Solar Photovoltaic Installations Research Articles

Sustainable and cost-effective hybrid energy solution for arid regions: Floating solar photovoltaic with integrated pumped storage and conventional hydropower

Over the past decade, solar photovoltaic installations have grown significantly, and energy storage is crucial for integration. Pumped storage hydropower is a cost-effective and proven grid-scale energy storage technology, reducing variable renewable energy curtailment. Floating solar photovoltaics can address water availability issues in arid regions by floating on water bodies. This research article explores a sustainable and cost-effective approach to enhancing water, energy, food, and ecosystem nexus in arid regions. It proposes a hybrid configuration of 200 MW Paras pumped storage hydropower, 30 MWp floating solar photovoltaic integrated with 300 MW Balakot conventional hydropower for grid energy storage. This study calculates the levelized cost of energy storage using conventional hydropower resources, water stream considerations, and floating solar PV installations. The novelty is that the levelized cost of energy storage decreases by 28 %, benefit to cost ratio increases by 56 % and installed costs are reduced by 25 % as compared to greenfield closed-loop pumped storage hydropower. The hybrid configuration can deliver an additional 3693 GWh of clean energy, resulting in a 30 % increase in revenue over 30 years compared to greenfield closed-loop pumped storage hydropower. This can improve the water, energy, food, and ecosystem nexus by enabling fast-track deployment of variable renewable energy in arid regions, while integrated pumped storage hydropower supports essential energy storage to the grid.

Power Quality Impacts of Grid-Tied PV Inverters on Low Voltage Distribution Networks a Smart OpenDSS Model to Find Power Quality Threshold Limits

As a renewable initiative, risen attention can be seen in solar photovoltaic (PV) installations in the world. Since tropical countries and even many other countries during summer experience higher irradiance levels, there is great potential for PV generation in solar panels. However, power quality can be impacted when the number of PV modules is increased in a network due to the emergence of harmonics, unbalanced voltages, voltage flicker, neutral voltage variations, etc. Therefore, it is essential to empirically analyse power quality parameters, i.e., total harmonic distortion (THD), voltage unbalance, etc., and find threshold margins of PV capacities in a network. Based on a case study conducted in Negombo, Sri Lanka, this work determines power quality impacts with reference to the number of PV interconnections in a distributed network. For the task, a low-voltage distribution network was chosen which was fed by a 250kVA, 11/0.4kV transformer with domestic loads and grid-tied PV inverters. A simulation model was developed in Open Distribution System Simulator (OpenDSS) with time-varying load patterns and PV generation. Consequently, a smart efficient method was introduced to model domestic loads with unique time-varying demand patterns. A determination criterion was established to derive snapshot load flow instants using time-varying load flow results to analyse voltage profiles along distribution feeders. Then, the model was enhanced to quantify the power quality parameters such as individual harmonic content, THD of voltage as well as current and neutral voltage variation. The results reveal that node voltage has improved with PV interception without violating the upper limit. The THD of voltage and current have slightly increased with the addition of PV inverters.

Maximizing solar energy production in ASEAN region: Opportunity and challenges

The Southeast Asian (SEA) region has witnessed a relentless surge in energy demand, driven by rapid urbanization, industrialization, and economic growth. In response, the exploration and development of renewable energy sources have gained significant attention. Among these sources, solar energy has emerged as a highly promising candidate due to its remarkable growth rate. This comprehensive review article aims to analyze the challenges and opportunities involved in maximizing solar energy production in the SEA region. The article commences with a succinct introduction to electromagnetic wave spectra and emphasizes the significance of visible spectra. It then provides a comprehensive examination of gross horizontal irradiance (GHI) patterns across the SEA region. A systematic tabulation is presented, organizing the current and potential solar energy installations and outputs of ASEAN countries. The article explores the deployment of hybrid photovoltaic (PV) systems, particularly floating PV installations, as an effective strategy to reduce dependence on fossil fuels. Moreover, the utilization of Supervisory Control and Data Acquisition (SCADA) systems for optimizing solar PV output is investigated. The article further delves into critical maintenance protocols, encompassing corrective, emergency, preventive, and predictive measures, and explores the levelized cost of electricity (LCOE) to assess the profitability of solar PV installations. Lastly, the leadership of Malaysia, Indonesia, and Singapore in solar PV research is highlighted, with a specific focus on building integrated PV and floating PV research. By addressing these, this review article offers valuable insights into the challenges and opportunities for advancing solar energy production in the SEA region.

