Solar Photovoltaic Deployment Research Articles

Macroeconomic, energy, and emission effects of solar PV deployment at utility and distributed scales in Saudi Arabia

For the first time, this paper assesses the macroeconomic, energy, and emission impacts of solar photovoltaic (PV) deployment in Saudi Arabia by linking an energy-and-environment-sector augmented macroeconometric model with a power model and a distributed generation model while distinguishing between utility scale and distributed-generation scale of the PV deployment. We analyze three policy scenarios of deploying 5.5 TWh PV for the period 2021–2030: (S1) fully government-funded utility-scale PV deployment, (S2) half-government-funded utility-scale PV deployment, and (S3) household-funded distributed generation scale PV deployment with little government support. We find that: S1 is best suited if the objective is fiscal expansion, 2 is best suited to improve non-oil fiscal positions, increase non-oil value added and employment, and increase household consumption, and S3 is best suited to reduce energy consumption and associated emissions and enhance international trade. We show that government and foreign investment play an important role, and it would be desirable for the Saudi government to partner in financing renewable energy projects, saving budgetary resources to invest in other projects to support economic growth.

Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions

Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R2 value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R2 of 0.88, an MSE of 0.49, and an MAE of 0.63.This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation.

Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country

Setting solar photovoltaic capacity targets and implementing supportive policies is a widespread strategy among nations aiming to achieve decarbonisation goals. However, policy implementation without a thorough understanding of the intricate relationship between social, economic, and land-use factors and solar photovoltaic deployment can lead to unintended consequences, including over- or underdeployment and failure to reach targets. To address this challenge, an investigation was conducted into the relationship between 36 factors and solar photovoltaic deployment across 143 countries from 2001 to 2020 using correlation analysis and principal component analysis. From these factors, five key variables were identified that collectively explain 79% of the year-to-year variation in photovoltaic capacity. Using these variables, a neural network model was constructed, enabling the estimation of capacity additions by country with an error of less than 10%. Additionally, a solar photovoltaic deployment index was developed, serving as a benchmark for comparing a country’s actual historical photovoltaic deployment to similar nations. Furthermore, the model’s utility in evaluating the impact of solar photovoltaic policies was explored. Through three distinct use cases—forecasting solar photovoltaic capacity additions, developing a solar photovoltaic deployment index, and assessing the impact of solar photovoltaic policies—the model emerges as a potentially powerful tool for governments and policy makers to assess solar photovoltaic deployment effectively and formulate strategies to promote sustainable solar energy growth.

Promoting Sustainable Development Goals by Optimizing City-Level Solar Photovoltaic Deployment in China.

Solar photovoltaic (PV) installations, which enable carbon neutrality, are expected to surge in the coming decades. This growth will support sustainable development goals (SDGs) via reductions in power-generation-related environmental emissions and water consumption while generating new jobs. However, where and to what extent PVs should be utilized to support SDGs must be thoroughly addressed. Here, we use multiple PV deployment scenarios to compare the benefits of PVs and related SDGs progress in 366 prefectural-level cities in China. We developed an assessment framework that integrates a PV allocation model, an electricity system optimization model, and a benefit assessment approach. We identify vast differences in PV distribution and electricity transmission and elucidate trade-offs and synergies among the SDGs under various PV implementation scenarios. The water conservation-oriented scenario yields substantial carbon reduction, air pollutant mitigation, and water saving cobenefits, leading to the greatest SDGs improvements. Prioritizing employment creation enhances job-relevant SDGs but inhibits environmental resource benefits. SDGs in less developed cities present greater progress across all scenarios. This study highlights the need to consider spatial heterogeneity and the potential trade-offs between different SDGs and regions when designing energy transition strategies.

Potentials and opportunities of solar PV and wind energy sources in Saudi Arabia: Land suitability, techno-socio-economic feasibility, and future variability

