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At least 271 records · Page 15

Performance evaluation of stand alone hybrid PV-wind generator

This paper presents the performance evaluation of standalone hybrid system on Photovoltaic (PV)-Wind generator at Faculty of Electrical Engineering (FKE), UTeM. The hybrid PV-Wind in UTeM system is combining wind turbine system with the solar system and the energy capacity of this hybrid system can generate up to charge the battery and supply the LED street lighting load. The purpose of this project is to evaluate the performance of PV-Wind hybrid generator. Solar radiation meter has been used to measure the solar radiation and anemometer has been used to measure the wind speed. The effectiveness of the PV-Wind system is based on the various data that has been collected and compared between them. The result shows that hybrid system has greater reliability. Based on the solar result, the correlation coefficient shows strong relationship between the two variables of radiation and current. The reading output current followed by fluctuate of solar radiation. However, the correlation coefficient is shows moderate relationship between the two variables of wind speed and voltage. Hence, the wind turbine system in FKE show does not operate consistently to produce energy source for this hybrid system compare to PV system. When the wind system does not fully operate due to inconsistent energy source, the other system which is PV will operate and supply the load for equilibrate the extra load demand.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

On the Path to SunShot - Emerging Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and Transmission System

Increasing the use of grid-flexibility options (improved grid management, demand response, and energy storage) could enable 25% or higher penetration of PV at low costs (see Denholm et al. 2016). Considering the large-scale integration of solar into electric-power systems complicates the calculation of the value of solar. In fact a comprehensive examination reveals that the value of solar technologies—or any other power-system technology or operating strategy—can only be understood in the context of the generation system as a whole. This is well illustrated by analysis of curtailment at high PV penetrations within the bulk power and transmission systems. As the deployment of PV increases, it is possible that during some sunny midday periods due to limited flexibility of conventional generators, system operators would need to reduce (curtail) PV output in order to maintain the crucial balance between electric supply and demand. As a result, PV’s value and cost competitiveness would degrade. For example, for utility-scale PV with a baseline SunShot LCOE of 6¢/kWh, increasing the annual energy demand met by solar energy from 10% to 20% would increase the marginal LCOE of PV from 6¢/kWh to almost 11¢/kWh in a California grid system with limited flexibility. However, this loss of value could be stemmed by increasing system flexibility via enhanced control of variable-generation resources, added energy storage, and the ability to motivate more electricity consumers to shift consumption to lower-demand periods. The combination of these measures would minimize solar curtailment and keep PV cost-competitive at penetrations at least as high as 25%. Efficient deployment of the grid-flexibility options needed to maintain solar’s value will require various innovations, from the development of communication, control, and energy storage technologies to the implementation of new market rules and operating procedures.

14 SOLAR ENERGY↗

Simulating PV System Performance with Component Reliability Distributions

A feature developed by Sandia National Laboratories (SNL) for simulating PV system component reliability has been integrated into the National Renewable Energy Laboratory's System Advisor Model (SAM). The PV Reliability Performance Model (PV-RPM) uses fault/failure and repair distributions to estimate impacts to power, energy production and operational costs. Realization results include the number of failures per component, failure rate, O&M costs incurred based on maintenance strategies, and energy lost based on the failure estimates. As the inputs are inherently uncertain and based on probability distributions, the model realizations include statistical confidence intervals to help bracket the probability that the sample interval contains the true mean value of the variable in question. This paper presents the current validation efforts and provides detail on how this feature is utilized in a PV performance model.

14 SOLAR ENERGY↗

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗

Using Willingness to Pay to Forecast the Adoption of Solar Photovoltaics: A ''Parameterization + Calibration'' Approach

Distributed energy resources, such as rooftop solar photovoltaics (PV), are likely to comprise a substantial fraction of new generation capacity in the United States. However, forecasting technology adoption based on people's willingness to pay (WTP) faces two major challenges: the stated-intention and omitted-variable biases. Previous solar adoption literature has neglected to address these two biases altogether. Here, we adopt a 'parameterization + calibration' approach to address both biases and estimate customers' WTP for PV. After collecting survey data on respondents' WTP for adopting PV, we characterize its empirical cumulative density function using a gamma distribution. We further calibrate the gamma distribution parameters using a national distributed PV adoption simulation model, finding the parameters that produce the best fit between simulated and historic solar adoption. We then show that the calibrated gamma distribution improves the raw WTP data after correcting for the two biases. Finally, we use our optimally-calibrated WTP to forecast market demand for residential PV at the county-level of the United States in 2020. Improving estimates of customer willingness to pay has significant implications for policy directly, e.g. estimating the effect of a proposed policy on technology adoption, and other regulatory processes that use forecasting, e.g. integrated resource planning.

