Rooftop Solar Research Articles

Rooftop Solar PV, Coal Plant Inflexibility and the Minimum Load Problem

Australia’s National Electricity Market (NEM) has amongst the highest take-up rates of rooftop solar PV in the world. As with California, this has produced a distinctive load shape termed the “duck curve.” The Queensland version is being principally driven by non-scheduled (i.e., uncontrolled) rooftop solar PV. When combined with inflexible coal plant, it leads to a “minimum load problem.” In this article, we examine the feasibility of dispatch with ever-expanding rooftop solar PV resources in the NEM’s Queensland region and minimal demand elasticity. We find episodes of intractable dispatch throughout the year with rising intensity in the winter and spring months. Furthermore, we find no ability to “export your way out of the problem” via larger interstate interconnectors because the same problem is emerging in the adjacent region at the same time. Resolution ultimately requires inflexible coal plant exit, and entry of flexible plant. JEL Classification: D25, D80, G32, L51, Q41

Application of PV on Commercial Building Facades: An Investigation into the Impact of Architectural and Structural Features

The rapid global transition toward renewable energy necessitates innovative solar PV deployment strategies beyond conventional roof installations. In this context, commercial building facades represent an expansive yet underutilized resource for solar energy harvesting in urban areas. However, existing studies on commercial rooftop solar PV predominantly focus on European contexts, neglecting the unique design constraints and performance trade-offs present in regions such as the Middle East. This study addresses this gap by specifically investigating the impact of architectural and structural features on the utilizable facade area for PV deployment in commercial buildings within the hot desert climate of Saudi Arabia. Detailed case studies of twelve representative buildings are conducted, combining architectural drawing analysis, on-site measurements, and stakeholder surveys. The methodology identified sixteen parameters across three categories—facade functionality, orientation suitability, and surrounding obstructions—that impose technical and non-technical restrictions on photovoltaic integration 3D modeling, and irradiance simulations revealed that, on average, just 31% of the total vertical facade area remained suitable for PV systems after accounting for the diverse architectural and contextual limitations. The study considered 698 kWh/m2 of solar irradiance as the minimum threshold for PV integration. Shopping malls displayed the lowest utilizability, with near-zero potential, as extensive opaque construction, brand signage, and shading diminish viability. Offices exhibited the highest utilizability of 36%, owing to glazed facades and unobstructed surroundings. Hotels and hospitals presented intermediate potential. Overall, the average facade utilizability factor across buildings was a mere 16%, highlighting the significant hurdles imposed by contemporary envelope configurations. Orientation unsuitability further eliminated 12% of the initially viable area. Surrounding shading contributed an additional 0.92% loss. The results quantify the sensitivity of facades to aspects such as material choices, geometric complexity, building form, and urban context. While posing challenges, the building facade resource holds immense untapped potential for solar-based urban renewal. The study highlights the need for early architectural integration, facade-specific PV product development, and urban planning interventions to maximize the renewable energy potential of commercial facades as our cities rapidly evolve into smart solar energy landscapes.

Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study.

Evaluation of transition to 100% electric vehicles (EVs) by 2052 in the United States

With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment.

Bi-Layer Model Predictive Control strategy for techno-economic operation of grid-connected microgrids

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy’s effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy’s effectiveness in optimizing BESS operations under real-world conditions.

Simulation of Rooftop Solar Photovoltaic for Tourist Accommodation Villa as a Mean to Promote Green Tourism in Bali – Indonesia

Abstract Indonesian government renewable energy mix can be accelerated if tourism industry also contributes to the generation of green electricity such as rooftop solar PV, especially for Bali Province as the world tourist destination with thousands of accommodations spread across the Bali island. In this research, the feasibility analysis to implement rooftop solar PV in Sanctoo Suite and Villa located in Gianyar Regency, Bali Province was carried out using Helioscope software simulation. The design was aimed to maximizing the benefit of free rooftop space with financial investment cost of installation. It was determined to reduce electricity bill of 345 kVA load capacity of the villa operation. Based on the simulation, the rooftop solar capacity of 69.3 kWp was available. Furthermore, the energy output generated by the rooftop solar PV annually was around 106.4 MWh. From financial analysis, the monthly electricity bill saving achieved was on average around 12 %. Further calculation on NPV and IRR shows positive value for feasible investment. It was estimated that the payback period for the investment will be around 18 years. These results showed that installation of rooftop solar PV for tourist accommodation is feasible and will promote green tourism for the industry in Bali.

