Estimation of Rooftop Solar Power Potential by Comparing Solar Radiation Data and Remote Sensing Data—A Case Study in Aichi, Japan
There have been significant advances in the shift from fossil-based energy systems to renewable energies in recent years. Decentralized solar photovoltaic (PV) is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and reduced cost of PV panels. The current modeling method using remote sensing data based on a geographic information system (GIS) is objective and accurate, but the analysis processes are complicated and time-consuming. In this study, we developed a method to estimate the rooftop solar power potential over a wide area using globally available solar radiation data from Solargis combined with a building polygon. Our study also utilized light detection and ranging (LiDAR) data and AW3D to estimate rooftop solar power potential in western Aichi, Japan, and the solar radiation was calculated using GIS. The estimation using LiDAR data took into account the slope and azimuth of rooftops. A regression analysis of the estimated solar power potential for each roof between the three methods was conducted, and the conversion factor 0.837 was obtained to improve the accuracy of the results from the Solargis data. The annual rooftop solar power potential of 3,351,960 buildings in Aichi Prefecture under Scenario A, B, and C was 6.92 × 10 7. 3.58 × 10 7. and 1.27 × 10 7 MWh/year, estimated using Solargis data after the adjustment. The estimated solar power potential under Scenario A could satisfy the total residential power demand in Aichi, revealing the crucial role of rooftop solar power in alleviating the energy crisis. This approach of combining Solargis data with building polygons can be easily applied in other parts of the world. These findings can provide useful information for policymakers and contribute to local planning for cleaner energy.
Accelerated urbanization with growing energy consumption increases the need to transition from fossil-based to renewable energy-dominated structures to ensure environmental sustainability. Many countries are striving to develop new energy-generation strategies in response to the call to keep global warming below 1.5 °C . Electricity generation by solar photovoltaic (PV) technology grew the fastest out of all renewable energy sources from 2018 to 2020, and the global total installed capacity was estimated to reach 760 GW by 2020, including both on-grid and off-grid [2,3]. Currently, decentralized PV is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and low cost of PV panels . Rooftop solar PVs are expanding rapidly in urban regions, and they facilitate low-emission, efficient, and resilient buildings [5,6]. over, installing solar PV allows households to take full advantage of their resources, thereby reducing the energy expenditure and dependence on government subsidies .
The “Green Growth Strategy Through Achieving Carbon Neutrality in 2050” was formulated in Japan by the Ministry of Economy, Trade and Industry, to achieve sustainable growth and innovation by expediting structural changes in the energy and industry sectors . To achieve this goal, Japan has made much effort to increase the share of renewable energy in the power generation mix, and solar PV has attracted extensive interest. The deployment of feed-in-tariff (FIT) in 2012 in Japan has driven the penetration of solar PV, contributing to a tenfold increase in the accumulative capacity . In addition, a policy based on the “NEDO PV Challenges” was established in 2014, which aims to reduce the power generation cost to 7 JPY/kWh by 2030 . Owing to different incentive policies, Japan now ranks third in terms of the global capacity of solar PV . In 2020, renewable energy accounted for 20.8% of all electricity generation in Japan, and electricity generated by solar PV accounted for 8.5% .
1.2. Previous Studies
The rooftop solar PV potential has been estimated in many countries using various methods, and geographic information systems (GIS) have become the dominant tools for this estimation. Light detection and ranging (LiDAR) is a popular remote sensing method that emits laser pulses to examine the Earth’s surface, and the result of LiDAR scanning is a series of 3D point clouds [12,13]. In recent years, many researchers have utilized LiDAR data combined with GIS to identify rooftop solar PV potential . Brito et al.  presented a 3D solar potential model for rooftops and facades using a digital surface model (DSM) with 1 m resolution created from LiDAR data and a solar radiation model. Mavsar et al.  proposed a simplified method to estimate rooftop PV potential in Slovenia, including physical, geographical, technical, and economic aspects, using LiDAR data and mathematical equations. The technical potential and suitability of rooftop solar PV in the US were estimated by combining 1 m resolution LiDAR data with a validated analytical method using GIS . The residential rooftop solar potential in Erie Country, New York, was identified using 0.91 m resolution LiDAR data considering rooftop azimuth, rooftop slope, shading, and contiguous area . Quirós et al.  utilized 1 m resolution LiDAR data and rooftop vectors to create a solar potential map of rooftops in Cáceres city, Spain, and the solar radiation was calculated using GIS. In addition, Nelson and Grubesic  compared the rooftop solar energy potential estimated by 1 m resolution LiDAR and unmanned aerial systems (UAS) and found that digital orthophotos from a UAS improved the aggregate irradiation estimates. Matsumoto et al.  estimated the annual power generation amount of rooftop solar PV in the western part of Nagoya City, Japan, using a 1 m resolution LiDAR DSM considering the slope and azimuth of individual roofs along with the shadow effect of the surrounding buildings.
