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DOE OSTI2016

Revenue Sufficiency and Reliability in a Zero Marginal Cost Future: Preprint

Features of existing wholesale electricity markets, such as administrative pricing rules and policy-based reliability standards, can distort market incentives from allowing generators sufficient opportunities to recover both fixed and variable costs. Moreover, these challenges can be amplified by other factors, including (1) inelastic demand resulting from a lack of price signal clarity, (2) low- or near-zero marginal cost generation, particularly arising from low natural gas fuel prices and variable generation (VG), such as wind and solar, and (3) the variability and uncertainty of this VG. As power systems begin to incorporate higher shares of VG, many questions arise about the suitability of the existing marginal-cost-based price formation, primarily within an energy-only market structure, to ensure the economic viability of resources that might be needed to provide system reliability. This article discusses these questions and provides a summary of completed and ongoing modelling-based work at the National Renewable Energy Laboratory to better understand the impacts of evolving power systems on reliability and revenue sufficiency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Revenue Sufficiency and Reliability in a Zero Marginal Cost Future

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗

DOE OSTI2017

8760-Based Method for Representing Variable Generation Capacity Value in Capacity Expansion Models

Capacity expansion models (CEMs) are widely used to evaluate the least-cost portfolio of electricity generators, transmission, and storage needed to reliably serve load over many years or decades. CEMs can be computationally complex and are often forced to estimate key parameters using simplified methods to achieve acceptable solve times or for other reasons. In this paper, we discuss one of these parameters -- capacity value (CV). We first provide a high-level motivation for and overview of CV. We next describe existing modeling simplifications and an alternate approach for estimating CV that utilizes hourly '8760' data of load and VG resources. We then apply this 8760 method to an established CEM, the National Renewable Energy Laboratory's (NREL's) Regional Energy Deployment System (ReEDS) model (Eurek et al. 2016). While this alternative approach for CV is not itself novel, it contributes to the broader CEM community by (1) demonstrating how a simplified 8760 hourly method, which can be easily implemented in other power sector models when data is available, more accurately captures CV trends than a statistical method within the ReEDS CEM, and (2) providing a flexible modeling framework from which other 8760-based system elements (e.g., demand response, storage, and transmission) can be added to further capture important dynamic interactions, such as curtailment.

14 SOLAR ENERGY↗

DOE OSTI2021

Stochastic Continuous-time Flexibility Scheduling and Pricing in Wholesale Electricity Markets

Large-scale integration of intermittent renewable energy sources (RES) is calling for additional flexibility resources as well as more advanced modeling and optimization techniques to account for the increasing uncertainty and variability in power systems operation. As the RES integration gains momentum, the magnitude and frequency of their variations increase, which may trigger ramping scarcity events in real-time power systems operation. This necessitates revisiting the present definition of power systems flexibility and reserve services to reflect their robustness and adequacy towards sub-interval variations of the load and RES, as well as adjusting the operation models to accommodate the new reserve services. This project took a fundamental approach and aimed at developing continuous-time scheduling and pricing model that accurately models the continuous-time variations of load and RES and efficiently deploys the ramping capability of flexible resources to compensate the sources of variability and uncertainty in the market. In this regard, this project pursued the following goals: Developing stochastic multi-fidelity continuous-time optimization models for scheduling of energy storage (ES) systems and flexible loads in wholesale energy markets; Developing the theory and practices of continuous-time locational marginal pricing for valuating energy storage systems and flexible loads in wholesale energy markets; Developing function space solution approach to convert the proposed stochastic multi-fidelity continuous-time optimization models into tractable mixed-integer linear optimization models; and Defining flexibility reserve as a new type of reserve in markets that would enable ultimate participation of energy storage devices in provision of services to compensate the variability and uncertainty of RES in electricity markets. This project successfully completed all five major tasks defined in the SOPO, and produced 8 high-impact journal papers, 6 conference papers, 3 published U.S. patents, and one web-based software for continuous-time operation optimization of power systems. The application of the proposed flexibility reserve and the stochastic multi-fidelity continuous-time operation scheduling models would modify the forward commitment and schedule of generating units, ES devices and flexible loads, and would line up the resources in such a way that the composition of available resources is better prepared to respond to the sub-hourly variations of the load and renewable resources in real-time operation. Therefore, this project paves the way to sustainable, reliable, and economic integration of renewable energy resources in power system, supporting the progress towards reaching the national targets on energy independence. Even if the proposed models offers a radically different point of view as compared to existing models, it does not alter fundamentally the architecture of power systems operations, nor the complexity of the scheduling problem, so the integration of this project in power systems is extremely practical.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Why Do Electricity Policy and Competitive Markets Fail to Use Advanced PV Systems to Improve Distribution Power Quality?