14 SOLAR ENERGY↗

Harmonic Modeling, Data Generation and Analysis of Power Electronics-Interfaced Residential Loads

The share of electronics-based residential load is expected to rise as devices such as variable frequency drives (VFDs), electric vehicle chargers, and inverter-based distributed energy resources (DERs), e.g., photovoltaic (PV) systems become more common. These loads may introduce significant harmonics into power networks that need to be closely studied in order to perform accurate load modeling and forecasting. However, it can be difficult to obtain harmonic-rich voltage and current data - necessary for identifying accurate load models - for residential electrical loads. Recognizing this need, we identify and model a set of electronics-based end-use loads and DERs in an electromagnetic transients program (EMTP) tool for a residence with a single- phase split-phase supply. Further, a procedure is developed to model harmonic interactions between end-use loads connected to the same non-ideal supply voltage in a residential setting. Finally, a harmonic-rich dataset produced via the proposed procedure is utilized to identify frequency coupling matrix (FCM) based load model. Numerical results demonstrate the accuracy of the model, and explore model identifiability with limited data points.

harmonics, power quality, load modeling↗

Grid-Integrated Distributed Solar: Addressing Challenges for Operations and Planning, Greening the Grid

Greening the Grid provides technical assistance to energy system planners, regulators, and grid operators to overcome challenges associated with integrating variable renewable energy into the grid. This document introduces a brief overview of common technical impacts of PV on distribution systems and operations, as well as emerging strategies for successfully addressing some of the priority issues.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Suggested Modifications for Bifacial Capacity Testing: Preprint

Capacity tests such as those described in ASTM 2848 and IEC 61724-2 are widely used during the contracting and acceptance testing of PV systems. With the increasing deployment of bifacial PV modules, there is a need to develop a standardized approach to capacity test these systems. Although variability and bias error was inherently higher for the measured capacity of bifacial systems, it could be reduced to a level consistent with the monofacial reference system by appropriate incorporation of rear irradiance- either measured or modeled. Three field installations provided bifacial system capacity that was measured with a mean bias error and standard deviation within 1% over the 2-10 month observation period. Capacity test accuracy could be improved further by utilizing measured back-of-module temperature and the IEC 61724-2 test method.

14 SOLAR ENERGY↗

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System

Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4% RMS error and resistance growth with 15% RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.

25 ENERGY STORAGE↗

How mesoscopic staircases condense to macroscopic barriers in confined plasma turbulence

This Rapid Communication sets forth the mechanism by which mesoscale staircase structures condense to form macroscopic states of enhanced confinement. Density, vorticity, and turbulent potential enstrophy are the variables for this model. Formation of the staircase structures is due to inhomogeneous mixing of (generalized) potential vorticity (PV). Such mixing results in the local sharpening of density and vorticity gradients. When PV gradients steepen, the density staircase structure develops into a lattice of mesoscale “jumps” and “steps,” which are, respectively, regions of local gradient steepening and flattening. The jumps then merge and migrate in radius, leading to the emergence of a new macroscale profile structure, so indicating that profile self-organization is a global process, which may be described by a local, but nonlinear model. This work predicts and demonstrates how mesoscale condensation of staircases leads to global states of enhanced confinement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Assessment and Control of Spacecraft Charging Risks on the International Space Station