Comparison of statistical and optimization models for projecting future PV installations at a sub-national scale

Spatially-disaggregated projections of new solar photovoltaic (PV) installations are essential for planning electricity grids and managing the electricity system at large scale. Such projections at sub-national level can be obtained by statistical models or by electricity system optimization models, but there is barely any study that compares the performances of these approaches. This study aims to compare methods for projecting PV installations at a level of 143 districts in Switzerland, using a simple extrapolation method (as a benchmark of the common practice today), a multiple linear regression model, two spatial regression models, and a spatially-explicit optimization model (EXPANSE) with various features to account for policy. The performance of different approaches is evaluated retrospectively for 2012–2020, using multiple accuracy indicators. The evaluation results show that statistical regression models, which account for socio-demographic and techno-economic characteristics as predictors of future PV growth, overall perform better than simple extrapolation or optimization. Although commonly used, extrapolation has the highest variability in accuracy, indicating the least robust performance. The optimization model tends to underestimate PV installations in its least-cost scenarios, if the role of policy is not considered. Incorporating solar PV policies and renewable electricity generation targets increases the overall accuracy of the optimization model at a national level, but not necessarily at a spatially-explicit level. We thus conclude that statistical models are preferred over extrapolation or optimization models for projecting future PV installations at a sub-national scale.

Impact of Personal Traits and Government Incentives on the Intention of Rooftop Solar Photovoltaic Installation in Sri Lanka

Sri Lanka, as a developing country, faces a significant obstacle of energy shortage due to efforts in economic development and enhancement of the living conditions of the citizens. However, the country accounts for a comparatively low carbon footprint, which at present is slowly increasing due to the development of the country and increasing power demand, as the country has taken desperate measures like burning fossil fuels to meet the rising demand. The country now intends to find more sustainable and environment-friendly measures to meet the demand. The introduction of renewable energy has made an effort to change the way that energy is produced. A non-depleting and renewable source of power, renewable energy is also referred to as green energy. It's common to refer to environmentally friendly energy sources as "green products" (in this case, "energy"). Fossil fuels are currently being replaced with renewable energy sources. Solar has been identified as the most promising renewable energy source, yet the land shortage has made the choice questionable. However, considering these issues and the country’s economic condition, the implementation of smaller-scale rooftop solar photovoltaic panels for individual houses may be the best solution. The research purpose of this study is to investigate and understand the impact of personnel traits and government incentives on the intention of rooftop solar photovoltaic (PV) installation in Sri Lanka. The data for the study was gathered using 401 questionnaires dispersed around the country. The questionnaire was self-administered. The IBM SPSS Statistics 23 software was used to analyze the data that had been gathered. The findings demonstrate that consumer innovation, ecological lifestyle, environmental concern, and government incentives have a favorable influence on the public's propensity to install rooftop solar panels. In order to determine the influence of these elements on customers' installation intentions, this research examines the consumer perception theory-based model based on planned behavior theory. Local legislators, solar industry executives, and other stakeholders may greatly benefit from the study's findings.

Economic Feasibility Investigation of On-Grid and Off-Grid Solar Photovoltaic System Installation in Central Java

Economic Feasibility Investigation of On-Grid and Off-Grid Solar Photovoltaic System Installation in Central Java

Greenhouse gas emissions embodied in the U.S. solar photovoltaic supply chain

Solar photovoltaic (PV) electricity is considered to be an important source of electricity generation in the quest for net-zero carbon emissions. However, the growth of solar electricity is creating both increased material demands and increased greenhouse gas (GHG) emissions from silicon and PV manufacturing (also referred to as embodied GHG emissions of solar electricity). Here we analyze the silicon and solar PV supply chain for the United States (U.S.) market and find that the embodied GHG emissions of solar PV panel materials (such as silicon), manufacture, logistics, and installation in the U.S. given the current supply chain are 36 g CO2e kWh−1 of solar electricity generated. Eighty-five percent of the embodied GHG emissions are from PV panel production processes in China and other Asia–Pacific countries. Moving the silicon and PV manufacturing to the U.S. would reduce the embodied GHG emissions of solar electricity by 16% from its current level, primarily because of the lower GHG emission intensity of the U.S. electrical grid and the lower GHG emissions for aluminum electrolysis in North America. Future scenario analysis shows that by 2030, with the U.S. PV domestic supply chain and its decarbonized grid electricity and aluminum production, as well as improving PV conversion efficiency, the embodied GHG emissions of solar electricity in the U.S. will be reduced to 21 g CO2e kWh−1.