Solar and wind energy sources hold significant potential to meet the escalating energy demand in Saudi Arabia sustainably. This research aims to assess the feasibility and prospects of deploying solar photovoltaic (PV) and wind energy systems in Saudi Arabia (SA). The study adopts a comprehensive approach, encompassing spatial analysis of land suitability, techno-socio-economic feasibility studies, and prediction of renewable energy (RE) resource variability and trends using machine learning techniques. Geographical Information System (GIS) spatial analysis is employed to identify suitable areas for establishing solar PV and wind farms based on multi-criteria evaluation methods. Techno-socio-economic feasibility analysis of solar PV and wind energy systems, under various configurations and scenarios, is conducted using the System Advisor Model (SAM) tool. Furthermore, a Support Vector Machine (SVM) machine learning algorithm is developed and deployed to forecast Global Horizontal Irradiation (GHI), wind speed, and influential weather parameters at different locations in SA. The projections of these variables derived from global climate models are utilized to analyze prospects and variabilities. The land suitability analysis identified four optimal locations for large-scale solar PV fields in Tabuk, Al Madinah, Makkah, and Riyadh provinces. Additionally, four promising wind farms were found in Al Madinah, Makkah, Riyadh, and Eastern provinces. The analysis also identified one location in Al Jouf province suitable for hybrid systems combining solar PV and wind energy. The techno-economic assessment revealed that wind farms performed best overall, achieving a capacity factor of 42.6 % in Al Madinah province. Although current tariffs render projects economically unviable, solar PV, wind energy, and hybrid solar PV-wind technologies are economically feasible in SA at Power Purchase Agreement (PPA) rates above $32.8/MWh, $26.1/MWh, and $50.6/MWh, respectively. The social development analysis estimated potential job creation from solar PV and wind energy deployment under different scenarios from 2020 to 2060, indicating that more ambitious climate targets could translate to millions of renewable energy jobs. The SVM model predicted solar irradiance with an R-squared value of 0.893. The CMIP6 model was then used to project the GHI for SA in 2049, suggesting an increase of approximately 19 %. Wind speed is also expected to rise roughly 5 % over the same period. The integration of GIS spatial analysis, techno-socio-economic modeling, and machine learning-based forecasting provides comprehensive insights into harnessing solar and wind energy in SA. This study facilitates evidence-based planning and risk assessment, crucial for a sustainable energy transition. The insights and roadmap derived from this research can inform policy frameworks, support the United Nations Sustainable Development Goals (UNSDGs), and attract private investments for RE development in SA. Consequently, this study establishes solar and wind energy as viable and promising solutions for meeting SA's growing energy demands sustainably, while minimizing the associated environmental impact.

Solar Photovoltaic Home Systems in Malaysia: A Comprehensive Review and Analysis

This paper presents a thorough review and analysis of solar photovoltaic (PV) home systems in Malaysia, offering a comprehensive exploration of their implementation, challenges, benefits, and future potential. As a nation striving to embrace sustainable and renewable energy solutions, Malaysia’s adoption of solar PV systems at the residential level is of paramount importance. The study delves into the current state of solar PV deployment, government initiatives, and policy frameworks that influence adoption trends. It examines the economic, environmental, and societal advantages of solar PV home systems, outlining their contribution to reducing electricity costs, carbon emissions, and energy dependence. Addressing barriers to wider acceptance, the paper scrutinizes challenges encompassing cost, awareness, intermittency, and regulatory constraints. Furthermore, the study evaluates the socio-economic implications of solar PV home systems, including potential job creation and energy independence. Technological advancements, such as enhanced efficiency and smart grid integration, are also explored for their role in surmounting obstacles. Drawing insights from practical case studies, the paper underscores successful installations and the lessons they offer. In examining policy and regulatory frameworks, it analyzes existing incentives while suggesting improvements to accelerate adoption. Finally, the paper offers a visionary perspective on the future trajectory of solar PV home systems in Malaysia, envisioning increased affordability, advanced energy storage solutions, and seamless integration with emerging technologies. This holistic analysis contributes valuable insights to propel Malaysia’s sustainable energy transition and inform strategic decision making for stakeholders across sectors.

Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network

Solar photovoltaic (PV) deployment plays a crucial role in the transition to renewable energy. However, comprehensive models that can effectively explain the variations in solar PV deployment are lacking. This study aims to address this gap by introducing two innovative models: (i) a computer vision model that can estimate spatial distribution of solar PV deployment across neighborhoods using satellite images and (ii) a machine learning (ML) model predicting such distribution based on 43 factors. Our computer vision model using Faster Regions with Convolutional Neural Network (Faster RCNN) achieved a mean Average Precision (mAP) of 81% for identifying solar panels and 95% for identifying roofs. Using this model, we analyzed 652,795 satellite images from Colorado, USA, and found that approximately 7% of households in Colorado have rooftop PV systems, while solar panels cover around 2.5% of roof areas in the state as of early 2021. Of our 16 predictive models, the XGBoost models performed the best, explaining approximately 70% of the variance in rooftop solar deployment. We also found that the share of Democratic party votes, hail and strong wind risks, median home value, the percentage of renters, and solar PV permitting timelines are the key predictors of rooftop solar deployment in Colorado. This study provides insights for business and policy decision making to support more efficient and equitable grid infrastructure investment and distributed energy resource management.

