sites. On the one hand, sites exhibit different base loads (degree-days-independent, e.g., for hot water supply), on the other hand thermostat setpoint temperatures might be different. Furthermore, the selected reference temperatures yielded the best fits.

Default sites

A fictive default site was constructed for each of the three sites. The assumed parameters were tabulated in Table G.1 in the appendix. This study assumed average building insulation qualities and average electricity and water usage efficiencies. A fictive medium-sized airport with 30 million passengers per year was considered (independent of the actual airport size at each use case location). The default business park could employ 5000 workers. 30,000 students could study at the default university campus, which also could employ 2000 university staff members. The maximum photovoltaic panel (PV) and wind turbine (WT) capacities were constrained due to limited spaces in practice. Figure F.1 in the appendix shows the load profiles determined for a fictive default airport located in Sydney.