Toward global rooftop PV detection with Deep Active Learning

It is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. However, these methods are based on annotating datasets and therefore often require relabeling when fine-tuned or extended to a different region. Recent advances in Deep Active Learning offer the opportunity to significantly reduce the number of required annotated images by intelligently selecting the images to label next based on their informative value for the model. In this study, we compare different Deep Active Learning algorithms using a variety of datasets from different regions and compare different model training variants. In the simulations, the entropy-based acquisition function shows the highest performance with only 3% of the data needed in case-imbalanced data, while remaining simple to implement. We believe that Deep Active Learning provides an elegant solution to maintain high model accuracy while reducing annotation effort substantially. This facilitates the development of generalizable models for worldwide rooftop PV detection.

Analysis of Rooftop Solar Power Development in Northwest Vietnam using the Analytic Hierarchy Process

Analysis of Rooftop Solar Power Development in Northwest Vietnam using the Analytic Hierarchy Process

DESIGN AND OPTIMIZATION OF A COST-EFFECTIVE NET-ZERO ENERGY MOSQUE LOCATED IN A HOT AND DRY CLIMATE

ABSTRACT This research paper presents a comprehensive analysis to develop an energy-efficient and net-zero-energy (NZE) model for a proposed hypothetical mosque model located in Riyadh, Saudi Arabia. The study employs a sequential optimization analysis using BEopt software (Building Energy Optimization Tool) to explore various energy conservation measures (ECMs) from three main categories: HVAC, lighting, and building envelope. The ECMs are evaluated based on energy savings and life-cycle cost, with the most cost-effective options selected for the proposed energy-efficient design package. Furthermore, a passive downdraught evaporative cooling (PDEC) system is introduced as a passive cooling strategy to further reduce the cooling energy consumption. Computational fluid dynamics (CFD) analysis is utilized to optimize the airflow around the building and PDEC tower, determining the optimal PDEC dimensions for enhanced ventilation performance. The PDEC system is found to contribute 12% to cooling energy savings, which further enhances the energy efficiency of the proposed mosque. A rooftop solar PV system is incorporated into the optimal energy-efficient design to achieve a NZE mosque. The results demonstrate that the NZE model achieves 80% energy savings, with the remaining 20% offset by the solar PV system. Moreover, life-cycle cost analysis reveals that the NZE model offers the lowest cost, making it the most cost-effective design option. This research provides valuable insights into designing sustainable mosques in hot and dry climates, such as Riyadh, offering a comprehensive solution to significantly reduce energy end-use and promote sustainable building practices.

Optimization and Feasibility Analysis of Rooftop Solar Photovoltaic (PV) Power Generation System Design

Optimization and Feasibility Analysis of Rooftop Solar Photovoltaic (PV) Power Generation System Design

Economic Optimization and Performance Enhancement of Rooftop Solar Power Systems Using Concentrated Solar Technology and Advanced Materials

Abstract: This research investigates the economic optimization and performance enhancement of rooftop solar power systems through the integration of concentrated solar technology and advanced materials. The aim is to assess the viability and effectiveness of these innovations in both residential and commercial settings. By focusing on improving energy efficiency and reducing costs, the study provides a comprehensive economic perspective on the adoption of these advanced solar technologies. Key aspects of the research include the evaluation of lifecycle costs, energy yield, and payback periods, offering insights into the long-term financial benefits and feasibility of implementing such systems. The integration of concentrated solar technology enhances the intensity of solar energy captured, thereby significantly boosting the efficiency of rooftop solar panels. Advanced materials, such as perovskites, multi-junction cells, and nanomaterials, are examined for their potential to improve energy absorption, durability, and overall performance of solar panels. The research employs simulation software to model the performance of these optimized systems under various environmental conditions, providing a detailed analysis of their energy output and efficiency.

Deep retrofitting toward net zero energy building in the Mediterranean region: A case study of Albania