Some studies have assessed the rooftop solar energy potential using other types of remote sensing images. ALOS World 3D (AW3D) is the world’s first 3D global map developed by the Japan Aerospace Exploration Agency (JAXA), the Remote Sensing Technology Center of Japan (RESTEC), and NTT DATA , and the products include AW3D Standard, AW3D Enhanced, and AW3D Ortho Imagery with varying resolutions . The AW3D and spatial data, including sun azimuth and sun altitude, were used to map the solar suitability for office buildings in Kuala Lumpur, Malaysia . Principe and Takeuchi  also utilized a 30 m resolution AW3D to generate a slope to assess rooftop solar PV installations. The technical potential of rooftop solar power was evaluated for Hanoi using 30 cm resolution WorldView 3 imagery combined with artificial intelligence algorithms . Song et al.  used the 0.9 m resolution Pleiades DSM and 0.2 m resolution satellite images from Google Maps to retrieve data on rooftops for the estimation of solar PV potential.
When evaluating the suitability of the rooftop for installing solar PVs, the rooftop projection area and the rooftop architectural morphology are two important factors . Slope and aspect were classified for each roof using digital elevation model (DEM) created from LiDAR, and viable and optimal areas for solar panels were calculated after subtracting setback value and object areas in . Stack and Narine  also determined rooftop suitability by slope and roof orientation from the DSM converted from LiDAR point Cloud. It was also found that the accurate estimation of roof geometry using LiDAR data required point clouds with density of 1 or 2 points/m 2 . A PV configuration over rooftops was proposed by AI-Quraan et al. , based on setting different scenarios for tilt angle and available rooftop areas. Ghaleb et al.  investigated features of commercial building roofs and the available roof area for PV system was calculated by subtracting areas affected by roof restrictions, maintenance and shadows. Wang et al.  classified rural building roofs into five categories (gabled, flat, hipped, complex and mono-pitched) according to roof texture and shape from UAV images. Monna et al.  also classified the targeted residential buildings into four types and the area for PV installation was determined for each type of the building. Studies which identified geometrical characterization for individual roofs were often conducted on a community scale; in terms of city scale, roofs were usually grouped into several categories and the available area for solar PV was assigned to each category.
Some studies did not conduct rooftop modeling [33,34,35]. The power generation potential for rooftop solar PV in the residential sector was explored in 13 major cities in the Kingdom of Saudi Arabia . When the PV design, local building construction, and cultural practices were considered, the estimated 51 TWh of annual electricity generation could satisfy 30% of the total national demand . Because the average solar radiation of each city was used to calculate the potential electricity generation, the results would be only a rough estimation for large regions.
The use of high-resolution remote sensing images or LiDAR data to model individual rooftop shapes is a popular approach for estimating rooftop solar PV potential and has a high accuracy level. However, this method is sophisticated and time-consuming and cannot be easily applied to large areas. Therefore, simple methods are required to estimate the rooftop solar PV potential over a large area.
This study aimed to develop a method for estimating rooftop solar PV potential over a large area by using globally available solar radiation data provided by Solargis and improving the outcome by comparing the results estimated by LiDAR and AW3D. After obtaining the estimated annual power generation amount by the three methods, regression analysis was conducted to determine the relationship between the results of the three methods for individual roofs. The estimates from the Solargis data can be extrapolated to give more precise results by applying the regression equations. This methodology can be easily applied for a large area for estimating rooftop solar PV potential when the high-resolution remote sensing data are not accessible.