The increasing pressure for network operators to meet distribution network power quality standards with increasing peak loads, renewable energy targets, and advances in automated distributed power electronics and communications is forcing policy-makers to understand new means to distribute costs and benefits within electricity markets. Discussions surrounding how distributed generation (DG) exhibits active voltage regulation and power factor/reactive power control and other power quality capabilities are complicated by uncertainties of baseline local distribution network power quality and to whom and how costs and benefits of improved electricity infrastructure will be allocated. DG providing ancillary services that dynamically respond to the network characteristics could lead to major network improvements. With proper market structures renewable energy systems could greatly improve power quality on distribution systems with nearly no additional cost to the grid operators. Renewable DG does have variability challenges, though this issue can be overcome with energy storage, forecasting, and advanced inverter functionality. This paper presents real data from a large-scale grid-connected PV array with large-scale storage and explores effective mitigation measures for PV system variability. We discuss useful inverter technical knowledge for policy-makers to mitigate ongoing inflation of electricity network tariff components by new DG interconnection requirements or electricity markets which value power quality and control.

14 SOLAR ENERGY↗

DOE OSTI2025

System Analysis of an Internal Combustion Engine (ICE) - Solid Oxide Fuel Cell (SOFC) Hybrid Cycle

The variability of renewable energy sources poses challenges for reliable grid operation. Conventional thermal power sources, though reliable, often lack operational flexibility. Hybrid energy systems that integrate Solid Oxide Fuel cells (SOFC) with Internal Combustion Engines (ICE) offer a promising solution by achieving relatively higher efficiency and grid-following capability. This study investigates performance of a 100-kW pressurized SOFC-ICE hybrid cycle. In this configuration, unutilized SOFC fuel is used to drive the engine, with a turbocharger providing air supply and an external reformer generating syngas. System components were numerically modeled using MATLAB/SIMULINK for the SOFC and reformer, and EBSILON® for the ICE and balance of plant. Parametric studies varied fuel utilization (70-90%), reformer temperature (600 – 1000K), anode off-gas recirculation (0 – 70%), and current density (0.2 – 0.55 A/cm2). Results show that the SOFC and ICE operate as thermally independent topping and bottoming cycles, achieving peak efficiency of 62% under optimized conditions.