Fairness-Aware Distributed Energy Coordination for Voltage Regulation in Power Distribution Systems

The accelerating deployment of solar photovoltaics into low-voltage distribution networks can cause reverse power flow and overvoltage problems. However, if coordinated properly, the real and reactive power flexibility of these resources enables distribution operators to manage their networks more efficiently. Existing literature is rich in droop-based control (Volt-Watt and Volt-VAr) and optimization-based distributed energy coordination for four-quadrant control of photovoltaics to prevent overvoltage issues. While optimal coordination can effectively mitigate overvoltage, it treats resources at sensitive parts of the grid unfairly. To address this concern, we propose a distributed optimal power flow formulation that incorporates fairness in curtailing photovoltaic generation and utilizes the reactive power capability of smart inverters. Fair curtailment of photovoltaic systems is studied with aggregation at each of two layers in a distribution network: 1) area-level fairness and 2) feeder-level fairness to demonstrate the flexibility of the proposed approach in resource aggregation. To explore the trade-off, the fairness-aware control actions are compared against the performance of a centralized controller that aims to maximize the aggregate PV generation without incorporating fairness. Simulation results show that introducing area-level fairness increased curtailment by 1.11 percentage points, and feeder-level fairness increased curtailment by 4.7 percentage points compared to a fairness-agnostic control.

Rooftop Solar PV Policy Assessment of Global Best Practices and Lessons Learned for the Kingdom of Saudi Arabia

The growth in global electricity demand, price volatility, and global warming is diverting the attention of power producers to look for alternative green energy sources, more specifically, solar photovoltaic (SPV). Rooftop solar PV (RSPV) is a significant contributor to the successful development and deployment of SPV in any country. Therefore, developing countries such as the Kingdom of Saudi Arabia (KSA) are seeking alternative energy sources. According to climatological studies, Saudi Arabia has an average of 3230 sun hours annually, indicating significant potential for producing solar energy. The article investigated the characteristics of policies of countries that have had massive success in developing RSPV systems like China, the US, Germany, Italy, Spain, Japan, and India). Each country has its unique policies that result in many policy structures. Therefore, it is beyond the content of this report to provide a complete overview of all policies for the selected countries. Instead, information will focus on specific aspects of solar policy in each of the seven countries. As part of the RSPV policy assessment in Saudi Arabia, this study shows the key vectors of the selected countries’ success in their rooftop policies’ examination, and eventually, it presents a clear policy assessment of KSA’s rooftop solar PV policy.

Transforming Saudi Arabia’s Energy Landscape towards a Sustainable Future: Progress of Solar Photovoltaic Energy Deployment

The Kingdom of Saudi Arabia’s electricity sector has undergone several distinct phases, and the country’s commitment to renewable energy development has resulted in a modern phase that includes the deployment of renewable energy power plants since 2010. Due to Saudi Arabia’s diverse topographical position, the exploration of renewable energy technologies is of interest, particularly solar energy, and its progress in renewable energy development could serve as a model for other countries seeking to transition to clean energy. This article explores the progress of solar photovoltaic (PV) energy deployment in Saudi Arabia, with a focus on the policies and regulations that have facilitated its growth. The article provides an overview of the energy landscape in Saudi Arabia and investigates the progress of solar PV deployment in Saudi Arabia, analyzing growth trends, capacity additions, and the role of policies and regulations in supporting the sector. The focus on renewable energy development in Saudi Arabia, particularly solar PV technology, could have far-reaching implications globally as the world seeks to transition to cleaner sources of energy. This research article highlights the importance of a comprehensive renewable energy policy for transforming the country’s energy landscape towards a sustainable future.

