Optimizing photovoltaic self-consumption in office buildings

Optimizing photovoltaic self-consumption in office buildings

Accepted Manuscript Title: Optimizing photovoltaic self-consumption in office buildings Authors: Nuria Mart´ın-Chivelet, David Montero-G´omez PII: DOI...

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Accepted Manuscript Title: Optimizing photovoltaic self-consumption in office buildings Authors: Nuria Mart´ın-Chivelet, David Montero-G´omez PII: DOI: Reference:

S0378-7788(17)30479-6 http://dx.doi.org/doi:10.1016/j.enbuild.2017.05.073 ENB 7654

To appear in:

ENB

Received date: Revised date: Accepted date:

10-2-2017 16-5-2017 27-5-2017

Please cite this article as: Nuria Mart´ın-Chivelet, David Montero-G´omez, Optimizing photovoltaic self-consumption in office buildings, Energy and Buildingshttp://dx.doi.org/10.1016/j.enbuild.2017.05.073 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.

Optimizing photovoltaic self-consumption in office buildings Nuria Martín-Chivelet, David Montero-Gómez CIEMAT, Avda. Complutense, 40; 28040 Madrid (Spain) Tel: +34 91 3466531; [email protected]

ABSTRACT Building Integrated Photovoltaics (BIPV) in commercial and office buildings can be designed with the aim of reducing the electricity consumption from the conventional local grid. This paper stresses the importance of matching the photovoltaic (PV) generation local profile with the building’s load shape to reach around 100 % self-consumption indexes and to improve the selfsufficiency degree of the building, without storage nor load management. For that purpose, this paper proposes a methodology that helps BIPV designers to achieve these targets. Its application to a real case shows that the different façades of the building envelope may play a role in the local PV production if their orientations receive sufficient insolation during the hours of electrical consumption. In particular, in the North Hemisphere the possibilities that offer all the external surfaces of the building that are not oriented to the North should be taken into consideration in a first approach, and then those which achieve the best production-consumption match should be analyzed and compared to select the optimum solution. Keywords: photovoltaic self-consumption; load profile; electric consumption; self-sufficiency; PV generation; BIPV.

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1. INTRODUCTION Building Integrated Photovoltaics (BIPV) is the result of architectonically integrating the photovoltaic (PV) modules of a PV system in the building’s envelope. In the last decades this solution has been increasing in residential and commercial buildings, in new projects and in retrofit works [1][2][3], demonstrating to be a successful way to combine the local production of renewable electricity with the constructive possibilities that offer the PV modules, which become active elements of ventilated façades, curtain walls, roofs or different types of shading devices. Within the commercial sector, office buildings are, together with retail, those with the biggest consumption and CO2 emissions [4], heating, ventilation and air conditioning (HVAC) accounting for around 50% of their total consumed electricity. Energy saving strategies combined with the integration of PV can definitely improve their energy efficiency, in line with the European Directives [5][6]. Considering that in most commercial and office buildings the largest available surfaces to integrate PV modules are the façades, special attention should be paid to select which façades best fit the load needs of the building. One important criterion is to bet for selfconsumption. PV self-consumption, which probably would better be named as PV self-supply, means consuming electricity from the own local photovoltaic system, reducing the use of the conventional grid[7][8]. If during some periods PV generation exceeds the building consumption, generally this PV electricity will be injected into the grid. There are typically two main incentive schemes that allow compensating grid consumption with PV production: net-metering, which means exchanging energy, and net-billing, which results in a costs exchange. In some scarce cases, as it is at this moment in Spain, compensation includes not only the price of electricity,

but also the grid costs and taxes[9]. While established markets such as Belgium or Denmark are progressively moving away from net-metering, emerging PV markets such as Dubai or Chile are setting up net-metering schemes[10][11].

