Solar Penetration Research Articles

(Mis)allocation of Renewable Energy Sources

Policies to incentivize the adoption of renewable energy sources (RES) usually offer little flexibility to adapt to heterogeneous benefits across locations. We evaluate the geographical misallocation of RES associated with the uniform nature of subsidies. We estimate the dispersion of marginal benefits from solar production in Germany and compute the social and private benefits from optimal reallocations of residential solar installations keeping total capacity fixed. We find that total value of solar would increase by 6.4% relative to the current allocation using conservative values for solar penetration. Reallocating all solar and taking into account transmission would yield considerably larger gains.

A Short-Term PV Power Forecasting Method Using a Hybrid Kmeans-GRA-SVR Model under Ideal Weather Condition

With the continuous increase of solar penetration rate, it has brought challenges to the smooth operation of the power grid. Therefore, to make photovoltaic power generation not affect the smooth operation of the grid, accurate photovoltaic power prediction is required. And short-term forecasting is essential for the deployment of daily power generation plans. In this paper, A short-term photovoltaic power generation forecast method based on K-means++, grey relational analysis (GRA) and support vector regression (SVR) (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed. The historical power data was clustered through the multi-index K-means++ algorithm. And the similar days and the nearest neighbor similar day of the prediction day were selected by the GRA algorithm. Then, similar days and nearest neighbor similar days were used to train SVR to obtain an accurate photovoltaic power prediction model. Under ideal weather, the average values of MAE, RMSE, and R2 were 0.8101 kW, 0.9608 kW, and 99.66%, respectively. The average computation time was 1.7487 s, which was significantly better than the SVR model. Thus, the demonstrated numerical results verify the effectiveness of the proposed model for short-term PV power prediction.

Residual load probabilistic forecast for reserve assessment: A real case study

Distributed generation from wind and solar acts on regional electric demand as a reduced consumption, giving rise to a “load shadowing effect”. The net load becomes much more difficult to predict due to its dependence on the meteorological conditions. As a consequence, the growing penetration of variable generation increases the imbalance between demand and scheduled supply (net load forecast) and the reserve margins (net load uncertainty).The aim of this work is to quantify the benefit of the use of advanced probabilistic approaches rather than a traditional time-series method to assess the day-ahead reserves. For this purpose, several methods for load and net load uncertainty assessment have been developed and applied to a real case study considering also future solar penetration scenarios.The results show that, when forecasting only the load both traditional and probabilistic methods exhibit similar accuracy. Instead, in the case of net load prediction, i.e. when solar power is present, the probabilistic forecast can effectively limit the reserve margin needed to arrange the imbalance between residual demand and supply. The developed probabilistic approach provides a notable reduction of the Following Reserve which increases with the solar penetration: from 32.5% to 68.3% at 7% and 45% of penetration.

Economic drivers of wind and solar penetration in the US

Much has been made of the potential for wind and solar generation to supply cheap, low-emissions electricity, but considerable disagreement exists as to which combinations of many potential drivers will enable deep penetration of these technologies. Most existing analyses consider limited factors in isolation, such as investment costs or energy storage, and do not provide rigorous support for understanding which combinations of factors could underpin a leading role for wind and solar. This study addresses this gap by undertaking a systematic sensitivity analysis using a state-of-the-art energy-economic model to comprehensively evaluate the relative magnitudes of five key drivers that may influence future wind and solar deployment in the United States. We find future wind and solar capital costs and carbon policy are the dominant factors, causing the average wind and solar share to vary by 38 and 31 percentage points, respectively. Transmission and storage availability have much smaller effects, causing the average share to vary by no more than 15 and 5 percentage points, respectively. No single factor unilaterally determines wind and solar deployment. The variable renewable share of electricity generation never reaches 100% nationally in any scenario even with low-cost storage, as decreasing marginal returns at higher deployments eventually outpace cost reductions. Average wind and solar shares and ranges of possible outcomes are higher in this study relative to recent multi-model comparison studies due to lower renewable costs and the potential for more stringent policies. Understanding drivers and barriers to renewable deployment has important ramifications for technology developers, infrastructure, market design, and policymakers, and this research provides insights as to which combinations of drivers lead to the greatest share of economic wind and solar deployment and why.

