Rooftop Solar Energy Research Articles

Towards Auto-Calibrated UBEM Using Readily Available, Underutilized Urban Data: A Case Study for Ithaca, NY

The shift toward urban decarbonization, which involves transitioning to heat pumps and integrating photovoltaic (PV) energy generation, poses substantial challenges to electricity grids in cities. Ithaca, NY, with its commitment to the Green New Deal, serves as a case study for cities confronted with these challenges. This paper explores how various building electrification scenarios might affect Ithaca's net-zero ambitions. We show that our reduced-order Urban Building Energy Modeling (UBEM) approach can accurately predict building energy consumption and rooftop solar PV energy generation for the 6114 buildings in the City of Ithaca. Building on that, we present three increasingly ambitious decarbonization scenarios that offer a holistic perspective on Ithaca's future electrification potential. In automating the reconstruction of building geometry from readily available LiDAR data, along with automated BEM setup and calibration, our methodology offers a robust toolkit to plan holistic decarbonization efforts. We argue that our approach, which forecasts both UBEM and PV for all residential and commercial buildings within a single framework, provides insights that cannot be reached with siloed methods. In the future, we believe that the abundance of readily available urban data sets will allow us to transition our approach to other jurisdictions across the US.

Least-cost solutions to household energy supply decarbonisation in temperate and sub-tropical climates

Decarbonisation of building energy supply is key to achieving current targets of greenhouse gas emission reduction. However, the least-cost, net-zero supply of electrical and thermal loads in residential buildings is affected by building type, local climate and grid emissions intensity and is subject to uncertain future energy prices. This work investigated a typical detached house and an apartment in two climates – temperate and sub-tropical – in Australia. Least-cost optimisation of the technology mixes required to serve building energy loads was undertaken, including a range of appliances powered by electricity, natural gas and hydrogen and the use of distributed energy resourced, notably rooftop solar and residential battery energy storage. Solutions were investigated with increasingly stringent greenhouse gas emission abatement constraints and a range of future prices for electricity and hydrogen, demonstrating: i) progressive electrification is the likely cheapest pathway to net-zero emissions for the two building types studied; ii) should grid decarbonisation lag the abatement constraint applied to the homes, a net-zero dwelling is likely unachievable and the deepest achievable abatement is very expensive; and iii) network-delivered hydrogen may be optimal in a subset of buildings with limited distributed energy resources potential and more peaky heating and hot water demands. In such cases, bio-methane or even synthetic methane may also be preferred. Through significant adoption of distributed energy resources, results suggest an affordable route to deep (i.e. > 50 %) abatement for homeowners that relies less on grid emissions and, beyond the Australian case study, can be extended to a significant proportion of dwellings in other countries.

A Study of Households’ Perception towards Solar Panel Installation Case Study: Villagers of Techno-park nearby Faculty of Technology, University of Sri Jayewardenepura

In Sri Lanka, energy demand has been exponentially increasing with the outage of non-renewable energy sources. There is significant solar intensity in most of the areas in Sri Lanka. This study focused on the domestic solar power system since domestic solar panel usage (700 MW) is the country's highest contributor to solar energy usage. A photovoltaic (PV) system can supply electric energy to loads by directly converting solar energy through the photovoltaic effect with a flexible structure. This study aimed to analyze households' perceptions of solar panel installation. The study was conducted within a 5 km radius around the Faculty of Technology, the University of Sri Jayewardenepura, to analyze the awareness of solar-related technologies. A systematically developed questionnaire was used for the survey to collect the peoples' awareness based on environmental, social & economic beliefs. Thirty residential places (30) were interviewed. According to the results, a residential house's average monthly electricity consumption is 137.67 kWh. The mean monthly income in the area is sixty-eight thousand one hundred and sixty-six rupees and sixty-seven cents. Moreover, most people had solar energy knowledge (93.33%). Among them, 53.57% of the majority were willing to install Domestic Solar PV systems for residential places. From the consumers, 76.66% of the majority had an average level of awareness of the Environmental benefits of using renewable energy and 70.00% of the majority had an excellent awareness of government intervention in rooftop solar energy projects such as "Soorya Bala Sangramaya‖. From the defined awareness levels, further analysis was done on the lowest level of Awareness of Solar Energy to identify their characteristics. The majority of them were those who had lower energy consumption, were over the age of 60 and had a monthly income below twenty thousand rupees. According to the consumers, the best strategy for promoting Solar PV Systems was to lower the prices of existing Solar PV systems. Regulating the efficiency of the Solar PV system was identified by the existing Solar PV system owners. 
 Keywords: Solar PV system, Techno-park, Photovoltaic panels

