Rooftop Photovoltaic Research Articles

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Buildings are important components of urban areas, and the construction of rooftop photovoltaic systems plays a critical role in the transition to renewable energy generation. With rooftop solar photovoltaics receiving increased attention, the problem of how to estimate rooftop photovoltaics is under discussion; building detection from remote sensing images is one way to address it. In this study, we presented an available approach to estimate a building’s rooftop solar photovoltaic potential. A rapid and accurate rooftop extraction method was developed using object-based image classification combining normalized difference vegetation index (NDVI) and digital surface models (DSMs), and a method for the identification of suitable rooftops for solar panel installation by analysing the geographical restrictions was proposed. The approach was validated using six scenes from Beijing that were taken using Chinese Gaofen-2 (GF-2) satellite imagery and Pleiades imagery. A total of 176 roofs in six scenarios were suitable for PV installation, and the estimated photovoltaic panel area was 205,827 m2. The rooftop photovoltaic potential was estimated to total 22,551 GWh. The results indicated that the rooftop photovoltaic potential estimation method performs well.

Shading effect and energy-saving potential of rooftop photovoltaic on the top-floor room

Rooftop photovoltaic panels can serve as external shading devices on buildings, effectively reducing indoor heat gain caused by sunlight. This paper uses a numerical model to analyze rooftop photovoltaic panels' thermal conduction, convection, and radiation in hot summer areas as shading devices. The researcher builds an experimental platform to verify the model, exploring the potential for energy savings of photovoltaic rooftop units in the Wuhan area. The results show that after installing photovoltaic panels, the delay performance of the roof increases by 0.5 h, the roof heat flux is reduced by 41.7%, the peak temperature of the roof is reduced by 22.9 °C, and the daily heat gain is reduced by 74.84%. Finally, this paper discusses the impact of high-reflectance and low-reflectance roofs on the shading effect. The study finds that low-reflectance roofs are more energy-efficient in the hot summer, as high-reflectance roofs lead to a 10.8% increase in indoor heat gain when the photovoltaic panel is installed. Finally, a quantitative method for evaluating the comprehensive potential for energy savings is proposed, considering the electricity generation gain of photovoltaic panels and the comprehensive energy-saving efficiency of photovoltaic roofs, which generates a total potential for energy savings rate of 61.06%. The model presented in this paper provides theoretical guidance for analyzing the comprehensive energy-saving effects of photovoltaic rooftop systems and reveals the potential for energy savings of rooftop photovoltaic panels as external shading devices.

Partial shading losses mitigation of a rooftop PV system with DC power optimizers based on operation and simulation-based evaluation

Building-applied photovoltaic (PV) systems improve urban building energy efficiency but are vulnerable to partial shading (PS) from near obstructions. This causes electrical losses owing to mismatch as well as reduction in module surface’s irradiance. Module level power electronics (MLPE) can mitigate these electrical losses caused by PS.The evaluation of the effectiveness of MLPEs in mitigating mismatch losses serves as the primary basis for determining its application during the system-design stage. However, research regarding the effects of applying MLPE is lacking. Further, the studies lack comparisons with various general PV system types, such as inverter types and array configurations.Therefore, the purpose of this study is to evaluate the mismatch losses mitigation effect of the DC power optimizer (DCPO), one of MLPEs. A rooftop PV (RTPV) system with DCPOs is selected for this study, and PS and operational performance are analyzed for RTPV system. Then, comparative analysis for the performance of the mimic RTPV model and seven general PV models, which are modeled for array configurations and different inverter types, is conducted using the simulation software.We developed four PV models with a central inverter and two with a string inverter, each with different shaded module connections. The annual array yields of these models decreased by up to 3.23% than mimic RTPV model, except for the model with micro inverters categorized as MLPE, which only experienced a 0.3% decrease. In the mismatch, it is expected that the PV system with MLPE can improve performance compared to the without MLPE.