Investigation of Daytime Peak Loads to Improve the Power Generation Costs of Solar-Integrated Power Systems

Improving daytime loads can mitigate some of the challenges posed by solar variations in solar-integrated power systems. Thus, this simulation study investigated the different levels of daytime peak loads under varying solar penetration conditions in solar-integrated power systems to improve power generation cost performance based on different load profiles and to mitigate the challenges encountered due to solar variation. The daytime peak loads during solar photovoltaic generation hours were determined by measuring the solar load correlation coefficients between each load profile and the solar irradiation, and the generation costs were determined using a dynamic economic dispatch method with particle swarm optimization in a MATLAB environment. The results revealed that the lowest generation costs were generally associated with load profiles that had low solar load correlation coefficients. Conversely, the load profile with the highest positive solar load correlation coefficient exhibited the highest generation costs, which were mainly associated with violations of the supply-demand balance requirement. However, this profile also exhibited the lowest generation costs at high levels of solar penetration. This result indicates that improving daytime load management could improve generation costs under high solar penetration conditions. However, if the generation system lacks sufficient ramping capability, this technique could pose operational challenges that adversely impact power generation costs.

The cannibalization effect of wind and solar in the California wholesale electricity market

Increasing penetration of zero marginal cost variable renewable technologies cause the decline of wholesale electricity prices due to the merit-order effect. This causes a “cannibalization effect” through which increasing renewable technologies’ penetration undermines their own value. We calculate solar and wind daily unit revenues (generation weighted electricity prices) and value factors (unit revenues divided by average electricity prices) from hourly data of the day-ahead California wholesale electricity market (CAISO) for the period January 2013 to June 2017. We then perform a time series econometric analysis to test the absolute (unit revenues) and relative (value factors) cannibalization effect of solar and wind technologies, as well as the cross-cannibalization effects between technologies. We find both absolute and relative cannibalization effect for both solar and wind, but while wind penetration reduces the value factor of solar, solar penetration increases wind value factor, at least at high penetration and low consumption levels. We explore non-linearities and also find that the cannibalization effect is stronger at low consumption and high wind/solar penetration levels. This entails that wind and (mainly) solar competitiveness could be jeopardized unless additional mitigation measures such as storage, demand management or intercontinental interconnections are taken.

Shadow-camera based solar nowcasting system for shortest-term forecasts

The rapid growth of solar power generation and the variable nature of the solar resource pose challenges for our electricity grids. Forecasting future changes in the irradiance might help to cost-efficiently manage this variability both for photovoltaic and concentration solar plants as well as grids with high solar penetrations. So far, for shortest-term forecasts with lead times of a few minutes, all-sky imager based nowcasting systems are used. However, due to the complexity of dynamically changing 3d cloud shapes as well as certain geometrical effects such as self-occlusion or near-horizon saturation, all-sky imager based nowcasting systems exhibit inherent weaknesses. Here, we present a novel system to generate shortest-term solar forecasts, which is located at Plataforma Solar de Almeria in southern Spain. This approach is based on downward-facing cameras (shadow cameras), taking images of the ground. From these images, spatially resolved irradiance maps are derived. By tracking cloud shadows, future irradiances are predicted. A demonstration system is achieved, which provides shortest-term forecasts for the next 2min. To the best of our knowledge, this is the first time such a system is developed. We benchmark several possible algorithmic approaches on 16 days and compare the deviations to a state-of-the-art all-sky imager based nowcasting system on 22 days. The root-mean-squared deviation (RMSD) of this shadow camera based nowcasting system for direct normal irradiance (DNI) and 1-min temporal averages is 15.6% for lead times of 2min (MAD, DNI: 9.6 %). In comparison to an all-sky imager system, this is an improvement as the all-sky imager system only reaches 22.0% RMSD and 14.8% MAD (both DNI). This demonstrates the feasibility and attractiveness in terms of accuracy of the proposed concept.