Creation of an Internet of Things (IoT) system for the live and remote monitoring of solar photovoltaic facilities

Reliable and widely accepted, renewable energy sources stand as the optimal substitute for fossil fuels in meeting our growing energy demands. Specifically, solar energy can be harnessed into electrical power through solar cells. Many solar installations are situated in remote locations like rooftops, mountains, and deserts. Effective monitoring of these solar photovoltaic systems is crucial to maximize their performance. Monitoring solutions come in various configurations. The simplest approach involves offline data collection, entailing the placement of a data collection device on the installation which then stores data locally for a specific timeframe. However, this conventional monitoring method falls short in providing real-time data. In contrast, leveraging Internet of Things (IoT) technology to oversee solar photovoltaic power generation offers a substantial performance boost. This project aims to develop an IoT-powered system for real-time remote monitoring of solar photovoltaic installations. The collected data is stored in the IoT cloud, accessible through an application via an active internet connection from anywhere worldwide. This grants immediate insights into the installation’s status, facilitating maintenance and prompt fault detection.

COMPARISON OF POTENTIAL ENERGY OF SOLAR RADIATION IN ROOFTOP MODELING USING DIFFERENT BUILDING LEVELS OF DETAIL

Abstract. The potential for future energy crises is a problem the world is currently facing. Many countries are switching from fossil to renewable energy to prevent an energy crisis. One of the most developed renewable energy today is solar energy. Easy installation makes solar energy installation not only on a large scale but also on a home scale. Urban areas will be very suitable for building solar photovoltaic (PV) roofs due to minimal open areas. In installing rooftop solar PV, sound planning is needed to predict the energy potential that can be provided by solar energy on the rooftop of a building. Spatial modeling can be done to determine the energy potential and suitable location for rooftop solar PV installation. In building rooftop solar PV modeling, the level of detail of the building will affect the results of the model. The rooftop's shape and the building's height will affect the amount of solar radiation going into the building. However, the higher the level of detail of the building, the higher the cost and processing time will be. This study will review the differences in modeling the potential of rooftop solar PV using different levels of detail. This research will integrate solar radiation data from remote sensing to determine the energy potential of solar radiation and digital surface model data from photogrammetry to create a level of detail for buildings. Integration of solar radiation data and the level of detail of the building will use hillshade analysis. Hillshade analysis can review the shadow effect on the rooftop of a building which will be directly related to the potential of solar energy on the rooftop of the building. This study determines the energy potential on the rooftop of the building with different levels of detail, namely 0, 1, and actual shape, to determine the difference in energy potential in the three scenarios. Hopefully, this research will determine the best level of detail for modeling rooftop solar PV. The best model that can show high accuracy value but at a lower price. Hopefully, this research can also assist policymakers and the public in planning for rooftop solar PV installations to develop renewable energy.

Power Efficiency Analysis based on Lightning Effect on Large-Scale Solar Photovoltaic System

Currently, a large-scale solar photovoltaic (LSSPV) has become one of the fastest developments of electrical generation power for Malaysian Renewable Energy. However, lightning strikes are common in Malaysia due to its geographical position and tropical climate, especially in the open space of large-scale site power plants. This paper discussed the scientific basis and key assumptions that should be included in the layout of a lightning protection scheme in a large-scale solar photovoltaic installation. The framework proposal aims to ensure compliance with standards and guidelines, mitigating the risk of significantly induced currents in the LSSPV power plant. A 2.5 MW solar PV farm system was modelled using MATLAB software for assessing a solar PV farm’s electrical performance under various lightning current waveforms and lightning magnitude currents. These lightning strikes will impact photovoltaic modules, thus deteriorating the system’s ratio generation. The effects of lightning current on LSSPV were accessed based on simulation work in MATLAB Simulink. The induced current protection scheme covered rating selection and collecting data on lightning that may hit the modules or arrays. This data was then analysed according to the standard guidelines to ascertain the most effective protection measures for the 50MW PV farm within the LSSPV system. Minimising the maintenance and replacement cost of equipment is possible. The performance of a 2.5MW solar PV power plant with a grid connection was evaluated. A maximum and minimum power output of 2.47MW and 522.50kW, respectively, were observed. However, when the lightning impulsive current is included through the system, the power output decrease to 407.33kW due to the high magnitude induced.