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Compensated or not, with financial incentives or without them, PV self-consumption presents several additional advantages [12]. For instance, it contributes to reduce peaks of production and consumption and helps to avoid congestion and bottlenecks. Besides, self-consumption helps consumers to control their energy bill and to be protected against unstable electricity prices, making them active players of a transition towards a more sustainable energy system. The European Commission encourages renewable energy and self-consumption for all these reasons, and to reach the transformation of Europe's energy system [13]. Lower prices of PV electricity, on the way to grid parity, contribute to increase the competitiveness of self-consumption, but success will be even better guaranteed if instantaneous, with no storage, self-consumption ratios are as high as possible [9]. Regarding in what extent PV production covers the needs of the building, a self-sufficient building would be the one that only consumes its own PV generated electricity, thus reaching a 100 % self-sufficiency index (SSI). SSI is of special importance under non incentive policies, and typically its maximum value oscillates between 20 % and 25 % [14][15] if no additional measures are applied, such as load shifting via demand responsive appliances or energy storage. This paper demonstrates that these limits could be easily increased if the PV project selects where to install de PV modules after taking into account the load demand of the building. In the literature, several publications address design and improvement of PV self-consumption in residential buildings with demand-side management (DSM) combined with electricity storage [16][17][18][19][20][21]. Nevertheless, there is a lack of studies that deal with direct or natural self-consumption in any type of building, which may result especially interesting in some no residential applications, such as office buildings. While DSM may play a role in residential buildings, where shifting some loads to most favorable time of the day results feasible, it is not the case in most commercial and office buildings, where daily load profiles follow typical fix patterns along the year. On the other side, to date, energy storage is not an effective solution for most office buildings, because although it would increase in some extent the self-sufficiency index, it would also increase considerably the investment and

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maintenance costs of the PV system. Nevertheless, batteries could be included in the final PV design, but always after having arrived to the best possible solution in terms of direct selfconsumption. Apart from the above mentioned considerations, loads in office buildings, are typically coincident in time with solar radiation, starting in the morning and having peak loads in the central hours of the day. Aware of all of this, and because there is a lack of studies that offer guidelines for direct self-consumption, this paper proposes a methodology to improve BIPV designs in office buildings, helping to increase self-consumption and self-sufficiency indexes without consideration of DSM nor battery storage.

2. APPROACH 2.1 The method Based on [9] and [22], the self-consumption index can be defined as the percentage of the generated photovoltaic energy that is locally consumed (Equation 1). The self-sufficiency index indicates what percentage of all of the energy consumed over a time period is PV energy locally generated (Equation 2). Self − consumption index (𝑆𝐶𝐼)(%) =

Self − sufficiency index (𝑆𝑆𝐼)(%) =

PV energy consumed (PVEC) PV energy produced (PVEP)

PV energy consumed (PVEC) Electricity consumed (EC)

∙ 100

∙ 100

(1)

(2)

In this paper only direct SCI and direct SSI are considered, which means that the PV energy is directly consumed in the building. The method proposed aims at designing direct PV self-consumption systems integrated in office buildings achieving around 100% self-consumption index and high direct self-sufficiency index. These two requirements condition both the envelope surfaces to consider for integrating the PV modules and the final PV power to install. If the building doesn’t have enough envelope area to integrate the desired power, the self-sufficiency index will become lower. On the other hand, an

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excess of installed power may decrease the self-consumption index, due to the emergence of additional PV electricity not directly consumed in the building. The different surfaces of the building envelope may result more or less attractive, depending on if their orientations receive more or less insolation during the consumption periods. In particular, in the North Hemisphere, east, south and west facades, and any other with an orientation comprised among these three, should be taken into consideration in a first approach. The proposed method is summarized in the following steps:

1. Analysis of the building’s daily electric load profiles during a year, differentiating between working days and weekend days, and extraction of two load shapes per month, one for each type of day. 2. Description of the building: number of facades and roofs, orientation and available surface in each one. Final determination of the suitable surface area and estimation of the maximum peak power limit at each plane. 3. Analysis of the daily evolution of the solar irradiance on each considered surface, and extraction of one typical day per month and one annual average. Estimation of the corresponding PV generation daily profiles. 4. Analysis of the matching degree of the annual load profile with the annual PV generation curve in weekdays. Calculation of the self-consumption indicators: self-consumption index and self-sufficiency index for each surface. 5. In general, among the solutions that allow 100 % self-consumption index, the best solutions would be those leading to the highest self-sufficiency indexes. 6. If the load profile is seasonal or monthly dependent, the study should be done on a seasonal or monthly basis.