Conservation Spillovers: The Effect of Rooftop Solar on Climate Change Beliefs

Biased beliefs about climate change may lead to under-regulation of emissions. We study a new channel by which the public form beliefs about climate change: visible mitigation actions. By exploiting the rapid growth of rooftop solar panels, a large survey, and differences in incentives to install solar, we find that visible mitigation actions have a positive impact on belief in basic climate science. However, we also find that higher solar penetration reduces concern about the impacts of climate change, which may dampen demand for additional mitigation policy and individual abatement effort. Our results suggest that government policies that incentivize technology adoption can have subtle but important spillover effects on beliefs and other behaviors.

Shaping photovoltaic array output to align with changing wholesale electricity price profiles

Large-scale deployment of solar photovoltaics (PV) contributes to the occurrence of depressed—and sometimes negative—electricity prices during daylight hours as PV displaces higher-cost generators in the merit-order dispatch stack. These changes in electricity price provide an opportunity to increase the wholesale energy revenue of PV generators through temporal shaping of PV output. Here, we explore the impact of three output-shaping strategies on PV wholesale energy revenue and capacity factor: utilization of 1-axis tracking, curtailment during negative-price hours, and modification of fixed-tilt array orientation. Utility-scale PV arrays are modeled at more than 10,000 pricing nodes across six United States electricity markets over the 2010–2017 time period. Large changes in revenue-optimized output profiles are observed for the California system, where solar capacity penetration has increased from ∼2% of peak load in 2010 to ∼28% of peak load in 2017, and the wholesale revenue benefits of temporal output shaping are increasing with time. On the California real-time market in 2017, compared to capacity-factor-optimized fixed-tilt arrays with must-run operation, curtailment increases revenues by 9%, curtailment in conjunction with fixed-tilt orientation optimization increases revenues by 20%, 1-axis tracking without curtailment increases revenues by 32%, and 1-axis tracking with curtailment increases revenues by 42% for the median node. Median optimal fixed-tilt azimuths for PV on the real-time market in California have increased from 192° in 2010 to 235° (i.e. 55° west of south) in 2017. Among the markets and years studied, the California market in 2017 demonstrates the largest potential benefit from temporal output shaping. These results highlight mechanisms for mitigating some of the decline in PV wholesale value at high solar penetrations, and illustrate the importance of adapting PV installation and dispatch strategies to changing power system conditions.

Field Investigation of Indoor Thermal Performance in Malaysia Air-Welled Terraced House

This paper aims to determine the performance of the air well system in a hot and humid tropical climate with existing full-scale air well of single-storey terraced house. The application of air well in residential building widely applied in a traditional building, especially in the Middle East and Europe countries. However, resulted from the development, the application of passive cooling strategies such as air well is gradually replaced by a mechanical cooling system. The aim of the study is to investigate the cooling system role in tropics, where hot and humid climate the overheating of building interior are a critical dilemma due to solar penetration through building fenestration. A field measurement has been carried out in a single storey terraced house with built-in air well in Kuching, Sarawak for 5 days. The field measurement investigates the thermal performance of the single-storey terrace house air well under tropical context. The investigation was measured with U-12 HOBO data logger for temperature and humidity while the air velocity was measured with HD32.3 DeltaOhm measurement logger. Both types of the instrument placed in the air well in a vertical position while another U12 HOBO datalogger placed in a test room with window connected to air well. The outdoor weather data set were measured with HOBO U30. Findings show that the under Malaysia tropical climate, the mean air velocity induced by the air well throughout the measurement days marked as 0.91m/s while during the hottest hour of the measurement days, the air velocity induced in the upper air well could reach 1.09m/s with an outdoor air temperature of 33.6°C and solar radiation of 198 Wh/m². The findings of the study have explained the effectiveness of the air well in providing the thermal performance in the indoor environment and further study on modification of the air well configuration could enhance the airflow and air temperature