Electrical interactions between the F2 region ionospheric plasma and the 160V photovoltaic (PV) electrical power system on the International Space Station (ISS) can produce floating potentials (FP) on the ISS conducting structure of greater magnitude than are usually observed on spacecraft in low-Earth orbit. Flight through the geomagnetic field also causes magnetic induction charging of ISS conducting structure. Charging processes resulting from interaction of ISS with auroral electrons may also contribute to charging albeit rarely. The magnitude and frequency of occurrence of possibly hazardous charging events depends on the ISS assembly stage (six more 160V PV arrays will be added to ISS), ISS flight configuration, ISS position (latitude and longitude), and the natural variability in the ionospheric flight environment. At present, ISS is equipped with two plasma contactors designed to control ISS FP to within 40 volts of the ambient F2 plasma. The negative-polarity grounding scheme utilized in the ISS 160V power system leads, naturally, to negative values of ISS FP. A negative ISS structural FP leads to application of electrostatic fields across the dielectrics that separate conducting structure from the ambient F2 plasma, thereby enabling dielectric breakdown and arcing. Degradation of some thermal control coatings and noise in electrical systems can result. Continued review and evaluation of the putative charging hazards, as required by the ISS Program Office, revealed that ISS charging could produce a risk of electric shock to the ISS crew during extra vehicular activity. ISS charging risks are being evaluated in ongoing ISS charging measurements and analysis campaigns. The results of ISS charging measurements are combined with a recently developed detailed model of the ISS charging process and an extensive analysis of historical ionospheric variability data, to assess ISS charging risks using Probabilistic Risk Assessment (PRA) methods. The PRA analysis (estimated frequency of occurrence and severity of the charging hazards) are then used to select the hazard control strategy that provides the best overall safety and mission success environment for ISS and the ISS crew. This paper presents: 1) a summary of ISS spacecraft charging analysis, measurements, observations made to date, 2) plans for future ISS spacecraft charging measurement campaigns, and 3) a detailed discussion of the PRA strategy used to assess ISS spacecraft charging risks and select charging hazard control strategies

Koontz, Steve↗

Comprehensive Solutions for Integration of Solar Resources into Grid Operations

The need for proactive closed-loop integration of uncertainty information into system operations and probability-based controls is widely recognized, but rarely implemented in system operations. Proactive integration for this project means that the information concerning expected uncertainty ranges for net load and balancing requirements, including required balancing capacity, ramping and ramp duration characteristics, will be fed back into the generation commitment and dispatch algorithms to modify their performance so that potential shortages of these characteristics can be prevented. This basic, yet important, premise is the motivating factor for this project. The achieved project goal is to demonstrate the benefit of such a system. The project quantifies future uncertainties, predicts additional system balancing needs including the prediction intervals for capacity and ramping requirements of future dispatch intervals, evaluates the impacts of uncertainties on transmission including the risk of overloads and voltage problems, and explores opportunities for intra-hour generation adjustments helping to provide more flexibility for system operators. The resulting benefits culminate in more reliable grid operation in the face of increased system uncertainty and variability caused by solar power. The project identifies that solar power does not require special separate penetration level restrictions or penalization for its intermittency. Ultimately, the collective consideration of all sources of intermittency distributed over a wide area unified with the comprehensive evaluation of various elements of balancing process, i.e. capacity, ramping, and energy requirements, help system operators more robustly and effectively balance generation against load and interchange. This project showed that doing so can facilitate more solar and other renewable resources on the grid without compromising reliability and control performance. Efforts during the project included developing and integrating advanced probabilistic solar forecasts, including distributed PV forecasts, into closed –loop decision making processes. Additionally, new uncertainty quantifications methods and tools for the direct integration of uncertainty and variability information into grid operations at the transmission and distribution levels were developed and tested. During Phase 1, project work focused heavily on the design, development and demonstration of a set of processes and tools that could reliably and efficiently incorporate solar power into California’s grid operations. In Phase 2, connectivity between the ramping analysis tools and market applications software were completed, multiple dispatch scenarios demonstrated a successful reduction of overall uncertainty and an analysis to quantify increases in system operator reliability, and the transmission and distribution system uncertainty prediction tool was introduced to system operation engineers in a live webinar. The project met its goals, the experiments prove the advancements to methods and tools, when working together, are beneficial to not only the California Independent System Operator, but the benefits are transferable to other system operators in the United States.

14 SOLAR ENERGY↗

PV Operations Software Transparency: A PVMAC Industry Snapshot

The rapid growth of photovoltaic (PV) deployment has increased reliance on software platforms for monitoring, workflow automation, diagnostics, and performance analytics. As these tools play a central role in asset management and operations and maintenance (O&M), greater transparency in methodologies, data handling, and validation practices benefits the broader PV ecosystem. To better understand current practices and identify opportunities for improved clarity and interoperability, 24 software providers contributed detailed responses through the PV O&M Analytics Collaborative (PVMAC) initiative, the first structured questionnaire of its kind in the industry, covering onboarding, interoperability, data quality, diagnostics, AI/ML, and other operational categories. These providers represent over 1.1 TW of solar assets under management. The analysis shows broad adoption of digital twins, AI/ML, and API integrations, but also highlights challenges in onboarding processes, inconsistent definitions and methodologies, variability in key performance indicator (KPI) calculations, and limited independent validation. Greater standardization, clearer documentation, and stronger validation frameworks could improve transparency, comparability, and trust across PV operations software platforms.