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7. The best solution for a determined PV power installed would be the one with around 100 % SCI, highest average SSI, and lowest variability of SSI along the year.

2.1 The load profile The load profile, also named as load shape, is a graph representing the daily variation with the time of the total electrical energy used by a building. Load profiles can vary widely, according to customer type, occupancy, local climate, type of day (weekdays or weekends) and season. Characterizing or even modeling daily load shape has been the aim of several research works, as for instance [22], where the impacts of weather on lighting levels, heating, ventilating, and air conditioning are analyzed, or the forecasting research reported in [23]. An interesting work performed in New Zealand provides low, medium and high load profiles for office, retail and mixed/other buildings, after data from more than 100 buildings collected over a four year period[24]. Also the Spanish Electric System Operator (REE) has published typical load profiles, result of the Reasearch Proyect INDEL, obtained from direct consumption measures at 2500 electricity meters in three different sectors: residential, commercial and tourism [25]. In this case, offices are not specifically contemplated.

In this work the load profile measures of two recently analyzed office and service buildings at CIEMAT’s headquarters in Madrid (40.45° N) have been considered[26], in order to compare them with the found profiles of the literature review, and to better illustrate the method proposed in this paper. Building A is a five story office building which also houses a cafeteria and Building B is a two story building with offices and mechanical workshops. Their electricity demand has been monitored by the electric maintenance service with the power analyzer CVMk2 and the software PowerStudio Scada, from Circuitor[27]. Data have been exported on an hourly basis and have been analyzed for each month, differentiating between weekdays and weekends.

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Figure 1 shows the average load profile of Building A at one average weekday per month. Figures 2 and 3 compare the annual load profiles of each of the two buildings during weekdays with two of the typical load shapes proposed in ref [29] for office buildings in New Zealand (NZ), having low and medium consumption, respectively. In order to focus on the shapes of the loads, all curves are normalized to the maximum power consumption of each building. This way, it doesn’t matter the size of the buildings, but their load consumption patterns.

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load power, normalized (kW/kWc)

0.7

Building A

0.6

Low consumption (NZ)

0.5 0.4

0.3 0.2

0.1 0 0

2

4

6

8

10

12 14 Hour

16

18

20

22

24

Figure 2. Annual average load profile of Building A compared to the typical low consumption profile of a New Zealand office building in weekdays [29].

0.9

Building B

load power, normalized (kW/kWc)

0.8

Medium consumption (NZ)

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

2

4

6

8

10

12 14 Hour

16

18

20

22

24

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0.4

South facade

PV Power (kW/kWp)

0.35

West facade

0.3

East + South

0.25

South +West

0.2 East facade 0.15 East + West

0.1

East + South + West

0.05 0 0

2

4

6

8

10

12

14

16

18

20

22

24

Hour Figure 4. PV Generation profiles per unit of peak power, from three different vertical façades in Madrid (40.4 5°N, 3.7 °W): east, south and west, and considering combinations of them equally shared.

South façade 140

POA Global Irradiation (kWh/m2)

Hamburg (53.65 N)

120

Madrid (40.45 N) Casablanca (33.57 N)

100 80 60 40 20 0

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East façade 140

POA Global Irradiation (kWh/m2)

Hamburg (53.65 N) Madrid (40.45 N)

120

Casablanca (33.57 N)

100 80 60 40 20 0

Figure 5. Yearly evolution of the plane of array (POA) global irradiation per unit of surface on a south oriented façade (up) and an east oriented façade (down), in three different sites.

2500

2500 Annual average

2000

September

September

EFV (kWh)

EFV (kWh)

January

Annual Average 2000

January

1500

1500

1000

1000

500

500

0

0 0

2

4

6

8 10 12 14 16 18 20 22 24

0

2

4

6

8 10 12 14 16 18 20 22 24

Hour

Hour

Figure 6. Daily average PV generation curves obtained in September, January and in a nine month period, from measurements (left) and simulating with PV Syst (right).

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600

EFV/APV (kWh/m²)

500 400 300 200 simulated 100

measured

0

Figure 7. Monthly PV energy normalized to the generator area (APV) simulated with PVSyst, and compared to real measured data, from September 2015 to May 2016.