Energy and emissions analysis of ice thermal energy storage in the western US

Energy storage systems provide a variety of benefits to the electricity sector, including taking better advantage of renewable electricity when available and smoothing demand by shifting peaks to times when electricity prices and demand are lower. When low electricity demand occurs during the nighttime, additional benefits of overnight energy storage can also occur. Lower nighttime ambient temperatures can lead to efficiency improvements throughout the grid, including power generators, transmission and distribution systems, and chillers. For ice thermal energy storage, these benefits have even been shown to enable net reductions in primary energy consumption compared to normal cooling operations. The energy and emissions impacts of ice storage depend strongly on the diurnal temperature variation and power fleet characteristics in a given region, but previous analysis only examined a single location with relatively high humidity. This study extends the previous analysis to arid regions with high diurnal temperature variation, using Phoenix, AZ and Flagstaff, AZ as case studies, and to a region with high solar penetration, using Los Angeles, CA as a case study. This study demonstrates that the larger temperature variations in Arizona lead to higher round-trip efficiencies, with average primary energy-based effective round-trip efficiencies of 127% in Flagstaff, 107% in Phoenix, and 90% in Los Angeles, with site round-trip efficiencies of 133%, 127%, and 99%, respectively. These high round-trip efficiencies result in net reductions in carbon emissions of 34% in Flagstaff and 24% in Phoenix, but an increase of 53% in Los Angeles due to the absence of solar production overnight. Additionally, the lower icemaking efficiency during warm daytime temperatures makes ice storage poorly-suited for storing excess solar generation compared to other storage approaches. However, the presence of a mature commercial market and the possibility of gaining over 100% effective round-trip efficiency create a strong case for ice thermal energy storage as an energy storage approach for reducing both primary energy consumption and carbon emissions, particularly if fleet efficiencies are higher and carbon intensities are lower overnight.

Quantifying net energy metering subsidies

Net energy metering (NEM) is the policy available in many states that promotes customer-owned rooftop solar power generation by compensating rooftop solar owners for each kWh that it generates at the retail rates. To help understand the magnitude of the residential net metering subsidies, we conducted a study to assess the subsidies for 16 US utilities with varying geographic location, size, rooftop solar penetration, and NEM policy.

Evaluating resource adequacy impacts on energy market prices across wind and solar penetration levels

This study evaluates market impacts of excess capacity across various wind and solar system penetrations. First, different fleet compositions are simulated in a production cost model to yield price outputs. Price statistics reveal a strong relationship between scarcity price events, resource adequacy, and the mean and standard deviation of prices. Next, a method is presented for adjusting resource adequacy to a chosen level, which links a capacity expansion model with a probabilistic resource adequacy model.

Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM

Photovoltaic (PV) solar power generation is always associated with uncertainties due to weather parameters intermittency. This poses difficulties in grid management as solar penetration rate rise continuously. Thus, accurate Photovoltaic (PV) power prediction is required for the successful integration of solar energy into the power grid, and short-term forecasting (minutes-1 day ahead) is significant for real-time power dispatching. Day-ahead power output time-series forecasting methods are proposed in this paper, in which ideal weather type and non-ideal weather types have been separately discussed. For ideal weather conditions, a forecasting method is proposed based on meteorology data of next day for ideal weather condition, using long short term memory (LSTM) networks. For non-ideal weather conditions, time-series relevance and specific non-ideal weather type characteristic are considered in LSTM model by introducing adjacent day time-series and typical weather type information. Specifically, daily total power, which is obtained by discrete grey model (DGM), is regarded as input variables and applied to correct power output time-series prediction. Prediction performance comparison between proposed methods with traditional algorithms reveal that the RMSE accuracy of forecasting methods based on LSTM networks can reach 4.62% for ideal weather condition. For non-ideal weather condition, the dynamic characteristic is effectively described by proposed methods and the proposed methods obtained superior prediction accuracy.