Solar photovoltaic deployment acceleration model to advance the sustainability of buildings in public universities in South Africa

The need to construct sustainable buildings has triggered the deployment of solar photovoltaics (PV) but tools are required to facilitate the process and accelerate the transition at a faster pace. Thus, the purpose of this paper is to present a PV deployment acceleration model (PVDAM) to advance the sustainability of buildings in public universities in South Africa. The data were collected using questionnaire surveys and analysed using the partial least squares structural equation modeling (PLS-SEM). The PLS-SEM results and recommendations from panellists were used to develop and validate the PVDAM. It was found that the category of drivers that have a significant positive influence on deployment was ‘direct benefits’; these include reduction of energy cost, enhancement of university image, boosting university sustainability performance and contribution to sustainable development goals (SDG) realisation. In contrast, the lack of incentives, lack of demand from project financiers, and inadequate funding were found to be factors that contributed to the significant negative influence of ‘government-related barriers’ on PV deployment. To boost deployment, six major actions were recommended to reinforce the ‘direct benefits’ and eliminate the ‘government-related barriers’. Overall, the study provided empirical evidence which can help stakeholders/policymakers take action to accelerate deployment in the public university sector.

Income and racial disparities in financial returns from solar PV deployment

Transitioning the U.S. energy system towards renewable sources of energy generation is critical to reducing global CO2 emissions. Solar photovoltaic (PV) technology is a promising source of renewable energy. Federal and state mandates, incentives, and subsidies increase solar PV adoption and hasten the energy transition. However, these policies are designed with adoption as the main metric of success and fail to consider differences in the stream of financial returns that accrue to households depending on the mode of adoption. Our research investigates financial returns to solar PV adoption by system ownership status (leased or owned). We examine the relationship of total and average financial returns to income and race using data on solar PV systems installed as part of the Massachusetts Solar Renewable Energy Certificate (SREC) program from 2014–2018. We find that financial returns that accrue to households for owned systems are over 300% higher on average than for leased systems. We also find that neighborhoods with more low-income and non-White households receive lower financial returns compared to neighborhoods with higher income and more White households, mostly because these households tend to lease their solar panels. Our results illustrate that the form of participation in the solar PV market (leasing or owning) has significant implications for the distribution of financial returns. Policymakers interested in prioritizing equity in the energy transition should account for this difference in financial returns when designing solar adoption programs, especially those targeted towards low-income and non-White communities.

Quantifying the cost savings of global solar photovoltaic supply chains.

Achieving carbon neutrality requires deploying renewable energy at unprecedented speed and scale1,2, yet countries sometimes implement policies that increase costs by restricting the free flow of capital, talent and innovation in favour of localizing benefits such as economic growth, employment and trade surpluses3,4. Here we assess the cost savings from a globalized solar photovoltaic (PV) module supply chain. We develop a two-factor learning model using historical capacity, component and input material price data of solar PV deployment in the United States, Germany and China. We estimate that the globalized PV module market has saved PV installers US$24 (19-31) billion in the United States, US$7 (5-9) billion in Germany and US$36 (26-45) billion in China from 2008 to 2020 compared with a counterfactual scenario in which domestic manufacturers supply an increasing proportion of installed capacities over a ten-year period. Projecting the same scenario forwards from 2020 results in estimated solar module prices that are approximately 20-25 per cent higher in 2030 compared with a future with globalized supply chains. International climate policy benefits from a globalized low-carbon value chain4, and these results point to the need for complementary policies to mitigate welfare distribution effects and potential impacts on technological crowding out.

Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia

The world faces the threat of an energy crisis that is exacerbated by the dominance of fossil energy sources that negatively impact the sustainability of the earth’s ecosystem. Currently, efforts to increase the supply of renewable energy have become a global agenda, including using solar energy which is one of the rapidly developing clean energies. However, studies in solar photovoltaic (PV) modelling that integrates geospatial information of urban morphological building characters, solar radiation, and multiple meteorological parameters in low-cost scope have not been explored fully. Therefore, this research aims to model the urban rooftop solar PV development in the Global South using Bandung, Indonesia, as a case study. This research also has several specific purposes: developing a building height model as well as determining the energy potential of rooftop solar PV, the energy needs of each building, and the residential property index. This study is among the first to develop the national digital surface model (DSM) of buildings. In addition, the analysis of meteorological effects integrated with the hillshade parameter was used to obtain the solar PV potential value of the roof in more detail. The process of integrating building parameters in the form of rooftop solar PV development potential, energy requirements, and residential property index of a building was expected to increase the accuracy of determining priority buildings for rooftop solar PV deployment in Bandung. This study shows that the estimated results of effective solar PV in Bandung ranges from 351.833 to 493.813 W/m2, with a total of 1316 and 36,372 buildings in scenarios 1 and 2 being at a high level of priority for solar PV development. This study is expected to be a reference for the Indonesian government in planning the construction of large-scale rooftop solar PV in urban areas to encourage the rapid use of clean energy. Furthermore, this study has general potential for other jurisdictions for the governments focusing on clean energy using geospatial information in relation with buildings and their energy consumption.

