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At least 217 records · Page 12

High Energy Yield Distributed Architecture for Large Commercial and Utility-Scale PV Systems

Photovoltaic energy has come a long way from its early days when small cells were fabricated in the labs in the early 50s. While it took a long time for PV modules to mature into what we now see in the market and in rooftops across the world, the power electronics has been lagging behind & was always the weakest point in the PV plant, being responsible for the majority of failures & robbing the PV plants of their full potential by losing precious Watts in the process of converting power from DC to AC. Since the early days of PV power, it has been recognized that the modules need to operate at their maximum power point in order to extract as much power as is available from the PV modules at a given time of day and year. Numerous maximum power point devices were developed to do just this. While effective at extracting as much power as they can, soon after there was a realization that in a PV installation where a central inverter is used, many losses occur, hence energy yield losses. This has led to thinking about alternative architectures where the maximum power point tracking (MPPT) is performed much closer to the modules rather than at a central inverter. Several architectures were proposed ranging from a microinverter, microconverter, string inverter, string optimizer, and to a multi-string inverter or optimizer. While these architectures promise to increase the energy yield by 5% to 25% (depending where MPPT is located), & recovering losses due to shading, soiling, mismatch … there were no real studies and implementations in the field to corroborate these numbers. The program that GE Global Research proposed & received funding for is aimed at a type of distributed architectures, namely the dc architectures where only the MPPT function is distributed across the PV plant. Further, the project is concerned with choosing the right architecture for large scale commercial & utility scale plants but not residential installations. Findings from Task 1- Define Requirements & Develop Architecture- showed that for large scale commercial & utility PV plants, locating the MPPT at the string level with a string size of >3kW is the most cost effective solution & that the increase in energy yield of 6% to 8% is more than enough to make up for the increased cost due to additional power electronics. Our study also shows that in addition to an increase in energy yield, having a dc/dc converter at the string level brings additional benefits such as increased array visibility, allowing for increased monitoring and diagnostics; high and constant dc bus voltage which allows for reducing losses further and optimizing the inverter; and ability to detect ground and arc faults. Task 2 focused on designing a suitable dc/dc converter that meets the requirements defined in Task 1: Efficiency, Cost, & Reliability. For this task, we surveyed and simulated all candidate topologies known by the practitioners in the field. We also generated a novel topology, simulated it, & compared it to existing topologies. Our novel topology being a partial power processing converter came out on top due to its simplicity (fewer parts), it high efficiency, its reliability, and its lower cost. In the remaining tasks of the project, we built the hardware, tested it first in the lab, then in the field and once we worked out all the bugs and issues and were confident our system was robust, we installed it on the rooftop and started collecting the data by mid-Q3 2012. We shared our work with the scientific community in the form of conference & Journal papers, hence contributing to the general understanding of this technology area. The methods, techniques, and hardware developed under this project have all been tested in the field over long periods of time, and under many conditions and have been shown to be robust and reliable. The converters built and developed under this program have been in operation in the field since mid-June 2012, and no hardware failures have been recorded. While some software issues have been seen, none have been showstoppers. The cost analysis of the hardware showed that it meets the cost target set in the requirement document developed in the trade-off study. Improving the performance of solar installations for commercial and utility scale plants contributes to reducing their cost of ownership, hence spreading them to a larger section of the society and making them affordable. Our project contributed to the understanding of the distributed architectures, showed that PV plants’ cost of electricity can be reduced through additional power electronics which allows for harvesting more energy from the plant. When the project started in 2009, the cost of PV energy was still high, hence there was a real need to increase the energy yield, hence reduce the levelized cost of electricity (LCOE). While costs have come down significantly, the need for increasing the energy yield is still relevant, hence the project results, hardware, and techniques developed are still pertinent today. By taking this project from concept to rooftop demonstration, we showed that the technology is mature enough, robust enough, and has the potential to be used by many more customers, hence benefiting the public in general through the spread of PV energy, which ultimately contributes to reducing our dependency on fossil fuel, improving the environment, and creating more jobs.

