Residential Solar Photovoltaic Systems Research Articles

A System Dynamics Model for Rooftop Solar PV System Development in Indonesia

Despite of Indonesia’s vast solar energy potential according to the Indonesian Ministry of Energy and Mineral Resources, Indonesia’s total installed capacity of rooftop solar photovoltaic (PV) is very far from Indonesia’s target of 3.6 GW in 2025. Indonesia once applied net metering scheme for rooftop solar PV policy and was expected to be able to boost rooftop solar PV growth. A system dynamics approach is used in the research to develop an assessment model to evaluate the policies’ impact on residential rooftop solar PV system growth. A Causal Loop Diagram was established then transformed into Stock and Flow Diagram (SFD) using software Vensim PLE 10.1.3, which was used to simulate several policies’ scenarios related to residential rooftop solar PV adoption and CO2 emissions reduction. Ten scenarios were simulated in this study involving three groups of intervention: initial net metering tariff, reduction on initial solar PV cost, upper limit of ROI, and combination of initial net metering tariff and initial solar PV cost reduction. The simulations revealed that combination of increasing net metering tariff to 80% & initial cost reduction 30% has the highest potential solar PV installations, the highest CO2 emissions reduction, and the lowest accumulation cost of net metering in 2030. This study can be used as reference by the policy makers in Indonesia to formulate the optimum policy to boost rooftop solar PV growth as the simulations shows that residential rooftop solar PV with the right intervention can meet the government’s target of rooftop solar PV in 2030.

Sensitivity analysis of distributed photovoltaic system capacity estimation based on artificial neural network

Residential solar photovoltaic (PV) system installations are expected to continue increasing due to their growing cost competitiveness and supportive government policies. However, excessive installations of unknown behind-the-meter solar panels present a challenge for accurate load prediction and reliable operations of power networks. To address such growing concerns of distribution network operators (DNOs), this research proposes a novel model for distributed PV system capacity estimations. Innovative extracted features from 24-hour substation net load curves were fed into a deep neural network to estimate the PV capacity linked to the substation feeder. A comprehensive study into the sensitivity of the model’s accuracy to specific temporal scales of data collection, number of households served by a substation, and proportion of PV-equipped properties was conducted. This study revealed that a model developed to be used exclusively in summer achieved a 18.1% decrease in estimation root mean squared error (RMSE) compared to an all-year model, whilst using only a third of the training data amount. Similarly, compared to an all-year model, RMSE decreased by 26.9% when only data from Mondays to Thursdays were used to train and test the model. Also, for the all-year model, the most accurate estimations occur when 20% to 80% of households have PV systems installed and estimation percentage error tend to remain constant at around 10% when more than 20% of households have PV systems installed. A machine learning-ready dataset of substations with known PV capacity and experiment results are both useful to inform DNOs on the potential of the proposed method in reducing grid operation costs.

Formulation of billing policy for residential scale solar PV systems and its impact in the Kingdom of Saudi Arabia

Governments worldwide have introduced diverse billing methods and regulations to promote the adoption of photovoltaic (PV) systems, aiming to reduce greenhouse gas emissions. This paper aims to develop a billing policy for installing rooftop PV systems in the Kingdom of Saudi Arabia by studying policies implemented to support installing PV systems in leading countries. Initially, the system is evaluated using the tariff predetermined by Saudi Electricity Cogeneration Regulation Authority under three different policies: a Feed-in Tariff (FIT), net metering, and net billing for different sizes of solar systems 10 kW, 15 kW, and 20 kW. The findings suggest a considerable discounted payback period ranging from 14 to 20 years under the net metering framework with the current tariff structure. In contrast, investments under FIT and net billing schemes do not appear to be viable. Thus, a tariff schedule, tailored according to the consumer-preferred payback period, is proposed. For a payback timeframe of 6–10 years, the tariff should range between $0.09–0.06 $/kWh within the FIT scheme. However, in the case of a net billing policy, the tariff should vary between $0.123–0.066 $/kWh. These findings suggest the supremacy of the FIT scheme, particularly if the government determined to use the low tariff policy.