Feasibility Study for the Use of an Autonomous Solar Photovoltaic Water-Lifting Installation in the Karakum Desert

Feasibility Study for the Use of an Autonomous Solar Photovoltaic Water-Lifting Installation in the Karakum Desert

Economic and energetic assessment of a hybrid vanadium redox flow and lithium-ion batteries, considering power sharing strategies impact

Hybrid energy storage systems (HESS) combine different energy storage technologies aiming at overall system performance and lifetime improvement compared to a single technology system. In this work, control combinations for a vanadium redox flow battery (VRFB, 5/60 kW/kWh) and a lithium-ion battery (LIB, 3.3/9.8 kW/kWh) are investigated for the design of a HESS. A literature review presents the available energy management/power allocation options that are being studied and applied worldwide in batteries. There is an identified need for opportunities to address better HESS configuration's economic and energy perspective for building applications. The justification of investment in such HESS should improve indicators on use scenarios based on energy management compared to single-battery scenarios. In that context, four scenarios for real-time algorithms application approaches are considered to operate the hybrid storage solution through a 15-year economic and energetic analysis using experimentally validated battery performance models. The results obtained for each scenario are compared with a single technology battery performance to analyse this HESS pair competitiveness and the relevance of the power-sharing techniques among the different ESS technologies, which should be weighted. In the definition of the scenarios, real electricity generation is considered from two solar photovoltaic installations (3.2 kWp and 6.7 kWp) and an estimated representative load of a services building. HESS performance is evaluated through specific energy and economic key performance indicators. The results indicate that using customised energy management strategies (EMSs) renders the VRFB and LIB characteristics complementary, besides enhancing the competitiveness of VRFB as a single technology. Moreover, the HESS management impacts the seasonality factor, contributing to the overall electric system smart management.

Improving the methodology for determining the main parameters of solar batteries

The article cites the need for a reliable and economical power supply to rural consumers. At the same time, it is important to develop distributed generation, including on the basis of renewable sources, using solar and wind energy to reduce costs. The necessity of choosing solar panels with optimal parameters, taking into account the influence of external factors, is shown. To determine the actual generated power of solar batteries, the concept of "power loss during the conversion of the solar radiation flux into electrical power" is introduced. It has been established that insufficient studies of the regime parameters of photocells, in particular, the temperature of its heating and the corresponding change in efficiency, taking into account external factors, do not allow choosing a module with effective power. To study the parameters of solar batteries, an equivalent circuit and methods for determining its parameters are presented. The established dependence of the excess of the SM temperature over the ambient temperature on the power of solar radiation is presented, which formed the basis of an improved method for determining the parameters of solar batteries. The presented results of the study allow at the design stages to determine the expected power of a solar photovoltaic installation and choose its optimal parameters.

Analysis of various degradations of five years aged mono c-Si, poly c-Si, and thin-film photovoltaic modules from rooftop solar installations in Dhaka’s tropical wet and dry climate conditions

This study evaluates the degradation of mono, poly, and thin-film silicon solar photovoltaic (PV) modules through visual and electrical measurements in Dhaka’s tropical wet and dry climate conditions. For this, several sites of rooftop solar PV installations in Dhaka, aged at least 5 years, were selected for field investigation. A visible inspection for visual degradation followed by experimental testing on various electrical characteristics of the PV modules installed on the rooftop using a portable Seaward I–V Tracer for measuring the electrical degradation was done. The electrical data were used to evaluate the degradation of short circuit current, open circuit voltage, fill factor, and maximum power in the field as compared to the nameplate values. Experimental results showed the varying types of visual degradation types such as visible cracks, snail trails, moisture intrusion, front glass, and discoloration for the investigated PV modules. The degradation of PV modules in terms of short circuit current was 0.7%/year for 85Wp mono c-Si, 7.6%/year for 150Wp poly c-Si, and 9.7%/year for 105Wp silicon thin-film, respectively. We believe this study would guide the maintenance of the solar PV systems installed on Dhaka city’s rooftop buildings.