Materials and Methods
2.1. Structural Framework of the Research
In this study, we utilized three different methods to estimate the annual solar power generated by each roof (Figure 1). For each method, the results were calculated under three scenarios (maximum, medium, and minimum potential) at the current technical level. Method 1 was based on the study conducted by Matsumoto et al. . First, the original LiDAR data were converted to LAS data; subsequently, a DSM was created after eliminating errors. The digital canopy model (DCM) was created by subtracting the digital terrain model (DTM) from DSM. The DCM was then combined with the building polygon data to compute the building height data, and the slope and azimuth of rooftops were estimated during the analysis of the building’s roof structure. Next, the solar radiation amount was calculated using the DSM through solar radiation analysis, considering shadow effects. Finally, the introduction potential (kW) and annual power generation amount (kWh/year) of the rooftop PV for each building were estimated.
For Method 2, the data from the AW3D Standard were utilized to create the DSM and obtain the DCM by subtracting the DEM. The remaining processes were similar to those of Method 1, without considering the slope and azimuth of rooftops. For Method 3, the introduction potential was calculated solely using the building polygon and was then combined with the direct normal irradiation (DNI) (kWh/m 2 /day) provided by Solargis to estimate the annual solar power generation by each roof. For all three methods, buildings with rooftop areas smaller than 10 m 2 were excluded from the estimation, as described by Schunder et al.  and Gagnon et al. . For Method 1 and Method 2, buildings with heights less than 1.5 m, as determined by the DCM, were ruled out. In addition, because buildings along the study area boundary might have their sunlight blocked by buildings outside the area, a −100 m buffer was created for the study area. Building polygons that were completely within the −100 m buffer were utilized in this study.
After obtaining the estimated annual power generation amount for each rooftop using the three methods, regression analysis was conducted to determine the relationship among the results obtained using the three methods. Finally, the annual solar power generation amount in Aichi Prefecture was estimated by combining all building polygons in Aichi and DNI from Solargis and then extrapolated to the results estimated by Method 1 through the coefficient.
ArcGIS Pro 2.7.2 (ESRI Japan) was used for the spatial analysis, Microsoft ® Excel ® for Microsoft 365 MSO (Version 2112) was used for the calculation, and IBM SPSS Statistics (188.8.131.52) was used for the regression analysis in this study.
2.2.1. Study Area
The target area is the western part of Aichi Prefecture in Japan, covering 229.43 km 2. as shown in Figure 2. The total number of building polygons in this area is 490,203, and the number of building polygons completely within the −100 m buffer that were included in this study was 475,764. This study used the same LiDAR data as that used in Matsumoto et al.’s study , but the study area, including the suburban area of Nagoya City, was expanded to approximately 1.5 times that of the previous study (152.51 km 2 ). Aichi Prefecture (34°34′ N–35°25′ N, 136°40′ E–137°50′ E) is in the central part of Japan with a population of 7.5 million and a total area of 5173 km 2 . The climate of Aichi Prefecture is influenced by the Pacific Ocean’s warm current; thus, it is hot and rainy in summer and dry in winter.
An overview of the datsource was shown in Table 1. For LiDAR data, the original and ground data from Shonai River and Tokigawa River Aviation Laser Survey Service (2016) provided by Geospatial Information Authority of Japan (GSI) (https://www.gsi.go.jp/ (accessed on 17 February 2022)) were used, which were the latest LiDAR data that we could obtain from the government for our study area. The AW3D data (2.5 m grid) from satellite JAXA-ALOS were purchased from JAXA, RESTEC, and NTT DATA (https://www.aw3d.jp/en/products/ (accessed on 17 February 2022)). The DNI data (250 m grid) from Solargis were downloaded from https://globalsolaratlas.info/download (accessed on 17 February 2022). The building polygon data and DEM data were obtained from GSI (https://fgd.gsi.go.jp/download/menu.php (accessed on 17 February 2022)). We used building polygon data surveyed between 2015 and 2016 to minimize the time lag with LiDAR data.
2.2.3. Method 1: Estimation of Solar Power Potential Using LiDAR Data
To estimate the maximum rooftop solar power generation potential using LiDAR data, we adopted the scheme developed by Matsumoto et al. , as summarized below.
