Multi-criteria assesment of building integrated photovoltaics/ Daugiakriteris i pastata integruotu saules fotoelementu vertinimas.
Increase of energy efficiency in buildings sector is a key objective of the European Union's (EU) energy policy, Member States of the EU are obliged by 2020 to move towards new and retrofitted nearly-zero energy buildings. Thus buildings have to become not just very energy efficient, but they also have to use renewable energy to cover their energy demand. One of the essential technologies to reach the goal of nearly-zero energy buildings is PV solar energy. In late 1990s, interest on building integrated photovoltaic (BIPV) started to grow. During the last decades, the photovoltaic (PV) modules and their associated architectural materials are increasingly being integrated into the construction of the building envelope such as faeade, roof and skylights (Ceron et al. 2013) and are becoming an important part of modern low- and high-rise buildings (Bayoumi, Fink 2014). The ability of buildings to supply their own electricity through PV system is considered as an attractive technology for a sustainable architecture and ecological buildings (Agrawal, Tiwari 2010; Benemann et al. 2001; Yoon et al. 2011; Chel et al. 2009; Park et al. 2010; Yoo 2011; Norton et al. 2011).
Advantages of BIPV system are that energy production can be combined with other functional features of buildings, such as solar shading (decrease of cooling energy), protection of the building envelope, preheating air or water (Friling et al. 2009; Lu, Law 2013; Yoo 2011; Fossa et al. 2008). Currently adoption of this technologies varies greatly, and within, by country depending upon climate, built environment, electricity industry structure, government policies, local product offerings, market stimulation mechanisms, consumer demand, existing industrial capabilities and the forms of tariff arrangement for grid-connected PV power generation (Norton et al. 2011). Cost and efficiency remain barriers to the widespread use of BIPV.
The semi-transparent BIPV facades produce electricity, reduce solar heat gain and facilitate daylighting schemes that save lighting energy consumption and lower cooling requirements. When semi-transparent PV panels together with the dimming controls were used, the annual building electricity saving and peak cooling load reduction was found to be significant (Li et al. 2009; Lu, Law 2013). The effect of the PV window on energy consumption of office buildings in terms of heating and cooling loads, daylighting, and electricity production was also analysed by Miyazaki et al. (Miyazaki et al. 2005). The study have found the optimum solar cell transmittance and window to wall ratio, and energy savings of the building were estimated. Chow et al. (2007) presented an assessment of overall performance of PV ventilated window system executed for different window orientations, using a small office room in Hong Kong. Authors have found that a solar cell transmittance in the range of 0.45-0.55 could achieve the best electricity saving.
Lu and Law (2013) have developed an overall methodology for investigating the thermal and power behaviours of semi-transparent single-glazed photovoltaic window for office buildings in Hong Kong. Their findings show that thermal performance is the primary consideration of energy saving in the entire system whereas electricity consumption of artificial lighting is the secondary one.
According to Wong, research on the optimization effect of semi-transparent PV on power generation, daylighting and thermal utilization on total energy balance is scarce (Wong et al. 2008).
Summarizing, just a few studies investigate overall building energy consumption, when BIPV are used. These studies are performed in Asian countries and there are no studies performed in Northern European climate. In order to fill this gap, the purpose of this research is to study the overall energy performance of semi-transparent BIPV module (PV window) by taking into account power generation, daylight utilization and effects on thermal energy demand. Final judgment on total performance of BIPV is done using multi-criteria analysis.
The object of the study is an office room. Two office room cases--one with transparent faeade (Fig. 1a), another with BIPV faeade (PV window) (Fig. 1b) are analysed. Dimensions of the room model--3 x 5 x 2.5 m, it is south oriented. All internal partitions are assumed to be adiabatic, U-value of the window--0.8 W/([m.sup.2]K). Room has typical for office buildings engineering systems--fancoil heating and cooling system, LED lighting system with linear control. Ventilation and domestic hot water systems are not simulated, since their energy demand is not influenced by glazing.
Analysis is performed by following steps:
1. Energy simulation of the room cases with Design Builder software with the purpose to define overall effect (Fig. 2) of PV window on the room's energy demand and comfort. For this purpose, based on monitoring data, a weather data file with Meteonorm software was generated;
2. Estimation of electricity generation of simulated PV window based on energy monitoring data of Vilnius Gediminas Technical University;
3. Life cycle analysis of both glazing alternatives, estimating C[O.sub.2] emissions of production and demolotion phases;
4. Calculation of investments required for both cases;
5. Multi-criteria analysis based on 4 criteria: energy (total primary energy demand), ecology (C[O.sub.2] emissions), economy (investments), comfort (annual discomfort hours)--3e+c criteria.