14 SOLAR ENERGY↗

Will Solar Panels Help When the Power Goes Out? Planning for PV Resilience

PV systems, like all energy systems, must chart a difficult course in order to improve resilience. They need to simultaneously withstand extreme conditions that cause grid power disruptions, be configured in a way that allows electricity to be delivered to meet the PV user's needs, and overcome disruptions arising from solar resource intermittency. Certain considerations reveal themselves as key to a PV system's resilience benefits. Choice of hardware is critical, as is site selection for the PV array. Voluminous evidence in a hurricane context demonstrates that durable hardware, properly maintained and sited, leads to PV systems surviving extreme weather with their functionality intact. Structurally weak or poorly maintained PV equipment, on the other hand, has demonstrated failures during extreme weather events. PV system stakeholders should ensure that best practices are followed for hardware procurement and siting. It is also important to understand that PV systems are not one-size-fits-all. While incorporating islanding capabilities and energy storage into the system's electrical topology will typically enhance resilience, the user's needs will determine the optimally resilient conformation of any given system. These factors - appropriate hardware, siting, maintenance, and system design - are the primary drivers of PV resilience. PV systems possess high potential for resilient energy generation due to their distributed, abundant, and free fuel supply, but the technology itself does not inherently lead to resilient outcomes. Realizing PV's potential for resilience requires consideration of these determinative factors.

14 SOLAR ENERGY↗

Regional Real-Time PV Spinning Reserve Estimator

Curtailed photovoltaic (PV) generation is a zero-marginal-cost spinning reserve that can be used for a number of active power control services. Unlike traditional spinning reserve providers, however, i.e., fossil-fueled generators, which have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate knowledge of the PV PHL is essential. It ensures that enough headroom is reserved by the PV plants to deliver the award services in real time and informs feasible dispatch decisions made by the market operator. To tackle this challenge, a novel reference-control grouping-based PV plant reserve estimation method has been proposed by the National Renewable Energy Laboratory under past projects funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The estimation method separates inverters within a plant into two groups: a control group and a reference group. While the reference group is reserved to operate at its PHL, the control group can be curtailed to provide the grid services. Real-time outputs from the reference inverters are used to estimate the PHL for the whole plant based on the ratio between capacities of the reference group and of the plant. This work further enhances the methodology by (1) improving the model accuracy through machine learning; (2) automating the reference inverter selection through correlation analysis; (3) considering estimation look-ahead windows; and (4) applying to regional spinning reserve estimation. Significant performance improvement has been observed based on real-world data collected by CAISO, Southern Company, and Terabase Energy. Compared with the original scaling method, the newly proposed machine learning-based approach reduces the estimation errors by 30% and 13% at the plant level and region level, respectively. Results obtained from this project are intended to be used by grid operators, market operators, balancing authorities, and PV plant owners and operators to facilitate PV participation in ancillary service markets. Regulators, policymakers, and system planners can also consider the results of this work in their decision-making processes. In addition to the performance improvement on the existing reference-control based grouping method, we also investigated how the variability of PV generation from a single PV inverter can be used to represent the variability of PV generation at the plant level.

24 POWER TRANSMISSION AND DISTRIBUTION↗

On the relationship factor between the PV module temperature and the solar radiation on it for various BIPV configurations

Temperatures of c-Si, pc-Si and a-Si PV modules making part of a roof in a building or hanging outside windows with various inclinations were measured with respect to the Intensity of the solar radiation on them under various environmental conditions. A relationship coefficient f was provided whose values are compared to those from a PV array operating in a free standing mode on a terrace. A theoretical model to predict f was elaborated. According to the analysis, the coefficient f takes higher values for PV modules embedded on a roof compared to the free standing PV array. The wind effect is much stronger for the free standing PV than for any BIPV configuration, either the PV is part of the roof, or placed upon the roof, or is placed outside a window like a shadow hanger. The f coefficient depends on various parameters such as angle of inclination, wind speed and direction, as well as solar radiation. For very low wind speeds the effect of the angle of inclination, β, of the PV module with respect to the horizontal on PV temperature is clear. As the wind speed increases, the heat transfer from the PV module shifts from natural flow to forced flow and this effect vanishes. The coefficient f values range from almost 0.01 m{sup 2°}C/W for free standing PV arrays at strong wind speeds, v{sub W}>7m/s, up to around 0.05 m{sup 2°}C/W for the case of flexible PV modules which make part of the roof in a BIPV system.