A Study of the Relevant Parameters for Converting Water Supply to Small Towns in the Province of Alicante to Systems Powered by Photovoltaic Solar Panels

Solar energy is presented as the main alternative to conventional energy sources that often rely on burning fossil fuels. However, one major obstacle to its wider adoption is the limited ability to store the energy produced that can only be generated for a few hours daily. One way to overcome this limitation is using photovoltaic energy to power urban water supply pumps. This allows the energy to be stored as potential energy in regulating reservoirs while also taking advantage of the temporal coincidence between the generation of solar energy and the daily and annual water and energy supply consumption. Given that implementing solar energy in pumping devices involves an enormous investment, the optimal payback period is identified as the key indicator to know which population is one in which this action is more advisable. This work aims to find the key factors influencing the payback period of solar photovoltaic installation in urban water supply networks. To accomplish this goal, this study analyzes all 20 municipalities in the province of Alicante (which consume groundwater) where these systems can be implemented. Furthermore, this study facilitates the identification of variables that influence the decision to install a solar photovoltaic system in an urban water supply. By measuring two or three parameters, it becomes possible to easily determine the economic viability of such an investment in towns supplied with groundwater. Furthermore, these results can be extrapolated to other municipalities with similar features (irradiance, inhabitants, etc.). This study also presents a straightforward formula that supply managers can utilize to calculate the payback period of the installation using readily available data. The main factors that affect the recovery period of a photovoltaic solar installation are the difference in monthly supply consumption between winter and summer months and the average water depth.

Off-grid solar PV–wind power–battery–water electrolyzer plant: Simultaneous optimization of component capacities and system control

Green hydrogen production systems will play an important role in the energy transition from fossil-based fuels to zero-carbon technologies. This paper investigates a concept of an off-grid alkaline water electrolyzer plant integrated with solar photovoltaic (PV), wind power, and a battery energy storage system (BESS). The operation of the plant is simulated over 30years with 5min time resolution based on measured power generation data collected from a solar photovoltaic installation and a wind farm located in southeastern Finland. Levelized cost of hydrogen (LCOH) is calculated based on the capital expenditures (CAPEX), the operating expenses (OPEX), and the respective learning curves for each of the components. Component degradation and replacements during the operational lifetime are included in the model, and the capacity of the components and the system control are simultaneously optimized to obtain the minimum LCOH. A sensitivity analysis performed over different installation years and discount rates reveals that for the off-grid alkaline system, the implementation of a wind farm as the sole power supply is the most economical solution until the installation years 2035–2040. Solar PV and a BESS are found to increase the full-load hours of the electrolyzer and reduce the electricity curtailed in the off-grid plant to less than 8%. However, with the current component prices and the climate in the studied region, they are not economically beneficial. It is found that the cost of hydrogen can be reduced to 2 €/kg by the year 2030.

Spatial modelling the location choice of large-scale solar photovoltaic power plants: Application of interpretable machine learning techniques and the national inventory

The optimum site selection of solar photovoltaics power plant across a given geographic space is usually assessed by using the geographic information system based multi-criteria decision making methods with various restriction criteria, while such evaluation results vary with criteria weights and are difficult to be validated in real life practices. To address this issue, this paper uses a national inventory dataset of large-scale solar photovoltaics installations (the land coverage area ≥ 1 hm2) to investigate the spatial location choices of solar power plants with the aids of interpretable machine learning techniques. A total of 21 geospatial conditioning factors of solar energy development are considered. The location choices of solar photovoltaics installation are then modeled with the multi-Layer perceptron, random forest, extreme gradient boosting models for each land cover type (e.g. cropland, forest, grassland, and barren). The SHapley additive explanation and variable importance measure methods are adopted to identify key criteria and their influences on the solar photovoltaics installation location selection. Results indicate that the random forest model presented the better performance among three machine learning models. The relative importance of conditioning factors revealed that the vegetation index and distance to power grid were always the most important predictors of solar photovoltaics installation location. Furthermore, topographical factors and transportation convenience may have a moderate impact on the spatial distribution of solar photovoltaics power stations. Unexpectedly, most of resources endowment and socio-economic factors play a negligible role in determining the optimal siting of solar power farms. Simulated solar photovoltaics installations probability maps illustrated that the most suitable regions account for 4.6 % of China’s total land area. The evidence-based method proposed in this research can not only help identify suitable solar photovoltaics farm locations in terms of various decision-making criterion, but also provide a robust planning tool for sustainable development of solar energy sources.

Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness

The incorporation of decentralized energy solutions, including solar photovoltaic (PV) installations and energy storage systems (ESSs), into residential communities has received substantial interest in recent times. This is driven by the objective of enhancing the self-reliance of residential zones while decreasing their reliance on centralized power grids. Effective management of these systems can yield advantages such as load equalization, cost reduction, and diminished emissions. The process of optimizing energy management in residential communities entails addressing multiple factors, including uncertainty, equity, and efficiency.This study introduces an innovative methodology for real-time energy management in a residential community, taking into account both the equitable utilization of solar PV and ESS and the minimization of costs. The suggested technique employs rolling horizon optimization and mixed-integer linear programming (MILP) to tackle uncertainties in PV production and guarantee a fair allocation of shared resources. The outcomes of this novel approach showcase its capability to sustain cost-effectiveness while fostering equitable energy consumption.

Development of a Solar-Tracking System for Horizontal Single-Axis PV Arrays Using Spatial Projection Analysis

Uniaxial trackers are widely employed as the frame for solar photovoltaic (PV) panel installation. However, when used in sloping terrain scenarios such as mountain and hill regions, it is essential to apply a solar-tracking strategy with the sloping factors considered, to eliminate the shading effects between arrays and reduce the electricity production loss due to terrain changes. Based on a uniaxial tracker on the sloping terrain of a PV farm located in Ningxia, this study established a uniaxial solar-tracking strategy for sloping terrain by integrating a spatial projection model with a dynamic shadow assessment method. In the proposed strategy, the optimal tilt angle of the PV array and related desirable adjustment are identified taking into consideration major parameters such as the shadow area ratio S and the average solar irradiance intensity G. A tool underpinned by Matlab Simulink has also been developed to realize the proposed solar-tracking strategy. With the input of a simulated ramp signal β and the dynamically changed time parameters, the tracking angle of PV arrays over the simulated duration is accurately predicted, followed by a series of experimental validations conducted on the winter solstice and a typical sunny day (15 September). Moreover, the study also explored the terrain impacts on solar tracking by comparing the sloping terrain and flat terrain applications. The analytic and experimental results indicate that (a) the maximum value of the G(β) function could serve as the input to identify the optimal tracking angle; (b) the application of the flat terrain tracking (FTT) strategy in sloping terrain would result in a reduction of average solar irradiance intensity harvested by the PV arrays with varying degrees; (c) in the context of an east–west −7° sloping terrain, compared with the FTT strategy, the sloping terrain tracking (STT) strategy enabled anti-shading tracking, and then increased the daily PV electricity yield by 0.094 kWh/kWp, which is around 1.48% of the daily energy production; (d) given a measurement with annual scale, the STT strategy could cause a 1.26% increase in the energy harvesting with a flat uniaxial PV array on a −7° slope terrain, achieving an annual increase of 25.16 kWh/kWp. The experimental comparative analysis validated the precision of the proposed solar-tracking model, which has far-reaching significance for achieving automatic solar-tracking of PV modules, as well as improving the capacity and efficiency of PV systems.

A Community Building Energy Modelling – Life Cycle Cost Analysis framework to design and operate net zero energy communities

Despite building codes and standards helping build energy-efficient structures, most focus only on typical individual buildings. Second, they do not discuss community-based renewable energy adoption strategies as part of the standards. However, to facilitate the transition to net zero emissions in the upcoming decades, policies and recommendations from standards must enhance their scope from the buildings to communities and exploit the maximum energy retrofit opportunities available within the community premises. This study proposes a Community Building Energy Modelling – Life Cycle Cost Analysis (CBEM - LCCA) decision-making framework to analyze the techno-economical feasibility of the residential community-wide implementation of any building retrofit scheme. The framework developed is applied in a 14-building residential community in Mumbai, India, to compare the techno-economic benefit of implementing community-wide renewable installations against the existing national Energy Conservation Building Code (ECBC). The results reveal that all the communities together have the potential to generate 333 MWh of electricity per year from solar rooftops alone against the consumption of 369 MWh. The communities are observed to be capable of achieving nearly net-zero energy after adopting a community-wide rooftop solar photovoltaic installation with fewer than eight years of simple payback period (SPP). A thorough sensitivity analysis also reveals that the community rooftop solar strategy proves economically far superior to ECBC compliance of individual building envelopes. Among the retrofits, the roof-wall combination gave the minimum SPP of 8.38 years at a 100 % cooling space fraction. The findings provide evidence for why only a small number of states support ECBC code compliance, and it is recommended that policymakers use the proposed CBEM-LCCA framework to create appropriate energy policies for developing community-wide rather than individual building-level retrofit strategies.