Multi-criteria analysis is performed with the purpose to assess BIPV according to different criteria: ecology, economy, energy, comfort (3e+c). To perform multi-criteria analysis, all criteria must be recalculated into non-dimensional values. Also weight coefficients for each criterion must be estimated/assumed. The best alternative is the one with the lowest value of criteria 3e+c.
There are many methods attempting to determine weight coefficients used to perform multi-criteria analysis. In the study authors seek to estimate BIPV used to make building more sustainable, therefore criteria weights are set using one of the international methodologies of green building certification--Green Star--Office (Heincke, Olsson 2012). This methodology includes many criteria; the sum of their weights is equal 100%. According to this methodology - energy criteria weight is 22%, emissions--10%, materials--9%, comfort--18%. Proportionally in the study we assume that sum of weights of 4 analysed criteria is 1: energy--0.37, ecology (emissions)--0.17, economy--0.15, comfort--0.31. Non-dimensional values of each criteria are calculated according to Eqs (1-4) (Rogoza et al. 2006):
[e.sup.en] = 1 - [[[E.sup.en.sub.max] - [E.sup.en.sub.n]]/[[E.sup.en.sub.max] - [E.sup.en.sub.min]]], (1)
[e.sup.em] = 1 - [[[E.sup.em.sub.max] - [E.sup.em.sub.n]]/[[E.sup.em.sub.max] - [E.sup.em.sub.min]]], (1)
[e.sup.eco] = 1 - [[[E.sup.eco.sub.max] - [E.sup.eco.sub.n]]/[[E.sup.eco.sub.max] - [E.sup.eco.sub.min]]], (3)
c = 1 - [[[C.sub.max] - [C.sub.n]]/[[C.sub.max] - [C.sub.min]]], (4)
where: [e.sup.en], [e.sup.em], [e.sup.eco], c--non-dimensional criteria values; [E.sup.en]--primary energy demand (kWh/[m.sup.2] year), [E.sup.em] - C[O.sub.2] emission (kg C[O.sub.2] ekv./[m.sup.2]), [E.sup.eco]--investments ([euro]), C--discomfort hours per year (h/year); indexes: "max"--maximum criteria values, "min"--minimum criteria values, "n"--value of calculated alternative. General criteria 3e+c is calculated:
3e + c = [e.sup.en] x [S.sub.en] + [e.sup.em] x [s.sub.em] + [e.sup.eco] x [s.sub.eco] + c x [s.sub.c], (5)
where: [s.sub.en], [s.sub.em], [s.sub.em], [s.sub.c]--weight coefficient of certain criteria (energy, ecology, economy, comfort).
The energy simulation of both alternatives comfirmed the assumptions that PV window influenced thermal and visual comfort of the room as well as energy demand for heating, cooling and lighting. Figure 3a shows that solar heat gains when PV window is used decreases almost twice--from 181 kWh/[m.sup.2] x year to 94 kWh/[m.sup.2] x year. This could be expected because of almost twice smaller transparent area of the window (see Fig. 1a). Within the day different alternative gives also different heat loss, therefore total fluctuation of daily energy balance of the window within the year is given in Figure 3b. It can be seen that higher differences between alternatives are noticed during the cold season--transparent window has much higher heat loss.
Simulation results of thermal comfort in both models show that in both cases overheating in the room occurs even in winter. Reason for that is high internal and solar heat gains and low energy loss because of envelope. Since cooling system operates just from March until October during the rest months overheating problems cause discomfort. Discomfort hours are defined as the time when the combination of zone humidity ratio and operative temperature is not in the ASHRAE 55-2004 summer or winter clothes region (DesignBuilder 2015).
For the room with transparent window average annual air temperature is 30.7[degrees]C, annual discomfort based on Simple ASHRAE 55-2004 methodology--2332 hours, time when setpoint temperature is not met during occupied cooling period--260 hours, for heating period--0 hours. Meanwhile for the oom with PV window internal temperatures are much lower, average annual temperature is 26.4[degrees]C, discomfort hours--1653, cooling set temperature not met--175 hours, heating--0. This means that to keep good thermal comfort--cooling system has to be switched on all year round and this would cause additional energy demand for cooling.
Simulation of visual comfort (Fig. 4) in the room was performed at sky model CIE overcast day (10 000 Lux). Both for transparent window as well as for PV window case daylighting of the working plane meets requirements of LEED (the LEED plaque on a building is a mark of quality and achievement in green building) and BREEAM (BREEAM is the world's foremost environmental assessment method and rating system for buildings) standards (Heincke, Olsson 2012). Also 100% of area meets daylight factor requirements (4%).