Like many other Mediterranean countries, Albania faces unique challenges and opportunities to achieve an efficient and fully decarbonized household sector by applying real energy efficiency measures and numbers toward nearly zero-energy buildings. The study findings showed that nZEB in 2030 can be achieved by combining active and passive energy efficiency measures. Behind the study's state-of-the-art stands a multivariable regression analysis of both electricity consumption and electricity generation executed for three validated consecutive years, 2021, 2022, and 2023, including demand side (electricity bills) and supply side generation provided from a nearby existing onsite PV with an installed capacity of 5.5 kWp. A good mathematical correlation is carried out using Durbin – Watson criteria on statistics using RETScreen software to correlate electricity consumption and generation as a function of weather parameters for the tested household and selected region. The goals to meet the nZEB concept require the insulation of the existing exterior wall and window replacements in compliance with the Energy Performance Methodology on Building and installing rooftop Solar Water Heating (SWH) panels of 3.73 m2 as expostulated in scenario 7. The simulation results show that integrating a rooftop PV system with an installed capacity of 5.5 kWp would provide enough electricity to convert residential buildings into Positive Energy Buildings in 2050 based on the selected dwelling and location (Mediterranean region). At the national level, the proposed model would reduce the national annual electricity import level by 1.64 %, saving annually around €31,021,162 and reducing at least 0.068 MtCO2 per year. Subsidizing passive energy efficiency measures at a rate of 3 % per year coupled with onsite renewable energy sources schemes and other incentivization pathways from the government and other interested parties at a rate of 80 % in the roadmap towards nZEB and PEB is mandatory.

Optimal planning and modelling of the solar roof-top PV system with different incentive policy schemes for residential prosumers: a real-time case study

Optimal planning and modelling of the solar roof-top PV system with different incentive policy schemes for residential prosumers: a real-time case study

Determinants of rooftop solar uptake: A comparative analysis of the residential and non-residential sectors in the Basque Country (Spain)

Rooftop solar, both in the residential and the non-residential sector, is emerging rapidly as a popular source of clean electricity. Together with utility-scale photovoltaics, its future growth is essential to achieve decarbonization targets. Therefore, understanding adoption determinants for firms and households is key to efficiently promoting its diffusion. There is a gap, however, in the knowledge of non-residential adoption determinants, as less attention has been given to this sector compared to the residential sector. As a result of this gap, there is an absence of comparative analysis across sectors. As determinants of adoption cannot be assumed to be the same in both sectors, the objective of this research is threefold. First, to analyze whether the residential and non-residential sectors share key determinants of rooftop solar investment; second, to compare the sectoral differences in these determinants; and third, to assess the policy implications of the results obtained to further promote distributed solar photovoltaic energy. For this purpose, a regional case study in the Basque Country (Spain) was conducted, applying key theoretical frameworks to both sectors in a way that maximized the comparability of the results obtained across them. The results showed that adoption determinants are very different across sectors and, therefore, sector-specific policy actions need to be taken in each sector to efficiently promote rooftop solar. For the residential sector, policy actions could build upon behavioral aspects; for the non-residential sector, economic incentives are expected to be more successful, especially among medium size businesses, which are identified as the most promising segment.

The new service development process of green FinTech innovation: A multi-case study

In light of increasing global challenges like climate change, carbon neutrality, and biodiversity loss, the need for sustainable solutions is essential. Green FinTech innovation, which combines financial resources, services, and technologies, has become a significant area of focus for addressing these issues. However, despite growing interest from various stakeholders, progress towards sustainable development remains slow due to fragmented academic knowledge. This study aims to bridge this gap by offering practical guidelines for those involved in green FinTech innovation. By examining the new service development process, including both the front-end and back-end stages, the study will identify key influencers such as customers, organizations, and partners. Semi-structured interviews will be conducted with three green FinTech case studies in Thailand, specifically focusing on rental electric bike services, energy trading systems, and solar rooftop platforms. The research will investigate the concepts, methods, and critical success factors that drive the innovation processes of these projects through a comparative multi-case study. The findings will reveal different paths for B2B and B2C green FinTech innovation, emphasizing the importance of external factors. Successful innovation requires a thorough understanding of customer behavior, beyond just pro-environmental tendencies. These insights aim to accelerate green FinTech innovation in emerging economies and underscore the need for further quantitative research to validate these findings. This research will provide valuable insights for policymakers, financial institutions, and innovators, supporting the advancement of sustainable development through green FinTech solutions.

Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach

Solar energy shines as a beacon for sustainable development, with rooftop solar photovoltaic (PV) installations playing a crucial role. This study proposes a novel framework to precisely assess citywide existing solar power generation and analyze future potential under various rooftop utilization scenarios (10–50 %). To illustrate the methodology, the existing solar PV yield and future potential of Islamabad are explored as a case study. Employing open-source satellite data and deep learning (DL), this study quantified the electricity generation from current solar infrastructure and projected future possibilities. DL models like Mask R–CNN and Deeplab-v3 are trained on extensive custom datasets for solar panels and rooftops extraction. The study delineated 19,000 solar arrays, covering an area of 0.8 sqkm, and extracted 75.08 sqkm of suitable rooftops for future installations. The analysis revealed, current solar infrastructure generates 141.42 GWh electricity, satisfying 6.34 % of Islamabad's annual energy demand. Utilizing 50 % rooftop area could generate 6578 GWh annually, meeting 294 % of the city's electricity needs. This research contributes to the strategic planning for solar energy infrastructure, demonstrating the substantial role rooftop solar can play in meeting urban energy needs. This can inform policy decisions and guide investment opportunities for expanding clean energy in the city.