184.108.40.206. Data Preparation
The average point density of the original and ground 3D point Cloud LiDAR data was 13.1 points/m 2 , which was sufficient for the analysis of aspect and angle of roof slope . The original and ground LiDAR data were first converted to an LAS format file and then arranged into a 2D grid structure after classification. The classification of original LiDAR data was first conducted using the “Classify LAS Noise” tool in ArcGIS Pro, and then wire guard, power line, transmission tower, and temporary error points such as birds, smoke, and cranes were classified manually.
The “LAS Dataset to Raster” tool was used to convert original LiDAR data to DSM to analyze building height and roof structure after removing low noise, wire guard, power line, transmission tower, and high noise as classified in the original LiDAR data. The ground LiDAR data were converted to a DTM. The cell size of DSM and DTM was 1.0 × 1.0 m. The DCM, which represents the height of buildings and trees above ground level, was calculated by subtracting the DTM from the DSM, and negative values were set to 0.
The DCM was extracted by building polygon data with a buffer of −50 cm to create raster data representing the height of each building (Figure 3). The buffer was created for the building polygon data owing to the misalignment of the building polygon data and LiDAR data . This reduces the error wherein the building height data are wrongly assigned to the adjacent building when the buildings are located very close to each other. After the buffer was created, 1953 building polygons disappeared because the width of the building was less than 1 m. A buffer of −50 cm was set by empirical judgment .
220.127.116.11. Analysis of Building Height and Roof Structure
The azimuth and slope of the roof were identified by the “Aspect” and “Slope” tools in ArcGIS Pro based on the DCM obtained in Section 18.104.22.168. The roof azimuth was expressed in 0–360° clockwise with north as 0°, and the output value was represented by −1 as the flat roof. The roof slope was expressed in 0–90°.
Building height and roof structure analyses was performed following the processes shown in Figure 4 to determine the annual average slope solar radiation “H” for flat roofs and inclined roofs, as discussed in a later section. Since the LiDAR data used in this study were surveyed in 2016, and the building polygon data were surveyed between 2015 and 2016, there is a possibility that some buildings were demolished or reconstructed during the time difference in the LiDAR data and building polygon data. Therefore, the building polygon with a height of less than 1.5 m, which was different from the 0.5 m in Matsumoto et al. , was removed from the study, considering the actual height of ceilings in Japan. Flat roofs were extracted using the slope data. The raster value of the slope data was rounded to the integer and approximated in 10-degree increments, and the most frequent value of the slope was calculated for each building polygon. If the most frequent value was smaller than 5°, it was assumed to be a flat roof . Subsequently, the remaining inclined roofs were divided into four directions (north, east, south, and west) based on the azimuth of the rooftop. The area of each direction that was smaller than 10 m 2 was excluded from the estimation, which was different from the 2 m 2 cut-off area used by Matsumoto et al. , considering the actual floor area. The annual average slope solar radiation “H” per day of the installation surface was set based on the most common slope and aspect values in each direction.
22.214.171.124. Solar Radiation Analysis
The raster for solar radiation analysis was created using the LAS dataset described in Section 126.96.36.199 after removing low noise, power lines, and high noise. The output cell size was 1.0 × 1.0 m.
The “Area Solar Radiation” tool was used to calculate the global solar radiation of a specific area, which is the sum of direct solar and scattered solar radiations. In this study, the default values (0.3 and 0.5 of scatter rate and transmittance, respectively) were used to calculate the global solar radiation for a normal sunny day. The sky size was set to 100 m, and the daily time interval was set to 2 h, considering the capacity and time of processing. All parameters used were the same as those used by Matsumoto et al. . The daily solar radiation for four particular days—the summer solstice, spring equinox, autumn equinox, and winter solstice—was calculated. By revealing the shadow from surrounding buildings, this exercise enabled the calculation of the global solar radiation (Wh/m 2 ) for a sunny day.
The shadow factor S (Equations (1) and (2) from ) was used as the solar radiation amount in this study, which is the ratio of the average value of solar radiation of the four special days in each roof direction to the maximum average solar radiation amount in the target area.