Incorporation of multiple random features kernel mean p‐power controller to elevate power quality in reconfigurable grid‐connected residential solar photovoltaic systems

SummaryProblems like intermittent photovoltaic (PV) power, disconnected PV arrays, etc. are brought on by the extensive integration of PV generation. In this article, a single‐stage PV‐integrated grid‐tethered system is controlled using a reconfigurative multiple random features kernel mean p‐power (MRFKMP) control algorithm. The algorithm circumvents the dependence of kernel parameter in the classic random Fourier features mapping and lower the computing complexity in the traditional multiple kernel learning adaptive filter. MRFKMP algorithm with a linear filter structure can reduce both the computational and storage cost. This is the novel application of MRFKMP algorithm to extract fundamental component of nonlinear load currents in PV‐integrated grid‐tied system. Furthermore, to increase the reliability of the system, a reconfigurable control is developed, which adaptive regulates DC link voltage from PV mode (PVM) during PV hours to active power filtering mode (AFM) during PV nonavailability/PV disconnection. On a created laboratory testbed, the applied control's practical feasibility for numerous scenarios is evaluated. Comparison with existing control algorithms reveals that the MRFKMP controller outperforms the LMF based PV system by 79% less peak‐to‐peak load current ripple and 60% less than the best performing SRF control algorithm. The grid current THD is 2.60%, which is 30% low than that presented by SRF technique.

On-site solar PV generation and use: Self-consumption and self-sufficiency

As energy storage systems are typically not installed with residential solar photovoltaic (PV) systems, any “excess” solar energy exceeding the house load remains unharvested or is exported to the grid. This paper introduces an approach towards a system design for improved PV self-consumption and self-sufficiency. As a result, a polyvalent heat pump, offering heating, cooling and domestic hot water, is considered alongside water storage tanks and batteries. Our method of system analysis begins with annual hourly thermal loads for heating and cooling a typical Australian house in Geelong, Victoria. These hourly heating and cooling loads are determined using Transient System Simulation (TRNSYS) software. The house’s annual hourly electricity consumption is analysed using smart meter data downloaded from the power supplier and PV generation data measured with a PV system controller. The results reveal that the proposed system could increase PV self-consumption and self-sufficiency to 41.96% and 86.34%, respectively, resulting in the annual imported energy being reduced by about 74%. The paper also provides sensitivity analyses for the hot and cold storage tank sizes, the coefficient of performance of the heat pump, solar PV and battery sizes. After establishing the limits of thermal storage size, a significant impact on self-efficiency can be realised through battery storage. This study demonstrates the feasibility of using a polyvalent heat pump together with water storage tanks and, ultimately, batteries to increase PV self-consumption and self-sufficiency. Future work will concentrate on determining a best-fit approach to system sizing embedded within the TRNSYS simulation tool.

Disentangle the price dispersion of residential solar photovoltaic systems: Evidence from Germany

Although Germany has the largest capacity of installed residential photovoltaic (PV) systems in Europe, comprehensive evidence on transparent pricing information remains missing. This study disentangles why PV quote prices are subject to significant dispersion and analyzes which factors influence particularly low- and high-priced systems in Germany. We create a comprehensive cross-sectional dataset of 19 561 PV system quotes from 2011 to 2022 and use regression analyses to investigate the effects of system characteristics, installation scope, and location-related parameters on quoted prices. Our results reveal highly volatile annual price dispersion consistent over 11 years and large price differences despite similar system characteristics. Applying hedonic regression techniques, we reveal spatially fine-resolved price heterogeneity with up to 20% difference in the German PV market. System characteristics such as battery usage, installation scope, and system capacity have the most significant effect sizes and are instead control variables. More insightful, the installer density shows price-lowering effects, whereas more PV installations per region, higher solar radiation, and higher labor wages cause price-increasing effects. Quantile regression results reveal that installer density promotes the price reduction of high-priced systems more. Scaffolding, AC installation, and elevation are significant price-increasing factors but with small effect sizes. Finally, DC optimizers affect the levels of high-priced systems more than low-priced ones.

Current and future prospective for battery controllers of solar PV integrated battery energy storage systems

Solar photovoltaic (PV) microgrids have gained popularity in recent years as a way to improve the stability of intermittent renewable energy generation in systems, both off-grid and on-grid, and to meet the needs of emergency settings during natural catastrophes. Over the last several decades, researchers have been interested in improving the efficiency of photovoltaic (PV) systems. Solar-battery charge controllers based on various algorithms are continuously and intensively employed to improve energy transfer efficiency and reduce charging time. This paper presents state-of-the-art solar photovoltaic (PV) integrated battery energy storage systems (BESS). An overview of and motivations for PV-battery systems is initially introduced, followed by the survey methodology and its contributions. In addition, this study classifies residential solar PV systems and battery charge controllers with their corresponding references in the review structure, which also provides details on battery charger topologies. Subsequently, an analytical review of the PV-Battery charge controller and the failure probability of such systems is discussed to determine the system components that mostly fail and their importance in the system. Finally, recommendation amendments to the existing charge controller that potentially contribute to increasing the system efficiency, reducing the failure probabilities, and reducing the cost are presented as future design concepts for the entire system.