14 SOLAR ENERGY↗

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗

Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data

A library of load variability classes is created to produce scalable synthetic data sets using historical high-speed raw data. These data are collected from distribution monitoring units connected at the secondary side of a distribution transformer. Because of the irregular patterns and large volume of historical high-speed data sets, the utilization of current load characterization and modeling techniques are challenging. Multi-resolution analysis techniques are applied to extract the necessary components and eliminate the unnecessary components from the historical high-speed raw data to create the library of classes, which are then utilized to create new synthetic load data sets. A validation is performed to ensure that the synthesized data sets contain the same variability characteristics as the training data sets. The synthesized data sets are intended to be utilized in quasi-static time-series studies for distribution system planning studies on a granular scale, such as detailed PV interconnection studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Demonstration of Active Power Controls by Utility-Scale PV Power Plant in an Island Grid: Preprint

The National Renewable Energy Laboratory (NREL), AES, and the Puerto Rico Electric Power Authority conducted a demonstration project on a utility-scale photovoltaic (PV) plant to test the viability of providing important ancillary services from this facility. As solar generation increases globally, there is a need for innovation and increased operational flexibility. A typical PV power plant consists of multiple power electronic inverters and can contribute to grid stability and reliability through sophisticated 'grid-friendly' controls. In this way, it may mitigate the impact of its variability on the grid and contribute to important system requirements more like traditional generators. In 2015, testing was completed on a 20-MW AES plant in Puerto Rico, and a large amount of test data was produced and analyzed that demonstrates the ability of PV power plants to provide various types of new grid-friendly controls. This data showed how active power controls can leverage PV's value from being simply an intermittent energy resource to providing additional ancillary services for an isolated island grid. Specifically, the tests conducted included PV plant participation in automatic generation control, provision of droop response, and fast frequency response.

14 SOLAR ENERGY↗

Assessing the PACE of California residential solar deployment: Impacts of Property Assessed Clean Energy programs on residential solar photovoltaic deployment in California, 2010-2015

A new study by Berkeley Lab found that residential Property Assessed Clean Energy (R-PACE) programs increased deployment of residential solar photovoltaic (PV) systems in California, raising it by about 7-12% in cities that adopt these programs. R-PACE is a financing mechanism that uses a voluntary property tax assessment, paid off over time, to facilitate energy improvements and, in some jurisdictions, water and resilience measures. While previous studies demonstrated that early, regional R-PACE programs increased solar PV deployment, this new analysis is the first to demonstrate these impacts from the large, statewide R-PACE programs dominating the California market today, which use private capital to fund the upfront costs of the improvements. Berkeley Lab estimated the impacts using econometric techniques on two samples: -Large cities only, allowing annual demographic and economic data as control variables -All California cities, without these annual data Analysis of both samples controls for several factors other than R-PACE that would be expected to drive solar PV deployment. We infer that on average, cities with R-PACE programs were associated with greater solar PV deployment in our study period (2010-2015). In the large cities sample, solar PV deployment in jurisdictions with R-PACE programs was higher by 1.1 watts per owner-occupied household per month, or 12%. Across all cities, solar PV deployment in jurisdictions with R-PACE programs was higher by 0.6 watts per owner-occupied household per month, or 7%. The large cities results are statistically significant at conventional levels; the all-cities results are not. The estimates imply that the majority of solar PV deployment financed by R-PACE programs would likely not have occurred in their absence. Results suggest that R-PACE programs have increased PV deployment in California even in relatively recent years, as R-PACE programs have grown in market share and as alternate approaches for financing solar PV have developed. The U.S. Department of Energy’s Building Technologies Office supported this research.

14 SOLAR ENERGY↗

Insights into metastability of photovoltaic materials at the mesoscale through massive I–V analytics

The authors demonstrate the feasibility of quantifying cell-level performance heterogeneity from module-level I–V curves by determining conditions of bypass diode turn-on. Analysis of these curves falls outside of typical diode-based models of photovoltaic (PV) performance. The authors show that this approach can leverage statistical and machine learning techniques for broad application to massive datasets, and combine those insights with simulations and laboratory-based experiments to provide useful information into the metastability of the interfaces of a PV cell. The authors find good agreement between the experimentally determined curves and the simulated curves, which guide the variable selection in the massive dataset collected from sites in Cleveland, OH, USA, the Negev Desert, Israel, Isla Gran Canaria, Spain, and Mount Zugspitze, Germany.

Peshek, Timothy J.↗