Optimizing energy consumption in smart homes: Load scheduling approaches

AbstractRising fuel prices, global warming, and environmental damage are leading to increased demand for rooftop solar energy systems connected to the power grid. The development of smart grids, modern metering systems, and energy management could promote energy conservation in households. In this study, two different meta‐heuristic optimization techniques were employed to schedule the shiftable load in suitable hours for decreasing electricity costs and minimizing peak to average ratio in a smart home while maintaining optimum user comfort. For power generation and storing energy, a grid‐connected residential load with rooftop solar panels, a battery, and an inverter is considered. First, the problem is theoretically described using a load model and an objective function, with the primary goal of reducing power costs by moving the time of usage for certain home appliances. Simulations validate the proposed strategies, which effectively reduce power costs by 4.5% by shifting the time of use, with both optimization algorithms showing similar output. The residential electricity cost before optimization was 507.12 BDT/day, which decreased to 484.33 BDT/day after optimization without compromising load turn‐off.

Evaluation of economic feasibility of rooftop solar energy systems under multiple variables

<p>Rooftop harvesting of solar energy is a promising method to provide a great portion of household energy requirements in many parts of the world. However, the cost of solar energy systems sometimes makes the exploration of rooftop solar energy systems not attractive to property owners. This study evaluates the economic factors that could affect the decision on whether to consider the installation of solar energy systems using the estimated time that the cumulative solar savings would become positive. The economic implication of increasing the micro-generation capacity of individual households, and the impact of varied interest rates, and subsidies were also evaluated. Among the three factors that were presented, the result showed that increasing the amount of electricity that is allowed to be generated from individual rooftops will result in the highest economic attractiveness for end-users. This is also expected to move the world closer to the goal of sustainable management of non-renewable resources for present and future generations. Increasing the micro-generation capacity of electricity from photovoltaic (PV) rooftops by individual households without increasing the electricity distribution fees results in a reduction of the time to reach positive solar savings. In addition, increasing the micro-generation capacity of electricity from PV rooftops is expected to contribute to a reduction in the greenhouse gas (GHG) emissions from the electricity grid for the entire community. This study recommends the encouragement of policies that allow for the maximization of electricity generation potential from rooftops of residential and industrial buildings.</p>

A district-scale spatial distribution evaluation method of rooftop solar energy potential based on deep learning

Accurate evaluation of rooftop solar potential is increasingly important in sustainable urban development. However, accurately evaluating the solar photovoltaic (PV) potential of rooftops in urban areas is a challenge due to the diversity of urban rooftop outlines and rooftop obstacles. This study proposes a generic framework for evaluating the potential of urban rooftop solar PV that integrates deep learning and geographic information systems (GIS). GIS is used to extract information about land use types and classify buildings based on land use types. A deep learning-based method for calculating the rooftop available area of multi-type buildings is proposed.To validate the proposed methodology, an area in Wuhan containing a variety of building features was used, combined with the utilization of the available rooftop area to estimate the solar photovoltaic potential, and solar irradiance measurement experiments were set up for validation. The results show that the classification of rooftops by building type can effectively improve the accuracy of rooftop availability identification, and the classification process improved the identification of rooftops by 12.68% and obstacles by 12.42% overall. The annual solar potential of urban rooftops in Hanyang District is 8864.37 GWh/year.

Towards Efficient Energy Usage at Ain Shams University Campus

In the light of global energy transition to renewable resources and energy efficiency usage, Ain Shams University (ASU) developed an ambitious plan to transform its campus into Green Campus. From an energy perspective, energy consumption data were continuously collected and audited to calculate the university campus carbon footprint. An energy usage strategy was established to tackle various pillars such as electrifying the campuses’ transportation system, improving energy efficiency usage, generating Renewable Energy (RE) for self-consumption, etc. Extensive research has been initiated on electric vehicles, wind and solar Photovoltaic (PV) energy generation with students’ activities/competitions. Thus, electric cars and buses were manufactured at the Faculty of Engineering (FoE) for elderly people and staff movement in ASU campus. Solar PV lighting poles with batteries were installed in the main campus. A small-scale Wind Turbine (WT) is manufactured and installed at the FoE and a pilot solar PV system is installed as well. Currently, an energy efficiency project is under implementation in various buildings/faculties and a parking lot that targets energy efficiency and solar PV energy generation. An energy efficiency measure is under implementation through replacing lamps with LED lamps, installing motion sensors, setting up a control center for monitoring and operation that is supported by Artificial Intelligence decision making algorithms. Rooftop solar PV energy systems are under design with smart meters. The project is targeting energy saving and bill reduction by at least 30% and as a result a reduction of carbon footprint will be achieved following the COP27 recommendations.

