Perspectives of the Energy Consumption in the Swiss Service Sector: Modelling and Results

Transcription

1 International Conference on the Perspectives of Energy Demand and Efficient Energy Use Tuesday, 8 October 1996, POSCO Centre, Seoul, Korea Perspectives of the Energy Consumption in the Swiss Service Sector: Modelling and Results Dr. Bernard Aebischer, Energy Analysis Research Group, Swiss Federal Institute of Technology, CH-8092 Zürich, Switzerland (2/10/96) In Switzerland, the service sector accounts for about one fourth of the national electricity demand. In some urban regions, its share can reach more than half of the total electricity consumption. However, the use of electricity and the driving forces for its steadily increase were hardly investigated. Sponsored by the Swiss Federal Office of Energy and the Union of the Swiss Utilities the Energy Analysis Research Group at ETH (Swiss Federal Institute of Energy) started in 1990 a three year research project with the aim to determine the feasibility of a bottom-up model for the electricity use in the service sector [1]. The city of Zürich, one of the big cities of Switzerland, was chosen as quite complete series of recent data of electricity consumption in different sub-sectors of the service sector were available. The evolution of the service sector in other Swiss cities is quite similar so that valuable insight of the overall electricity consumption of the Swiss service sector could be reached through this particular case study. Part I: Analysis of electricity use in the city of Zürich I-1 Overview The service sector accounts in 1993 for some 56% of the total electricity demand of the city of Zürich. This share is higher than the Swiss average due to the concentration of services in urban regions and to the importance of Zürich as an international banking centre. Table 1 shows the evolution of electricity demand in the main consumption sectors: City of Zürich share of total average annual average annual electricity demand growth rate growth rate Industry 13% 0.5% - 5.9% Households 22% 1.0% 1.0% Services 56% 4.6% - 0.8% Table 1: Evolution of electricity demand in the main consumption sectors of the city of Zurich

2 Inside the service sector itself, some sub-sectors are outstanding and exhibit electricity consumption with growth rates over the average. The banking sub-sector is exhibiting the highest growth rate over the period with over 6% per year. This example shows that the dynamics inside sub-sectors and the sector itself can be very different. In table 2, the electricity consumption is accounted according to the subdivisions used in national accounts City of Zürich share of total average annual average annual electricity demand growth rate growth rate Banks, Offices, 47% 6.2% - 1.7% Stores Transport, Hotel industry 24% 2.7% 0.6% Schools, Health, Misc. 29% 4.0% 1.7% Table 2: Electricity consumption in the service sector in Zurich accounted according to the subdivisions used in national accounts The influence on the electricity demand of the number of employees, which in turn depends on economic as well as technical factors, is a well known characteristic of the service sector. The concept of electricity consumption per employee has been extensively used in such analysis. Between 1978 and 1990 the increase of electricity demand in the service sector is directly related to the increasing number of employees and an annual increase of electricity use per employee of 2.6%, reaching 5.3 MWh/person year in After 1990 there is no more increase of electricity use per employee. The interpretation of this change in the evolution of the electricity consumption per employee, suddenly constant after having regularly increased during the preceding years is not easy and more analysis is needed to understand it in detail. A partial answer may be given by the observation that important differences in electricity consumption per employee exists among the distinct sub-sectors: in hotels and restaurants this figure is as high as 7.5 MWh/person year whereas it is around 3 MWh/person year in offices and retail-stores. Another element of explanation is the difference in the evolution of this parameter among the distinct groups of activities. The most striking example is the rapid doubling of this parameter in the banking sub-sector and the rather constant value in hotels and restaurants. A further analysis of the usage patterns of electricity in this sub-sectors will help to understand these mechanisms and allow the use of these parameters with more reliability in models.