Rooftop solar to roll out on China’s public buildings
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To meet its climate goals, China needs to more than double its wind and solar power generation capacity in the coming decade (Image: Alamy)
September 16, 2021 October 7, 2021
On Tiananmen Square, China’s very heart, an 850 square metre solar installation is in operation. The panels sit on the roof of the Great Hall of the People, generating 98,000 kilowatt hours (kWh) a year to run the building below. This is not a common arrangement. Nationally, next-to-no government or public buildings have rooftop solar installations.
In late June, the National Energy Administration (NEA) published a notice on county-level trials of distributed solar power generation, designed to boost rooftop solar. This may prompt a new spurt in solar installations, on both public and private buildings, over the next five years. Statistics collected by industry media outlet BJX indicate 75 county-level governments have picked firms to install distributed solar and are set to start trials.
Unlike a coal-fired power station feeding the grid, distributed power is electricity generated on a smaller scale. The power can either be used by the home, office or other facility that produced it, sold to other users in the vicinity or sold to the grid. Distributed power generation has the potential to improve energy security, lower power costs and reduce CO2 emissions.
The NEA notice encourages counties to sign up if they have appropriate rooftops, good grid access and the technical and financial capacity to roll out the programme. Electricity grid companies are expected to provide connections where possible, making upgrades if necessary, to ensure distributed solar power can be connected to the wider network.
To apply for the trial, at least half of the rooftops of a county’s Party and government buildings needs to be suitable for solar installation, and 40% of other public buildings such as hospitals and schools. Liu Yiyang, deputy secretary general of the China Photovoltaic Industry Association, told China Dialogue that solar power installations on the rooftops of Party and government, university and hospital buildings are rare, but the trials could change that.
Why promote distributed solar?
At last December’s Climate Ambition Summit, China set a couple of important goals for the year 2030. Non-fossil energy sources should account for about 25% of all primary energy consumption; and solar and wind power generation should reach over 1,200 GW. At the end of 2020, China had installed 281 GW of wind and 253 GW of solar power, making a total of 534 GW. To meet its 2030 goals, China needs to more than double its wind and solar power generation capacity in the coming decade. Solar power capacity needs to grow by 80 GW a year, according to Peng Peng, secretary general of the China New Energy Investment and Financing Alliance.
This is no easy task. Though growth in solar power farms, which account for the lion’s share of solar capacity in China, had been Rapid, it slowed this year. Peng points out that while it will be possible to build more solar farms to drive growth in solar capacity between 2021 and 2025, there will be a bottleneck in how much of it the grid can deal with. Solar power farms are mostly located in northern China. While distributed solar suits the more developed eastern coast, where there is a lot of consumers, easier access to the grid and plenty of room for growth. “Distributed solar will have to account for half of new capacity, if annual growth in solar power is to go past 80 GW,” said Peng.
At the end of 2020, distributed solar accounted for about 78 GW (30%) of the 253 GW of China’s installed solar generation capacity, according to data from the country’s National Energy Administration. Growth in distributed solar appears to be picking up in proportion to growth in solar farms. In the first half of this year, about 13 GW of new solar power capacity was installed, 7.65 GW of that distributed solar.
Rely on rooftops, or innovation?
Over 20 GW of the 78 GW of distributed solar capacity is residential, and around 58 GW is generated on offices and industrial buildings. There are virtually no solar installations on the rooftops of public buildings. The new trials aim to fix that by boosting installations on Party and government buildings, universities and hospitals.
Liu Yiyang told China Dialogue that public buildings are rarely covered with solar panels as they already enjoy relatively low electricity prices. If generating power costs about the same as taking it from the grid, building managers have little incentive to go for solar.
Investors will usually lease a rooftop, provide power to the building owner at a discount, and sell the remainder to the grid. But public buildings tend to be smaller than industrial and commercial buildings, meaning less excess electricity to sell. Also, the investors need to lease public building rooftops from the government, which means more red tape. Liu thinks the government needs to step in to coordinate this process, helping investors arrange grid connections, matching up rooftop solar providers with rooftop owners, and reducing investment costs. The government also needs to educate building owners and investors, increasing their eagerness to develop distributed solar and make better use of the public rooftops. “Before, the building owners didn’t see a need, so even if a solar power firm came knocking they wouldn’t be interested. That roadblock is being cleared now.”