Enhancing rooftop solar energy potential evaluation in high-density cities: A Deep Learning and GIS based approach

The assessment of rooftop solar potential is vital for optimal photovoltaic (PV) system placement and renewable energy policy in dense urban areas. Complex shading from buildings and diverse rooftop obstacles have posed significant challenges to this evaluation. We propose a method that leverages Deep Learning and Geographic Information Systems (GIS) to precisely gauge solar energy potential at the city scale, accounting for shading and obstacle effects. We integrated building data with height attributes to measure inter-building shading and employed the DeepLab-v3 Convolutional Neural Network to identify rooftop obstructions from high-resolution satellite imagery. Using this hybrid framework, we calculated the available rooftop area in Shanghai, excluding the Chongming Island, and produced a detailed map of PV potential. Results show that the estimated annual potential for rooftop solar radiation in Shanghai stands at 257,204 GWh, with a predicted annual PV electricity generation of 49,753 GWh. In the study area, obstacles occupy approximately 14.9 % of the rooftop area. Neglecting the impact of rooftop obstructions and shading effects would result in a 25.6 % overestimation of the rooftop PV capacity. This work advances the precision of renewable energy development and informs sustainable urban planning strategies.

Just sharing? Energy injustices in the Norwegian solar policy mix for collective prosuming

There is increasing evidence that the energy transition incorporates new types of inequalities for different types of energy consumers. In many places, detached dwellings are highly overrepresented among the building types with solar PV installed. Based on document analysis and high-level interviews, this article analyses the policy mix for rooftop solar energy in Norway through an energy justice lens, focusing on multi-apartment buildings and housing cooperatives. Such cooperatives resemble both a Renewable Energy Community and a Citizen Energy Community as defined in the EU's ‘Clean Energy for all Europeans’-package. We identify several energy injustices in the solar PV policy mix, which effectively impede residents in multi-apartment buildings from collectively producing their own electricity. As a result, very few multi-apartment buildings and housing cooperatives produce their own electricity. Most importantly, current regulations add electricity taxes, grid fees and VAT to the collectively self-consumed electricity as soon as it enters each individual dwelling. By contrast, detached households are allowed to practise hourly net-metering, thereby consuming their self-produced electricity behind the meter without any additional taxes or fees. Finally, our findings show that perceptions of a ‘fair’ policy mix vary considerably, and that equal rules affect various groups of residents differently.

The impact of rooftop solar on wholesale electricity demand in the Australian National Electricity Market

Solar energy from rooftop photovoltaic (PV) systems in Australia’s National Electricity Market (NEM) has been continuously increasing during the last decade. How much this change has affected power demand from electricity networks is an important question for both regulators and utility investors. This study aims to quantify the impact of rooftop solar energy generation on spot electricity demand and also to forecast power system load in the post-covid-19 era. Using half-hourly data from 2009 to 2019, we develop a novel approach to estimate rooftop solar energy generation before building regression models for wholesale electricity demand of each state. We find that the adoption of solar PV systems has significantly changed the levels and intra-day patterns of power demand, especially by reducing daytime power consumption from the grid and creating a “duck curve”. The results also show that most states in the NEM would see decreased electricity demand during 2019–2034.

