Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration

Accepted Manuscript Title: Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration Authors: Morgane Le Guen, Lucas Mosca, A.T.D. Perera, Silvia Coccolo, Nahid Mohajeri, Jean-Louis Scartezzini PII: DOI: Reference:

S0378-7788(17)32161-8 https://doi.org/10.1016/j.enbuild.2017.10.057 ENB 8077

To appear in:

ENB

Received date: Revised date: Accepted date:

27-6-2017 21-9-2017 16-10-2017

Please cite this article as: Morgane Le Guen, Lucas Mosca, A.T.D.Perera, Silvia Coccolo, Nahid Mohajeri, Jean-Louis Scartezzini, Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration, Energy and Buildings https://doi.org/10.1016/j.enbuild.2017.10.057 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration

Morgane Le Guen1, Lucas Mosca1, A.T.D. Perera1, Silvia Coccolo1, Nahid Mohajeri2, Jean-Louis Scartezzini1

1

Solar Energy and Building Physics Laboratory (LESO-PB), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland. 2

Sustainable Urban Development Programme, Department for Continuing Education, University of Oxford, Rewley House, 1 Wellington Square, Oxford OX1 2JA, United Kingdom.

Graphical Abstract

Abstract The integration of renewable energy technologies and building renovation are the two main procedures for improving energy sustainability of buildings at neighborhood scale. It is a difficult, however, to optimize these procedures simultaneously. This study focuses on improving energy sustainability of Hemberg, a Swiss village with a population of about 900, through optimizing these two procedures. For this purpose a computational platform was developed, combining software CitySim, HOMER Pro, QGIS and Rhinoceros.

The energy

demand on hourly basis for the buildings in the village was analyzed through comparing the current demand with that after retrofitting according to the Swiss energy labels (i) Minergie and (ii) Minergie-P. Swiss energy maps were used to identify the most promising renewable energy sources while three scenarios were considered for solar PV integration and energy system improvements. The first scenario presents the current condition in the village, while the second scenario explores improvements in electricity generation and the third in both 1

electricity and heat generation. The results show that retrofitting of all buildings according to Minergie reduces the space heating demand by 70-85% and reduces the fluctuations in energy demand, thereby allowing the integration of more renewable energy. According to the simulations, building-integrated solar PV panels can cover the total annual energy demand of the village when considering the Minergie and Minergie-P scenarios. However, the energy system assessment shows that it is difficult to reach beyond 60% when integrating nondispatchable renewable energy technologies. Finally, and more importantly, integration of wind energy at system level has an important impact in the hub.

Keywords: Energy sustainability, Building retrofitting, Building integrated PV panels, Energy hubs

1. Introduction Building sector consumes 40% of the total energy produced globally [1,2]. Consequently, it is important to improve the energy efficiency of the existing buildings stock and to introduce on-site sustainable power generation technologies [3]. The energy efficiency of buildings can be increased by improving the energy systems [4] [5], and the thermal properties of the envelope [6], [7]. Simultaneously, onsite power generation, such as through building integrated PV (BIPV) [8] and wind turbines [9], can be introduced which will cater a part of the building demand and increase the energy autonomy of the buildings. Distributed energy hubs can also be introduced at neighborhood scale to connect renewable energy technologies at building level with energy storage and dispatchable energy sources. Such procedures improve the energy autonomy at the neighborhood scale while facilitating to integrate more renewable energy technologies. Although building renovation and onsite energy generation both reduce the carbon foot print while minimizing the operating cost, these procedures belong to different expertize between which there is a lack of proper communication [10].

Energy efficiency of buildings have been widely discussed in the recent literature [11–14]. For example, improvements in component level such as window glazing [6], insulation materials for walls, and improvements in system level, such as HVAC[5], BMS[4], have received much attention [15,16]. However, it is important to move from building scale to the neighborhood scale to match the scale of demand of distributed energy systems such as virtual power plants, multi-energy hubs [17–20]. Information about the buildings such as type of building (domestic, commercial, industrial), building envelope, material used for building components (often assumed based on year of construction) need to be collected for the entire building stock. Several recent studies have emphasized the possibility of using GIS in this context [21]. It is important to simulate energy flow of buildings at an hourly rate and consider the thermal interaction between buildings. This interaction includes the effect of shadowing, long-wave radiation due to reflectance of the buildings, and thermal boundary layer for the cluster of buildings [22]. Simulation of energy flow for cluster of buildings is more difficult than simply multiplying the energy demand of one building by the number of the buildings, as it has been done in many studies [23]. In addition, simulation of energy flow for building stock needs to be extended further considering the renovation scenarios and the possibility of generating BIPV to find promising directions to improve energy efficiency and minimize carbon footprint. Such a comprehensive assessment on existing building stocks has not performed in existing literature especially considering energy system improvements. 2

Renewable energy integration into distributed energy systems has been widely discussed in recent literature starting from simple stand-alone energy systems [24–26] up to poly-generation systems connected [27,28] to multi-energy grids [29]. Several of these studies have focused on catering the demand of domestic applications starting from single building up to neighborhood scale [29]. Sizing the energy system, however, has been the sole objective of these studies without a detail investigation about the building stock either from renovation perspective or renewable energy integration. A comprehensive study has been conducted by Orehounig et al. [30] and Karunathilaka et al. [31] on the promising energy mix for community energy systems classifying different energy technologies based on the end use of the applications (building stock). Energy system sizing, however, has not been considered in these studies. A simple energy hub including BIPV and energy storage connected to grid was optimized by Mavromatidis et al. [32] for a Swiss village, but did not consider ways of improving the energy efficiency of the building stock. This work was later extended by Wu et al. [33] who considered the building renovation but not the renewable energy integration. A representative set of buildings is considered because it is difficult to consider the building renovation requirements for all buildings in the village in the optimization. By using a representative set of buildings, the impact of the neighboring buildings and building geometry on thermal demand cannot be properly addressed. This simplification is partly because a direct coupling of building energy flow simulation and energy system optimization at the community level for more than 100 buildings is a computationally demanding task. Improving energy efficiency of buildings, integrating renewable energy technologies into buildings, and upgrading distributed energy systems to cater the demand of building stocks at neighborhood scale are important and closely related procedures. Building renovation and energy system design cannot be formulated as a simple optimization problem so as to give the decision makers the best solution. Each procedure, namely building renovation, renewable energy integration into buildings, and energy system improvement must be assessed separately and then linked to provide the best strategy. Here we present a computational platform using several existing software to handle the aforementioned research problem, focusing on the energy hub optimisation. Then, a comprehensive assessment of the building stock and energy system is conducted to evaluate the impact of building renovation and renewable energy integration at both building and neighborhood level. More specifically, we first present a brief overview of the village of Hemberg, including a qualitative analysis of promising energy technologies for the village using energy maps. Next we describe the computational platform, which combines several software, 3D modeling tools, a building simulation model, and an energy system optimization model. Included is a description of the flow from one computational tool to the other and the parameter selection for the models. The results of the study focus on the (i) impact of building renovation based on two Swiss standards namely, the Minergie and Minergie-P, (ii) renewable energy integration and (iii) energy system improvements in order to address the research gaps highlighted as well as a sustainable energy strategy for the village. 2. Hemberg An energy system at community scale was designed for the village of Hemberg (47°18′N, 9°10′ E), which is a small village with approximately 900 population, situated in Canton of St. Gallen, Switzerland. According to the Koeppen – Geiger classification [34], the village is characterized by a Cfb climate (C: warm temperate; f: fully 3

