Rooftop Photovoltaic Installations Research Articles

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

We consider the problem of estimating the unobserved amount of photovoltaic (PV) generation and demand in a power distribution network starting from measurements of the aggregated power flow at the point of common coupling (PCC) and local global horizontal irradiance (GHI). The estimation principle relies on modeling the PV generation as a function of the measured GHI, enabling the identification of PV production patterns in the aggregated power flow measurements. Four estimation algorithms are proposed: the first assumes that variability in the aggregated PV generation is given by variations of PV generation, the next two use a model of the demand to improve estimation performance, and the fourth assumes that, in a certain frequency range, the aggregated power flow is dominated by PV generation dynamics. These algorithms leverage irradiance transposition models to explore several azimuth/tilt configurations and explain PV generation patterns from multiple plants with non-uniform installation characteristics. Their estimation performance is compared and validated with measurements from a real-life setup including 4 houses with rooftop PV installations and battery systems for PV self-consumption.

Stakeholders’ Perspectives of Design Options for a Rooftop Solar PV Self-consumption Scheme in Thailand

Adoption of solar photovoltaic (PV) power generation systems has been accelerating around the world, contributing to the debate about the future of policy and regulation in a high distributed energy resources future. As one of the leaders in solar investment in Southeast Asia, Thailand has recently shifted its policy framework for the support of small scale, distributed solar PV systems from subsidizing power export through feed-in tariff toward a policy that is focused on self-consumption. This paper investigates stakeholder perspectives of the detailed design options for self-consumption schemes for supporting rooftop solar PV installations. The research methodology employed questionnaires and focus group discussion in order to capture stake-holder perspectives for each element of rooftop solar PV self-consumption schemes. In all, the data derived from questionnaires and focus group discussion involved a total of 72 stakeholders. The results indicate that most stakeholder groups expressed a strong desire for compensation for excess generation of PV electricity from rooftop PV systems. While the majority of electric utilities prefer a system of net billing with real-time buyback, designed to minimize revenue losses, consumers and policymakers preferred a net-metering-based compensation scheme for supporting use of rooftop PV electricity in Thailand.

Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests

Photovoltaic (PV) panels are a very promising technology that answers part of the increasing need for global renewable energy production, particularly in urban areas. We present a novel methodology combining Geographic Information Systems (GIS), solar models and a Machine Learning (ML) algorithm, Random Forests, to estimate the potential for rooftop PV solar energy at the scale of a country. We use a hierarchical approach which divides the computation of the final potential into several steps. Each step is reached by estimating multiple variables of interest using widely available data, and combining these variables into potential values. The method for estimating each variable of interest is as follows: (1) collect all the data related to the variable, (2) train a Random Forest model based on the collected data and (3) use the model to predict the variables in unknown locations. The variables of interest include available area for PV installation on rooftops, shape, slope and direction of rooftops, global solar horizontal and tilted radiations, as well as shading factors over rooftops. The study focuses on Switzerland and provides the rooftop PV technical potential for each (200 × 200) [m2] pixel of a grid covering the entire country. The methodology, however, is generalizable to any region for which similar data is available and could therefore be useful for researchers, energy service companies, stockholders and municipalities to assess the rooftop PV capacity of the region. Prediction Intervals are also provided for the different estimated variables, in order to measure the uncertainty of the estimations. The results show that Switzerland has a large potential for rooftop PV installations. More specifically, for roofs orientated at ±90° from due south, the total estimated potential PV electricity production is about 16.29 TWh/year, which corresponds to 25.3% of the total electricity demand in 2017.