30 DIRECT ENERGY CONVERSION↗

Exploring California PV Home Premiums

Although photovoltaic (PV) penetration in the United States is increasing rapidly, properly valuing homes with PV systems remains a barrier to PV deployment. Previous studies show that PV homes command sales price premiums. Still, some appraisers and other home valuers assign no value to a home’s PV system, and those who do often cannot find comparable home sales to help determine the PV premium. This has spurred the development of alternative methods of valuing PV homes, including the use of an income approach (based on the present value of PV energy produced over its useful lifetime) and the replacement cost approach (based on the present installed cost equivalent of the PV system). However, those approaches have just begun to have been validated against actual market premiums. Moreover, the drivers underlying PV home premiums are not well understood, which may deter some appraisers from assigning value to PV systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Integrating Solar PV in Utility System Operations

This study develops a systematic framework for estimating the increase in operating costs due to uncertainty and variability in renewable resources, uses the framework to quantify the integration costs associated with sub-hourly solar power variability and uncertainty, and shows how changes in system operations may affect these costs. Toward this end, we present a statistical method for estimating the required balancing reserves to maintain system reliability along with a model for commitment and dispatch of the portfolio of thermal and renewable resources at different stages of system operations. We estimate the costs of sub-hourly solar variability, short-term forecast errors, and day-ahead (DA) forecast errors as the difference in production costs between a case with “realistic” PV (i.e., subhourly solar variability and uncertainty are fully included in the modeling) and a case with “well behaved” PV (i.e., PV is assumed to have no sub-hourly variability and can be perfectly forecasted). In addition, we highlight current practices that allow utilities to compensate for the issues encountered at the sub-hourly time frame with increased levels of PV penetration. In this analysis we use the analytical framework to simulate utility operations with increasing deployment of PV in a case study of Arizona Public Service Company (APS), a utility in the southwestern United States. In our analysis, we focus on three processes that are important in understanding the management of PV variability and uncertainty in power system operations. First, we represent the decisions made the day before the operating day through a DA commitment model that relies on imperfect DA forecasts of load and wind as well as PV generation. Second, we represent the decisions made by schedulers in the operating day through hour-ahead (HA) scheduling. Peaking units can be committed or decommitted in the HA schedules and online units can be redispatched using forecasts that are improved relative to DA forecasts, but still imperfect. Finally, we represent decisions within the operating hour by schedulers and transmission system operators as real-time (RT) balancing. We simulate the DA and HA scheduling processes with a detailed unit-commitment (UC) and economic dispatch (ED) optimization model. This model creates a least-cost dispatch and commitment plan for the conventional generating units using forecasts and reserve requirements as inputs. We consider only the generation units and load of the utility in this analysis; we do not consider opportunities to trade power with neighboring utilities. We also do not consider provision of reserves from renewables or from demand-side options. We estimate dynamic reserve requirements in order to meet reliability requirements in the RT operations, considering the uncertainty and variability in load, solar PV, and wind resources. Balancing reserve requirements are based on the 2.5th and 97.5th percentile of 1-min deviations from the HA schedule in a previous year. We then simulate RT deployment of balancing reserves using a separate minute-by-minute simulation of deviations from the HA schedules in the operating year. In the simulations we assume that balancing reserves can be fully deployed in 10 min. The minute-by-minute deviations account for HA forecasting errors and the actual variability of the load, wind, and solar generation. Using these minute-by-minute deviations and deployment of balancing reserves, we evaluate the impact of PV on system reliability through the calculation of the standard reliability metric called Control Performance Standard 2 (CPS2). Broadly speaking, the CPS2 score measures the percentage of 10-min periods in which a balancing area is able to balance supply and demand within a specific threshold. Compliance with the North American Electric Reliability Corporation (NERC) reliability standards requires that the CPS2 score must exceed 90% (i.e., the balancing area must maintain adequate balance for 90% of the 10-min periods). The combination of representing DA forecast errors in the DA commitments, using 1-min PV data to simulate RT balancing, and estimates of reliability performance through the CPS2 metric, all factors that are important to operating systems with increasing amounts of PV, makes this study unique in its scope.