hybrid↗

DOE OSTI2012

Appendix I3-1 to Wind HUI Initiative 1: AWST-WindNET-Phase 1 Final Report

This report is an appendix to the Hawaii WindHUI efforts to develop and operationalize short-term wind forecasting and wind ramp event forecasting capabilities. The report summarizes the WindNET Phase 1 efforts on the Big Island of Hawaii and includes descriptions of modeling methodologies, use of field validation data, results and recommendations. The objective of the WindNET project was to investigate the improvement that could be obtained in short-term wind power forecasting for wind generation facilities operating on the island grids operated by Hawaiian Electric Companies through the use of atmospheric sensors deployed at targeted locations. WindNET is envisioned as a multiphase project that will address the short-term wind forecasting issues of all of the wind generation facilities on the all of the Hawaiian Electric Companies' island grid systems. The first phase of the WindNET effort (referred to as WindNET-1) was focused on the wind generation facilities on the Big Island of Hawaii. With complex terrain and marine environment, emphasis was on improving the 0 to 6 hour forecasts of wind power ramps and periods of wind variability, with a particular interest in the intra-hour (0-1 hour) look-ahead period. The WindNET project was built upon a foundation that was constructed with the results from a previously completed observation targeting study for the Big Island that was conducted as part of a project supported by the National Renewable Energy Laboratory (NREL) and interactions with the western utilities. The observational targeting study provided guidance on which variables to measure and at what locations to get the most improvement in forecast performance at a target forecast site. The recommendations of the observation targeting study were based on the application two techniques: (1) an objective method called ensemble sensitivity analysis (ESA) (Ancell and Hakim, 2007; Torn and Hakim, 2008; Zack et al, 2010); and (2) a subjective method based on a diagnostic analysis of large ramp events. The analysis was completed for both the wind farm on the southern tip of the Big Island and on the northern tip of the island. The WindNET project was designed to also deploy sensors to validate the Big Island observational targeting study and enhance operator's understanding of predominate causes of wind variability conditions at the wind facilities. Compromises had to be made with the results from the observation targeting study to accommodate project resource limitations, availability of suitable sites, and other factors. To focus efforts, field sensor deployment activities focused on the wind facility on the southern point of Big Island.

14 SOLAR ENERGY↗

DOE OSTI2017

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↗

DOE OSTI2025

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.

Demographic evolution↗

DOE OSTI2023

Grid Forming Fast Frequency Response for PMSG-Based Wind Turbines

Electric power systems are undergoing a rapid transition from fuel-based generation using synchronous generators to renewable generation interfaced by power electronics. In this context, a key challenge is using renewable generation with limited controllability to contribute to power system stability. In this work, we investigate grid-forming control of permanent magnet synchronous generator (PMSG) wind turbines. The proposed control and curtailment strategy supports the entire spectrum of standard functions of grid-following (e.g. maximum power point tracking (MPPT) and grid-forming control (e.g., primary frequency control) without explicit mode switching. A detailed case study is used to compare the performance of the proposed control at operating points corresponding to MPPT and frequency control with standard grid-forming and grid-following controls. Further, the results demonstrate that (i) the proposed energy balancing grid forming control is self-synchronizing in MPPT mode, (ii) the (limited) energy storage and controllability of wind turbines can be adequately utilized to provide grid support, and (ii) the proposed control exhibits good performance under variable wind speeds and does not result in a significant increase of wind turbine fatigue loads.

17 WIND ENERGY↗

DOE OSTI2015

High Penetration of Photovoltaic Generation Study – Flagstaff Community Power (Final Technical Report, Results of Phases 2-5)

APS’s renewable energy portfolio, driven in part by Arizona’s Renewable Energy Standard (RES) currently includes more than 1100 MW of installed capacity, equating to roughly 3000 GWh of annual production. Overall renewable production is expected to grow to 6000 GWh by 2025. It is expected that distributed photovoltaics, driven primarily by lower cost, will contribute to much of this growth and that by 2025, distributed installations will account for half of all renewable production (3000GHW). As solar penetration increases, additional analysis may be required for routine utility processes to ensure continued safe and reliable operation of the electric distribution network. Such processes include residential or commercial interconnection requests and load shifting during normal feeder operations. Circuits with existing high solar penetration will also have to be studied and results will need to be evaluated for adherence to utility practices or strategy. Increased distributed PV penetration may offer benefits such as load offsetting, but it also has the potential to adversely impact distribution system operation. These effects may be exacerbated by the rapid variability of PV production. Detailed effects of these phenomena in distributed PV applications continue to be studied. Comprehensive, high-resolution electrical models of the distribution system were developed to analyze the impacts of PV on distribution circuit protection systems (including coordination and anti-islanding), predict voltage regulation and phase balance issues, and develop volt/VAr control schemes. Modeling methods were refined by validating against field measurements. To augment the field measurements, methods were developed to synthesize high resolution load and PV generation data to facilitate quasi-static time series simulations. The models were then extended to explore boundary conditions for PV hosting capability of the feeder and to simulate common utility practices such as feeder reconfiguration. The modeling and analysis methodology was implemented using open source tools and a process was developed to aid utility engineers in future interconnection requests. Methods to increase PV hosting capacity were also explored during the course of the study. A 700kVA grid-supportive inverter was deployed on the feeder and each grid support mode was demonstrated. Energy storage was explored through simulation and models were developed to calculate the optimum size and placement needed to increase PV hosting capacity. A tool was developed to aid planners in assigning relative costs and benefits to various strategies for increasing PV hosting capacity beyond current levels. Following the completion of the project, APS intends to use the tools and methods to improve the framework of future PV integration on its system. The tools and methods are also expected to aid other utilities to accelerate distributed PV deployment.

