A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling.

We develop and present a k-nearest neighbor space-time simulator that accounts for the spatiotemporal dependence in high-dimensional hydroclimatic fields (e.g., wind and solar) and can simulate synthetic realizations of arbitrary length. We illustrate how this statistical simulation tool can be used in the context of regional power system planning under a scenario of high reliance on wind and solar generation and when long historical records of wind and solar power generation potential are not available. We show how our simulation model can be used to assess the probability distribution of the severity and duration of energy "droughts" at the network scale that need to be managed by long-duration storage or alternate energy sources. We present this estimation of supply-side shortages for the Texas Interconnection.

The quantity-quality transition in the value of expanding wind and solar power generation.

Wind and solar photovoltaic generators are projected to play important roles in achieving a net-zero-carbon electricity system that meets current and future energy needs. Here, we show potential advantages of long-term site planning of wind and solar power plants in deeply decarbonized electricity systems using a macro-scale energy model. With weak carbon emission constraints and substantial amounts of flexible electricity sources on the grid (e.g., dispatchable power), relatively high value is placed on sites with high capacity factors because the added wind or solar capacity can efficiently substitute for running natural gas power plants. With strict carbon emission constraints, relatively high value is placed on sites with high correlation with residual demand because resource complementarity can efficiently compensate for lower system flexibility. Our results suggest that decisions regarding long-term wind and solar farm siting may benefit from consideration of the spatial and temporal evolution of mismatches in electricity demand and generation capacity.

Geophysical constraints on the reliability of solar and wind power worldwide.

If future net-zero emissions energy systems rely heavily on solar and wind resources, spatial and temporal mismatches between resource availability and electricity demand may challenge system reliability. Using 39 years of hourly reanalysis data (1980-2018), we analyze the ability of solar and wind resources to meet electricity demand in 42 countries, varying the hypothetical scale and mix of renewable generation as well as energy storage capacity. Assuming perfect transmission and annual generation equal to annual demand, but no energy storage, we find the most reliable renewable electricity systems are wind-heavy and satisfy countries' electricity demand in 72-91% of hours (83-94% by adding 12 h of storage). Yet even in systems which meet >90% of demand, hundreds of hours of unmet demand may occur annually. Our analysis helps quantify the power, energy, and utilization rates of additional energy storage, demand management, or curtailment, as well as the benefits of regional aggregation.

Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study.

This paper illustrates the potential for seasonal prediction of wind and solar energy resources through a case study in the Yangtze River estuary. Sea surface temperature and geopotential height-based climate predictors, each with high correlation to ensuing seasonal wind speed and solar radiation at the Baoshan weather observing station, are identified and used to build statistical models to predict seasonal wind speed and solar radiation. Leave-one-out-cross-validation is applied to verify the predictive skill of the best performing candidate model for each season. We find that predictive skill is highest for both wind speed and solar radiation during winter, and lowest during summer. Specifically, we find the most skill when using climate information from the July-September season to predict wind speed or solar radiation during the subsequent November-January season. The ability to predict wind and solar energy availability in the upcoming season can help energy system planners and operators anticipate seasonal surpluses or shortfalls and take precautionary actions.

Developing reliable hourly electricity demand data through screening and imputation.

Electricity usage (demand) data are used by utilities, governments, and academics to model electric grids for a variety of planning (e.g., capacity expansion and system operation) purposes. The U.S. Energy Information Administration collects hourly demand data from all balancing authorities (BAs) in the contiguous United States. As of September 2019, we find 2.2% of the demand data in their database are missing. Additionally, 0.5% of reported quantities are either negative values or are otherwise identified as outliers. With the goal of attaining non-missing, continuous, and physically plausible demand data to facilitate analysis, we developed a screening process to identify anomalous values. We then applied a Multiple Imputation by Chained Equations (MICE) technique to impute replacements for missing and anomalous values. We conduct cross-validation on the MICE technique by marking subsets of plausible data as missing, and using the remaining data to predict this "missing" data. The mean absolute percentage error of imputed values is 3.5% across all BAs. The cleaned data are published and available open access: https://doi.org/10.5281/zenodo.3690240.

Citizen science-based water quality monitoring: Constructing a large database to characterize the impacts of combined sewer overflow in New York City.

To protect recreational water users from waterborne pathogen exposure, it is crucial that waterways are monitored for the presence of harmful bacteria. In NYC, a citizen science campaign is monitoring waterways impacted by inputs of storm water and untreated sewage during periods of rainfall. However, the spatial and temporal scales over which the monitoring program can sample are constrained by cost and time, thus hindering the construction of databases that benefit both scientists and citizens. In this study, we first illustrate the scientific value of a citizen scientist monitoring campaign by using the data collected through the campaign to characterize the seasonal variability of sampled bacterial concentration as well as its response to antecedent rainfall. Second, we examine the efficacy of the HyServe Compact Dry ETC method, a lower cost and time-efficient alternative to the EPA-approved IDEXX Enterolert method for fecal indicator monitoring, through a paired sample comparison of IDEXX and HyServe (total of 424 paired samples). The HyServe and IDEXX methods return the same result for over 80% of the samples with regard to whether a water sample is above or below the EPA's recreational water quality criteria for a single sample of 110 enterococci per 100mL. The HyServe method classified as unsafe 90% of the 119 water samples that were classified as having unsafe enterococci concentrations by the more established IDEXX method. This study seeks to encourage other scientists to engage with citizen scientist communities and to also pursue the development of cost- and time-efficient methodologies to sample environmental variables that are not easily collected or analyzed in an automated manner.

Predictive statistical models linking antecedent meteorological conditions and waterway bacterial contamination in urban waterways.

Although the relationships between meteorological conditions and waterway bacterial contamination are being better understood, statistical models capable of fully leveraging these links have not been developed for highly urbanized settings. We present a hierarchical Bayesian regression model for predicting transient fecal indicator bacteria contamination episodes in urban waterways. Canals, creeks, and rivers of the New York City harbor system are used to examine the model. The model configuration facilitates the hierarchical structure of the underlying system with weekly observations nested within sampling sites, which in turn were nested inside of the harbor network. Models are compared using cross-validation and a variety of Bayesian and classical model fit statistics. The uncertainty of predicted enterococci concentration values is reflected by sampling from the posterior predictive distribution. Issuing predictions with the uncertainty reasonably reflected allows a water manager or a monitoring agency to issue warnings that better reflect the underlying risk of exposure. A model using only antecedent meteorological conditions is shown to correctly classify safe and unsafe levels of enterococci with good accuracy. The hierarchical Bayesian regression approach is most valuable where transient fecal indicator bacteria contamination is problematic and drainage network data are scarce.