The use of magnetic resonance imaging for the assessment of distal limb wounds in horses: A pilot study

SummaryThe utility of magnetic resonance (MR) imaging in the evaluation of equine solar foot penetrations is well established. The objective of this pilot study was to evaluate the utility of MR imaging in assessment of equine distal limb wounds excluding solar penetrations. Low‐field MR images of 23 horses that had previously sustained distal limb wounds were reviewed in consensus by two ECVDI diplomats. Structures (bone; synovial structure; subcutaneous tissue and skin; and ligament/tendon) were identified as normal or abnormal on MR images, radiographs and ultrasound images and reports. All abnormalities were described. The presence of artefacts and their effect on image interpretation were also noted for each modality. Comparisons were made between imaging modalities, and it was noted if MR imaging influenced case management. Abnormalities of the bone were identified in 26% of horses on MR images and 17% of horses on radiographs; there were no osseous abnormalities identified on radiographs that were not identified on MR images, and additional features and better characterisation of lesions were noted on MR images. Tendon/ligament abnormalities were identified in 57% horses on MR and 47% of horses on ultrasound images. Magnetic susceptibility artefacts compromised MR image interpretation in 17% of horses. MR imaging of equine distal limb wounds allowed identification of both osseous and tendon/ligament abnormalities in more cases than either radiography or ultrasonography, and altered case management in 20/23 horses. Although MR imaging should not replace conventional imaging, this study highlights that MR imaging of equine distal limb wounds can yield information not detected on conventional imaging which may direct treatment and affect prognostication.

Potential lighting and thermal demand reduction in office buildings using blind control considering surrounding buildings

ABSTRACTAutomated blind control has been found to be more effective in controlling solar penetration than manual blind operation. Existing automated blind control methods mainly focus on improving indoor conditions, but considering the outside obstructions may offer a potential method to admit diffused solar radiation, reducing lighting, cooling, and heating demands. Accordingly, in this study, blind control considering surrounding buildings (BCCSB) was applied to determine potential demand reductions considering 20 736 cases of different surrounding building heights, orientations, locations, and climate change projections using EnergyPlus. The results demonstrate that BCCSB can reduce lighting demand by up to 30% when the building obstruction angle is greater 30°; however, this reduction decreased as the angle factor between the fenestration surface and sky decreased due to the accompanying reduction in admitted diffused solar radiation. The reduction in lighting demand also reduced the cooling demand as fewer active lighting fixtures were required. The use of BCCSB did not significantly reduce heating demand. The BCCSB can thus facilitate the reduction of lighting and cooling demand in highly glazed buildings, especially in warm climates. This evaluation of the positive effects of BCCSB can help to develop related design guidelines, standards, and practices for automated blind control strategies.

Coordination of Distributed Energy Storage Under Spatial and Temporal Data Asymmetry

We consider the problem of controlling distributed storage in a network with renewable distributed generation to minimize operational cost while satisfying power quality constraints. We assume control is distributed between a global controller (GC) and local controllers (LCs) located at the nodes with storage units. Each LC has access to its most recent net load data and runs at every time step while the GC has delayed data due to smart meter infrastructure or communication network delay, hence runs less frequently. We describe three control schemes: 1) direct storage controller (DSC) in which the GC computes the storage control signals for an upcoming window and the LCs directly use these signals; 2) net load following controller (NLFC) in which the GC computes a net load profile for each node and each LC tries to track its set profile; and 3) nodal slack controller (NSC) in which the GC computes upper and lower bounds on the net load at each node and the LC optimizes the local control action constrained by these bounds. We use a radial network with real load data to compare the performance of these schemes based on arbitrage profit and maximum solar penetration relative to a perfect foresight controller. We find that NSC and NLFC increase the supported maximum solar penetration to 29% and 40%, respectively, as compared to 10% for DSC. Moreover, NSC is able to capture 90.7% of the available arbitrage profits which is significantly higher than that achievable with NLFC and DSC.