Consumer attitude and purchase intention toward rooftop photovoltaic installation: The roles of personal trait, psychological benefit, and government incentives

Rooftop photovoltaic system is a cheap and abundant energy source that addresses the threat of global warming, and its future success relies on government incentives and marketing strategies designed to improve consumers’ benefit perception. The present study aimed to examine the relationship among personal traits (including environmental concern, an ecological lifestyle, and consumer innovativeness), psychological benefits (including a warm glow and a “nature experience”), attitudes toward rooftop photovoltaic, government incentives, and intentions to install rooftop photovoltaic. Empirical data were collected from one nationwide company in Taiwan, and 300 valid questionnaires were collected. The collected data were analyzed using a structural equation model. The results show that an ecological lifestyle, consumer innovativeness, and warm glow affect rooftop photovoltaic installation intention through the attitude toward rooftop photovoltaic. Moreover, government incentives have the strongest influence on this intention. This study integrates personal traits, psychological benefits, attitudes toward rooftop photovoltaic, government incentives, and intentions to install rooftop photovoltaic in a model from the consumer perception theory perspective; it expands the theory regarding planned behavior in the solar photovoltaic research field. This research also provides suggestions for government policymakers and offers a strong theoretical and practical framework for photovoltaic industry marketers.

Photovoltaic-Model-Based Solar Irradiance Estimators: Performance Comparison and Application to Maximum Power Forecasting

Due to the increasing proportion of distributed photovoltaic (PV) production in the generation mix, the knowledge of the PV generation capacity has become a key factor. In this paper, we propose to compute the PV plant maximum power starting from the indirectly estimated irradiance. Three estimators are compared in terms of ability to compute the PV plant maximum power, bandwidth, and robustness against measurements noise. The approaches rely on measurements of the DC voltage, current, and cell temperature, and on a model of the PV array. We show that the considered methods can accurately reconstruct the PV maximum generation even during curtailment periods, i.e., when the measured PV power is not representative of the maximum potential of the PV array. Performance evaluation is carried out by using a dedicated experimental setup on a 14.3 kWp rooftop PV installation. Results also proved that the analyzed methods can outperform pyranometer-based estimations with a simple sensing system. We show how the obtained PV maximum power values can be applied to train time series based solar maximum power forecasting techniques. This is beneficial when the measured power values, commonly used as training, are not representative of the maximum PV potential.

Benchmarking the Performance of Solar Installers and Rooftop Photovoltaic Installations in California

Rooftop photovoltaic (PV) systems are rapidly proliferating around the world. Whether the PV systems have been efficiently installed is an issue of utmost importance for both solar installers and policymakers. However, the impact of solar installers on PV performance is not well understood. In this paper, we investigate the performance of rooftop PV installations and the solar installers using a dataset of 1035 projects developed by 213 installers in California. Based on data envelopment analysis (DEA), our study takes the PV system capacity, electricity generation, cost, modules, solar irradiance, and ambient temperature into account simultaneously to construct a unified measure for the efficiency of PV installations. We analyze the relationship between installer characteristics and PV system performance. We find PV installations with the installer also being the module manufacturer, exhibit significantly better performance than other installations. PV installations by subsidiaries of oil firms have inferior performance. PV installations by large installers on average do not perform better than the installations by small installers. Geographic diversification of an installer’s operations is significantly and inversely related to the performance of installations. We demonstrate the aforementioned findings have significant implications for policymakers and the solar installation industry.

Analysis of inhomogeneous local distribution of potential induced degradation at a rooftop photovoltaic installation

Potential induced degradation (PID) of photovoltaic (PV) modules is one of the frequently observed failures in PV installations nowadays. This study investigates the inhomogeneous and complex PID generation on rooftop installations on industrial buildings as well as its impact on the module performance. The PID development is exemplarily presented for a 314kWp PV-plant installed in the Atlantic coastal climate. Due to the complex plant geometry and resulting irradiation situation the existence of PID could not be identified based on the annual yield data. By Infrared imaging PID was clearly identified. Evaluating historic monitoring data, the impact of PID on the string and plant performance could be quantified. A linear correlation between the defect ratio and the performance rate as well as the degradation loss rate could be formulated.

