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Hodge, Brian S

Publications and source records attributed to Hodge, Brian S.

At least 19 records
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DOE OSTI2020

Grid-Following Inverters and Synchronous Condensers: A Grid-Forming Pair?: Preprint

As power systems across the globe continue to achieve higher instantaneous penetrations of power electronic converterinterfaced renewable sources, the stability of these power systems is challenged because of the removal of synchronous machines, from which stability is traditionally derived. Although technological solutions to these stability challenges are on the horizon, such as the use of grid-forming inverters, they are not yet widely applied to larger power systems, which presents operational challenges for power systems achieving these high instantaneous penetrations today. An interim solution using existing technologies is to pair synchronous condensers with grid-following inverters, which might prolong the stability of an operating power system while synchronous generators are turned off during periods of high renewable energy availability. This work examines the transient stability of such a solution using PSCAD simulations of a two-bus system coupled to a variedlength transmission line, with a synchronous condenser at one bus and a grid-following inverter with grid-support functionality at the other. The system is exposed to load step, balanced fault, and unbalanced fault perturbations. It was found that this simple system returned to a steady state for transmission line lengths up to 125 km after 10% load steps, or a variety of fault types.

14 SOLAR ENERGY↗
DOE OSTI2020

Linear Approximation Line Pack Model for Integrated Electricity and Natural Gas Systems OPF

With the increasing installation of renewable energy electricity generation capacity, the penetration of natural gas fired generation is also substantailly increasing in power systems due to their fast response capability to mitigate system variability and uncertainty. The dependencies between the electricity system and the natural gas system are becoming more important to maintain the reliability of both systems. In integrated electricity and natural gas systems (IEGSs) operations, the nonlinearity of the natural gas system brings challenges to the system operators. In this paper, the IEGSs optimal power flow (OPF) model is proposed that includes the line pack of the gas system via linear approximation. The simulation results demonstrate that the proposed model can obtain optimal solutions efficiently that are comparable in accuracy as the nonlinear line pack model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Compounding climate change impacts during high stress periods for a high wind and solar power system in Texas

Power system planning aims at ensuring that sufficient supply- and demand-side assets exist to meet electricity demand at all times. For a Texas electric power system with high wind and solar penetrations, we quantify how climate change will affect supply and demand during three types of high stress periods for the power grid: high demand hours, high net demand hours, and high system ramp hours. We specifically quantify effects on demand, reductions in available thermal capacity (i.e. thermal deratings), wind and solar generation, and net demand. We estimate each using meteorological variables from five climate change projections (2041-2050) assuming Representative Concentration Pathway 8.5 and from a reference period (1996-2005). All five projections indicate that climate change will increase demand by up to 2 GWh during high demand hours (4% of demand in the reference period) and increase net demand by up to 3 GWh during high net demand periods (6% of net demand in the reference period). All five projections also indicate thermal deratings will increase during high demand and net demand periods by up to 2 GWh and high net demand ramps will increase by up to 2 GW. Overall, our results indicate compounding effects of climate change in Texas will necessitate greater investment in peak and flexible capacity.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Analysis of Hybrid Smart Grid Communication Network Designs for Distributed Energy Resources Coordination

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications. This paper develops a new method to investigate the impact of DER penetrations and applications on hybrid communications network designs. A suite of hybrid communications simulation models are developed using Network Simulator 3 (ns-3). The smart grid communications topologies with different DER penetrations are built on the utility-scale distribution grid. The simulation results show that (i) with increasing photovoltaic penetrations, nine hybrid communications designs achieve the expected latency albeit with higher packet loss rate; and (ii) both BPLC-Ethernet and BPLC-WiMAX designs can accommodate the DER applications with a high data rate up to 100 Mbps.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

OpenSolar: Promoting the Openness and Accessibility of Diverse Public Solar Datasets

Observational solar data is the foundation of data-driven research in solar power grid integration and power system operations. Compared to other fields in data science, the openness and accessibility of solar data is lacking, which prevents solar data science from catching up with the emerging trends of data science (e.g., deep learning). In this paper, OpenSolar, a package with both R and Python versions, is developed to enhance the openness and accessibility of publicly available solar datasets. The OpenSolar package provides access to multiple types of solar data, primarily from four datasets: (1) the National Renewable Energy Laboratory (NREL) Solar Power Data for Integration Studies dataset, (2) the NREL Solar Radiation Research Laboratory dataset, (3) the Sheffield Solar-Microgen database, and (4) the Dataport database. Unlike other open solar datasets that only contain meteorological data, the four datasets in the OpenSolar package also contain behind-the-meter power data, sky images, and solar power data with satisfactory temporal and spatial resolution and coverage. The overview, quality-control methods, and potential usage of the datasets, in conjunction with sample code implementing the OpenSolar functions, are described in this paper. The package is expected to assist in bridging the gaps between the research fields of solar energy, power systems, and data science.