0.7 Load

0.6

Normalized power

PV

0.5 0.4

C

0.3 A 0.2 B

0.1

A

0.0 0

2

4

6

8

10

12

14

16

18

20

22

24

Hour

Figure 8. Load and PV generation profiles and the three possible regions delimited by them: A: energy demanded to the grid, B: PV energy directly consumed, C: PV energy generated but not consumed.

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PV power (kW/kWc) , Load (kW/kWc)

0.7

East South

0.6

West S+E

0.5

S+W 0.4

Load

0.3 0.2 0.1 0.0 0

2

4

6

8

10

12

14

16

18

20

22

24

Hour

Figure 9. Load and PV generation profiles corresponding to different solutions reaching the maximum SSI in each case, with a SCI of 100 %.

120 SCI 100

East

SCI (%), SSI (%)

80

South West

60

S+E S+W E +W

40

E +S +W

20 SSI

0 0.0

0.5

1.0 Pp (kWp) / max load (kWc)

1.5

2.0

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Figure 10. Self-consumption index (SCI) and self-efficiency index (SSI) versus PV peak power normalized to the maximum load consumption, for a building in Madrid. For each façade or combination of façades, labeled with East (E), South (S) and West (W), there is a couple of curves describing the evolution of SCI and SSI, respectively. In all cases, the PV generation profile for a yearly average day has been considered.

120 SCI

SCI (%), SSI (%)

100

East(year)

80

East (June)

60

East (Dec)

40 20 SSI 0 0.0

0.5

1.0 Pp (kWp)/ max load (kWc)

1.5

2.0

1.0 Pp (kWp)/ max load (kWc)

1.5

2.0

120

SCI

SCI (%), SSI (%)

100

South (year)

80

South (Sep)

60

South (June)

40 20

SSI 0 0.0

0.5

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120 SCI

SCI (%), SSI (%)

100

South + East (year)

80

South + East (Aug)

60

South + East (Dec)

40 20 SSI 0 0.0

0.5

1.0 Pp (kWp)/ max load (kWc)

1.5

2.0

Figure 11. Self-consumption index (SCI) and self-efficiency index (SCI), versus the peak power normalized to the maximum load consumption for a building in Madrid. The three graphs compare three different solutions: east, south, and south + east façades. In each case the annual average and the best and worst months in terms of PV generation are plotted.

Table 1. For each façade or combination of façades of Figure 9, and with the boundary condition of having 100 % SCI: maximum possible SSI, corresponding peak power normalized to the maximum consumption load, and productivity (PV Yield). East

South

West

S+E

S+W

Peak power (kWp/kWc)

1.6

1.1

0.6

1.8

1.6

SCI (%)

100

100

100

100

100

Maximum SSI (%)

45

39

17

57

25

Annual productivity (kWh/kWp)

786

1040

786

932

913

Figure 3. Annual average load profile of Building B compared to the typical medium consumption profile of a New Zealand office building in weekdays[29]. In Figure 1 it can be seen that there are not strong seasonal variations among months in the load profiles of Building A (apart from differentiating between weekdays and weekends). Besides, the annual average in weekdays shows a high similarity with the typical low consumption load profile of an office building in New Zealand[29], as shown in Figure 2. Following the parameters

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recommended in reference [26] to characterize a load shape, it can be said that the base load (consumption at night time) is in both cases around 0.11 kW/kWc, the rise time and the width of the high load periods are also similar, although in the NZ curve the peak value is slightly lower, and the fall time from the end of the high load period to the base load is a bit longer in the NZ case. In Figure 3, where it is compared the annual average load of Building B in weekdays with the typical medium consumption load profile for an office building in New Zealand, it can be observed that base load is about 45 % higher in the case of the NZ building than in Building B, and the high load period is about two hours wider, although the load shapes are relatively similar. The main cause to have such high base load values seems to be that part of the equipment is switched on during unoccupied hours. Probably, a substantial energy saving in annual terms could be achieved by turning equipment off overnight in offices.