Extend theory of planned behaviour model to explain rooftop solar energy adoption in emerging market. Moderating mechanism of personal innovativeness

Switching to eco-friendly electrical equipment is a solution promoted by many countries toward the goal of sustainable development. This study develops the Theory of Planned Behavior (TPB) model to evaluate the influence of several factors on customers' adoption intention for rooftop solar energy products in emerging markets. i.e., Vietnam. The results of Structural Equation Modeling (SEM) analysis have shown that government incentive policies and environmental knowledge positively affect attitude towards adoption. In addition, adoption intention is motivated by subjective norms, environmental knowledge and attitude towards adoption. In contrast, the perceived monetary barrier significantly impedes adoption intention, but it can be alleviated by the factor of environmental knowledge. Among all independent factors, government incentive policies is found not to leave significant impact on customer intention adoption. In particular, the study also demonstrated the moderating role of personal innovativeness on the relationship between attitude and intention as well as perceived monetary barriers and intention adoption. These findings help to increase understanding of sustainable consumption behaviour in emerging countries and allow authors to suggest some implications for policy-makers and rooftop solar energy technology providers.

Research on applying machine learning models to predict the electricity generation capacity of rooftop solar energy systems on buildings

Research on applying machine learning models to predict the electricity generation capacity of rooftop solar energy systems on buildings

Solar adoption and the decisive role of the feed-in tariff policy

Rooftop solar energy is a key piece of infrastructure in lowering carbon emissions. In this paper, we use survival analysis to study the effects of a feed-in tariff (FIT) policy on installation rates. We show that the option to resell domestic solar energy to the grid dramatically increases the total amount of home solar uptake and exponentially speeds up the adoption rate.

The Parametric Design of a Photovoltaic Power System over a Parking Lot

The effective planning of urban areas is important in terms of improving both the urban density and the traffic problem. Parking lots should be arranged to manage spaces and traffic at the points where the population is highly dense. On the other hand, installing rooftop solar energy plants over parking lots are highly advantageous in terms of renewable energy production. During the design process of such urban power plants, the area-energy optimization and the shading caused by surrounding buildings have direct effect on overall performance. In this study, firstly a parking lot was designed on an empty parcel within the urban area of İzmir province, with the capacity of 1.704 vehicles, 64 motorcycles and 64 electric vehicle charging stations. In addition, a solar power plant was planned over the parking lot canopies, then a power system was designed by selecting the proper photovoltaic panels and inverters. On the parametric simulation software, which was used to determine the system performance, the solar radiation and shadow simulations on the designed parking lot were run. After that, the electricity generation performances were investigated and comparisons between the parking lot parcels and the selected dates were made. In the conclusion, it was found that 7.006 photovoltaic panels placed over the canopies with an area of 24.995 m2 are able to generate 8.084 MWh/year electricity.

Fault Detection and Importance of Infrared Thermal Diagnostics in Rooftop Solar Energy System

Bu çalışmanın temel amacı, Türkiye'deki arıza türlerini görmek ve fotovoltaik santrallerde yatırımcılara ve işletmelere enerji üretimindeki iyileştirmeler hakkında daha iyi ve daha hızlı analizlerle bilgi vermektir. Kızıl ötesi termal teşhis ve termal görüntü işleme ile saha alanlarında tespit edilen arızaların sonuçlarının bir tartışmasını sunar. Türkiye'de kurulu Güneş enerji santralinin gerçek zamanlı saha ölçüm sonuçlarını içermektedir. Türkiye gibi gelişmekte olan ülkeler yıllar içinde daha fazla enerjiye ihtiyaç duyacakları için sürdürülebilir bir yenilenebilir enerji kaynağı önemlidir. Dolayısıyla termal görüntüleme kullanan fotovoltaik enerji santrallerinde güç üretiminde daha iyi performans geliştirme ve iyileştirmeler sağlanmaktadır. Türkiye'de bulunan 600 kW 'lık güneş enerji sisteminde arıza tespiti yapılmış, arızaların nedenleri ve çeşitleri analiz edilmiştir. Ölçüm testlerine göre, termal drone ile elde edilen sonuçlarda 600 kW'lık güneş enerjisi sisteminde bağlantı hataları sorunu panellerin çalışmamasına neden olmaktadır. Bağlantı hatasının sistemi önemli ölçüde etkilediği ve üretim açısından önemli bir etkinliğe sahip olduğu görülmektedir. Sahada yaşanan teknik bağlantı hatası sorunu giderildiğinde % 0.16'lık bir enerji üretiminde iyileştirme sağlanmıştır.

Site Assessment and Layout Optimization for Rooftop Solar Energy Generation in Worldview-3 Imagery

With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability. We demonstrate a fully automated layout design pipeline that attempts to solve a more general formulation with greater geometric flexibility that accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis rather than imposing any predefined layouts. Our results demonstrate that shading plays a critical role in automated rooftop PV optimization and significantly changes the resulting layouts. Additionally, they suggest that, although several common heuristics are often effective, they may not be universally suitable due to complications resulting from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the literature and propose a potential new rule of thumb that may help improve rooftop solar energy potential when shading effects are considered.