Liu is enthusiastic about the business model: “For the solar power firms, generation from public buildings might be low, but it’s reliable profit.” Governments, hospitals and universities don’t relocate every few years, and can be relied upon to pay their bills. Business and factory owners, in contrast, may use less electricity in a quiet period, or even close down. And if bills aren’t paid, it’s the investors that lose out.
Public buildings have only limited roof space. Peng Peng told China Dialogue that a county government will only have control over several thousand square metres of rooftop space – ten thousand, at most. “Even if you reach the 50% coverage proposed by the NEA, that’s a limited amount of power generation.” She emphasised that the key to developing distributed solar is expansion on the rooftops of industrial, commercial and residential buildings.
Will these new trials promote that? She doesn’t expect to see the trials alone make much difference – it will depend largely on profitability.
Installations on industrial and commercial buildings are either entirely for the company’s own consumption, or excess generation is sold to the grid. Residential installations come in a third variety too – where all power generated is sold to the grid. On-site consumption is the most profitable process, as it removes the need to buy power from the grid at business rates. When power is sold to the grid, the benchmark coal power tariff is paid, which is lower. Li Hui, founder of Adzar Energy, a distributed solar solutions provider, said that in Shanghai large industrial companies pay 0.80 yuan per kWh during the day, which they can save by generating their own electricity. Power sold back to the grid earns 0.41 yuan per kWh.
In neither case are market mechanisms fully in play. Peng Peng points out that reform is required to generate more enthusiasm for distributed solar. The existing system, where sold power can only enter the grid, should be changed to allow generators to deal directly with nearby consumers. That, she says, would make distributed generation more profitable, reducing financing costs and bringing many more projects online.
Li Hui doesn’t expect the trials to have much impact on his business. He told China Dialogue that Adzar Energy specialises in distributed solar installations for industrial and commercial facilities on city outskirts, and has also worked on residential installations. Government-controlled rooftops primarily belong to state-owned enterprises or government institutions, he said.
But he does think the trials will make more building owners realise they have a social responsibility to generate green and renewable energy. The NEA’s document sets targets for distributed solar installations on industrial and commercial buildings, and residential buildings in rural areas. Li said: “All companies will see that, sooner or later, they’re going to end up with solar panels, and the government encouragement may mean those who saw it as unprofitable get on board. That means we can win new business as the policy rolls out.”
Droves of local governments have applied to run trials, but small and medium-sized enterprises have some concerns. One commenter on the NEA website asked how the government would protect the interest of small-scale solar installers, who are at risk of being frozen out as county governments sign up with larger firms.
The NEA replied that trials should be run competitively, rather than as monopolies, and be market-led, with all suitable companies able and encouraged to participate in bidding.
The NEA also pointed out that local governments can choose whether and how many trials to run, and that there is no requirement to gain NEA approval. The reply also explained the government’s role: it is to coordinate resources, rather than take over the entire process. The building owners should choose contractors freely, via the market. Also, the trials should not cause any ongoing approvals processes or grid connections to be paused or delayed.
The publicly available data so far shows that most local governments have opted to use state-owned enterprises to develop distributed solar. But as few such enterprises have previously done this in their own right, many are choosing to cooperate with private firms. “Currently I haven’t heard of any local governments signing exclusive agreements which lock other firms out,” said Li Hui.
Solar deployed on rooftops could match annual U.S. electricity generation
Researchers conducted a global assessment of rooftop solar PV potential using high-resolution imagery, big data, and machine learning.
The United States has enough usable rooftop space to deploy an amount of solar equal to its current nationwide generation levels, according to recent research on global photovoltaic potential. Researchers at Ireland’s University of Cork leveraged big data, machine learning, and geospatial analysis to reach their findings, which were published in Nature Communications
In the report, about 77,000 square miles of rooftop area worldwide was demarcated as usable PV surface area (for context, the state of Florida is roughly 65,000 square miles). The researchers said this area could result the production of 27 petawatt-hours, or 27 million GWh, if completely covered by conventional photovoltaics.