14 SOLAR ENERGY↗

Net Metering and Market Feedback Loops: Exploring the Impact of Retail Rate Design on Distributed PV Deployment

The substantial increase in deployment of customer-sited solar photovoltaics (PV) in the United States has been driven by a combination of steeply declining costs, financing innovations, and supportive policies. Among those supportive policies is net metering, which in most states effectively allows customers to receive compensation for distributed PV generation at the full retail electricity price. The current design of retail electricity rates and the presence of net metering have elicited concerns that the possible under-recovery of fixed utility costs from PV system owners may lead to a feedback loop of increasing retail prices that accelerate PV adoption and further rate increases. However, a separate and opposing feedback loop could offset this effect: increased PV deployment may lead to a shift in the timing of peak-period electricity prices that could reduce the bill savings received under net metering where time-varying retail electricity rates are used, thereby dampening further PV adoption. In this paper, we examine the impacts of these two competing feedback dynamics on U.S. distributed PV deployment through 2050 for both residential and commercial customers, across states. Our results indicate that, at the aggregate national level, the two feedback effects nearly offset one another and therefore produce a modest net effect, although their magnitude and direction vary by customer segment and by state. We also model aggregate PV deployment trends under various rate designs and net-metering rules, accounting for feedback dynamics. Our results demonstrate that future adoption of distributed PV is highly sensitive to retail rate structures. Whereas flat, time-invariant rates with net metering lead to higher aggregate national deployment levels than the current mix of rate structures (+5% in 2050), rate structures with higher monthly fixed customer charges or PV compensation at levels lower than the full retail rate can dramatically erode aggregate customer adoption of PV (from -14% to -61%, depending on the design). Moving towards time-varying rates, on the other hand, may accelerate near- and medium-term deployment (through 2030), but is found to slow adoption in the longer term (-22% in 2050).

14 SOLAR ENERGY↗

Supported PV module assembly

A supported PV assembly may include a PV module comprising a PV panel and PV module supports including module supports having a support surface supporting the module, a module registration member engaging the PV module to properly position the PV module on the module support, and a mounting element. In some embodiments the PV module registration members engage only the external surfaces of the PV modules at the corners. In some embodiments the assembly includes a wind deflector with ballast secured to a least one of the PV module supports and the wind deflector. An array of the assemblies can be secured to one another at their corners to prevent horizontal separation of the adjacent corners while permitting the PV modules to flex relative to one another so to permit the array of PV modules to follow a contour of the support surface.

42 ENGINEERING↗

ENVI-PV: An Interactive Web Client for Multi-Criteria Life Cycle Assessment of Photovoltaic Systems Worldwide

Solar photovoltaics (PV) is the second largest source of new capacity among renewable energies. The worldwide capacity encompassed 135 GW in 2013 and is estimated to increase to 1721 GW in 2030 and 4674 GW in 2050, according to a prospective high-renewable scenario. To achieve this production level while minimizing environmental impacts, decision makers must have access to environmental performance data that reflect their high spatial variability accurately. We propose ENVI-PV (http://viewer.webservice-energy.org/project_iea), a new interactive tool that provides maps and screening level data, based on weighted average supply chains, for the environmental performance of common PV technologies. Environmental impacts of PV systems are evaluated according to a life cycle assessment approach. ENVI-PV was developed using a state-of-the-art interoperable and open standard Web Service framework from the Open Geospatial Consortium (OGC). It combines the latest life cycle inventories, published in 2015 by the International Energy Agency (IEA) under the Photovoltaic Power Systems Program (PVPS) Task 12, and some inventories previously published from Ecoinvent v2.2 database with solar irradiation estimates computed from the worldwide NASA SSE database. ENVI-PV is the first tool to propose a worldwide coverage of environmental performance of PV systems using a multi-criteria assessment. The user can compare the PV environmental performance to the environmental footprint of country electricity mixes. ENVI-PV is designed as an environmental interactive tool to generate PV technological options and evaluate their performance in different spatial and techno-economic contexts. Its potential applications are illustrated in this paper with several examples.