3 I-2 Bottom-up analysis A next step towards a better understanding of the electricity consumption evolution is to analyse the ways electricity is used: the equipment and the evolution of its use. The key-idea of the bottom-up approach is to build up the demand by starting from the equipment and its patterns of use, for homogeneous groups of energy-users. The first drawback of such a method is the quantity of data it requires. Another is the variability in the patterns of use of technically similar equipment. Changes in societal values are of importance and are usually not captured by simple technical parameters. It is therefore necessary to find characteristics that help limiting the analysis. Electricity consumption indicators defined on sub-groups that exhibit enough homogeneity is one of these methods. Another way is to focus on the most important consumers, as they cover most of the electricity consumption to be explained and as they usually bother much about their electricity bill and keep better account of how it has been used. Both methods were used in investigating more in detail electricity use in four sub-sectors: Banking, Education, Health Care and Retail Stores. Banking Banking is an important sub-sector in Zürich. Its share in the electricity consumption of the service sector increased from 25 to 37% between 1977 to 1990 with for Switzerland an extraordinary annual growth rate of more than 9%. A great part of it can be explained by the installation of large computing centres. Apart from these exceptional events during the 80's, the changes occurring now are the results of the improvements in efficiency and the introduction of new appliances. This is very similar to what is happening in the other sub-sectors of the service sector. A specific study of an important bank in Zürich confirmed these results: the computing centre can amount to as much as half of the total electricity consumption. It was in the mid-eighties that the computing capacities expanded very fast, leading to a doubling of the electricity consumption in five years. Since then, the growth rate has been steeply declining and is negative since The decline of the electricity consumption of computing centres results from the replacement of large computer units by computers of newer generations characterised by lower requirements in electricity for larger computing capacities. In the banking sub-sector, this trend is likely to continue in the near future. In other sub-sectors electricity use for computing facilities may still increase, but some levelling out similar to the banking sector in the 90's is probable.

4 Education In this sub-sector two consumers are predominant: The electricity demand of the Swiss Federal Institute of Technology (ETH) amounts to 50% and that of the University of Zurich to 25% of the total sub-sector. At ETH one can distinguish two main driving forces for electricity increase: the number of students and the electricity use per student. The second is more interesting since it allows a comparison with other schools. It increased from 1 MWh/student year in 1960 to 8 MWh/student year in 1980, and remained now since several years at a value of somewhat below 7 MWh/student year. Electricity consumption per student at the University is somewhere around 2 MWh/student year. This large difference is due to the difference in floor space per student and to the somewhat higher specific electricity consumption per floor area at ETH. A student at the Institute of Technology uses besides the lecture hall and seminar rooms facilities such as laboratories and computing rooms, i.e. not only more space but also space with more electricity consuming equipment. Electricity per surface area has increased at ETH from 25 kwh/m2.year in 1960 to over 120 kwh/m2.year in The mean value at the University was somewhere around 50 kwh/m2.year as late as 1978, but with its last extension of mainly laboratory buildings and computer centres it is today only slightly lower than ETH. Health Care Hospitals are taking the lion s share with 70% of the total electricity consumption of this subsector, the reminder being distributed among homes and medical consulting rooms, with 21% and 19% respectively. At the largest hospital, the University Hospital, the number of patients treated is quite stable, but the number of beds is steadily decreasing (-33% since 1960) and the number of employees increasing (+ 50% since 1960). The impressive increase of the number of employees per patient or per bed can be related to the constant increase of new medical activities and research. Most of these new medical, therapy and research activities are not replacing old ones but come in addition to them. The related spatial expansion of the period constitutes the most relevant explicative factor of the evolution of the electricity consumption of the University Hospital. The other hospitals are likely to catch up in the next 5 to 10 years. Retail Stores One of the most important results of this study was the validation of the floor area as a stable reference factor in the analysis of the electricity consumption. An important step was the introduction of a new concept: the sorted distribution of electricity intensity of a group of buildings. The electricity consumption per unit floor area (or "electricity intensity") of the different buildings of a company or an sub-sector are sketched in decreasing order in function of

5 the total cumulated area. This representation the "electricity intensity profile" is shown in figure 1 for an important company with many retail stores in the city of Zurich. kwh/m2.year Type C Type B 100 Type A Share on total floor area, in [%] Figure 1: Sorted distribution of the electricity intensity of the food stores of a large retail company It is useful to distinguish between stores with butchery (A), using a lot of commercial refrigeration, stores without any commercial refrigeration (C) and stores laying in between (B). A comparison of the electricity intensity profiles in the years 1980 and 1990 demonstrates that the increase in electricity consumption is due to the increase of the number of stores with butchery, the electricity intensity for each of the three groups remaining almost constant during this period. In the three groups, the effects of improvements in electricity efficiency and of the increase of the amounts of services delivered are compensating each other, but the relative increase of the number of the energy intensive type of store results in an increase of the mean electricity intensity of all stores. I-3 Key findings from the Zürich case study The main results of the historical analysis performed in the study can be summarised by the following conclusions: A classical bottom-up analysis, starting from the equipment and its pattern of use in each subsector, is not well-suited to the present state of the database. In the service sector, the number of different types of equipment and the multiplicity of their patterns of use are an important obstacle to model building. The lighting and HVAC installations represent 70% of the electricity use of the sector and it is particularly difficult to describe quantitatively the usage of them. The floor area is a stable reference parameter to describe the evolution of the sub-sectors. Important internal structural changes occurred in the distinct groups of economic activities during the 80's and can explain as much as 50% of the evolution of the electricity