2.2 The PV generation profile The main component of a PV system is the PV generator, made up of all the PV modules grouped in arrays. In BIPV the modules are mounted in the building envelope (roof, façades), or integrated into structures such as awnings or louvers. The PV systems also include power conditioning devices, mainly inverters, which convert the DC (direct-current) generated by the PV generator into AC (alternate-current) suitable to be fed into the grid. Although PV systems may also contain batteries, this work deals with direct self-consumption, so storage is not considered.

2.2.3. Simulation of the PV generation profile The PV generation profile depends mainly on the irradiance received by each building surface over a typical day and, secondly, on the performance of the PV installation, described by the performance ratio, PR, which accounts for all the production losses that affect the output of the PV system, such as temperature, soiling, wiring or the inverter performance. Yearly average values of PR typically fall between 0,65 and 0,8 in BIPV.

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The peak power (Pp) of a PV system is the nominal power of its PV generator, the sum of the nominal power of every PV module it is comprised of. Nominal power is rated at STC (standard test conditions): irradiance of 1 kW/m2, cell temperature of 25 °C, and AM1.5 solar spectrum (the standard global spectrum related to an air mass of 1.5) [28]. The peak power of a PV system depends on module efficiency, size and number. The PV module's efficiency is the ratio of power density supplied by the module to the solar irradiance it receives. Thus, the annually energy generated by a PV system is found from the peak power Pp of the system, the annual irradiation on the plane of array Ia, and the performance ratio PR: 𝐸𝑃𝑉 = (𝑃𝑝 ⁄𝐺𝑆𝑇𝐶 ) ∙ 𝐼𝑎 ∙ 𝑃𝑅

(3a)

𝐸𝑃𝑉 = 𝑃𝑝 ∙ 𝑌𝑟 ∙ 𝑃𝑅

(3b)

or

where GSTC stands for the irradiance at standard test conditions, which as above stated is 1 kW/m², and Yr is the reference yield, also named as sun-peak hours or rated sun-hours over the period considered. This parameter is calculated from the ratio of the total irradiation to the reference irradiance and represents the duration that the irradiance would need to be at reference irradiance levels to contribute the same incident irradiation as actually occurred[29]. The higher the Yr on a plane, the bigger its PV generation potential [30]. Figure 3 shows different PV generation profiles corresponding to some vertical façades in Madrid, which are east, south or west oriented, or to equal combinations of two or the three of them, as indicated. Profiles have been generated using the software PVSyst[31], which by default uses the model of Perez and Ineichen[32] to estimate the irradiance on tilted surfaces form global horizontal data that are obtained from Typical Meteorological Year data (TMY), on an hourly basis. For the simulation of the PV generator it is used the PV module Shockley's simple "one diode" model as described, for example, in [33]. In this case, the average performance ratio is 0.75, after considering the different losses factors, such as inverter efficiency losses of 2.7 % and PV module temperature losses of 3.5 %.

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Observe the shift of the PV generation curve towards the morning hours in the case of the east or south-east façades, and towards the evening in the case of the west or south-west façades. The same tendency is observed in all kind of climates, although for each location the relative irradiation of each façade, and consequently its comparative PV potential, is mainly a function of the latitude. To illustrate this, Figures 5 and 6 show the monthly irradiation along a TMY of a south façade in three different locations, having different climates and latitudes. It is observed that in high latitudes like in Hamburg (53.65 °N) the best choice is the south façade, because it receives the highest irradiation annually and gets the best monthly irradiation share along the year (less differences between winter and summer). Nevertheless, as latitude gets lower, it may become interesting to combine the south façade with the east or the west, as it is the case in Casablanca (33.57 °N). Madrid case will be studied deeply in the next paragraphs.

2.2.3. About measured and simulated values Commonly, software to simulate PV generation use TMY on an hourly basis, as it is the case of PVSyst. Differences between generated and measured data are normal, as real irradiance differs in some extent with typical data. Nevertheless, results of the simulations are helpful to design PV plants and to analyze and compare different configurations and locations. To compare the simulation results with real measured data, we have considered a PV sub-system integrated in the west façade of a PV building at CIEMAT[34][33]. The PV array is formed by 16 single-crystalline PV modules of 305 watts peak each, connected to a 4 kW grid-connected single-phase inverter. Considering the PV energy measured from September 2015 to May 2016 in an hourly basis, a comparative analysis has been performed between simulated and measured data. Average daily curves for two different months and for the whole period of time are shown in Figure 6. As it is shown in Figure 7, in some cases the simulated monthly profiles are higher than the measured ones, and in some others is the opposite. Besides irradiation differences between TMY and real data as the main reason of the discrepancies, a slight lower tendency is observed in the measurements, due to the ageing of the PV modules.