humid; b: warm summer). The village has 150 buildings, distributed primarily along the main street, Scherbstrasse, as shown in Figure 1. In order to perform the energy analyses of the site, all buildings were categorized according to their function (i.e housing, hotel, office, school, supermarket, restaurant, industry and private parking). The buildings include 95 residential, 7 commercial, 4 administrations, 10 agricultural. The village includes buildings dating back to 1920, although the majorities were built after 1960.

2.1 Available renewable energy resources Available renewable energy resources play a vital role in making energy infrastructure sustainable. This study limits its scope to evaluate the potential of the renewables wind, solar and hydro power for the energy system. Availability of wind energy. Hemberg has a good wind energy potential: the annual average wind speed at the village at a height of 50 m is 4.7 m·s-1, which corresponds to a light breeze of the Beaufort wind scale. However, already about one kilometer south from the village center, there is higher wind potential is evident, with about 5.8 m·s-1, as a yearly average. This location is ideal, as it provides strong wind potential, but it is away from the village, reducing the noise produced by the wind turbines. Availability of solar energy. The total amount of energy received from the sun by the urban areas of Switzerland, independently of urban characteristics, was estimated using a machine learning method [35], as well as by the software Meteonorm [36]. The yearly mean global horizontal irradiation for the entire country is about 1,400 kWh·m-2 whereas that for the urban areas is about 1,280 kWh·m-2. At the location of Hemberg the yearly mean global horizontal irradiation is about 1,250 kWh·m-2, slightly below the average value for the urban areas. Hydropower potential. Switzerland is well known for its hydroelectric power. The mountainous topography provides for a high head, one of the most important conditions for a hydro power plant. At present, hydro power accounts for around 56% of the total Swiss electricity generation and is used to store the excess of electricity by using the pumped hydro storage. The feasibility for a micro hydro-power plant has been investigated in this study with the help of the GIS program. The potential to build a micro-hydro power plant close to Hemberg is negligible. There are no streams that could be used to install a micro-hydro power plant. In order to reach at least 20 kW, it is required to have a head of 200 m when using a stream with a potential of 0.1 kW·m-1. Such a high flow rate is reachable even when combining several streams. It is thus concluded that there is no potential for a micro-hydro power plant.

2.2 Importance of hybrid energy system combining solar and wind energy The actual energy production of solar and wind energy sources at a given location varies over time. If their maximum and minimum productions coincide, for a given period, it is a challenge to meet the demand. To assess the complementarity of these resources, power output of set of PV panels (with a capacity of 600 kW) and wind turbines (eight wind turbines each one having a rated power of 95 kW) neglecting the losses (discussed in detailed in Section 4) are plotted in Figure 2. Clearly, the wind speed in winter is higher than in summer. 4

Therefore wind can, to a degree, compensate the reduction of electricity generation from the PV panels during winter. By contrast, PV becomes dominant during the summer when power generation from wind less than in the winter. This demonstrates that in an energy system which includes both PV panels and wind turbines they partly compensate each other and, thereby, even out great variations in the overall energy production.

3. Computational platform combining GIS, building simulation and energy system designing It is difficult to conduct a comprehensive assessment of energy efficiency, renewable energy integration, and energy system improvements for the entire buildings in the village using a single software. Here we therefore develop a design platform consisting of several existing (commercial and open source) tools so as to make the assessment. A graphical overview of the design platform used for this study is presented in Figure 3.

The study begins with collecting basic information for the buildings in the village using QGIS which is an opensource geographic information system (GIS). The 3D geometries of the buildings in the village are modelled using Rhinoceros, based on the information from QGIS. This is done to prepare the DXF data files as input for CitySim Pro[37], a building/urban energy simulation tool (citysim.epfl.ch). CitySim Pro is then used to simulate the energy flow of the building stock in the village. It allows simulating building-related energy resource flows by taking into account the location (climate and topographic profile), the buildings characteristics (shape, material, occupancy), and the energy conversion systems. The climate of the site and the topography data are provided by the software Meteonorm [36] and from the Swisstopo databases [38]. In order to perform the energy analyses, all the physical characteristics of the buildings as well as the outdoor environment are defined within CitySim Pro. These include physical properties of the building’s envelope (e.g. U-value and g-value of the windows and the opaque surfaces), infiltration rate, occupancy profile, outdoor materials etc. The specific model developed for Hemberg is presented in detail under Section 3.1. More importantly, CitySim Pro considers the interaction among buildings, that is, mutual shadings, and the outdoor radiative environment. To conduct a detailed analysis on renewable energy integration and energy system improvements the model HOMER Pro [39] is used. HOMER Pro is a micro-power optimization model initially developed by the US National Renewable Energy Laboratory and currently running as an independent service provider. Renewable energy potential, demand for multiple energy services, technical details for energy conversion devices, market prices of system components, etc. are the input data for the software. The energy demand of buildings (heating and cooling), as well as the electricity produced by renewable energy sources (e.g. BiPV) are the inputs for HOMER Pro. Subsequently, HOMER Pro optimizes the configuration of the energy system. Besides optimizing the energy system, HOMER Pro provides a comprehensive overview of the energy flow of the system on hourly basis. A detailed description about the computational model used in HOMER Pro is presented in Section 3.2. 3.1 Building simulation model