Identification of causes and associated risk factors for lameness in working donkeys in and around Bishoftu

The present study was conducted from November 2014 to April 2015 in and around Bishoftu town to determine causes, associated risk factors and owner's response to causes of lameness for lame donkeys presented to Donkey Health and Welfare Project Clinic. Accordingly, 325 lame donkeys were purposively selected for this study. Questionnaire survey and physical examination were employed as study design. Results of 325 donkey owners interviewed revealed that various causes of lameness. Among these interviewed 222 and 221 respondents has better knowledge about the lameness caused by hyena bites and various wounds respectively, whereas 2, 8 and 41 respondents have information about varies diseases, solar penetration and overloading as a causes of lameness respectively. The current study includes 2 young and 323 adult donkeys. Out of the 325 lame donkeys, 179 were females and the remaining 146 were males. Upon physical examination of all donkeys taken into study, seventeen types of musculoskeletal abnormalities were identified, among these, higher frequency of foreign material in hoof, crackling and hoof overgrowth with 293, 245 and 208 cases respectively. Low frequency of hygroma (3) and bone fracture (8) were also observed. Grade analysis of lame donkeys revealed 3 (0.92%) mild cases, 84 (25.85%) moderate, 234 (72.92%) severe and 1 (0.31%) non-weight bearing cases. Grade three was most frequently observed which appeared in one out of two cases of lameness. The average grade of lameness was 2.72 ± 0.47. There was no statistically significant difference (P>0.05) between the two sexes, body condition scores and age of animals regarding identified abnormalities and grade of lameness. However, the high number of donkeys which had lameness (79.08%) were found with poor body condition scores. Lameness was seen on both front and hind limbs but majority (97.23%) was unilateral type, of which 60% was presented with front limb while 37.5% was with hind limb. Significant difference was observed between front and hind limbs (P<0.05) for occurrence of lameness. Poor husbandry practices and lack of knowledge or information on causes of lameness among owners remain the factor for occurrence and severe grade of lameness.

Performance assessment of a neuro-fuzzy load frequency controller in the presence of system non-linearities and renewable penetration

The existing literature demonstrates the application of neuro-fuzzy based control techniques to load frequency control in interconnected power systems. However, their performance has not been evaluated in the combined presence of renewable resources and system non-linearities. The focus of this paper is to present the design and simulation of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller for a power network possessing non-linearities such as boiler dynamics, generation rate constraint, governor dead-band and time delay. A proportional integral (PI) controller was tuned with the Bode plot approach to obtain the training data set for the proposed controller. In the simulation model, multiple scenarios of wind and solar penetration levels in a two-area system were considered. The dynamic performance of the ANFIS controller was found to be superior when compared to a conventional controller in regards to peak overshoot and settling time.

Factors driving Indian consumer’s purchase intention of roof top solar

PurposeThe solar energy sector has been growing with dramatic reduction in commercial pricing, improved efficiencies and improved deployments/usage conveniences. The purpose of this study is to understand the drivers of the purchase intent for rooftop (RT) solar. This will enable policymakers to improve the penetration of this new and promising green technology.Design/methodology/approachThis research leverages the framework of unified theory of acceptance and use of technology 2 (UTAUT2) to identify and build a quantitative approach to identify factors (and their relative impact on) the purchase intent of a domestic RT solar buyer. The 400 respondents’ field data were collected in Bangalore (a high RT solar penetration region) and Delhi NCR (relatively lower RT solar penetration region).FindingsThe exploratory factor analysis study revealed that the consumers’ purchase intention of RT Solar is shaped by seven main factors, namely, environmental concerns, social beliefs, hedonic motivation, performance expectancy, price value, self-efficacy and effort expectancy, and that these factors explain 79.2 per cent of the field data. This study finds that social beliefs followed by effort expectance concerns are key factors explaining approximately 20 per cent of the purchase intent each, while unit change in price value beliefs explain about 18 per cent of the purchase intent.Practical implicationsSuggested policy measures include building on strengthening emergence of local solar evangelist groups in the communities and easing effort expectance items (e.g. building legal, regulatory frameworks and financial tools for solar penetration models such as renewable energy services companies).Originality/valueThis paper is the first attempt to model the consumer behavior of the Indian RT solar photovoltaic buyer leveraging the UTAUT2.