Sensing anomaly of photovoltaic systems with sequential conditional variational autoencoder

The market for urban distributed photovoltaics (DPV) is expected to take off in the next decade. However, these systems are often subject to complex urban contexts and sub-optimal conditions, requiring scalable and comprehensive solutions to detect their underperformances. In recent years, deep generative models (DGMs) have exhibited outstanding performance in the anomaly detection domain, dealing with generic high-dimensional time series data. Nevertheless, the existing applications of DGMs in the photovoltaic (PV) sector are still unable to account for environmental information, limiting their performance under various environmental conditions. This study proposes the Sequential Conditional Variational Autoencoder (SCVAE), which can cope with the sequential impacts of the environment on PV power generation. Using real-world data collected from 30 rooftop PV sites located across China, a data processing pipeline is developed to construct the training datasets which contain mostly normal samples for unsupervised SCVAE model training. This work also constructs a synthetic dataset with a wide variety of artificial anomalies in reference to the domain insights and engineering practice of DPV systems. After checking and refining by experts, the synthetic dataset can finally be used to validate the anomaly detection models. The results demonstrate that the SCVAE model outperforms existing state-of-the-art unsupervised anomaly detection models and can be effectively generalized to unseen PV sites. Moreover, the latent variables of SCVAE could be used to identify the type of DPV failure, thereby enabling more targeted diagnostics of anomaly mechanisms.

Cost-reliability trade-offs for grid-connected rooftop PV in emerging economies: A case of Indonesia's urban residential households

This study explores the potential of grid-connected rooftop photovoltaic (PV) systems in terms of how they can be better planned and utilised by understanding possible trade-offs between cost and reliability while acknowledging challenges to utility supply security in the context of emerging economies. The study particularly examines the implications of unserved energy targets, PV capacity, and billing deduction factors on grid-connected rooftop PV's trade-offs in terms of total net present cost and unserved energy. This study considers four residential household segments in Indonesia's urban area as a case study, with four cases applied in each segment representing scenarios on PV capacities and billing deduction factors. Using HOMER software, the analyses highlight the role of cost components in trade-offs involving potential PV capacity cases. Systems with maximum PV capacity exhibit cheaper total net present costs than those of half PV capacity within the same unserved energy. While the optimisations pushed PV capacity up to the maximum size across all unserved energies, higher unserved energy resulted in lower grid capacity required to meet demand associated with the system's maximum unserved energy limit. This study provides residential customers and stakeholders with insights to better plan and implement grid-connected rooftop PV systems and policies.

Economic analysis of rooftop photovoltaics system under different shadowing conditions for 20 cities in China

Economic analysis of rooftop photovoltaics system under different shadowing conditions for 20 cities in China

Comparing PV-green and PV-cool roofs to diverse rooftop options using decision analysis

Flat roofs can employ a range of technologies to improve sustainability, such as photovoltaic (PV) panels, green roofs, cool roofs, or a combination of these options. Yet, weighing the benefits, costs, and performance of different roofing technologies is complex, especially when different stakeholders are involved. Decision analysis techniques, such as multi-criteria decision analysis (MCDA), can be used to systematically evaluate a diverse range of rooftop options to assess trade-offs in a quantitative way and avoid decision biases. This study offers a holistic comparison of different roof types, considering stakeholder preferences and uncertainty using MCDA. Ten flat roof options are compared, including black, gravel, cool, extensive green and semi-intensive green roofs, each with or without a rooftop PV installation, for nine objectives and three hypothetical stakeholder profiles. Performance is evaluated using building energy simulation, hydrologic modeling, and literature research. Uncertainty analyses are used to evaluate the effects of model assumptions on the MCDA results. For assumed preferences of an urban planner and environmentalist, semi-intensive green roofs with integrated PV installation are the best performing option; however, for a hypothetical building owner more concerned with costs, a gravel roof with PV ranks best. Uncertainty plays a role in the results, in particular, the uncertainty of the predicted outcome of options for the building owner, which can change the top-ranking options considerably. The uncertainty analyses are useful to identify consensus options over all three stakeholder types. Despite considerable uncertainty, extensive and semi-intensive green roofs with PV are recommended as relatively robust best-performing options.