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When working at the urban scale, it may be difficult to define the tradeoff between all the required input parameters [40]. Here we use the software CitySim a urban energy modelling tool, to define the energy demand, as well as the renewable energy production, from individual building to the city scale [41]. The core model of CitySim is subdivided into the following modules: i) thermal, ii) radiation, iii) behavioral, iv) equipment [42], v) greening and vi) human comfort [43]. The tool CitySim was previously validated on site with monitoring [44], as well as through the software EnergyPlus [45], showing a good correlation between the results. CitySim was also succesfully validated with the BESTEST procedure [46]. To perform the simulations, we include enclosed space for each building, properties of the thermal shell, visible surface properties, occupancy profile, openings of the building through doors and windows, etc. It is, however, difficult to consider all these factors for the entire village. It follows that we made several simplifications in the energy demand simulations of the buildings of the village, as describe below. In our models we included all the physical available data (obtained from GIS and onsite visits); the missing data were obtained from the scientific litterature [47]. 3.1.1 The enclosed space ◦ Building envelope details are taken from QGIS. The minimum and maximum temperatures (T) are fixed at 20 C ◦ ◦ and 26 C, respectively. This means that heating starts when temperature is less than 20 C in winter and the ◦ cooling starts when temperature exceeds 26 C in summer, as required by the Swiss norm [48]. 3.1.2 Properties of the thermal shell The thermal shell properties include the insulation of the roof, the floor and the walls. The thickness of each material layer as well as their physical properties - thermal conductivity, specific heat capacity and density – is selected as desired. For simplicity, the thermal shell is assumed homogeneous between walls, floor and roof. Three different thermal shells have been simulated: (i) Base Thermal Shell (BTS), (ii) Minergie and (iii) Minergie-P. The BTS is the closest to the current situation in Hemberg. The properties of the shell depend on the construction year and the data are taken from a study performed for the city of Neuchâtel in Switzerland [47]. BTS is divided into seven classes based on the construction period (Table 1). The second and third thermal simulations follow the Minergie and Minergie-P label of Switzerland. The Minergie is a Swiss registered label, certifying that the buildings are of high quality energy performance and ensure a comfortable indoor environment for users. Minergie label can be applied for new as well as for refurbished buildings, improving their energy efficiency. The label is widely applied in Switzerland, and was recently used in Dubai [49]. These labels aim to improve the energy efficiency of buildings and notably require a U-value under 0.2 W·m-2K-1 for Minergie and under 0.1 W·m-2K-1 for Minergie-P [50]. Introducing Minergie and Minergie-P would imply to retrofit most of the buildings in Hemberg. The properties of the thermal shell considered for BTS is summarized in Table 1. 3.1.3 Glazing ratio and opening characteristics The glazing ratio is the ratio between the total window area and the total facade area. The openable ratio represents the maximum proportion of the window which can be opened. The opening characteristics include the glazing ratio, which can be adapted according to the facade direction (North N, South S, East E and West W), the openable ratio and the properties of the window (U-value and g-value). Three different layouts or types have 6

been simulated for the windows. The first window type is the closest to the current situation in Hemberg and named the base case. The thermal characteristics of this window depend on the construction year. The corresponding values are taken from the village Zernez in Switzerland and are based on the study of Orehounig et al. [30]. As for the thermal shell, the second and third simulations follow the Minergie and Minergie-P label of Switzerland respectively [50]. The U-values of the windows, according to the period of construction and to the label, are summarized in Table 2. 3.1.4 Surface properties and installation of roof top PV The reflectance measures the effectiveness of a surface in reflecting radiant energy. The reflectance from all the visible surfaces including solar panels is considered in this study. The reflectance is assumed to be 0.2 for both the ground and the building facades of Hemberg. Solar thermal panels were not considered in this study. The PV ratio is calculated for each surface of the scene, which is defined as the ratio between the PV panel area and the roof area. The PV installation is considered at different levels. Initially, simulation of the building stock is conducted without PV panels. Subsequently, PV panels are installed on all the building roof tops and facades. However, due to the low power generation of facade PV panels, only rooftops considered in the third simulation. Taking into account the requirements for connecting equipment and the various shapes of the roofs, the assumed maximum PV ratio is 75%. 3.1.5 Thermal demand simulation Thermal demand in a building is affected by occupants and electrical appliances. Occupancy profile and use of electrical appliances are well connected to each other. The thermal load in a building due to the occupants depends on thermal gain of an occupant, the number of occupants per building, and the occupancy profile. A person who is sitting generally produces 70 W·m-2 whereas a person who is walking produces around 110 W·m-2 [51]. It is assumed that occupants get involved in activities such as sleeping, cooking, cleaning etc. while they are in house. The latent heat is consequently assumed to be equal to 90 W for houses. The number of occupants per building has been calculated based on the Swiss norm SIA 2024 which provides the surface area per person [m2/P] depending on the function of the building. In a similar manner, the thermal demand due to electrical appliances is calculated. Although thermal demand is calculated, it is not considered in the sizing of the energy system. 3.1.6 Electricity and water heating demand simulation The hourly electricity demand of Hemberg is simulated based on Swiss norm SIA 2024. The norm SIA 2024 provides the annual electricity needs [kWh·m-2] for lighting and appliances depending on the function of a building. Although the norm SIA 2024 provides the hourly usage profiles for appliances, the lighting profiles are missing. The lighting profiles have been created based on the hourly short-wave irradiation data simulated using CitySim tool. We consider that lighting is on if the solar irradiance corresponds to 0 W·m-2 during the daytime (from 7:00 to 18:00 hours), according to the occupancy schedule, and 5 hours during the night time [48]. Influence of the season and the building type are considered when calculating the electricity demand for lighting. Based on hourly usage profiles and the annual electricity demand [kWh·m-2], hourly electricity demand [kWh·m-