14 SOLAR ENERGY↗
DOE OSTI2018

Simulation-Based Parameter Optimization Framework for Large-Scale Hybrid Smart Grid Communications Systems Design

The design of reliable, dynamic, fault-tolerant hybrid smart grid communication networks is a challenge to achieve for autonomous power grids. Hybrid networks use different communications technologies for different area networks. A simulation-based parameter optimization framework is proposed to tune parameters of hybrid communication technologies to achieve the optimal network performance. It consists of three main components: a parallel executor used to speedup a list of simulations; a sampler running simulations using the parallel executor at each generation; and a hybrid stochastic optimization algorithm for tuning configurable parameters of hybrid designs and applications. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient method to quickly achieve an approximately global optimum solution. Three optimization test functions are employed to train the adjustable parameters of the hybrid algorithm. Results show the proposed parameter optimization framework can help the designer choose the right hybrid architecture with an optimal parameter set for a large-scale broadband PLC-WiMAX hybrid smart grid communication network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Capacity Market Model Considering Flexible Resource Requirements

With increasing installations of renewable generation, such as wind and solar, electric power systems need significant amounts of flexible resources to provide fast responses to mitigate the additional variability and uncertainty in generation. Current market designs have been shown to be insufficient in their mechanisms to compensate flexible resources for their services. Ancillary service markets can cover some of these costs, but they are likely not enough to attract additional flexible resources into the system. Although some independent system operators have started to design markets for flexible resources, their focus is on the short-term operating time horizon. This time horizon has significant real-time price uncertainties for flexible resources and might provide insufficient motivation for them to participate in the market. The capacity market has traditionally been the incentive to invest in new generation resources. Therefore, if a system needs new flexible resources, it is necessary to introduce flexible resource requirements into the capacity market to directly attract more flexible resource investment. This paper proposes a simplified capacity market model including flexible resource requirements. Case studies are examined to validate the proposed method.

17 WIND ENERGY↗
DOE OSTI2018

Unsupervised Clustering-Based Short-Term Solar Forecasting

Solar forecasting accuracy is highly affected by weather conditions, therefore, weather awareness forecasting models are expected to improve the forecasting performance. However, it may not be available or reliable to classify different forecasting tasks by only using predefined meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting method is developed for short-term (1-h-ahead) global horizontal irradiance (GHI) forecasting. This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition is adopted to recognize the category of a certain day using the first four hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer machine learning based multi-model (M3) forecasting framework. The developed UC-M3 method is validated by using 1-year of data with 13 solar features from three information sources. Numerical results show that 1) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; and 2) M3-based models also outperform the single-algorithm machine learning models by approximately 20%.

14 SOLAR ENERGY↗
DOE OSTI2018

Capacity Market Model Considering Flexible Resource Requirements: Preprint

With increasing installations of renewable generation, such as wind and solar, electric power systems need significant amounts of flexible resources to provide fast responses to mitigate the additional variability and uncertainty in generation. Current market designs have been shown to be insufficient in their mechanisms to compensate flexible resources for their services. Ancillary service markets can cover some of these costs, but they are likely not enough to attract additional flexible resources into the system. Although some independent system operators have started to design markets for flexible resources, their focus is on the short-term operating time horizon. This time horizon has significant real-time price uncertainties for flexible resources and might provide insufficient motivation for them to participate in the market. The capacity market has traditionally been the incentive to invest in new generation resources. Therefore, if a system needs new flexible resources, it is necessary to introduce flexible resource requirements into the capacity market to directly attract more flexible resource investment. This paper proposes a simplified capacity market model including flexible resource requirements. Case studies are examined to validate the proposed method.

17 WIND ENERGY↗
DOE OSTI2018

Potential of Wind Power to Provide Flexible Ramping Products and Operating Reserve: Preprint

With increasing penetration levels of variable renewable energy sources, such as wind and solar, electric power systems need flexible resources that can provide fast-response ramping capabilities to counter the variability and uncertainty of renewable generation. Although the California Independent System Operator and Midcontinent System Operator have lauched ramp products, determining how wind power can participate using their flexible ramping models is still largely unclear. This paper proposes a security-constrained unit commitment (SCUC) model considering a flexible ramping constraint representing the system's flexible ramping requirements that account for the net load probabilistic forecast uncertainty. Through this explicit modeling of the uncertainty, the true cost of the flexible ramp service can be determined after solving the SCUC problem. In addition, the potential of wind power to provide operating reserves is analyzed. Numerical case studies demonstrate that with wind power providing both flexible ramping products and operating reserve, the system operating costs and ramp shortages will be reduced significantly.

17 WIND ENERGY↗
DOE OSTI2018

Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization

Probabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the point-forecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model.

17 WIND ENERGY↗
DOE OSTI2018

Analyzing the Impacts of Variable Renewable Resources on California Net-Load Ramp Events: Preprint

This paper characterizes the ramping features of multiple renewable resources, load and net-load in California using 2016 measured data. The goal of this analysis is to understand the interactions between variable renewable resource ramps and net-load ramps, and delineate the positive and negative impacts. The findings indicated that although the frequency and uncertainty in the system level net-load ramp occurrences increased due to variable renewable integration, there was also a decrease in the average ramp up and down magnitudes by about 20% and 30% respectively, along with decreases in their respective standard deviations by 40% and 20%. At the zonal level, the analyses also indicated a 4-5% decrease in the net-load standard deviations due to wind integration alone at various market scheduling periods. The paper finally concludes with future research needs to better integrate variable renewable ramp forecasts into system operations and planning for economic utilization of resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization: Preprint

Probabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the pointforecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model.

17 WIND ENERGY↗