Estimating Solar PV Potential for Sustainable Energy Planning in Tier-II Cities of India: Case of Bhopal City

With growing economy, several cities and towns in India are experiencing a substantial growth in their gross and peak electricity demands. While Municipal Corporations and the electricity supply agencies are struggling to cope with growing energy demand of cities, many think solar electricity generation through rooftop Photovoltaic (PV) is a viable solution for this problem. Quantification of available rooftop is necessary to estimate the potential of energy generation in Indian cities through rooftop solar PV panels. This paper proposes an automated object-oriented approach to extract total available area of residential rooftops. A smaller residential neighbourhood in Bhopal city has been selected as Test Site to test the proposed method. Data sets used are World View 2 Stereo pair and Multispectral images. It is found that the test area has the potential to meet 265% of its daily local energy demand through rooftop solar PV panels. Though Indian government had announced separate scheme under the Jawaharlal Nehru National Solar Mission (JNNSM) to promote installation of rooftop PV, development of this sector is still in nascent stages. This paper also suggests some policy level interventions to promote rooftop PV at domestic level in similar tier-II cities of India.

Coordination of single-phase rooftop pvs in unbalanced three-phase residential feeders for voltage profiles improvement

Installation of single-phase rooftop photovoltaic (PV) systems in low-voltage (LV) distribution networks is gaining increasing popularity in many countries including Australia. However, installation of rooftop PVs in residential feeders without controlling their ratings and locations may deteriorate the overall grid performance including power flows, losses and voltage profiles. Therefore, in recent years, the utilities have adopted limitations on the maximum allowable number of PVs in LV networks. To overcome these issues, this study proposes an intelligent and communicatoon-based voltage profile regulating technique which is composed of three different steps. This technique relies on the availibility of smart meters at the buses. The first objective of the method was to minimise voltage rise along the feeder by reducing the voltage at secondary side of distribution transformer. This is achieved by utilizing a distribution transformer with on load tap changing capability and a master controller located at the transformer. The algorithm also considers reactive power exchange and active power curtailment by the inverters of the single-phase rooftop PVs as the main steps for voltage profile regulation, as second objectives. The effectiveness and limitations of the proposed intelligent method are also investigated in this study with the help of MATLAB-based simulation studies.

Financial and environmental analysis of rooftop photovoltaic installations with battery storage in Australia

Recent trends in increased public environmental awareness coupled with the introduction of governmental incentives, has resulted in a remarkably rapid growth in rooftop photovoltaic (PV) installations at a residential level in Australia. Despite the widely perceived economic and environmental benefits of rooftop PV, the economic and environmental performance of rooftop PV under Australian conditions are yet to be thoroughly examined. Whilst prior studies on the subject of rooftop PV are extensively overseas based, the conditions under which they are performed differ immensely from the Australian conditions. Consequently, this paper focuses on evaluating the life cycle economic and environmental merit of rooftop PV in Australia. By making use of component-wise costs and embedded emissions associated with each component of the rooftop PV generation system, the life cycle cost and emission models have been developed. For this to occur, the Australian costs and, energy and emission intensities have been used. All the life cycle factors relating to the manufacturing, purchasing, installation and operation of rooftop PV are considered. A sensitivity analysis is performed with respect to electricity price, feed-in tariff, battery price, battery lifetime and emission intensities. The results of the cost and emissions analyses are collectively used to estimate the cost of emissions mitigation through the use of rooftop PV for current and potential future scenarios in Australia. The cost payback and the energy payback times are found to range between 11 to more than 25years and 1.75 to 14years, respectively. The study also reveals that local load profile can have a significant impact on payback times. For example, the cost payback without battery storage is found to be the least for Tasmania which hosts the least favourable solar insolation conditions and lowest feed-in tariff amongst the Australian states however possesses a high daily load and electricity price.

Solar, Solar Everywhere: Opportunities and Challenges for Australia's Rooftop PV Systems

AT THE END 2014, 16% OF AUSTRALIA'S detached and semidetached houses?nearly 1.4 million homes?had photovoltaic (PV) systems on their roofs. That makes Australia, by a wide margin, the global leader in rooftop PV installation per household. This outcome was achieved from a negligible base at the start of 2010, and such rapid expansion was widely unexpected.