Large-scale evaluation of the suitability of buildings for photovoltaic integration: Case study in Greater Geneva

In the context of a rapid and massive deployment or renewable energy and in particular solar photovoltaic, it is necessary to develop methods and tools to guide this deployment. To this end, this work proposes a multicriteria approach for evaluating the suitability of a building to be equipped with photovoltaic (PV) systems (PV suitability). In the present case, technical (roof complexity), economic (payback period), environmental (CO2 reduction), energetic (self-consumption), as well as social (heritage constraint) criteria are considered. These criteria are evaluated for each building of the Greater Geneva Agglomeration (GGA), a cross-border French–Swiss territory of nearly 270 000 buildings. A multicriteria method, ELECTRE TRI, makes it possible to sort these buildings into three categories, A, B, and C, that correspond to “very high,” “high,” and “moderate” PV suitabilities, respectively. Large differences are observed within the 210 municipalities of the GGA since some of them have almost no A-ranked buildings whereas others comprise more than 70% of these buildings. It is shown that, by prioritizing the A-ranked buildings, almost 50% of the annual electricity consumption of the Geneva Canton could be produced by PV systems. Finally, the method developed here offers a decision-aiding tool that could be used at a territory scale to achieve energy transition goals in terms of solar PV deployment.

100% renewable energy in Japan

Low-cost solar photovoltaics and wind offer a reliable and affordable pathway to deep decarbonization of energy, which accounts for three quarters of global emissions. However, large-scale deployment of solar photovoltaics and wind requires space and may be challenging for countries with dense population and high per capita energy consumption. This study investigates the future role of renewable energy in Japan as a case study. A 40-year hourly energy balance model is presented of a hypothetical 100% renewable Japanese electricity system using representative demand data and historical meteorological data. Pumped hydro energy storage, high voltage interconnection and dispatchable capacity (existing hydro and biomass and hydrogen energy produced from curtailed electricity) are included to balance variable generation and demand. Differential evolution is used to find the least-cost solution under various constraints. This study shows that Japan has 14 times more solar and offshore wind resources than needed to supply 100% renewable electricity and vast capacity for off-river pumped hydro energy storage. Assuming significant cost reductions of solar photovoltaics and offshore wind towards global norms in the coming decades driven by large-scale deployment locally and global convergence of renewable generation costs, the levelized cost of electricity is found to be US$86/Megawatt-hour for a solar-dominated system, and US$110/Megawatt-hour for a wind-dominated system. These costs can be compared with 2020 average system prices on the spot market in Japan of US$102/Megawatt-hour. Cost of balancing 100% renewable electricity in Japan ranges between US$20–27/Megawatt-hour for a range of scenarios. In summary, Japan can be self-sufficient for electricity supply at competitive costs, provided that the barriers to the mass deployment of solar photovoltaics and offshore wind in Japan are overcome.

Drivers of solar photovoltaic deployment in South African public universities: a Delphi study

PurposeSolar photovoltaic energy is the fastest growing renewable energy in South Africa, and deployment at public universities is critical in order to meet the high energy demand on campuses in a more sustainable manner. To promote and support deployment, it is necessary to know the factors that drive adoption of the technology. Thus, the aim of this study was to identify the factors which engender deployment of photovoltaic energy in public universities in South Africa.Design/methodology/approachThrough an extensive literature review, the factors which drive photovoltaic energy deployment were identified and a three round Delphi survey was conducted for panellists to rate the drivers. The data were analysed using SPSS Version 27, and the mean and inter-quartile range values were used to identify the significant drivers within the public university sector.FindingsThe findings suggested that the deployment of photovoltaic energy is engendered by the direct and indirect benefits, social influences and the relative advantage of photovoltaic energy usage. For instance, universities install photovoltaic energy because of the financial gains that are made through reduction of energy cost. The deployment is also motivated by the reduction of energy-related greenhouse gases, enhancement of university sustainability performance, and the contribution to the Sustainable Development Goals.Originality/valueA significant contribution to the body of knowledge regarding the drivers of photovoltaic energy deployment has been made in the paper from the perspective of a public university. The contribution fills the knowledge gap in South Africa by contributing valuable information to enable decision-makers to gain better understanding of the key issues that call for more attention in promoting and supporting photovoltaic energy deployment in the sector.