Techno-economic analysis of residential rooftop photovoltaics in Spain

In response to the European Commission's renewable energy targets for 2030, this study presents a comprehensive, data-driven evaluation of the potential for electricity self-consumption in the Spanish residential sector based on rooftop PV systems. Utilizing real-time hourly electricity demand data and various surplus compensation policies, the research highlights the significance of data granularity, indicating that annual data-based PV estimations can lead to rooftop PV self-consumption capacity overestimations when compared to hourly data assessments. Furthermore, geographical variations reveal distinct rooftop PV self-consumption capacities between urban and rural areas, driven mainly by the prevalent building typologies. This discrepancy suggests that while rural regions, with their predominance of single-family dwellings, offer higher PV generation potential due to more available rooftop space, urban areas, dominated by multi-story buildings, face significant constraints in rooftop surface availability. Economically, the current surplus compensation policy in Spain reduces the profitability of PV installations, underscoring the necessity for enhanced policies to fully utilize all the available rooftop areas in the residential sector. The study's findings, particularly the revelation that residential rooftops in Spain may not be enough to meet the current electricity demand, emphasize the need for adaptive, region-specific policies and the potential role of energy communities. Policymakers and industry stakeholders are urged to prioritize rooftop utilization, ensuring the deployment of PV systems is both promoted and economically viable, steering Spain closer to its renewable energy and sustainability aspirations.

The Technical and Economic Assessment of a Solar Rooftop Grid-Connected Photovoltaic System for a Dairy Farm

This study covers the technical and economic analysis of a grid-connected rooftop 216 kWp photovoltaic (PV) system to meet the average annual energy demand of 45,327 kWh of a dairy farm. According to the results of the analysis, an average of 326,819 kWh/year is received at the output of the panel arrays, while 294,838 kWh/year is supplied to the grid. The amount of energy to be received from the grid was found to be 25,013 kWh/year, and the performance ratio of the system was 0.808. The net present value (NPV) of the system was calculated as $36,463.39, and the levelized cost of electricity (LCOE) was calculated as 0.065 $/kWh. Considering that the annual electricity generation amount of the rooftop PV system evaluated within the scope of the study is 315,152 kWh, it is estimated that 127.419 tCO2 per year of emission release will be prevented annually with the installation of the system.

Assessing urban rooftop PV economics for regional deployment by integrating local socioeconomic, technological, and policy conditions

Assessing the urban rooftop photovoltaic (PV) economics is important for scaling up rooftop PVs for rapid decarbonization. In this study, socioeconomic, technological, and policy factors were integrated into a rooftop PV economic assessment. This comprehensive method was applied to 21 cities in the Guangdong province of China, in combination with local real parameters. The results show that, with higher urbanization levels, electricity tariffs, and PV self-consumption rates, cities in the Pearl River Delta area generally had better economic performance in terms of both economic scale and profitability. They accounted for 75% of the total rooftop PV potential in Guangdong Province and had an internal rate of return (IRR) as high as 14.6–19.2% for Commercial and Industrial buildings (C&I), and 9.9–15.9% for residential buildings (R). Socioeconomic, technological, and policy factors jointly affect the economic performance and create a promising future for Guangdong rooftop PVs. It is projected that the IRR of current worst performing cities will grow by 4.9–5.8, and 4.4–5.9 percentage points, and the payback period will be shortened by 5.8–6.7 and 3.4–4.1 years by 2030 for R and C&I buildings, respectively. Among the three factors, technological advancement had the most significant long-term effect, followed by socioeconomic development. Policy factors play an important role in alleviating short-term cash flow pressure. Integrating the three factors in the assessment provides us with a more comprehensive picture of how urban rooftop PV economics will evolve and a better idea of how to prioritize rooftop PV development. Suggestions for prioritizing the deployment of rooftop PV systems in Guangdong by 2030 were provided.