6 consumption of the whole sector. The remaining can be attributed to the growth of the sector itself. The internal structural change affecting the individual sub-sectors is in the first order the result of the economic and social evolution. It is possible to define homogeneous groups by means of electricity intensity profiles, which can be used to measure the internal structural changes. Policies intending to favour more rational use of energy can affect the electricity intensity value on the level of the homogenous sub-groups but will not change the internal structural change of the sub-sector. Part II: Perspectives II-1 Modellisation of the electricity demand The results of the study of electricity use in the service sector of Zurich and the analysis on a more aggregated level of the evolution of electricity demand in the Swiss service sector led us to develop a "bottom-up" model based on the relation Electricity demand = Electricity intensity. Floor area The approach is in fact quite similar to models used in the heating sector, but in the present approach the electricity intensity is calculated for subgroups of the commonly used sub-sectors. These subgroups are characterised by their economic activity. We refer to these subgroups as "homogenous groups", which suggests that their main activity is homogenous regarding electricity use (i.e. electricity consuming equipment and usage pattern of this equipment). In a second step, these electricity intensities are combined with the different growth patterns of the homogenous groups within one particular sub-sector which we call "internal structural change". The result is a mean electricity intensity for each sub-sector, which incorporates both a technical component (defined by the electricity intensity of the homogenous groups) and a socioeconomic component (reflected by the internal structural change). The speed of the internal structural change is related to the general economic growth in the service sector. The change in time of the electricity intensity at the level of the homogenous groups is described by: Electricity intensity = R - (R - N). S, where R corresponds to today's practice,

7 N is a target value (e.g. today s optimum value or some standard value such as the recommendation by the Swiss Association of Engineers and Architects SIA 380/4), and S represents the share of the new or renovated buildings which reach the target value. In order to describe the interdependence of the electricity intensity and the price of energy, socalled "supply curves of conserved energy" were determined. Supply curves were calculated by two methods. A first curve is the result of a detailed engineering analysis of cost and energy savings in nine commercial buildings. A second curve is based on the observation that in a new or completely renewed building, larger savings are possible with small or even negative costs. The marginal costs for the conserved energy were derived from projected investments. The two approaches are compared in figure [CHF/kWh] 1.0 a) 0.5 b) 0.0 c) [kwh/m2.an] Figure 2: Marginal costs for conserved energy derived from detailed engineering estimates in nine commercial buildings (a) and from projected investments for energy saving measures in new average (b) and new complex (c) office buildings (1CHF = 0.8 USD) In order to use these cost curves, which are defined for single objects, in our model, they have to be "transferred" to the level of the entire building stock. The explicit solution to this task could mean a monetarisation of the transaction and other additional costs, what is just not feasible today. In our applications of the cost curves we therefore only consider the gradient of these cost curves, and not their absolute value.

8 II-2 Application to the Swiss service sector In 1993, on behalf of the Swiss Federal Office of Energy, the Energy Analysis Research Group at ETH in Zurich was asked to do a long term perspective study of the energy demand in the Swiss service sector [2]. A first task was the description of the situation in The result is shown in figure % Electricity Floor space Fuels 90% Trade 80% 70% Banks, Insurance Ind. Hotels, Restaurants 60% Education 50% Health Care 40% 30% 20% Different Services - Public Administration - Culture, Sports - Consulting, Planning % *Others 0% * Others - publ. lighting - water treatment -... Agriculture 44 PJ 124 million m 2 77 PJ Figure 3: Distribution of floor area, electricity consumption and consumption of fuels in the Swiss service sector and in agriculture in the year Approximately 15% of the electricity consumption, public lighting for instance, is consumed outside the floor area. The future evolution of economy is an exogenous input to our model: roughly constant number of employees, but steady growth of the added value: +1.4%/year until 2000, +2.3%/year , 1.4%/year , resulting in an total increase of some 90% between 1990 and The resulting increase in the floor area was estimated to some 20% in With the "bottom-up" model described earlier we calculated the evolution from 1990 to 2030 of the average electricity intensity in the service sector for different energy policy scenario. These scenario were defined by the Federal Office of Energy; they describe the energy policy of today (I), the policy proposed by the government for the future (II) and proposition by environmentalists, which has to be voted in one or two years. A short summary is given in table 3.