As simulation results have shown, PV window has increased primary energy demand for heating almost twice--from 2.6 to 4.6 kWh/[m.sup.2]-per year (Fig. 5) (heat is produced in gas boiler, rated efficiency--0.9). This influence for tight and well-insulated building is insignificant taking into account all energy balance of the building, but can be more significant in less insulated one. Influence on lighting energy is also slight. This is because glazing WWR remains relatively high (46%) and ensures sufficient daylighting even when PV is used. The highest influence is made on cooling energy demand (PE energy factor used for electricity is 2.8, cooling system coefficient of performance (COP)--3.5), which is dominant component in energy balance of the office.
The BIPV system investigated in this study was applied for one of the buildings of Vilnius Gediminas Technical University (VGTU), which is located in Vilnius city (Lithuania) (the latitude 54[degrees]72' North and the longitude of 25[degrees] 33' East), cell efficiency 15.8%. Average electricity generation of monitored PV window system, which is installed in the same conditions as simulated office is on average 177 kWh/[m.sup.2] x year (Fig. 6). Area of monitored cells is in total 1.68 [m.sup.2] and area of simulated cells--3.64 [m.sup.2]. Simulated cells will produce proportionally--385 kWh, meaning that 25.7 kWh/[m.sup.2] of electricity demand of the office will be covered by solar energy and since it is produced inside the building, it needs no non-renewable primary energy (primary energy factor is 0). Annual primary energy demand will decrease also by 25.7 kWh/[m.sup.2]. This energy amount may cover almost 70% of rooms lighting energy demand.
Concluding energy simulation and electricity generation results, primary energy demand for transparent window case will consitute 171 kWh/[m.sup.2]-year, for room with PV window--96 kWh/[m.sup.2]-year.
Multi-criteria analysis (Table 1) shows that according to set weights of criteria analysed, alternative with PV window is more rational compared to transparent window.
Conclusions and discussion
1. Simulation results of two office room alternatives show that office with transparent window is less sustainable compared to the one with PV window. Solar gains of the office with PV window are twice smaller, therefore:
--thermal comfort is better--average internal annual air temperature is lower by 4.3[degrees]C;
--heating energy demand because of envelope is twice smaller, but insignificant in total energy balance because of good insulation of the building;
--influence on cooling energy demand is considerably lower--difference is 42%;
--lighting energy demand is influenced negatively, but just slightly--15% higher;
2. Daylighting is sufficient for both analysed cases and corresponds to requirements.
3. Annual primary energy demand because of electricity generated by PV window will decrease by 25.7 kWh/ [m.sup.2]. This energy amount may cover almost 70% of rooms lighting energy demand.
4. Total primary energy demand of the office after application of PV decreased from 171 kWh/[m.sup.2] x year to 96 kWh/[m.sup.2]-year.
5. Multi-criteria analysis shows that more sustainable is office with PV window, but we shouldn't forget that it depends a lot on the weights given for different criteria and with different priorities alternatives ranking may change.
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Violeta MOTUZIENE (1), Kestutis VALANCIUS (2)
Vilnius Gediminas Technical University, Vilnius, Lithuania
E-mails: (1) email@example.com; (2) firstname.lastname@example.org
Caption: Fig. 1. (a) case with transparent faeade (WWR--88%); (b) case with PV glazed faeade (WWR--88%, transparent part--47%). Note: WWR--window to wall ratio
Caption: Fig. 2. Overall assessment of BIPV performance
Caption: Fig. 3. Heat flows because of windows: (a) solar heat gains; (b) daily heat balance of the window
Caption: Fig. 4. Daylighting in the office room: (a) transparent window; (b) PV window
Caption: Fig. 5. Primary energy demand of the office room
Caption: Fig. 6. PV window electricity generation monitoring data
Table 1. Multi-criteria analysis Primary energy C[O.sub.2] demand, kWh/ emissions, [m.sup.2] x per kgC[O.sub.2]ekv/ year [m.sup.2] Weight coefficients 0.37 0.17 Window with PV 96 2670 Transparent window 171 1860 Investments, Discomfort, [euro] h/year Weight coefficients 0.15 0.31 Window with PV 6000 1653 Transparent window 600 2332 Non-dimensional criteria values 3e+c Weight coefficients [e.sup.en] [e.sup.em] [e.sup.eco] c Window with PV 0 1 1 0 0.32 Transparent window 1 0 0 1 0.68
Please note: Illustration(s) are not available due to copyright restrictions.
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|Author:||Motuziene, Violeta; Valancius, Kestutis|
|Publication:||Science - Future of Lithuania|
|Date:||Aug 1, 2015|
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