7

2

] is deduced for each building. Demand for hot water is also calculated based on SIA 2024 [48]. In this study it

is assumed that hot water is generated using electrical heater. 3.2 Outline of the energy systems and design tool The superstructure of the energy system plays a vital role when integrating renewable energy technologies. Three different scenarios with different superstructures for the energy system are considered in this study, that is, Version 0, Version 1 and Version 2 (Figure 4). Version 0 presents the current energy system in Hemberg and the two others present two promising superstructures for the energy systems. Subsequently, an energy-economic analysis of the system is conducted using HOMER Pro. Brief overview about the super structures of the energy system and computational model used in HOMER Pro is presented in this section. 3.2.1 Overview of the energy system The existing energy system in Hemberg is considered as the baseline for the assessment. Currently, only five houses have installed PV panels on the roofs. This is a small proportion of the available roof area for installing PV panels. At present, majority of the houses in Hemberg use boilers for space heating. Version 0 takes into account the present condition of the energy system in Hemberg. Version 1 considers the integration of both PV panels and wind turbines along with energy storage to satisfy the demand. However, improvements in heating system are not considered in both Version 0 and Version 1. In Version 2 it is assumed that heating demand is provided for by heat pumps. The village can be divided in two different sectors, i.e. the inner part which has a high density of houses and the outer part which has a low density of houses and consists of larger individual houses. In version 2, the inner part is assumed to have a centralized geothermal heat pump while the outer parts of the village are equipped with individual air-water heat pumps (Figure 5). Outline of the energy systems considered is presented in Table 3.

3.2.2 The computational model for energy conversion HOMER Pro uses hourly time series of demand and renewable energy potential as inputs. Demand for multiple energy services (heating and electricity) are taken from CitySim Pro. Hourly wind speed and global horizontal solar irradiation are the inputs. Wind speed at anemometer level is converted to wind-turbine hub level using Eq. 1. In this equation, Uhub and Uanem denotes wind speed [m/s] at wind-turbine hub level and the wind-turbine anemometer level. zhub and zanem denotes the height [m] of wind turbine and anemometer. z0 is taken as 0.4 considering the condition of a small village. T denotes the set of time steps considered.

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U

hub t

U

anem t

(1)

z anem ln( hub ), t  T z

Power output of wind turbine can be modelled in two different ways, i.e. using either presumed shape models or actual shape models of the wind turbines. Thapar et-al [52] showed that the latter is more accurate in many applications. HOMER Pro is using actual wind turbine power curve when calculating the wind power. In this study, wind turbines of Nothern Power NPS100C-24 (95 kW) are considered. Similar to wind power, solar power generation is calculated on hourly basis. HOMER Pro takes the hourly global horizontal solar irradiation as the input and uses an iso-tropic model to compute the tilted global solar irradiation. Finally, the power output from the PV panels is calculated using Eq. 2, namely:

Pt PV  Y PV f

PV

(

Gt )[1   p ( t   STC )], t  T G STC

(2)

where YPV, fPV, Gt and GSTC denote rated capacity of the PV array [kW], PV derating factor [%] (taken as 80 %), solar radiation incident on the PV array [kWh·m-2] and incident radiation at standard test conditions [1kWh·m-2]. Furthermore, αP, θt and θSTC denote temperature coefficient of power [%·0C-1], PV cell temperature [0C], PV cell temperature at standard test conditions [taken as 25 0C]. HOMER Pro considers the life time of the battery bank to be the minimum of battery float life defined by the manufacturer or the life time depending upon charging and discharging cycles. Lead acid batteries are considered as energy storage in this study with an assumed round cycle efficiency of 85%. Both levelized energy cost known as Cost of Energy (COE) and net present value (NPV) are used to evaluate the energy system. Present value of all the cash flows throughout the life time of the project is summed up while deducting the revenue form injecting to the grid when computing net present value. Included in the calculations are initial capital costs, replacement costs, operating and maintenance costs, fuel costs and the costs of buying electricity from the grid. Revenues include return from salvage and grid sales revenue. HOMER Pro ranks all systems configurations based on NPV or levelized energy cost/Cost of Energy (COE). NPV cannot be used directly to make decision related to renewable energy integration since it does not provide a levalized price for energy unit generated or catered which can be compared with the price of electricity in the grid. Hence, COE is used in this study for the assessment while the set of system solutions are ranked based on NPV (considered as the objective function.) 4 Results and discussion Building renovation, renewable energy integration, and energy system improvements can be directly evaluated based on the investment required and the subsequent lifetime returns, considering the building renovation or energy system improvements individually. For simplification, scenarios are defined considering building renovation, integration solar PV into buildings, and energy system improvements. Subsequently, different procedures (building renovation, renewable energy integration and energy system improvements) are compared based on the different scenarios in order to evaluate the contribution of each procedure. In the following analyses 9

we focus on the heating demand at the site, assuming that it is the predominant demand. As defined in the methodology, we assume that the cooling starts if the indoor air temperature is higher than 26°C. 4.1 Energy saving through building renovation Simulation of building energy demand can be used to extract useful information on promising directions towards improving the energy efficiency of the building stock. Among many outputs from Citysim Pro, hourly time series of heat demand is useful to determine buildings which consume more energy for heating. This is helpful to identify the most promising buildings for renovation. This section provides a detailed analysis of the energy demand of the village and the impact of building renovation on energy efficiency of the village. 4.1.1 Heating demand for Hemberg It is important to compare the order of magnitude of the space heating demand obtained from the simulation with existing demand data before analyzing the demand for Hemberg. Both annual space heating demand per building and the hourly variation of the space heating demand throughout the year have been examined. In order to compare the space heating demand among the buildings, the space heating demand per unit area [kWh·m-2] is calculated, considering both Base and Minergie thermal shells as described in Section 3.1. The space heating demand in Hemberg varies from 80 kWh·m-2 to 200 kWh·m-2 for the Base shell (Figure 6) and from 30 kWh·m-2 to 60 kWh·m-2 for Minergie shell for renovation. These annual heating demands are close to the order of annual average heating demand of Germany as reported in Ref. [53]. The main cause for the fluctuation in space heating demand for the Base scenario is the variation in building insulation which depends on the construction year of the building and renovation. However, the space heating demand for Base shell is notably high in compared with Minergie shell. More specifically, in the Minergie simulation we assume that all buildings have been retrofitted using efficient insulation materials. There are, however, buildings with higher heating demand even for Minergie shell such as buildings characterized by low compactness (internal volume to external envelope area ratio) and shaded by neighbouring buildings. The three simulated scenarios described in Section 3.1 focused mainly on renovation. In addition, the sensitivity of the occupancy and appliances are also investigated. A detailed analysis is performed for the month of December, taken to represent heating demand in winter for the purpose of comparison. Indeed, the thermal shell has greater effects on the space heating demand than either occupancy or appliances. The internal gains from people and appliances help to reduce the space heating demand by 10-12% when considering the Base shell. However, the gains from people and appliances are more significant for the Minergie shell. The peak demand is significantly reduced when moving from Base shell to Minergie shell and subsequently to Minergie-P. Peak demand reduces by 78% when moving from Base shell to Minergie. Hence, it is clear that peak demand for heating can be greatly reduced by retrofitting the buildings which will not change the minimum heating demand. Renovation of buildings can thus help to minimize the fluctuation of heating demand for the village. At the same time, similar reduction in annual heat demand can be observed when moving from Base shell to Minergie shell and subsequently to Minergie-P from 70% to 85% respectively (Figure 7). Therefore, building renovation helps to minimize both annual and peak demand notably.