How does residential solar PV system diffusion occur in Australia?-A logistic growth curve modelling approach

Australia is facing the twin problems of energy depletion and environmental pollution. This crisis can be alleviated by effective promotion of solar photovoltaic (PV) system installation. The question is: how does solar PV system diffusion occur in various regions in Australia? This study introduces an innovative five-parameter logistic (5PL) curve to analyse the diffusion of solar PV technology in all postcode areas of Australia. Furthermore, it combines principal component analysis (PCA) and cluster analysis to identify the major area types that have similar diffusion characteristics. The study identifies three main diffusion pathways and their corresponding distribution areas in Australia. The results provide a visual representation of the historical diffusion of residential PV systems in Australia. From a technology diffusion perspective, the results also show how residential solar panel diffusion occurs differently in areas at the postcode level. These findings provide insights on how to reduce regional disparities in solar panel adoption, thus facilitating renewable energy equity and justice.

Grid Parity of Residential Building Rooftop Solar PV in India

Rooftop solar photovoltaic(PV) installation in India have increased in lastdecade because of the flat 40 percent subsidy extended for rooftop solarPV systems (3 kWp and below) by the Indian government under the solarrooftop scheme. From the residential building owner’s perspective, solar PVis competitive when it can produce electricity at a cost less than or equalgrid electricity price, a condition referred as “grid parity”. For assessing gridparity of 3 kWp and 2 kWp residential solar PV system, 15 states capitaland 19 major cities were considered for the RET screen simulation by usingsolar isolation, utility grid tariff, system cost and other economic parameters.3 kWp and 2 kWp rooftop solar PV with and without subsidy scenarioswere considered for simulation using RETscreen software. We estimate thatwithout subsidy no state could achieve grid parity for 2kWp rooftop solarPV plant. However with 3 kWp rooftop solar PV plant only 5 states couldachieve grid parity without subsidy and with government subsidy number ofstates increased to 7, yet wide spread parity for residential rooftop solar PV isstill not achieved. We find that high installation costs, subsidized utility grid supply to low energy consumer and financing rates are major barriers to gridparity

Compensating Solar Prosumers Using Buy-All, Sell-All as an Alternative to Net Metering and Net Purchasing: Total Use, Rebound, and Cross Subsidization

Electric utilities are challenging net metering, which compensates owners of residential solar photovoltaic systems by crediting solar generation at the retail rate instead of reflecting the value of solar to the utility. In 2012, Austin Energy was the first US utility to replace net metering with Buy-All, Sell-All, where prosumers purchase all electricity at the retail rate and sell all electricity at the utility’s value of solar rate. Using Austin Energy customer data for hot weather months, a load curve comparison of comparably sized houses shows that prosumers have higher consumption than non-solar customers during system peak hours. Regression analysis finds that prosumers use more energy for air conditioning, with a rebound effect of 20%. A simulation shows that under net metering and net purchasing almost 7% of prosumer bills would be paid by non-solar customers while total subsidy would be 18% due to the inclusion of increasing block rates.

Techno-economic feasibility analysis of grid-connected residential PV systems in Algeria

Small-scale photovoltaic (PV) power systems have been proven to be successful in generating electricity, conserving fossil fuels, and reducing greenhouse gas emissions in the residential sector, which is one of the largest consumers of energy. In Algeria, to reduce energy consumption in this sector, the authorities are considering implementing a policy that would encourage grid-connected residential PV systems. This paper presents a techno-economic assessment of grid-connected residential PV systems in four climate zones in Algeria. This work was performed using HOMER software for two different PV system configurations, grid/PV and grid/PV/battery. The technical performances of the considered systems were evaluated through the assessment of the self-consumption and self-sufficiency, while the net present value (NPV), internal rate of return (IRR), profitability index (PI), and discounted payback period (DPBP) were used to determine their feasibility. A sensitivity analysis was carried out to evaluate the effects of feed-in tariff (FiT), battery costs, and PV array capacity on the profitability of the systems. The results revealed that the grid/PV systems are technically and economically feasible in all of the four climate zones. For the grid/PV/battery systems, the grant of battery costs and the development of a regional FiT system are recommended. This article provides a tool for policymakers to assess the technical and financial performance of residential solar PV systems to develop adequate policy supports and tariff structures for Algeria.

A Novel Data-Driven Method to Estimate Invisible Solar Power Generation: A Case Study in Taiwan

As the penetration of photovoltaic (PV) solar generation has increased, a significant number of residential and commercial solar PV systems have been installed without meters. The majority of these systems, however, are also not monitored by power system operators. Therefore, the uncertainty of the net load owing to this “invisible” solar power generation has raised additional challenges for power system operation. To reduce the impact of this, we propose a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that could assist in the identification of the representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating aggregated power generation compared to other existing methods.