14 SOLAR ENERGY↗

Reaching new solar heights: integrating high penetrations of PV into the power system

Installed capacity of solar photovoltaics (PV) continues to grow rapidly in the US. High penetrations and can create operational and reliability challenges. PV integration shares some attributes and mitigation strategies with wind, and differs in some key ways that will be discussed. This study investigates PV integration challenges and examines mitigation issues for utilities planning for high penetrations of PV, with a focus on the Western Interconnection of the US. System balancing becomes more difficult due to a duck‐shaped net load curve. Flexibility in the remaining generators, load, and the PV itself, is critical in balancing the system. Reliability impacts from PV have been studied less and addressed less than system balancing issues, but become more important at high penetrations. Understanding essential reliability services from PV, weak grid issues with PV, and distributed PV impacts on bulk power system reliability is critical for planning of high penetrations of PV.

Lew, Debra↗

A Non-Modeling Exploration of Residential Solar Photovoltaic (PV) Adoption and Non-Adoption

Although U.S. deployment of residential rooftop solar photovoltaic (PV) systems has accelerated in recent years, PV is still installed on less than 1 percent of single-family homes. Most research on household PV adoption focuses on scaling initial markets and modeling predicted growth rather than considering more broadly why adoption occurs. Among the studies that have investigated the characteristics of PV adoption, most collected data from adopters, sometimes with additional non-adopter data, and rarely from people who considered but did not adopt PV. Yet the vast majority of Americans are non-adopters, and they are a diverse group - understanding their ways of evaluating PV adoption is important. Similarly, PV is a unique consumer product, which makes it difficult to apply findings from studies of other technologies to PV. In addition, little research addresses the experience of households after they install PV. This report helps fill some of these gaps in the existing literature. The results inform a more detailed understanding of residential PV adoption, while helping ensure that adoption is sufficiently beneficial to adopters and even non-adopters.

14 SOLAR ENERGY↗

Solar Economies of Scope through the Intersection of Four Industries: PV Installation, Electrical, Construction, and Roofing

The U.S. solar photovoltaic (PV) installation industry largely grew from pre-existing industries that provide related services such as electrical contracting, construction, and roofing. In this report, we use a new data set based on data from the California Contractors State License Board (CSLB) to explore the ties between PV installation and these related-service industries. Based on a study period of 2010 to 2016, the CSLB data show that most PV installers hold some license other than the license designed specifically for PV installation, primarily electrical and construction licenses. Indeed, only about 9% of PV installers hold only the solar contractor license. The fact that most PV installers hold electrician and construction licenses illustrates the important ties between PV installation and these industries. The CSLB data, combined with residential PV price data, also show that installers with multiple non-solar licenses are associated with lower-priced systems. These findings suggest that installers with multiple skillsets may be able to reduce PV installation costs. Further, the data show that PV systems are lower-priced when installed during new construction, especially when new-construction systems are installed by licensed roofers.