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Similar to annual demand for heating, it is important evaluate the hourly variation of heating demand. In order to achieve this, hourly heating profile of one building is selected (Building number 23) and plotted in Figure 8. Building number 23 is located at the centre of the village, being covered by several other buildings. More specifically, this building was built in 1945 and has poor thermal insulation. The space heating demand fluctuates slightly on hourly and daily basis. However, a notable change is observed on monthly and seasonal basis where peak is during the winter. This fluctuation makes it challenging to integrate renewable energy technologies such as PV and wind energy. The main advantage of renovation is to minimize the peak demand and the fluctuation for heating demand so as to make it easy to cover the reasonable fraction of the demand using renewable energy sources.

4.1.2 Electricity demand for Hemberg Electricity demand of the village plays an important role especially during summer where there is little heating demand. The electricity demand for each building is presented in Figure 9. For individual houses in Hemberg the lowest electricity demand is between 2,000 and 10,000kWh per year. Buildings with industrial applications, supermarkets and block of flats have the highest electricity demand, 34,000kWh per year. According to the Association of Swiss Electricity Companies (VSE) average annual electricity demand of Swiss households and industries, respectively, are 4,500 kWh and 150,000 kWh. However, industrial buildings are quite small in Hemberg. Hence, it seems that the calculations performed with the norm SIA 2024 lead to a good order of magnitude for Hemberg electricity demand. Using norm SIA 2024, the annual electricity demand of Hemberg for lighting and appliances is 1,882 MWh. The ratio between the heating demand and the electricity demand is a good way to check the consistency of the energy data. The annual heating demand is usually three times the one needed for electricity in central Europe. Since this ratio is equal to 3.8 for Hemberg, the demand for electricity looks realistic. 4.2 Potential for building integrated PV We use CitySim in order to provide short-wave irradiation data for each surface of the buildings in Hemberg. The short-wave irradiation data is useful to determine which building facades are well exposed directly to the sun and thus suitable for installing PV panels. Figure 10 represents the annual short-wave irradiation of Hemberg on building facades and roofs. As shown in Figure 10, walls do not presents a notable potential for solar panels, although the roofs are all well-radiated. The walls marked in dark blue receive insignificant amount of irradiation due to the shadows of the other buildings as well as their non-favorite orientation toward sun. The CitySim Pro simulations of the buildings can provide the electricity production from the solar panels considering the solar irradiance received on each surface of the building which is considered having PV panels. Two different scenarios have been simulated in this study, that is, PV panels installed on all the roofs of Hemberg and PV panels installed only on roofs with a minimum irradiance of 1,000 kWh·m-2 which is shown to be the threshold limit for PV integration [54]. Figure 11 represents the annual PV electricity production per building for both scenarios.

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Buildings number 26, 92 and 128 represent individual houses with the highest PV electricity generation capacity due to their large roof areas. Furthermore, these houses are far from other buildings and thus not affected by shadowing of neighbouring buildings. The PV panels which have been removed are from the buildings having either lowest or highest PV generation potential. This is because small buildings are shadowed by others and not suitable for PV installation. Buildings with higher PV potential usually have very large roof areas. Therefore, they are likely to have one part shadowed by other buildings. The total annual electricity potential from PV in Hemberg is 5,294 MWh with PV on all the building roofs. However, 5,177 MWh can be generated using PV on the selected roofs, leading to a difference of 117 MWh per year. To estimate how much of the electricity generation from PV panels can be used to cater the present electricity demand in Hemberg. We consider electricity generation from PV panels with a minimal irradiance of 1,000 kWh·m-2 (Table 4). The ratio between the annual electricity generation using solar PV and electricity demand is equal to 274% for Hemberg. Further, it is interesting to compare the total energy demand including space heating, water heating and electricity with annual PV electricity generation (Figure 12 and Table 4) for the three different scenarios. However, having a percentage above 100% in Figure 12 does not guarantee that the total demand of the village can be solely catered using PV due to the hourly fluctuation of the solar energy potential. Additionally, it is evident that the electricity produced by PV should be then converted by a heat pump, in order to produce the required heating, consequently due to the efficiency of the machine, the production is lower than the calculated one. The period for peak electricity generation is not the same as for peak electricity consumption when considering the total energy demand including heat demand (taken into discussion in detail in Section 4.3).

4.3 Energy system improvement along with renewable energy integration Hemberg has a great potential for both wind and solar energy. The preliminary analysis on roof top PV potential shows that at least 40% of the total demand can be solely covered using roof-top PV panels. This suggests the possibility of attaining fully autonomous condition using both PV and wind energy. However, simple comparison of annual demand and generation using variable renewable energy technologies such as PV and wind may lead to incorrect conclusions. There are plenty of instances where demand does not match with generation and support of grid. In this case, energy storage or a dispatchable energy source such as internal combustion generator and gas turbine is required to full-fill the mismatch. The mismatch between demand and renewable power generation requires the support of the energy system to cater the demand while maintaining the system reliability. In some cases, the system needs to depend on the grid moving beyond the boundary of the system where energy storage and dispatchable sources are not sufficient enough to absorb the fluctuations in demand and renewable power generation. Frequent energy interactions may lead to instabilities in the grid. Hence, there are constraints that have been imposed when interacting with the grid which will result in poor utilization of renewable energy (need to be dumped due to grid curtailments). This will have negative impacts from both economic and energy efficiency perspectives. Hence, number of technoeconomic factors should be carefully considered when designing energy systems focusing on improving the renewable energy fraction. In this section, a detailed energy system assessment is conducted focusing on: (i)

12

sensitivity of grid restrictions, (ii) utilization of renewable energy, (iii) super structure of the energy system.