9 Scenario Policy status Important measures I policy today Energy Decree, Energy 2000 Program II policy planned Dynamic target values for energy using equipment and buildings, DSM, (tax on CO 2 -emissions) IIIa Initiative Tax on non-renewable energy with the objective: stabilisation of non-renewable energy until 2008, followed by a reduction of 1% per year (-> increase of electricity price by 1.5%/year) Table 3: Summary of the most important energy policy scenarios Besides the general scenario description, there are essentially two groups of input data used in our model: (1) technical and economic data, such as target values for electricity intensity in the various homogenous groups and cost estimations for energy conservation measures; (2) data describing the behaviour of individuals and groups, such as the degree of acceptance and application of energy policy measures. On the basis of this data the model-parameters are established, which determine the development of the average energy intensity. In the case of the scenario I, II and III with identical number of employees, value added and floor area the most important scenario-dependant model-parameter is the average electricity intensity in the new and renovated buildings of each homogenous group. An example is shown in figure 4 for complex office buildings. The share between the homogenous groups of the total surface of the sub-sectors has some minor importance in scenario III, where the high energy tax has some impact on the economic growth and the unemployment. 600 [MJ/m2.year] building stock average I average II new buildings 300 average I neu buildings target value average II Figure 4: Target value for electricity intensity in new complex office buildings, average value in new and renewed buildings in the scenarios I and II and average value of all buildings.

10 For a given scenario the evolution of electricity demand is quite different in the various subsectors. Besides the economic growth, it depends strongly on the technical saving potential (defined by the difference between today's average electricity intensity and the target value in the homogenous groups) and the internal structural change. An overview is given in Table 4 for scenario I. Sub-sector Variation in electricity consumption , in Scenario I Trade 17% Banks, Insurance Industry -19% Hotels, Restaurants 26% Education 38% Health Care 54% Other Activities 68% Table 4: Variation of electricity consumption between 1990 and 2030 for different sub-sectors in scenario I (energy policy of today) The results of our calculations for the major scenarios are summarised in table 5. Assuming today s energy policy (scenario I), electricity consumption will increase steadily (by some 1%/year to the year 2010 and 0.5%/year between 2010 and 2030), but at a slower pace than in the past (2.5%/year in the 80's). The consequences of reinforced and additional policy measures proposed by the government for the coming years (scenario II) are substantial: in the year 2010 a 2% and in the year 2030 a 11% reduction of electricity consumption compared to scenario I. After the year 2015 electricity consumption is levelling off and slowly decreasing. Our calculations show a rather strong long-term impact on electricity demand (-9% in 2030 compared to scenario II) of the steadily increasing tax on electricity (inducing an electricity price increase of 1.5%/year) in scenario III: the resulting long term price elasticity is of the order of 0.2. Negative side-effects of this important price increase (+ 50% in 2020) are small due to the fact that the revenues of this tax are redistributed to consumers. Year scenario I scenario II scenario III Table 5: Electricity consumption in the Swiss service sector, in [PJ/year]

11 It is interesting to analyse the evolution of electricity demand in terms of explanatory variables. Figure 5 shows for all three scenario the annual variation of electricity demand due to the variation of floor space, structural change between sub-sectors, internal structural change and electricity intensity in the homogenous groups. In scenario I the effect of the internal structural change counterbalances the reduction of electricity intensity in the homogenous groups, and electricity consumption is increasing close to the rate of floor space. In the scenario II and III, due to new and reinforced energy policy measures, the energy reduction rate due to the reduction of electricity intensity in the homogenous groups gets stronger and finally dominates the other factors after the year Resulting variation of electricity demand [%/year] Floor space I nternal s tructural change El. intensity hom. groups Szenario I Szenario II Szenario III 1.5 Struct. change between sub-sectors Figure 5: Annual variation, in %/year, of electricity demand in the Swiss service sector due to the variation of floor space, structural change between sub-sectors, internal structural change within sub-sectors and electricity intensity in the homogenous groups References [1] B. Aebischer and D. Spreng, Electricity Demand in the Service Sector of a Large City, Swiss Federal Office of Energy, 1994 (in German) [2] B. Aebischer, J. Schwarz and D. Spreng, Perspectives of the Energy Demand in the Service Sector in Switzerland, Swiss Federal Office of Energy, 1996 (in German)