Economic Dispatch of Integrated Electricity–Heat–Hydrogen System Considering Hydrogen Production by Water Electrolysis

Water electrolysis is a clean, non-polluting way of producing hydrogen that has seen rapid development in recent years. It offers the possibility of resolving the issue of excessive carbon emissions in conventional hydrogen production methods. In addition, waste heat recovery in hydrogen fuel cells can significantly increase the efficiency of energy use. Thus, to combine the electric power system, the hydrogen energy system, and the district heating system, this research suggests a novel optimal multi-energy complementary electricity–hydrogen–heat model. Rooftop photovoltaics, energy storage batteries, electric boilers, and hydrogen energy systems made up of hydrogen generation, hydrogen storage, and hydrogen fuel cells are all included in the suggested model. Furthermore, the electricity–hydrogen–heat system can be connected successfully using waste heat recovery in hydrogen fuel cells to create a coordinated supply of heat and power. In this work, the waste heat of hydrogen fuel cells is taken into account to increase the efficiency of energy use. To show the effectiveness of the suggested optimal multi-energy complementary model, many case studies have been conducted.

Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.

Evaluasi Emergent Strategy Bisnis PLTS Atap PT X sehubungan dengan Ketidakpastian Strategis Permintaan Pasar

PT X is one of the developers of rooftop solar photovoltaic (PV) power plants in the commercial and industrial sectors in Indonesia. Over time, there has been a strategic uncertainty of market demand since 2022, mainly affected by the strong bargaining position of Wilayah Usaha's state-owned enterprise (Wilus SOE). Wilus SOE plays a significant role, is given priority in the provision of electricity for the public interest as stipulated in the regulation, and also has a dominant share (95 percent in 2020), making it a de facto monopoly in the electricity business in Indonesia. Wilus SOE has legitimacy as a power distributor and therefore has the authority to approve the capacity of rooftop solar PV power plants. Referring to this situation, this research aims to identify the intended strategy of PT X and the emergent strategy in dealing with the strategic uncertainty of market demand. PESTEL analysis (political, economic, sociocultural, technological, environmental, legal) and five-forces framework (competitive rivalry, new entrants, substitute products, suppliers, customers), as well as emergent strategy evaluation, were used as the framework of this research. To strengthen the analysis results, in-depth interviews were also conducted with the management team of PT X. Based on the series of analyses, it can be concluded that PT X's business is strongly influenced by legal factors while the competitive rivalry and customers' factor have very strong forces. On the other hand, PT X has implemented an emergent strategy well in dealing with the strategic uncertainty of market demand, as an effort to maintain the company's profitability. This research is expected to provide benefits for PT X and other rooftop solar PV power plant developers to understand the emergent strategy in order to remain competitive in its business implementation.

Balancing DSO interests and PV system economics with alternative tariffs

Distributed rooftop photovoltaics (PV) is one of the pillars of the energy transition yet their widespread integration strains grids, leading to over-voltage, reverse power flow, and infrastructure strain. Furthermore, increased PV self-consumption reduces imported electricity, posing challenges for cost recovery by Transmission System Operators (TSOs) and Distribution System Operators (DSOs), whose grid costs were traditionally tied to volumetric tariffs. To investigate whether alternative tariffs could mitigate PV impacts at the distribution level without hampering PV development, we assess five electricity tariffs that could help the DSOs to recover the costs of maintaining the distribution grid. We also analyze their effects on private storage investment and their implications for urban, semi-urban, and rural low-voltage networks. We found that tariffs with a capacity-based component promote further adoption of PV and storage. At the same time, they allow the DSOs to recover the grid cost without incurring relevant economic differences for the customer. Our study found that alternative tariffs like dynamic and capacity-based tariffs promote the adoption of storage and PV systems. While no single tariff alone can fully mitigate PV impacts at the distribution level, our results point towards the need of managing PV export through solutions like PV curtailment.