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2.3.

Matching the generation and the consumption profiles

The higher the compatibility between the generation and the consumption, the more efficiency will be achieved [9]. The calculation of direct photovoltaic consumption can be done on a monthly basis, considering separately weekdays and weekends. Each type of day is described by two graphs, one with the load shape and another with the photovoltaic generation profile. If the monthly load profiles are very similar along the year, as it is the case of Builing A, the annual average of the load profile can be a first approach to look for a solution. The superposition of the generation with the load curve can give rise to three types of regions of interest, as shown in the example of Figure 8. The area of region A represents the energy demanded to the grid, the area of region B the PV energy directly consumed, and the area of C the PV energy generated but not consumed. If this area reduces to cero, the self-consumption index will become 100 %. To estimate the monthly characteristic energy values the three areas should be multiplied by the number of days of the month.

3. CASE STUDY In order to illustrate self-consumption and self-sufficiency indexes with a real load profile, we have chosen the annual load shape obtained for Building A (see Figure 1). Besides, we have analyzed the south, east and west façades of a generic quadrangular building in Madrid to obtain more generalizable results for buildings at similar latitudes. Figure 9 shows some possible

solutions with 100 % SCI, and the maximum achievable SSI in each case. Results are summarized in Table 1. Observe that the combination east + south achieves the highest self-sufficient ratio (57%) but the power needed to reach this value is also the highest (1.8 kWp/kWc). Also the east and south, separately, could be good solutions and should be analyzed, because they lead to high SSI with lower necessary peak powers. For a fixed power Figure 10 shows the 18 | P á g i n a

performance of different solutions in an annual average basis. Observe that there is a threshold value of PV power installed at which SCI starts to decrease from 100 %. Besides, SSI increases continuously inside the range of the PV power considered. For a low PV power installed (Pp under 0.5 of the maximum load) all solutions are quite similar, getting 100 % of SCI and between 13 % and 17 % SSI, except the combination west + south façades that leads to very low values of SSI (8 %). From this value of peak power on, the west façade reduces significantly the SCI, so this solution should be then avoided. Something similar occurs with the other possibilities considered, sequentially as peak power continues to increase. With Pp equal to the maximum load (1 kWp/kWc) there are three solutions reaching 100 % SCI and average SSI values above 25 %: east (28 %), south (34 %), south + east (31 %). With Pp at 1.2 kWp/kWc, SCI keeps at 100 % in those three cases and SSI increases to above 34 %: east (34 %), south (41 %), south + east (38 %). Nevertheless, the different façades or combinations of them that appear to be interesting in an annual basis, should be analyzed also month by month. Alternatively, the best and worst months in terms of PV generation for each solution can be studied. This way the limits of the maximum SSI are bounded between the highest and lowest values obtained with the best and worst months respectively. September, June and August result to be the best months for the south, east and south + east solutions, respectively. On the other side, June is the worst month for the south façade, while December is the worst for both the east and the east + south solutions. Special attention should be paid to the worst month, and to the variability obtained in SCI and SSI in between the best and worst months each case. Figure 11 shows the graphs obtained for the east, south and east + south cases. According Figure 11, the east façade alone should be rejected, even if the peak power is low, because it leads to higher dispersion in SCI and SSI along the year and it shows no 19 | P á g i n a