This level of potential energy would exceed 2018 total global electricity consumption, a year in which 6 petawatt-hours (PWh) of electricity was consumed by homes alone, and over 23 PWh consumed in total globally, per Statista.com.
The report further said that rooftops in the United States could host enough capacity to produce an annual 4.2 PWh per year, effectively matching the nation’s current total energy output of about 4 PWh per year.
In 2020, about 60% of U.S. electric generation came from fossil fuels, and roughly 20% each from nuclear and renewable energy sources, according to data from the Energy Information Administration (EIA). EIA estimated that the U.S. additionally produced 42 billion kWh of electricity from distributed, small-scale solar, about 1% of total generation. Large-scale solar accounted for 2.2% of total generation that year.
The authors of the paper in Nature Communications said there are mitigating factors in this potential PV outlook, including constraints around transmission, and the need for storage due to the intermittent generation cycles of solar energy.
This high potential for rooftop solar may come as good news for American ratepayers, as it may put some savings back in their s. Recently, it was estimated by Local Solar for All that collectively 109 billion in utility bill payments could be avoided by 2030 if rooftop solar were to scale up 2-4 times faster, vs. an all-utility-scale solar deployment scenario.
The report is an important talisman to wield for those who are concerned about energy sprawl, or the development of land for energy generation. A study by Clemson University showed that in the U.S., including spacing and zoning requirements, roughly 500,000 square miles of land would need to be dedicated to new energy development by 2040, an area larger than Texas. Rooftops represent an alternative to using up otherwise useful lands.
“The open data generated in this research helps to quantify, locate and prioritize investment in zero-carbon electricity systems,” said study coauthor James Glynn, senior researcher at Columbia University.
Potential costs for rooftop solar across the globe were also evaluated in the Nature Communications paper, and it was found that costs range depending on location. The lowest per-megawatt-hour costs were in China (68) and India (66), and the highest costs were in the U.K. (251) and the U.S. (238).
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Solar Rooftop: How Does a Solar Panel Work?
Fossil fuels are quickly being seen as a non-sustainable means of energy with global fluctuations in price and volume, causing the world to turn towards alternative solutions. The sun is an inexhaustible source of energy, which can be harnessed into clean solar energy. The developments in solar technology have made turning solar energy into electricity much more convenient and efficient. As it is independent of traditional and conventional power generation systems, it can help you save on costs as well as protect the environment.
It is also a myth that extracting the full potential of this energy requires large solar farms. With today’s advanced solar rooftop systems, even residential homes and small to medium businesses can take advantage of this energy source.
But how do Solar Panels work?
Solar rooftop panels rely on photovoltaic cells within the panels to absorb the energy emitted by the sun and converts it into electricity. These cells are made of conducive materials like silicon, acting as a semiconductor. When these solar cells receive sunlight, a chemical reaction occurs within them, which then releases electrons, which generates an electric current.
The core component of these photovoltaic cells in a solar rooftop system are layers of doped silicon crystal-based semiconductor materials. A positive charge is created by the bottom layer of the cell, that is laden with boron which bonds with silicon. While the negative charge is created by the top layer that is doped with phosphorus. An electric field is produced by the movement of the surface between these two layers called the P-N junction.
When the cells receive sunlight, photons knock electrons out of both the layers. The flow of electrons then occurs, since the top and bottom layers possess opposite charges. The electrons then travel to an external circuit through which electricity is carried for usage purposes, controlled by the P-N junction.
How is this converted electricity utilised and tracked?
The electricity is in the form of DC current, that is unusable for home appliances. A solar inverter converts it into AC current, flows through a service panel, after which it can be safely used for all purposes. Simultaneously, a solar meter keeps track of the solarrooftop power production and bring to notice any potential problems. This, in a nutshell is the basic process through which solar energy is converted into useable electricity.
Thus, solar energy can be easily harnessed for all purposes from residential to commercial purposes. And if you are looking for solar financing, look no further than Electronica Finance Limited. EFL offers Rooftop Solar Loans with up to 75% of the installation value, for a loan term of up to 4 years at flexible interest rates and offer collateral-free loan for up to 15 lakhs.
So, harness the power of clean energy and be independent of rising power and frequent power cuts, with your very own rooftop solar unit.
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