4.3.1 Energy autonomy of the system Energy system improvements should be assessed considering all the relevant components, including wind turbines, BIPV, energy storage etc. Hence, energy system optimization is conducted considering both system configuration and operation strategy. In this section we consider the renewable energy integration (both wind and PV) in general from system integration perspective without limiting to BIPV. In order to assess the effects of grid restrictions, the energy system is optimized considering Net Present Value (NPV) as the objective function and yearly net purchased energy as a constraint. As the first step, sensitivity of grid interactions on COE and renewable energy fraction is evaluated based on the results of the optimization (Figure 13 and Table 5). COE presents the unit price of electricity (NPV is divided by number of units served which include energy units used for the system and injected to the grid). Due to grid restrictions, system tends to integrate more renewable energy sources which will reduce the number of units purchased from the grid while increasing the number of units served. Hence, COE will drop with the grid restriction although the NPV increases since initial investment required to integrate renewable energy technologies is quite high compared to the return. It follows that minimum NPV occurs when there are no grid restrictions while minimum COE will take place when grid restrictions are 50% (this particular instant). Excess power generated within the system cannot be entirely absorbed by the grid due to the grid curtailments. This will result in excess energy that needs to be dumped. As a result, when increasing grid restrictions beyond the minimum COE, resulting renewable energy integration will create a significant amount of excess energy that cannot be utilized. Therefore COE will start to increase when grid restrictions are beyond 50% (minimum). Grid restrictions are having a direct impact on the contribution of renewable energy sources. Imposing stringent restrictions for purchasing electricity from the grid allows integration of more renewable energy sources. However, the increase in renewable energy fraction is trivial. 15 % of the energy demand of Version 1 can be catered using renewable energy sources when considering the instance with minimum NPV. Figure 14 presents the renewable power generation as a fraction of the demand. When analysing Figure 14, it is prudent that large part of the renewable energy generated within the system is used to cater the demand of the system. Furthermore, restrictions to purchase electricity from the grid will increase the energy injected to the grid.

It is important to analyse the influence of restricting the energy units that are injected to the grid (the opposite of restricting energy units that can be purchased from the grid). In order to achieve this, energy system is optimized using Net Present Value (NPV) as the objective function and annual net purchased energy as the constraint. The optimization is conducted considering three scenarios i.e. 35 %, 50% and 65% constraint to selling energy to the grid. The contributions of renewable energy sources and COE for the three scenarios are presented respectively in Figure 15 and 16. Figure 15 shows that imposing more restrictions for selling electricity into grid will retard the renewable energy integration. The difference in COE when moving from 65% to 50% restrictions is quite significant when compared to the change in 50% to 35 %. The renewable energy fraction tends to reach the same

13

value irrespective of the grid restrictions to sell when reaching 80% restrictions to purchase electricity from the grid. This is due to the reason that the system approaches to the condition of a stand-alone system when restricting the net purchase energy from grid above 80%. Hence, renewable energy fraction finally approaches to the same value although the COE change due to different interaction levels due to selling. 4.3.2 Renewable energy utilization Electricity grid is not designed to absorb any quantity of power injected to it. This is the reason to impose curtailments micro-grids are interacting with grid. Renewable energy utilization becomes challenging when the capacity of non-dispatchable renewable energy sources become large that the electricity produced cannot be used or stored (and usually goes beyond the limits of grid curtailments). For grid integrated energy systems, excess energy produced is expected to be kept below 10% of the annual demand. However, there are exceptions due to the seasonal change in demand and generation. When analysing Figure 17, it is clear that when grid sale restrictions are less than 35%, the excess power generation is within the limit of 10%. For both other scenarios namely, 50% and 65%, the excess electricity generated goes beyond 10%; especially for the case where grid restrictions for selling are 65%. These results clearly indicate that grid restrictions can increase the excess electricity generated notably which will hinder the renewable energy integration. However, when considering the case of Hemberg it is within 10% for the most of the instances except for extreme scenarios. 4.3.3 Sensitivity of energy system configuration Version 1 which has be the focus so far in this section employs fossil fuel based boilers to supply the thermal load (heating). Hence, there is an upper bound renewable fraction can reach since heating demand is catered using fossil fuel based energy technology. When moving from Version 1 to Version 2 heat pumps are used to cater the thermal demand (power to heat) in which the upper bound for renewable fraction is extended further. The electricity demand is the main difference in these two systems. Version 2 (Table 6) is having a much higher electricity demand due to power to heat demand. Furthermore, a notable change in demand profile is observed when moving from Version 1 to Version 2 due to the power to heat demand (Figure 18). The demand remains the same in summer in both versions since no heating is required. However, a significant increase in winter is observed in Version 2 due to the heating demand of the village which results in an oversized energy system during the summer.

When analysing the optimum results of two systems, it is observed that when restricting the grid interactions COE tends to decrease in Version 2 (similar to Version 1). However, the minimum COE for Version 2 is reached at a later stage in compared (grid interactions are 70%) with Version 1 as shown in Figure 19. At the same time, renewable energy contribution is notably high in Version 2 since total demand can be covered using the energy system (electrical part) whereas only 25% of the total demand can be covered in Version 1. The generation of excess electricity is less in Version 2 (Figure 20) which falls below 10% except for one scenario (restrictions with 65%).