advantages when compared to the south and south + east solutions. Although the two other solutions are quite similar up to a peak power of about 1.5 times the maximum load, south façade shows always higher SSI values. Besides, this solution leads to smaller dispersion along the year in SSI values, for any peak power. Nevertheless, from around 1.3 kWp/kWc on, the SCI index of the south façade decreases gradually, and its dispersion increases among monthly values along the year. At this point the south + east solution can be a good option, especially if the target is to keep high SCI values. Looking at the curves of Figure 11 and at Table 1, it can be concluded that the south façade is the best solution if the peak power is under 1.3 kWp/kWc, although any of the solutions, except the south + west one, could be acceptable for peak power values under 0.5 kWp/kWc. Generally, the limit of possible PV power installed is determined by the available building surface, together with the efficiency of the selected PV modules. For example, choosing PV modules with an efficiency of 20 % would double the limit of peak power, in reference to considering PV modules with an efficiency of 10 %, because the corresponding needed area would be 5 m²/ kWp, against 10 m²/ kWp. This would allow higher SSI, although not always keeping the 100 % SCI condition, as it is shown in Figure 10. In general, high efficiency technologies are especially recommended if the available area is low. But in BIPV participate some other criteria, like esthetics, the constructive role of the PV modules, or the planned investment costs. The final decision has to be taken with all the inputs together, although one criterion might prevail over the others. In any case, after fixing the peak power of the PV installation, among all the possible façade options, the one that gets the highest SSI keeping SCI to 100 % should be chosen, 20 | P á g i n a

but after checking that the annual dispersions of the monthly values of SCI and SSI are not a problem. Finally, it has to be stated that the theoretical SSI values obtained have a tolerance that is mainly affected by the deviations between TMY and real irradiation data, and by deviations between typical load profiles and real load shapes, which, at the same time, are affected by real irradiation and by other climatic parameters, such as temperature. Differences between SSI values from measured and simulated data in our particular case have varied between 2 % and 30 %, depending on the month. Monitoring a BIPV selfconsumption system, including the irradiance on the plane of array, the PV output energy, the PV energy consumed, and the total electricity consumed in the building would help to determine and characterize in a more precise manner the self-consumption parameters of a particular building.

4. CONCLUSIONS This paper proposes a method to increase direct self-consumption in office and commercial buildings, which is based on the selection of the external surfaces of the building that get the best fit of the PV generation profile to the building’s load shape. A good match will lead not only to 100 % direct self-consumption, but also to high self-sufficiency indexes. The proposed method starts considering all the building envelope’s surfaces that lead to 100 % direct self-consumption in an annual basis. Then, the different solutions are compared, in reference to the maximum selfsufficiency index they can reach as a function of the installed power. The best solutions should be then analyzed in a monthly basis, to discard those which lead to high dispersion of the SSI values along the year. After determining the peak power of the PV installation, the option

that gets the highest SSI keeping the SCI to 100 % should be chosen.

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In the case study, the best solutions from the point of view of self-consumption and selfsufficiency are the south and the south + east. For a installed PV power up to 1.2 kWp/kWc –

which, e.g., means an area of PV generator of 6 m² per unit of maximum load (6 m² /kWc) if considering PV modules with 20 % efficiency, or an area of (12 m²/kWc) if modules have an efficiency of 10 % –, the south is the best option, leading to the highest average SSI (up to 41 %). From this power on, if the highest SCI criteria prevails, the south + east combination could be the best solution, because the south would lead to a SCI lower that 100 %. The west façade in office buildings having similar load profiles as the studied one should be the last option to consider, because it shifts the PV profiles towards the evening hours of the day. Nevertheless, differences between façades diminish as the PV installed power reduces, although SSI also decreases.

4.1 Further considerations 

In order to improve the self-consumption solution for BIPV, further work related to the load profile of the particular building would help. In particular, occupancy patterns should be studied in each case. The reason is that occupancy is a key factor in the determination of building energy consumption, especially if there are occupancy-controlled loads, such as air-conditioning, lighting or ventilation [35].



The use of batteries would enhance both SCI and SSI, although it would be at the expense of increasing the investment and maintenance costs. Further advances in PV batteries will make BIPV self-consumption to be become progressively tied to storage in the future.



DSM, when possible, could enhance both SCI and SSI. Usually, this is not possible in office buildings.

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Other architectonic appliances, such as awnings, could improve the PV energy output, when compared to PV vertical façades.



It is important to stress that applying the methodology proposed in this paper makes really sense if energy saving measures have been previously taken into account. The baseline load profile, for instance, should be as low as possible.

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Hour Figure 1. Monthly load profiles of Building A, at weekdays and at weekends (flatter curves).

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