A holistic techno-economic feasibility analysis of residential renewable energy systems: An insight into Turkish case

This study conducts a detailed techno-economic evaluation of residential renewable energy system (RRES) in Türkiye, focusing on the city of Kahramanmaras with high solar potential and utilizing real-site measured PV data in the city. To this end, a comprehensive RRES model, incorporating various system configurations are developed. In order to represent all PV-Battery energy storage system (BESS) operating configurations, six different cases are defined. Then, novel energy management strategies are proposed for the cost-efficient allocation of available energy and to improve the techno-economic performance of the RRES. To conduct a comprehensive feasibility analysis from all aspects, various economic metrics such as net present value, simple payback period (SPP), discounted payback period (DPP), internal rate of return (IRR), profitability index (PI), and levelized cost of energy (LCOE), together with energy-related performance metrics like self-sufficiency and grid-dependency rates are employed. Furthermore, a sensitivity analysis is carried to observe the impacts of pricing scheme and system cost&sizing on the evaluation metrics. On the other hand, as a technical assessment, reliability index (RI) is calculated for each case to discuss the loss of load probability. MATLAB R2020b software is utilized for modelling and energy management purposes. The numerical results indicate that the most feasible system under current financial circumstances and renewable energy policy is rooftop PV system with a SPP of 8.38 years, a DPP of 7.71 years, an IRR of 11.64 %, a PI of 2.72, and a LCOE of 0.026 $/kWh, whereas the least feasible system is proven to be sun tracking PV-BESS system on PV charge mode with a SPP of 12.08 years, a DPP of 10.73 years, an IRR of 7.61 %, a PI of 1.69, and a LCOE of 0.227 $/kWh. Moreover, it is observed that charging the BESS from the grid instead of PV energy procures higher financial returns for the end-user such that SPP, DPP, IRR, PI, and LCOE improve to 10.95 years, 9.83 years, 8.40 %, 1.87, and 0.198 $/kWh, respectively for the system of configuration of sun tracking PV with BESS on grid charge mode compared to sun tracking PV with BESS on PV charge mode. Also, all PV-BESS operational configurations can meet the feasibility criteria through improved incentives and cost reductions although the cost-benefit ratio of BESSs is not yet at the desired level. The results of technical evaluation indicate that charging the BESS from the grid and smaller BESS sizes serve to reduce the loss of load probability, resulting in an improvement in the reliability index of the RRES. Overall, this study provides a holistic insight for promoting RRESs in Türkiye through a detailed techno-economic performance analysis.

Modeling the potential effects of rooftop solar on household energy burden in the United States

Policymakers at the federal and state level have begun to incorporate energy burden into equity goals and program evaluations, aiming to reduce energy burden below a high level of 6% for lower income households in the United States. Pairing an empirical household-level dataset spanning United States geographies together with modeled hourly energy demand curves, we show that rooftop solar reduces energy burden across a majority of adopters during our study period from a median of 3.3% to 2.6%. For low- and moderate-income adopters (at or below 80% and 120% of area median income, respectively), solar reduces median 2021 energy burden from 7.7% to 6.2%, and 4.1% to 3.3%, respectively. Importantly, solar reduces the rate of high or severe energy burden from 67% of all low-income households before adoption to 52% of households following adoption, and correspondingly from 21% to 13% for moderate-income households. Here, we show rooftop solar can support policy goals to reduce energy burden along with strategies such as weatherization and bill assistance.

Rooftop Solar Power System for EV Charging Station of Household Customers in Indonesia: A review and an Opportunity for Developing Countries

Rooftop Solar Power System for EV Charging Station of Household Customers in Indonesia: A review and an Opportunity for Developing Countries

Evaluation of the Impact of Rooftop Solar Power on the Power Quality of Vietnam Urban Distribution Networks according to Relevant Vietnamese and IEEE and IEC Standards

Evaluation of the Impact of Rooftop Solar Power on the Power Quality of Vietnam Urban Distribution Networks according to Relevant Vietnamese and IEEE and IEC Standards