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A notable change in system configuration is observed when moving from Version 1 to Version 2 (Tables 5 and 6). Renewable energy capacity significantly increases when moving from Version 1 to Version 2 due to the increase in peak demand. This leads to a notable increase in renewable power generation within the system which can reach above 60 % of the total demand. More importantly, this is reached while maintaining excess energy generation below 10%. This shows that heat pumps will facilitates to integrate more renewable energy while maintaining the energy efficiency in the renewable energy integration process. However, PV panel capacity does not follow the trend of renewable energy capacity (Table 5 and 6). Therefore, the ratio between installed capacities of wind energy to solar energy notably increases when moving from Version 1 to Version 2 (Figure 21). This can be explained by analysing the yearly power output of PV panels and wind turbines. Wind energy potential is notably high during first and last quarter of the year while solar energy potential is notable high during the summer. When moving from Version 1 to Version 2 power to heat demand notably increases during winter and partially in spring and autumn being align with the period having higher wind energy potential. Hence, in order to cater the power to heat demand, the wind turbine capacity must increase. However, contribution of renewable energy sources notably increase in both the scenarios of Version 1 and Version 2 in compared to Version 0 which is the present situation. 5. Conclusions This paper presents a comprehensive study on how to improve the energy sustainability of the Swiss village Hemberg based on building renovation, renewable energy integration, and energy system improvement. A computational platform developed is used to determine the energy demand, building integrated PV potential and to optimize energy system configuration. The results indicate that the present heating demand of the village can be reduced by 78% by renovating buildings according to Minergie standards; this can be reduced further by implementing Minergie-P label. Roof tops are considered suitable for PV installation when considering the economic limitations. The PV panels have the potential to cater more than half of the energy demand of the village when considering the present condition of the buildings (base case). However, the annual power generation potential of PV panels can reach more than 1.5 times the annual energy demand of the village when moving into Minergie-P renovation scenarios are used. Although the PV generation can theoretically cater more than half of the energy demand of the village, energy system assessment indicates that it is difficult to reach beyond 20% for both the proposed system configurations. Nonetheless, both scenarios present a notable improvement in PV integration in compared to present status of the village. A detailed assessment of the energy system indicates that wind and solar energy can contribute at least 14% of the total energy demand considering stringent grid restrictions based of Version 1 where the possibility of power to heat is not considered. Contribution of renewable energy sources can reach 60% of the total demand considering Version 2. Wind energy plays a vital role in complementing solar energy in Version 1. This reveals the importance of having a hybrid energy system, that is, a system where one energy source complements the other. A notable improvement in wind energy potential is observed when moving from Version 1 to Version 2 since wind energy potential is following the heating demand. Although PV has a large potential, it is not economically feasible to integrate it at a large scale since PV generation does not follow the demand when considering power to heat. The long-term energy storage, which could potentially make up for the mismatch in the peak energy production and demand, is, however, not economical at present. In conclusion, the results show 15

that the present thermal efficiency of the buildings in Hemberg and renewable energy integration can be notably improved. That improvement should, however, be made with care through selecting appropriate energy technologies while considering the interactions with the grid and appropriate building renovation methods. The optimization of buildings and energy system at community level followed by a detailed assessment can be helpful in the move to the energy sustainability in Switzerland.

Acknowledgements This research has been financed partly by the EPFL Middle East and partly by the CTI (Commission for Technology and Innovation) within the SCCER Future Energy Efficient Buildings and Districts, FEEB&D, (CTI.2014.0119).

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Figure 1: (a) Map of the village of Hemberg (source: Google Maps). (b) 2D view of the site (source: Swisstopo).

Hour

Power generation (kWh)

Day

Hour

Power generation (kWh)

Day Figure 2: Yearly PV panels power output (top) and yearly wind turbines power output (bottom).

20

Figure 3: Overview of the computational platform developed to assess building renovation and energy system improvements of Hemberg.

Version 0

Present energy system of the village 1.Fully connected to the grid with BIPV 2.Boilers are used for heating

Version 1

Modified energy system of the village 1.Partially connected to the grid with BIPV and wind turbines 2.Boilers used for heating

Figure 4: Outline of the super structure of the energy systems.

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Version 2 Modified energy system of the village 1.Partially connected to the grid with BIPV and wind turbines 2.Heat pumps used for heating

Figure 5: Representation of the two areas with high and low house densities in the village (the inner, high-density part is encircled in red and the outer low-density parts are encircled in blue).

Figure 6: Annual heating demand of the village for the Base shell, considering occupancy and appliances.

22

Figure 7: Annual space heating demand of Hemberg for the simulations B-empty, B-occupancy, M-empty, M-occupancy, MP-empty and MP- occupancy.

Figure 8: Hourly space heating demand of the building number 23.

23

Figure 9: Annual electricity demand per building in Hemberg, calculated based on the norm SIA 2024.

Figure 10: Annual shortwave irradiation received by the building facades and roofs in Hemberg.

24

Figure 11: Annual PV electricity production for each building in Hemberg.

Figure 12: The percentage of total demand that PV can cater if PV panels install on all the roofs with a minimum irradiance of 1,000 kWh·m-2.

25

35

0.134 0.132

Renewable Fraction COE

25

0.130

20

0.128

15

0.126

10

0.124

5

0.122

0

0.120 0

20

40

60

80

100

Restrictions for annual net purchased energy [%] Figure 13: renewable energy fraction as A function of the restrictions for annual net purchased energy (Version 1).

25

Renewable fraction [%]

20

15

10

with sales to grid

5

w/o sales to grid 0 0

20 40 60 Restrictions for annual net purchased energy[%]

80

Figure 14: renewable energy fraction as A function of Restrictions for annual net purchased energy (Version 1).

26

100

COE [CHF/kWh]

Renewable Fraction [%]

30

24

Renewable fraction [%]

22 20 18 16 65%

Restriction for grid injection

14

50% 35%

12 10 0

20

40

60

80

100

Restrictions for annual net purchased energy [%]

Figure 15: Restrictions for annual net purchased energy as A function of the renewable fraction (Version 1).

0.16 Restriction for grid injection

0.15

65% 50%

COE [CHF/kWh]

35% 0.14 0.13 0.12 0.11 0.1 0

20

40

60

80

Restrictions for annual net purchased energy [%]

Figure 16: Restrictions for annual net purchased energy as A function of the COE (Version 1).

27

100

Renewable energy dumped [%]

30

Restriction for grid injection

25 20

65% 50% 35%

15 10 5 0 0

20

40

60

80

Restrictions for anual net purchased energy [%]

Figure 17: Evolution of excess electricity (Version 1).

Figure 18: Electricity Profile for a) Version 1 (top) b) Version 2 (bottom).