Optimum energy management strategy with NEM-ETOU for campus buildings installed with solar PV using EPSO

In campus facilities, classrooms and labs often use a large amount of energy due to their variable consumption pattern and lack of use management. Installing solar photovoltaic (PV) rooftop in the campus building through net-energy-metering (NEM) scheme has recently increased to reduce energy usage as well as generate income from the sales of excess solar energy to the grid. Meanwhile, the Malaysian government has introduced the Enhanced Time of Use (ETOU) tariff to encourage consumers to shift their electricity usage to reduce their bills through demand response. Despite offering different rates at various times of the day, the ETOU tariff may lead to an increase in electricity expenses if consumers do not effectively shift their consumption to lower rate hours and eventually using more during peak hours. Additionally, there has been lack of quantitative analysis conducted to determine how effective the compensation scheme provided by NEM. Therefore, considering a university building that holds a commercial electricity tariff, this study aims to develop an optimum energy management strategy integrating demand response and solar energy rooftop using improved Evolutionary- Particle Swarm Optimisation (EPSO). To determine the best load shifting strategy, the model was used to evaluate two Universiti Teknologi MARA (UiTM) campuses where NEM solar PV rooftop systems were installed. The analysis involved testing various weightage factors for controlling the loads, ranging from 5% to 20% under 2 different electricity tariffs offered in Malaysia. Results show the electricity cost under EPSO provides a significant cost reduction compared to PSO for both tariffs with a lesser computational time. Furthermore, EToU tariff with a weightage factor of 20% gives a higher energy and cost saving compared to fixed tariff. The findings from this research will assist the energy users in managing the load and generation from solar, whilst supporting the policymakers in designing effective compensation schemes.

Control techniques for operation of roof-top solar photovoltaics based microgrid in islanded mode

This paper presents the control techniques for the operation of roof-top solar photovoltaics based single-phase ac microgrid in islanded mode. The proposed microgrid consists of four roof-top photovoltaics on various buildings, battery energy storage system in each building, and loads. In each building, the roof-top photovoltaic system is connected to the single-phase ac loads and proposed single-phase ac microgrid through the two-stage converter, which consists of dc-dc boost converter and bidirectional single-phase voltage source converter. A bidirectional dc-dc converter is used to connect the battery energy storage system to the photovoltaic system. The control technique of the bidirectional single-phase voltage source converter, using the feed-forward and feed-back control signals, has been suggested into a rotating d-q synchronous reference frame, with the dual controllers, for controlling the power mismatch and rated sinusoidal ac voltage of the microgrid. The control technique of the bidirectional dc-dc converter, used for the battery energy storage system, is implemented with two voltage controllers and one current controller for managing of the power mismatch in the proposed microgrid system. In this work, the power control algorithms have also been developed, which are aimed to manage the power balance in the single roof-top PV based building as well as in the proposed microgrid, under islanded mode, for various operating scenarios including fault condition.

The assessment of the value of electricity saving and economic benefit to residential solar rooftop PV customer: The case of Thailand

A rise in residential solar rooftop PV (SRPV) has been observed in Thailand because of the decreasing costs of solar photovoltaics (PV) and implementation of incentive programs for SRPV. However, without financial assistance, it is not economically feasible to invest in a SRPV system with Stationary Battery Storage (SBS) due to the high up-front cost of SBS and the low power retail price for household. This study examines the benefit to Thai households with SRPV under different technology regimes, incentives, rate structures, and trading mechanisms such as Peer-to-Peer (P2P), and financial aid programs. It is found that the households who install solar with SBS, or a Battery Electric Vehicle see a minor benefit. Net Energy Metering saves more money than Net Billing (NB) under different retail rate structures and price escalation as well as with different load patterns in pre-COVID19 and during COVID19 conditions. In addition, SRPV can deliver even greater financial return if it is supported by measures, such as a lower loan interest rate and a substantial share of the loan amount. Compared to NB, P2P energy trading is favorable for SRPV that purchase and sell excess solar rooftop energy after self-consumed with the neighboring household.

