# Urban Environment and Solar PV Performance: The Case of the Netherlands

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

^{2}, (b) the plot ratio, as the ratio of building surface to the total surface of the area and (c) the Average Building Distance (ABD) as it is measured from the centroids of each building. The most straightforward indicator to describe the 3D aspect of the urban compactness is the (d) the Average Building Height (ABH) in an area. However, a uniform distribution of building heights can have nearly zero shading effects and therefore the (e) Standard Deviation of Building Height (SDBH) is also examined in this research, as high values of SDBH can potentially lead to shading from adjusting buildings. In an ideal situation, a flat roof free of other constructions is the most suitable place to install a PV system as it can give unlimited freedom to tilt and orientation angles while having a direct and unobstructed view to the sun. The (f) Internal Building Height Standard Deviation (IBHSD) shows the distribution of height within a building polygon when it is projected to a GIS map and then it is averaged over the entire neighborhood. This indicator is suitable for quantifying the effect of any object that is placed on a rooftop such as chimneys, air condition and ventilation equipment or even architectural elements that exceed 0.5 × 0.5 m

^{2}in size. In case the average internal height standard deviation (std) is zero, then the roof of the building is completely flat without tilted parts or any extra constructions.

_{p}of capacity installed. This indicator has the advantage of being straightforward and easy to calculate; however, since the energy generated is dependent on several factors such as the Plane of Array (POA) irradiance and temperature, the system yield is inadequate to properly evaluate the PV system by itself, or in comparison with other systems. For a homogeneous and systematic assessment of the technical quality of PV installations, the comparison is set between the actual energy production of a system, and a reference system that hypothetically operates with the exact same characteristics under the same conditions, but it is free of losses. The most widely used indicator that follows this principle is Performance Ratio $\left(PR\right)$, which shows the degree of utilization of an entire PV system. It is a dimensionless quantity and it is calculated by dividing the final system yield ${Y}_{f}$ by the reference yield ${Y}_{r}$ during the same time interval:

^{2}[20]:

## 3. Results

#### 3.1. PV Systems Overview

_{p}system, in sub-urban locations, this capacity increases to 4.7 kW

_{p}, and, in rural areas, it almost doubles that figure reaching 9.2 kW

_{p}. The effects of urban compactness are illustrated in Figure 3 where 90% of installations placed in cities are smaller than 5 kW

_{p}and only 2% has a capacity higher than 15 kW

_{p}. The lower population density of rural areas results in higher availability of space, which can be better utilized by allowing room for installation of larger PV generators. Small size systems are still dominant in rural areas as 5 kW

_{p}are sufficient for the average household; however, large systems reach 19% of the total number of installations. This number accounts for the 54.3% of the total installed capacity in rural locations.

^{2}for PV generators oriented at 200° (with South being 180°) and having an inclination of 37°. The displacement from the South is due to the clearing of morning clouds in the afternoon, which results in a larger share of radiation in the afternoon, and thus leading to off-South optimum orientations [28]. In order for a PV system to be able to harvest at least 95% of the incoming irradiation the tilt and the orientation angle should fall within the grey shaped area in Figure 4b, which extends from 152° to 252° for the orientation and 10° to 64° for inclination. The grey colored area in Figure 4c represents the range of orientation and inclination angles that are necessary to collect at least 90% of incoming irradiation. Since, in most cases, the exact configuration of each installation is dictated by the roof that the PV is mounted on a large variation is revealed as expected. However, the extent of this variation is a an indicator of how well a system is planned before installation. By considering as good oriented systems the ones that were able to harvest at least 90% of the maximum incoming irradiation (grey colored area in Figure 4c), in the data set, we find that 90%, 88% and 92% of urban, suburban and rural systems, respectively, fall in this category. More specifically by setting the optimum harvested POA to 95% (shaded area in Figure 4b) the same values are 75%, 74% and 77%. These numbers demonstrate that, at least in this case, urban compactness is not leading to extreme misplacement of installations especially in locations with limited availability in rooftop area.

#### 3.2. Performance Analysis—Seasonal Variation

#### 3.3. Performance Analysis—Urban Indicators

#### 3.4. Errors and Uncertainties

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Map division in urban, suburban and rural areas in the Netherlands based on population density.

**Figure 4.**(

**a**) Plane of Array (POA) irradiation; (

**b**) optimum oriented systems; (

**c**) well oriented systems.

**Figure 7.**Difference in $PR$ between PV installations in rural and urban areas versus the solar elevation.

**Figure 8.**Comparison of the average $PR$ using linear regression with (

**a**) ABH; (

**b**) plot ratio; (

**c**) BHSD; (

**d**) IBHSD; (

**e**) number of buildings per km

^{2}; (

**f**) ABD.

**Figure 10.**Comparison of ${Y}_{f}$ using linear regression with (

**a**) ABH; (

**b**) plot ratio; (

**c**) BHSD; (

**d**) IBHSD; (

**e**) number of buildings per km

^{2}; (

**f**) ABD.

Year | Annual Yield kWh/kW_{p} | PR % |
---|---|---|

2014 | $919\pm 78$ | $79\pm 6$ |

2015 | $970\pm 126$ | $79\pm 6$ |

2016 | $945\pm 89$ | $80\pm 7$ |

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**MDPI and ACS Style**

Moraitis, P.; Kausika, B.B.; Nortier, N.; Van Sark, W.
Urban Environment and Solar PV Performance: The Case of the Netherlands. *Energies* **2018**, *11*, 1333.
https://doi.org/10.3390/en11061333

**AMA Style**

Moraitis P, Kausika BB, Nortier N, Van Sark W.
Urban Environment and Solar PV Performance: The Case of the Netherlands. *Energies*. 2018; 11(6):1333.
https://doi.org/10.3390/en11061333

**Chicago/Turabian Style**

Moraitis, Panagiotis, Bala Bhavya Kausika, Nick Nortier, and Wilfried Van Sark.
2018. "Urban Environment and Solar PV Performance: The Case of the Netherlands" *Energies* 11, no. 6: 1333.
https://doi.org/10.3390/en11061333