28

100

80

0.124 Renewable Fraction COE

70

50

0.120

40 0.118

30

COE [CHF·kWh-1]

Renewable fraction [%]

0.122 60

20 0.116 10 0

0.114 0

20

40

60

80

100

Yearly grid net purchased energy restriction [%]

Figure 19: Sensitivity of COE and renewable energy fraction (Version 2).

Renewable energy dumped [%]

25 65% Restrictions for grid injection 50%

20

35% 15

10

5

0 0

10

20

30 40 50 60 70 80 Restrictions for anual net purchased energy [%]

Figure 20: Evolution of excess electricity (Version 2).

29

90

100

PV

Generation of PV and wind

100%

Wind Turbines

80%

60%

40%

20%

0% 0

10

20

30

40

50

60

70

80

90

Restrictions for anual net purchased energy [%]

PV

Generation of PV and wind

100%

Wind Turbines

80%

60%

40%

20%

0% 0

10

20

30

40

50

60

70

Restrictions for anual net purchased energy [%]

Figure 21: Power generation ratio of PV to wind a) Version 1 (top) and b) Version 2 (bottom).

30

80

90

Table 1: Properties of Base Thermal Shell depending on the construction year [47].

Construction year

Before 1945

U-value [W·m-2K-1]

0.94

1946 - 1960

1.35

1961 - 1970

1.03

1971 - 1980

0.88

1981 - 1990

0.90

1991 - 2000

0.69

2001 - present

0.21

Specific Heat Cp [J·kg-1K-1] 1,000 1,045

Insulating rendering Rubble masonry

0.02 0.40

Thermal conductivity ?? [W·m-1K-1] 0.08 0.81

Insulating plaster Rendering Rubble masonry Air gap Hollow clay brick Plaster Rendering Hollow clay brick Expanded polystyrene Hollow clay brick Plaster Rendering Hollow clay brick Expanded polystyrene Hollow clay brick Plaster Rendering Expanded polystyrene Reinforced concrete Plaster Rendering Expanded polystyrene Reinforced concrete Plaster Rendering PS 30 polystyrene Reinforced concrete

0.02

0.21

800

900

0.02 0.20 0.06 0.08 0.01 0.02 0.15 0.03 0.06 0.01 0.02 0.15 0.04 0.06 0.01 0.02 0.05 0.17 0.01 0.02 0.07 0.17 0.01 0.02 0.16 0.17

0.87 0.81 0.33 0.80 0.43 0.87 0.80 0.06 0.80 0.43 0.87 0.80 0.06 0.80 0.43 0.87 0.06 2.40 0.43 0.87 0.06 2.40 0.43 0.87 0.036 2.40

1,100 1,045 1,005 900 1,000 1,100 900 1,450 900 1,000 1,100 900 1,450 900 1,000 1,100 1,450 1,000 1,000 1,100 1,450 1,000 1,000 1,100 1,400 1,000

1,800 1,600 1.2 1,600 1,200 1,800 1,600 30 1,600 1,200 1,800 1,600 30 1,600 1,200 1,800 30 2,350 1,200 1,800 30 2,350 1,200 1,800 30 2,350

Plaster

0.01

0.43

1,000

1,200

Thickness [m]

Layer material

Table 2: U-value of the windows, according to the period of construction and the label.

Construction year/ Label

U-value [W·m-²K-1]

Reference

Before 1980

2.7

Adapted from [30]

1981-2000

2.5

Adapted from [30]

2001 - present

2

Adapted from [30]

Minergie

1

Adapted from [50]

Minergie-P

0.8

Adapted from [50]

31

Density d [kg·m-3] 300 1,600

Table 3: Summary of scenarios.

Version 0

Version 1

Version 2

Thermal load [kWh]

7'274'677

7'274'677

0

Electrical load HP [kWh]

-

-

2'233'611

Total electrical load [kWh]

2'480'923

2'480'923

4'116'099

Max renewable fraction (without grid injection) [%]

0.18

25

100

Electrical peak load HP/rest [kW]

-

-

759/400

Total electrical peak load [kW]

537

537

1'152

Sale power capacity (50% of peak load) [kW]

298.5

298.5

576

Electrical average load [kW]

283

283

490

PV panels

17 kWp

optimized

optimized

Wind turbines

-

optimized

optimized

Battery bank

-

optimized

optimized

Heat pumps

-

-

considered

Grid

considered optimized

considered

Converter

considered optimized

Boiler

optimized

optimized

-

optimized

Table 4: Overview of Hemberg yearly heating demand, electricity demand and electricity production.

Description

Scenario

Value (GWh)

Space heating demand

B-empty B-occupancy M-empty M-occupancy MP-empty MP-occupancy

All roofs

8,2 7,3 2,3 1,6 1,5 0,9 0,6 1,9 5,3

Selected roofs

5,2

Water heating demand Electricity demand PV electricity production

32

Table 5: Version 1 with 50% sale power restriction1).

Yearly Restriction [%]

PV [kW]

Total Wind Production [nb*95kW] [kWh] Grid Sales [kWh]

0 (917,264 kWh)

400

6

2'976'544

387'436

10

500

6

3'001'247

410'342

20

600

6

3'061'267

445'952 508'756

30

500

7

3'187'592

40

600

7

3'268'506

540'660

50

700

7

3'346'708

568'077

60

700

8

3'526'683

643'975 687'382

70

800

8

3'611'550

80

800

9

3'797'953

745'054

90

1000

9

3'983'274

796'616

90 1)

1000

3'983'274

9

796'616

Battery banks were not present in the optimum set of results

Table 6: Version 2 with 50% sale power restriction2).

Yearly Restriction [%]

PV [kW]

Wind [nb*95kW] Total Production [kWh] Grid Sales [kWh]

0 (1,457,807 kWh)

300

11

4'923'093

731'456

10

300

12

5'085'031

856'551

20

500

12

5'207'028

920'260

30

600

12

5'270'969

959'794

40

600

13

5'444'485

1'069'748

50

600

14

5'622'394

1'168'440

60

800

14

5'779'367

1'244'026

70

1000

14

5'951'044

1'326'145

80

1200

14

6'131'977

1'423'315

90

1100

16

6'412'791

1'528'664

2) Battery banks were not present in the optimum set of results

33