Contrasting effects of electricity prices on retrofit and new-build installations of solar PV: Fukushima as a natural experiment

This study examines the effects of financial incentives, particularly electricity prices, on residential solar photovoltaic (PV) system installations. We highlight the importance of a factor in the adoption of low-carbon building technologies that the literature has largely overlooked: the distinction between retrofit and new-build installations. To address the endogeneity of electricity prices, we use the 2011 Fukushima nuclear accident and the subsequent shutdown of nuclear power plants in Japan as a natural experiment that caused substantial, exogenous, and regionally varying increases in electricity generation costs and prices. Electricity prices have a statistically significant, positive effect on PV system installations for existing homes (with a mean elasticity of 1.6), but a statistically insignificant, much smaller effect for new-build homes. A policy implication of these contrasting responses is that the cost-effectiveness of financial incentive schemes for low-carbon building technologies can be improved if they are targeted more at retrofits. We also find a large downward bias (40%–60%) in the estimated effect of electricity prices if they are not instrumented with exogenous cost-shifters.

The occupational safety implications of the California residential rooftop solar photovoltaic systems mandate

Introduction: A 2018 change to the California building code mandates that new residential construction in the state include rooftop solar photovoltaic power systems beginning in 2020. As residential construction (especially work on rooftops) is among the more dangerous occupations in the United States, this paper seeks to quantify the increased risks to workers as a result of this mandate. Method: An analysis of the trends by occupation of nonfatal safety incident rates in the United States combined with a Monte Carlo simulation provide an estimate of the uncertain impact of this new mandate. Results: Recordable safety incidents are anticipated to increase by a total of 16.6 incidents (standard deviation = 1.0 incidents) over the 2020–2029 time period as a result of this policy change. However, lessons from Germany and other industries offer potential avenues to reduce the negative social impact of this mandate. Conclusions: While it is not possible to increase employment in any sector without increasing the expected number of occupational injuries to some degree, these results indicate that risks could be considerably reduced by making solar PV system design decisions that increase worker productivity and reduce roof exposure time. Practical Applications: Changes such as eliminating work on roofs could decrease the expected number of recordable injuries over the 10-year period by 0.30 incidents per year (a reduction of 18%).

How the decision to accept or reject PV affects the behaviour of residential battery system adopters

When residential rooftop solar photovoltaic (PV) systems are widely accepted across society, the uptake of home battery energy storage systems is closely tied to the PV-status quo and the behaviour previously taken by households. This study proposes that a decision of acceptance or rejection of PV systems is the past behaviour of the battery adoption decision. This antecedent role of PV behaviour may spark two attitudinal changes: (a) feelings of regret, which may occur among PV adopters, stemming from a positive experience of using the system, or among non-adopters due to their rejection of the system, and (b) feelings of despair, which may arise among current PV users due to dissatisfaction and discontent with the system. While regret positively changes consumers’ attitude towards battery adoption expediting an earlier purchase, despair could preclude battery installation. Through a survey of 557 households in South East Queensland, Australia, this study investigated factors driving attitude change. Instead of traditional statistical methods, machine learning algorithms were adopted to derive data-driven models of attitude change, allowing for higher prediction accuracy and a determination of the latent causalities. The main findings indicate that perceived attitudes towards financial and non-financial benefits, followed by informal peers, best estimate the attitude change, whereas traditional sociodemographic factors, knowledge and affordability may not engender a shift. Leveraging from this new paradigm can encourage current PV consumers to take another step and become earlier battery adopters. Failure to recognise these dynamics may breed despair and turn current innovators and early adopters of PVs into late adopters or rejectors of battery systems.

Redirecting solar feed in tariffs to residential battery storage: Would it be worth it?

Government subsidies and premium feed in tariffs (PFiTs) have been effective in driving Australia’s high uptake of residential solar photovoltaic (PV) systems. PFiT policies have reduced bills for solar customers and the total greenhouse gas emissions associated with electricity generation. However, solar penetration is approaching technical limits in some areas of Australian distribution networks. Distributed batteries can overcome these limits allowing for a greater penetration of residential solar PV and a reduction in overall network costs through improvements in capacity utilisation. It is important for policies to align the interests of individual consumers with a reduction in total system costs. In this article we demonstrate how redirecting ongoing feed-in tariff payments towards residential battery storage has the potential to reduce overall system costs. We present some of the challenges associated with unlocking these benefits and provide a list of policy principles that can be utilised to guide policy development .