Financial Feasibility of Solar Energy for Household Consumption and Its Impact

Solar energy is one of the renewable energy sources that will be a leading renewable energy source for electricity generation in the future. Generation of electricity through solar power plant is clean, environment-friendly and reliable. Solar power is cheaper than the electricity grid. Thermal energy production comes more costly contrarily than rooftop solar energy which offers a one-time investment for your property.

Modeling, Load Profile Validation, and Assessment of Solar-Rooftop Energy Potential for Low-and-Moderate-Income Communities in the Caribbean

This document presents the modeling of load profile consumption for Low-and-Moderate-Income (LMI) communities in the Caribbean Islands, as well as an assessment of the solar-rooftop energy potential. In this work, real data, together with synthetic and electricity bill data, were collected to validate and improve the load profile models. The solar-rooftop energy potential was obtained through a National Renewable Energy Laboratory (NREL) software called the PVWatts calculator, and mathematical analysis. The analysis of rooftop solar energy potential was conducted to enable the minimum size of solar power systems to fit the energy demand in the community. The results obtained allow estimation of the capacity of the energy system for each house or an entire community.

Optimal sizing of grid‐connected rooftop photovoltaic and battery energy storage for houses with electric vehicle

AbstractA practical optimal sizing model is developed for grid‐connected rooftop solar photovoltaic (PV) and battery energy storage (BES) of homes with electric vehicle (EV) to minimise the net present cost of electricity. Two system configurations, (1) PV‐EV and (2) PV‐BES‐EV, are investigated for optimal sizing of PV and BES by creating new rule‐based home energy management systems. The uncertainties of EV availability (arrival and departure times) and its initial state of charge, when arrives home, are incorporated using stochastic functions. The effect of popular EV models in the market is investigated on the optimal sizing and electricity cost of the customers. Several sensitivity analyses are adopted based on variations in the grid constrains, retail price and feed in tariff. Uncertainty analysis is provided based on the variations of insolation, temperature, and load to approve the optimal results of the developed model. A practical guideline is presented for residential customers in a typical grid‐connected household to select the optimal capacity of PV or PV‐BES system considering the model of EV. While the proposed optimization model is general and can be used for various case studies, real annual data of solar insolation, temperature, household's load, electricity prices, as well as PV and BES market data are used for an Australian case study. The developed optimal sizing model is also applied to residential households in different Australian States.

Characterization of vulnerable communities in terms of the benefits and burdens of the energy transition in Pacific Northwest cities

Energy transition to renewable sources has occurred along with the development of various clean energy policies aimed at decarbonization and electrification. However, the transition can inadvertently lead to social inequity resulting in increasing burdens on vulnerable communities. Although many studies have tried to define and identify vulnerable communities, there has been no study specifically aimed at characterizing vulnerable communities in terms of the benefits and burdens of such energy transition. In response, the objective of this study is to characterize vulnerable communities by examining rooftop solar adoption and energy expenditure using spatial and mixed-effect models. Rooftop solar adoption operationalizes energy resilience and benefits, and energy expenditure operationalizes energy dependence and burdens of the transition. The study also investigates the link between rooftop solar adoption and energy expenditure by considering city-level variability in three Pacific Northwest cities. The results show that Bellevue has 50.4% less rooftop solar adoption than Portland, while Portland has 16.1% or $223 more energy expenditure than Seattle. On average, an increase in annual energy expenditure of $431 is associated with 29% increase in rooftop solar adoption, specifically Bellevue, Seattle, and Portland by 21.4%, 39.1%, and 26.2%, respectively, but not vice versa. Furthermore, the group of communities more vulnerable in housing attributes has 15.2% less rooftop solar adoption than the group of more vulnerable communities in socioeconomic attributes. In addition, the city centers, commercial areas, or mid-rise and high-rise zones are found to have lower rooftop solar adoption and energy expenditure than other areas. The results suggest that policymakers should consider between-city variability when identifying vulnerable communities. Policies should also be tailored to local communities based on their attributes as communities with similar attributes tend to cluster together. Furthermore, policymakers should focus more on housing and built environment attributes to promote resilient communities.