14 SOLAR ENERGY↗

Agent-Based Coordination Scheme for PV Integration (ABC4PV)

Renewables and especially photovoltaics (PV) have benefitted significantly from a host of incentives and policies targeted toward enhanced integration and adoption of specific energy technologies. However, with the push to move forward into a subsidy-free market framework, behind-the-meter residential PV applications have generally struggled to retain their value (unlike utility scale and commercial projects) [1]. This project focused on developing control-theoretic solutions aimed at improving the integration and interaction of behind-the-meter residential PV with other distribution system assets (controllable and non-controllable) to enhance the integrated value of residential PV. To this end, a suite of decentralized control methodologies have been developed to enable effective coordination and control of behind-the-meter residential load customers’ PV, battery storage systems (BSS), controllable loads and other similar assets within a distribution feeder. This interaction aims at procuring energy savings and, thus, energy bill savings. The main source of savings is drawn from reducing the effect of demand charge pricing and is realized at the feeder level, assuming community level interaction and management among the aforementioned assets. Optimal control of the assets is implemented with a distributed optimization methodology, leveraging consensus-based algorithms. The results gathered from the optimal control simulations demonstrates that the savings can be duly achieved and the algorithm decision times (to dynamically control asset set points, for example) are fast. As for the overall efficiency of PV+BSS systems, to procure energy savings from curtailment of the demand charge pricing effects, the optimal control is set up so as to minimize the variance of the load for all customers, throughout a feeder and throughout time in a rolling horizon scheduling with model predictive control. The control takes into account inter-temporal electrochemical storage (battery) degradation costs: specifically, we have developed a long-term lifetime model for the BSS that weighs in the effect of the degradation factor in the dispatch formulations, thus, a considerable operating cost that affects energy decision making. The levelized cost of energy (LCOE – redefined for the purpose of quantifying asset integration effectiveness through the customers’ energy cost) is shown to be below the threshold set for the combined PV+BSS topology of $ 0.14/kWh for multiple cases of PV penetration all the way up to 50%, provided that a policy of shared ownership of and savings is in place. Further, the LCOE calculated for the case before the deployment PV+BSS systems is also achievable, i.e. the deployment of PV+BSS, if planned and scheduled optimally. will have no effect on customers’ energy costs. From the control methodology viewpoint, the developed consensus-based algorithms are shown to converge for a wide range of problem cases (spanning normal operating scenarios and contingencies), guaranteeing dispatch solutions under forecasting errors, communication break-downs and cyber-security attacks. The proposed control solutions are scalable and real-time implementable, with dispatch computations and device set-point updates converging in less than 2s in most practical instances of the above events.

14 SOLAR ENERGY↗

PV Performance Modeling and Stakeholder Engagement (Final Technical Report)

This core capability project’s objective is to increase the value of photovoltaic (PV) performance models by improving their functionality, demonstrating, and quantifying their validity, and offering a wide range of stakeholder engagement opportunities. In FY22-24, we developed new and improved modeling algorithms and functions to represent PV performance more accurately in a variety of environments and conditions. The “Model parameter toolkit” was developed and includes functions to translate between different module temperature models, incidence angle modifier models, and single-diode models. A new modeling capability named “PV Atlas” was also developed leveraging Sandia’s High Performance Computing resources. This capability allows us to investigate several questions and provide climate-specific best practices and geographic data files; all these are hosted on an interactive website on Sandia’s GitHub and can be used for training, system optimization, or to provide best practices for uncertainty reduction. For model validation, we published high-quality PV performance, and weather data; these data are well documented, filtered, and processed for quality and include examples on how to run PV simulations. We also developed well documented, standardized methods for validating PV models and ran independent model validation and 2 blind modeling intercomparisons engaging with 49 organizations from 17 countries. We co-led and contributed to a growing, well documented and maintained suite of open-source functions for PV modeling (i.e., the pvlib-python) and we outreached to the PV modeling stakeholders via the PVPMC workshops and web resources. In addition, this project supported US representation and leadership for the International Energy Agency (IEA) PVPS Task 13; specifically, members of our team led and supported 3 subtasks on: 1) Best practices for the optimization of bifacial photovoltaic tracking, 2) Extreme weather events and their multiple impact on PV power plants: Risks, failure mechanisms and mitigation strategies, and 3) Best practice guidelines for the use of economic and technical Key Performance Indicators (KPIs). This project resulted in the publications of 14 peer reviewed journal papers, 37 conference presentations, 6 SAND reports, 5 public datasets and 6 new webpages on the PVPMC website. It supported the release of 13 pvlib-python versions where 28 enhancements were from this PV Performance Modeling project. We co-organized 5 PVPMC workshops in FY22-24 with the participation of 214 unique institutions and around 700 participants. The PVPMC website was redesigned, and its reliability was improved; it receives over 50,000 visitors/year from 202 unique countries.