Determining the influencing factors in the residential rooftop solar photovoltaic systems adoption: Evidence from a survey in Qatar

Renewable energy sources have developed rapidly, decreased in cost, and proven to have the potential to limit global warming. Renewable energy in the form of solar energy can be collected through decentralized rooftop solar systems. Solar energy development in Qatar is still at an early stage. The abundance of solar radiation, high GDP, and plentiful access to rooftop spaces make rooftop photovoltaic (PV) systems suitable. Unfortunately, the early development and adoption of residential rooftop solar PV systems are expected to face numerous constraints. This paper investigates the factors that impact the residential rooftop solar photovoltaic adoption in Qatar. Through analyzing the response of a general public sample, we hope to prove the hypothesized factors by aiming at testing their solar systems knowledge and awareness. The result of the study will provide insights on how the public perceives solar panels, along with factors the government needs to address to ensure successful public adoption of residential solar rooftop systems in Qatar.

The changing effectiveness of financial incentives: Theory and evidence from residential solar rebate programs in California

Financial incentives, such as rebate programs, are widely used to promote the adoption of residential solar photovoltaic systems. This paper studies the changing effectiveness of rebate programs as the solar market evolves. We develop a theoretical model to first characterize individual treatment effects then aggregate them to regional effect, and hypothesize that the aggregate treatment effect of rebates first increases and then decreases with declining costs and increasing net metering rate. We empirically test our hypotheses by estimating the treatment effect of rebate rate in California from 2006 to 2017 and its interaction terms with installation cost and residential electricity rate. Results confirm our hypotheses and identify an installation cost of $8.84/W and a residential electricity rate of 22.36c/kWh as the turning points where rebate impacts start to decline. Our findings suggest that front-loaded rebate rate design can be an effective and efficient tool at promoting residential solar adoption.

Modeling multi-criteria decision analysis in residential PV adoption

Multi-Criteria Decision Analysis (MCDA) is a sub-discipline of operations research that aims to solve multi-objective optimization problems by evaluating competing factors in decision-making. MCDA supports multidimensional decision-making processes through the analysis of diverse inputs at several levels of description, e.g. economic, technical, social, and environmental. The use of MCDA has been gaining momentum in the energy field, especially in endeavors that require evaluating the feasibility of different adoption scenarios for renewable energy technologies. The study presented in this paper investigates the combined use of multi-agent simulation, Bayesian modeling and sensitivity analysis for the development of a novel MCDA approach that supports the analysis of residential solar Photovoltaic (PV) adoption. The ensuing MCDA approach is evaluated alongside four popular MCDA methods (AHP, TOPSIS, SAW and ELECTREII) in a variety of tests aimed to assess overlap in criterion rankings and decision-making outcomes, covariation of criteria rankings in alternative scenarios, and the capacity to provide a model of correct decision-making. The comparative evaluation with AHP, TOPSIS, SAW and ELECTREII shows that overall ABM-BN-SA is well correlated with most of other MCDA methods and provides the best performing model of decision-making, with reference to the PV adoption use case under analysis. TOPSIS shows the closest fit with ABM-BN-SA, as expected since it used a ranking approach considerably closer to ABM-BN-SA as compared to the other MCDA treatments. ELECTREII yields the lowest degree of ranking overlap with ABM-BN-SA. All the reviewed methods have been illustrated and evaluated within our residential solar Photovoltaic (PV) adoption decision support system. In general, these methods enable a user to select an optimal solution out of a set of plausible alternatives according to multiple criteria and assist him in the design and exploration of the decision space. The ensuing decision-making methodology can be applied not only by Solar PV panel purchasers but also by stakeholders in other industries to logically and straightforwardly model and simulate the adoption decision-process of the public based on their individual preferences, behavioral rules, and interaction within a social network, with specific reference to a consumer utility function.

Questioning the Sun: Unexpected emissions implications from residential solar photovoltaic systems

Reaching carbon neutrality would require the retirement of conventional power sources and substitution with renewable energy sources. Given that immediate substitution from conventional to renewable power sources is not feasible in the status quo, we investigate whether adding residential solar photovoltaic (PV) technology in addition to conventional power sources would reduce residential emissions. We use a large survey dataset of more than 300,000 observations and employ a structural equation model (SEM) to validate our findings. Interestingly, emissions increase by 1.75% if residential PV is adopted, and Japanese citizens with residential PV systems end up using 3.02% more electricity. We also find that pro-environmental consumers may also produce more emissions with PVs. As a result, reaching target carbon reductions in the residential sector would necessitate eliminating conventional energy sources. We address the policy implications for pathways to reduce residential emissions.