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This poster presents an overview of the third edition of the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications.
As the world increasingly seeks low-carbon energy solutions, solar power emerges as the most abundant resource on our planet. However, the challenge of effectively harnessing this energy is crucial in the coming years. Solar energy applications such as photovoltaics, solar heating and cooling, and concentrating solar power use different technologies to capitalize on sunlight. Each system has unique capabilities and requirements, underscoring the need for reliable information about solar resources across diverse installations, from residential rooftops to large-scale power plants. This is especially important for substantial projects, often exceeding $1 billion in construction costs. Before embarking on such ventures, it is imperative to obtain accurate data concerning solar resource quality and reliability at specific sites. Developers require detailed historical information, including seasonal, daily, hourly, and, ideally, subhourly variability to effectively predict a power plant's annual performance. Without these vital data, financial analyses fall short. Moreover, with the growing adoption of distributed photovoltaics, integrating these generation sources becomes critical to maintaining grid reliability and stability. By accurately forecasting generation patterns, utilities and system operators can facilitate greater integration of solar energy, thus ensuring the operational stability of the grid. The complexity and importance of these issues have prompted the foremost experts in the field to collaborate under the auspices of the International Energy Agency's (IEA's) Photovoltaic Power Systems Programme (PVPS) Task 16 to publish this handbook, which summarizes state-of-the-art information about all these topics. The efforts focus on providing reliable data and insights that can help shape our investments in solar energy and drive a sustainable future.
The 2018 Renewable Energy Grid Integration Data Book identifies the status, key trends, challenges, and solutions of renewable energy grid integration in a highly visual format. It provides an overview of selected key grid integration metrics that represent complex interactions among generation characteristics, market rules, and environmental and safety factors, which may vary by geography, season, and time of day; and the data book presents metrics that either indicate how much variable renewable energy (VRE) is currently being integrated onto the grid or measure factors that may increase or decrease the challenges associated with integrating VRE generation onto the grid.
Power systems around the world are undergoing a major transformation because of the increasing shares of renewable energy, growing deployment of energy storage systems, proliferation of distributed energy resources, electrification of other sectors, and so on. In the United States, wind and solar provided almost 10% of electricity in 2019. The U.S. Energy Information Administration, in its 2020 Annual Energy Outlook, forecasted that the share of electricity from renewables will reach 38% by 2050, of which more than 80% will come from wind and solar. Wind and solar, along with battery energy storage systems, interface with the grid using power electronic inverters; hence, they are collectively referred to as inverter-based resources (IBRs). The increasing annual share of electricity from IBRs in a power system means that during more times of the year, the system will operate at a much higher concentration of IBRs. Figure 1 presents the hourly share of wind and solar generation in the Electric Reliability Council of Texas (ERCOT) system in Texas in 2019. While the annual wind share was at 20%, the instantaneous percentage share was much higher. Moments of high shares of IBRs (>50%) will continue to grow as more IBRs will be deployed in a power system.
Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. Furthermore, the design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.
The optimal power flow (OPF) problem for distribution systems can include network details down to the low-voltage (LV) points of interconnection of individual customers. This paper addresses the implementation of voltage magnitude constraints, and sets forth a practicable approach for capturing the effects on voltage from the switching behavior of loads (e.g., heat pumps, air conditioners, water heaters, or pool pumps) and from the variability of renewable generation (e.g., rooftop solar). The proposed method adjusts the OPF voltage constraints based on forecasts of load and generation upper and lower bounds, in conjunction with sensitivity factors derived from the power flow equations. An illustrative OPF formulation is also provided, which incorporates transformer models that include core loss. We demonstrate that accurate modeling of these LV network components is critical to avoid voltage violations at customer points of interconnection. Furthermore, the ideas are validated through numerical case studies on a realistic distribution feeder.
We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = -393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.
Over the last two decades, grid-connected solar photovoltaic systems have increased from a niche market to one of the leading power generation capacity additions annually. In 2018 the total installed photovoltaic electricity generation capacity exceeded 500 GW. Another doubling is forecasted until the early 2020s. Therefore, the further development is coupled with the question at what prices solar photovoltaic electricity can be provided and delivered to the customers. The installation of PV systems for self-consumption is already now an interesting option for many people but in general limited to those who have access to a rooftop they own or can use. A new envelopment to enable residents of multi apartment buildings to commonly use electricity generated by a PV system (collective self-consumption) is a relatively new development and is still facing a lot of administrative and regulatory challenges.
Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K p in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic K p predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead K p prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K p ≥ 5 – ) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.
The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.
To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncertainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.
The split incentive problem is particularly pronounced in rental markets, where landlords prioritize investments that directly increase property value or rental income. Since energy savings from solar photovoltaic (PV) systems primarily benefit tenants, landlords may perceive little return on investment unless mechanisms exist to recapture some of the financial gains. The primary objective of this project is to develop a publicly available, web-based tool to analyze the U.S. Department of Energy’s ResStock database, which models the U.S. residential building stock. The tool allows users to filter buildings by location, type, HVAC system, square footage, and other characteristics, and outputs typical electric load profiles. By leveraging location-specific electric load data, Fram Energy aims to advance business strategies that address the split incentive barrier and promote the adoption of solar PV installations in rental properties. In addition, a machine learning model will be developed to weigh the marginal contribution of building features across the dataset in predicting electricity demand, supporting guided decision making in forecasting electric load profiles. Lastly, based on each building’s location, load profile, and utility’s electricity rate, an optimized solar photovoltaic array and battery energy storage system will be sized to provide energy arbitrage opportunities.
Distributed wind-hybrid microgrids have the potential to provide key resilience and economic benefits to both the customers they serve and the utility grids they are connected to. Such microgrids will likely be a key part of the grid of the future, whether connected to large utility grids or linked together in multi-microgrid systems. Through the hybridization of distributed wind and solar photovoltaics, autonomous device-level and system-level controls, battery energy storage systems with smart inverters, and forecasting, these microgrids could maintain local stability and provide grid services - all with renewable power. In the literature, these elements have been considered individually. However, they have not been combined and demonstrated at a high fidelity, which is essential to prove the concept's operation before moving to hardware-in-the-loop and physical demonstrations. In this work, we develop a high-fidelity MATLAB-Simulink model of a real distributed wind-hybrid microgrid that includes all these elements. We demonstrate the microgrid maintaining stability and production in a variety of islanded, grid-connected, and transition scenarios. This includes riding through faults and grid transitions, handling resource variability, and providing grid services. The results demonstrate, at a high fidelity, how distributed wind-hybrid microgrids can operate in an economic and resilient fashion. Finally, we provide recommendations for future research to move advanced distributed wind-hybrid microgrids toward deployment.
The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of realtime, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m -2 ) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m -2 . For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m -2 for all seven locations. These results, together with the low-latency of the method (1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.
Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.
Probabilistic forecasts provide a distribution of possible outputs and so can capture the uncertainty and variability of Variable Renewable Energy (VRE). However, taking advantage of uncertainty information has practical challenges that make it difficult to integrate probabilistic forecasting into control room decision-making. This paper proposes a novel use-case for probabilistic forecasts by incorporating them into the hour-ahead operations for situational awareness via a risk-averse multi-stage stochastic program. We employ a Markovian representation of the probabilistic forecasts that enables the formulation of the multi-stage problem and avoids a scenario generation phase. We test the model on a realistically sized system to assess risk and showcase the capability of using probabilistic renewable forecast as input to produce probabilistic output forecasts of future system states. The results show that the model can capture time consistency in the reserves and Area Control Error (ACE) forecast. The solution times are adequate for risk profiling in hour-ahead timescales.
Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.
Emerging grid-interactive efficient buildings (GEBs) have great potential to provide much-needed demand flexibility to electric grids while fulfilling their own control targets by co-optimizing smart appliances, solar photovoltaics, electric vehicles, and energy storage at buildings. To enable the optimal operation of GEBs, site-specific weather information—such as temperature, solar irradiance, relative humidity, and wind speed—is crucial; however, this information is generally unavailable or expensive to obtain. This paper develops advanced machine learning methods to provide precise weather forecasts for individual building sites using readily available weather station data. Support vector regression and artificial neural networks have been employed to learn the spatiotemporal correlations between the weather conditions at nearby weather stations and the individual building site. The proposed site-specific weather forecasting methods have been validated using 1-year actual weather measurement data collected in the Denver metro area. Results show that the developed machine-learning-driven methods can accurately forecast the temperature at the target building site 1 hour ahead with mean absolute error less than 0.72°C and a 48% improvement over the persistence method. Site-specific weather forecasts will improve the understanding of the microclimate effect and its impact on building energy consumption. This information will drive efficiency upgrades and adjustments of building control strategies to improve energy savings and increase flexibility in building loads.