Robust space–time modeling of solar photovoltaic deployment

Solar photovoltaic (PV) has established itself as a fairly promising, fast-growing renewable energy source. The main determinants of solar PV deployment are thought to be physical and climatic factors – such as latitude and solar irradiance, not to mention terrain and built environment features – as well as socio-economic drivers — such as population density, household size, and education level. Besides, peer effects and neighborhood effects are found to affect the willingness to adopt solar photovoltaic systems strongly. This study aims to set up robust space–time models, which enable us to investigate the drivers of solar PV deployment using fine-grained spatial and temporal data. We use space–time auto-regressive models (STAR) with several exogenous covariates that are expected to explain the installed solar PV capacity. STAR models require the specification of spatial weight matrices (W). As in regular lattice data, we select causal (lower triangular) W matrices so that the consistency of least-squares (LS) estimators is warranted. We show that they can be extended to robust LS estimators, which are necessary because of strong outlier contamination. Models are tested on the Italian municipal data of residential and industrial PV plants installed under the support schemes in force between 2006 and 2011. Empirical results confirm the important role played by the space–time dynamic components. Significant exogenous predictors are found in the domains of the physical features (elevation and land area), demography (population), built environment (residential buildings), and socio-economic aspects (income, employment rate, commuter workers).

Spatial analysis of distribution grid capacity and costs to enable massive deployment of PV, electric mobility and electric heating

Rapid deployment of solar photovoltaics (PV), electric heat pumps (HP) and electric vehicles (EV) is needed to decarbonize the economy. However, the integration of these technologies into the power system creates challenges for the distribution grid infrastructure. It is therefore vital to analyse to which extent grid reinforcement is needed to enable decarbonization strategies while also studying alternative flexibility measures. In this GIS-based study, we model the impact of the deployment of PV, HP and EV on a low-voltage distribution grid network serving 170’000 households in Switzerland, and analyse scenarios for their penetration in the years 2035 and 2050. Using a detailed grid model in collaboration with a distribution system operator, we find that PV leads to 18.5% and 13.7% more voltage violation issues compared to HP and EV respectively, which on the other hand, cause slightly more line overloading, 0.5% and 2.5%, respectively. We also find that grid reinforcement costs markedly depend on the type of urban setting ranging between 51–213 CHF/kWp, 46–1’385 CHF/kW and 34–143 CHF/kW for PV, HP and EV, respectively, with the higher limit corresponding to rural areas. The total distribution grid reinforcement costs can amount up to 11 billion CHF until 2050 i.e. 2’900 CHF per household in Switzerland. Interestingly, we find that batteries, even with current costs, have the potential to defer grid reinforcement for up to 15% of the transformer stations with the highest specific grid reinforcement costs. Our study aims to inform various stakeholders about the required grid investments to enable the massive deployment of low carbon technologies.

The Motivation for Incorporation of Microgrid Technology in Rooftop Solar Photovoltaic Deployment to Enhance Energy Economics

Deployment of rooftop solar Photovoltaic technology in domestic premises plays a significant role in accomplishing renewable energy transformation. The majority of domestic consumers still do not have a positive perception about adopting rooftop solar PV technology, due to its high capital cost and prolonged payback period. In this aspect, the proposed work identifies the factors causing energy deprivation in the present distribution and utilization system. To explicitly express the importance of the present work, an extensive case study based on an Indian scenario has been carried out to investigate where the losses occur in the existing distribution system and how the solar power and its storage system have been ineffectively utilized. The deep investigation has thrown light on several issues that lead to the performance deterioration of PV technology. Finally, in this work, a scheme to incorporate hybrid microgrid technology in the domestic distribution network has been proposed to effectively manage the distribution system and to efficiently utilize solar power and its storage systems. The real-time electricity tariff data have been taken for cost comparison and payback period calculations to prove the effectiveness of the proposed method. Crucial comparisons have been presented based on energy saving and carbon dioxide CO2 emission reduction strategies.