Harnessing climate variables for predicting PV power output: A backpropagation neural network analysis in a subtropical climate region

This study examines the influence of weather conditions on the electricity production of photovoltaic (PV) panels in the Conghua district, a region in Southern China distinguished by its subtropical mountainous climate. The area's high humidity levels, high temperatures, and substantial rainfall often lead to predominantly cloudy skies, which alter solar irradiation patterns as a result. Utilizing the EnergyPlus simulation tool, the power generation of PV panels on rooftop application was simulated. A structured classification method is adopted to sort out weather conditions into categories of sunny, cloudy, and rainy. Through examining the influence of these varied weather scenarios on power generation, unique factors affecting electricity outputs are identified. As a result, this study illuminates the relationships between potential weather variables and PV power generation across each weather category. Subsequently, a back propagation neural network (BPNN) model is utilized to explore the relationship between weather categories and PV generation. The high coefficient of determination value (R2 > 0.95) from the comparison of simulated and predicted PV generation validates the model accuracy. This research enables the development of updated dataset and enriches the prediction of rooftop PV power generation. It equips designers and owners with a reliable, user-friendly, and real-time tool for PV power generation forecasting, and provides a working platform for PV performance evaluation during the design phase of PV projects. The insights derived from the BPNN model could guide the optimal design and implementation of PV systems in similar climates. Furthermore, these findings advance sustainable energy solutions in regions affected by complex weather patterns.

Grid abandonment potential of photovoltaic and storage-based system in China

Nowadays, owing to the price and technological advantages, photovoltaic (PV) and battery energy storage systems (BESS) have rapidly developed in China. The self-production and consumption of PV and BESS are causing consumers to abandon the power grid. However, this potential of grid abandonment in China's power sector remains unclear. Therefore, this paper deeply investigates the interaction between grid abandonment and the distribution price of utility, namely the utility death spiral to explore the potential in China. A method based on the innovation diffusion theory is proposed to reveal the causal loop of the death spiral. The Bass diffusion model, an approach of innovation diffusion theory, is applied to estimate the installations of rooftop PV systems and hybrid PV-BESS. After that, the distribution price, grid utilization level, and utility recovery cost based on China’s price structure are evaluated. Sensitivity analyses are conducted to investigate several significant inner drivers of the rooftop PV systems and hybrid PV-BESS expansions. Results indicate that the utility death spiral is unlikely to occur in China with low-solar radiation, while it will occur in a high-solar radiation scenario with 57% penetration of rooftop PV systems. Compared with the return rates of the utility requirement, the cost reduction of PV and BESS, and even a 1% growth in the retail price cause the death spiral. When the death spiral occurs, the permeations of PV and BESS in the cost reduction scenario are 51% and 56%, while the permeations are 52% and 51% in the scenario of 1% growth of the retail price. Additionally, several possible solutions to avoid the death spiral are discussed, such as discriminatory prices, business models, price mechanisms, and support mechanisms.

Impacts of Rooftop Photovoltaics on the Urban Thermal Microclimate: Metrological Investigations

Ambitious climate protection requires decentralized renewable energy production via building-related photovoltaics in cities. However, cities are also subject to the Urban Heat Island effect, which negatively impacts human health. In this context, the installation of PV modules in urban areas raises the question of how the energy-active building component affects its surrounding thermal microclimate. The topic is approached with a novel, twofold methodology: First, a horizontal ambient air temperature profile above a rooftop PV installation and the analysis of surface temperatures provide primal indications of the impacts of PV on the thermal microclimate. Secondly, establishing energy balances allows us to retrace the energy fluxes. The data required for the comparative analyses are gathered through metrological investigations of two rooftops with and without PVs in Munich, Germany. The results of the investigation show a daytime heating effect of photovoltaics on the ambient air temperature of up to +1.35 K and a nighttime cooling effect of up to −1.19 K. Cumulated daily, the daytime heating effect exceeds the impact of the nighttime cooling. The main driver of the heating and cooling is the additional convective heat transfer from the PV modules. However, the impacts of the PV modules are marginal and restricted to their direct surroundings.