14 SOLAR ENERGY↗

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

14 SOLAR ENERGY↗

PV module mounting method and mounting assembly

A method for mounting PV modules to a deck includes selecting PV module layout pattern so that adjacent PV module edges are spaced apart. PV mounting and support assemblies are secured to the deck according to the layout pattern using fasteners extending into the deck. The PV modules are placed on the PV mounting and support assemblies. Retaining elements are located over and secured against the upper peripheral edge surfaces of the PV modules so to secure them to the deck with the peripheral edges of the PV modules spaced apart from the deck. In some examples a PV module mounting assembly, for use on a shingled deck, comprises flashing, a base mountable on the flashing, a deck-penetrating fastener engageable with the base and securable to the deck so to secure the flashing and the base to the shingled deck, and PV module mounting hardware securable to the base.

42 ENGINEERING↗

Net Load Forecasting With Disaggregated Behind-the-Meter PV Generation

As worldwide use of residential photovoltaic (PV) systems grows, system operators and utilities will need to transition from forecasting pure demand to forecasting net load with behind-the-meter (BTM) PV generation. However, PV generation can be difficult to predict and the measurements of PV generation from BTM residential systems are often invisible behind a measurement of the net load, making net load forecasting challenging. This paper proposes a novel two-stage framework for net load forecasting in areas with limited observability and high BTM PV generation. First, the profiles of observable customers are used to disaggregate the net load measurements into the pure load and PV generation. Then, separate models are used to forecast the PV generation and pure load individually, and the results are combined for a net load forecast. Further, this paper also proposes a compensator for correcting the error of the net load forecast, using historical forecast errors of the PV generation, pure load, and net load. The proposed framework is tested through two case studies for areas with high BTM PV penetration and less than 10% observable customers. The two-stage forecasting model is compared to two benchmark methods - a time series forecasting model, and a model that forecasts the net load directly using historical net load measurements. Results show that the proposed disaggregation-forecasting framework reduces the error of the net load forecast compared to both benchmark models. In addition, when the net load forecast error is periodic, the compensator can correct the error to improve the forecast accuracy.

14 SOLAR ENERGY↗

New York Solar Smart DG Hub-Resilient Solar Project: Economic and Resiliency Impact of PV and Storage on New York Critical Infrastructure

Resilient PV, which is solar paired with storage ('solar-plus-storage'), provides value both during normal grid operation and power outages as opposed to traditional solar PV, which functions only when the electric grid is operating. During normal grid operations, resilient PV systems help host sites generate revenue and/or reduce electricity bill charges. During grid outages, resilient PV provides critical emergency power that can help people in need and ease demand on emergency fuel supplies. The combination of grid interruptions during recent storms, the proliferation of solar PV, and the growing deployment of battery storage technologies has generated significant interest in using these assets for both economic and resiliency benefits. This report analyzes the technical and economic viability for resilient PV on three critical infrastructure sites in New York City (NYC): a school that is part of a coastal storm shelter system, a fire station, and a NYCHA senior center that serves as a cooling center during heat emergencies. This analysis differs from previous solar-plus-storage studies by placing a monetary value on resiliency and thus, in essence, modeling a new revenue stream for the avoided cost of a power outage. Analysis results show that resilient PV is economically viable for NYC's critical infrastructure and that it may be similarly beneficial to other commercial buildings across the city. This report will help city building owners, managers, and policymakers better understand the economic and resiliency benefits of resilient PV. As NYC fortifies its building stock against future storms of increasing severity, resilient PV can play an important role in disaster response and recovery while also supporting city greenhouse gas emission reduction targets and relieving stress to the electric grid from growing power demands.

14 SOLAR ENERGY↗