Modelling PV Clouding Effects Using a Semi-Markov Process with Application to Energy Storage

Cloud-induced intermittency of photovoltaic (PV) generation forces equipment on the electrical grid to cycle excessively preventing PV from being considered as a reliable or dispatchable source of power. Energy storage units (ESU) are proposed to turn PV power dispatchable. In order to use an ESU most effectively, it must be controlled appropriately by considering cloud-induced effects. To this end, the cloud structure is modeled as a random sequence inferred from clouding data. The proposed model is valid for centralized PV installations and serves to develop not only a control methodology to coordinate an ESU with existing grid equipment but also as a sizing criterion for an ESU. The above methodology is demonstrated on both clouding data collected from a rooftop PV installation that includes a pyranometer.

Experimental and data collection methods for a large-scale smart grid deployment: Methods and first results

This paper has two objectives: 1) to describe the experimental and data collection methods for a large-scale smart grid deployment in Austin, Texas, and 2) to provide results based on those data. As of October 2012, the test bed was comprised of 1) 250 homes concentrated in a single neighborhood all built after 2007, and 2) 160 homes distributed throughout Austin with ages ranging from 10 to 92 years old. This experiment includes 200 electric monitoring systems (15-s resolution), 211 electric monitoring systems (1-min), 182 gas meters (2-cubic foot), and 51 water meters (1 gallon) and many of the monitored homes also have energy audits and homeowner surveys. The test bed also includes 185 rooftop PV (photovoltaic) installations and 50 electric vehicles in the same neighborhood. Data streams were automated and gathered at a supercomputing facility at UT-Austin yielding 250 GB (2.95 × 109 records) of data in the first year. This paper describes the baseline study and monitoring methods, characterizes the study participants, and provides some first results about residential energy use. These results include a negative correlation between energy use and knowledge about energy as well as a possible positive correlation between energy use and some rebates.

Façade and Rooftop PV Installation Strategy for Building Integrated Photo Voltaic Application

Building integrated photo voltaic (BIPV) is an emerged research topic to optimize building component replacement using certain types of photo voltaic (PV) module. This paper conducts a strategic review on the optimum PV module installation to generate electricity from the building envelope. The façades and rooftops would be an object of building envelope to be deposited with a specific characteristic installation of PV module. Façade installation will be affected by the geographical position of the site, so a certain directions shall perform a higher electric energy production than the other directions. Recent calculations of the inclination angle of attaching the PV module in the selected walls indicates that an optimum angle in horizontal and vertical inclination. The calculation also uses installation distance to module length ratio to achieve a greater solar insulation on the PV modules. Rooftop installation will consider the curved rooftop as a potential feeder either it was bonded or mounted. It is believed that overall performance of curved PV installation could outperform flat PV installation in certain time. Other calculation also performed to observe effective load carrying capacity (ELCC) against PV penetration level to perceive the optimum PV penetration level for high ELCC without resulting operational problems.

Statistically adjusted engineering (SAE) modeling of metered roof-top photovoltaic (PV) output: California evidence

Abstract Accurate hourly photovoltaic (PV) output data are useful for engineering design, cost-effectiveness evaluation, rate design, system operation, transmission planning, risk management, and policy analysis. However, a large sample of hourly metered PV data is seldom available, and engineering simulation is often the only practical means to obtain hourly PV output. Based on an analysis of net energy metering (NEM) funded by the California Public Utilities Commission (CPUC), this paper presents statistically adjusted engineering (SAE) modeling of metered output of 327 roof-top PV installations in California for the 12-month period of January–December 2008. The key findings are: (a) the metered PV output is on an average 80–90% of simulated performance; and (b) the simulated data have useful information for accurately predicting metered PV performance. Plausible causes for (a) include incomplete input data for PV simulation, occasional failures in metered data recording, and less than ideal conditions for PV performance in the real world.