14 SOLAR ENERGY↗

DOE OSTI2012

Probabilistic Approach to Quantifying the Contribution of Variable Generation and Transmission to System Reliability: Preprint

The increasing electrical load served by variable generation (VG), such as wind and solar energy, in the United States and many other countries has stimulated an interesting line of research to better quantify the capacity value of these resources. Methods applied traditionally to thermal units based on their average outage rates do not apply to VG because of their uncertain and non-dispatchablenature. The North American Electric Reliability Corporation's Integration of Variable Generation Task Force recently released a report that highlighted the need to develop and benchmark underlying loss-of-load expectation and related metrics that reasonably and fairly calculate the contribution to planning reserves, or capacity value, of solar and wind power. As the fraction of generation comingfrom VG becomes more significant, their estimated capacity value will have a larger impact on system planning. In this paper, we provide a method to include VG in traditional probabilistic-based adequacy methods. This method has been implemented in the Renewable Energy Probabilistic Resource Assessment tool developed at the National Renewable Energy Laboratory. Through an example based on the U.S. Western Interconnection, this method is applied to assess the effect that transmission can have on resource adequacy. We also analyze the interactions between available transmission and capacity value for VG.

14 SOLAR ENERGY↗

DOE OSTI2016

Dynamic Braking System of a Tidal Generator

Renewable energy generation has experienced significant cost reductions during the past decades, and it has become more accepted by the global population. In the beginning, wind generation dominated the development and deployment of renewable energy; however, during recent decades, photovoltaic (PV) generation has grown at a very significant pace due to the tremendous decrease in the cost of PV modules. The focus on renewable energy generation has now expanded to include new types with promising future applications, such as river and tidal generation. The input water flow to these types of resources is more predictable than wind or solar generation. The data used in this paper is representative of a typical river or tidal generator. The analysis is based on a generator with a power rating of 40 kW. The tidal generator under consideration is driven by two sets of helical turbines connected to each side of the generator located in between the turbines. The generator is operated in variable speed, and it is controlled to maximize the energy harvested as well as the operation of the turbine generator. The electrical system consists of a three-phase permanent magnet generator connected to a three-phase passive rectifier. The output of the rectifier is connected to a DC-DC converter to match the rectifier output to the DC bus voltage of the DC-AC inverter. The three-phase inverter is connected to the grid, and it is controlled to provide a good interface with the grid. One important aspect of river and tidal generation is the braking mechanism. In a tidal generator, the braking mechanism is important to avoid a runaway condition in case the connection to the grid is lost when there is a fault in the lines. A runaway condition may lead to an overspeed condition and cause extreme stresses on the turbine blade structure and eventual disintegration of the mechanical structure. In this paper, the concept of the dynamic braking system is developed and investigated for normal and abnormal operations. The main objective is to optimize the performance under emergency braking while designing the system to be as simple as possible to avoid overdesigning the power electronics or exceeding the target budget.

13 HYDRO ENERGY↗

DOE OSTI2013

Analysis of the Impacts of Distribution-Connected PV Using High-Speed Data Sets: Preprint

This paper, presented at the IEEE Green Technologies Conference 2013, utilizes information from high resolution data acquisition systems developed at the National Renewable Energy Laboratory and deployed on a high-penetration PV distribution system to analyze the variability of different electrical parameters. High-resolution solar irradiance data is also available in the same area which is used to characterize the available resource and how it affects the electrical characteristics of the study circuit. This paper takes a data-driven look at the variability caused by load and compares those results against times when significant PV production is present. Comparisons between the variability in system load and the variability of distributed PV generation are made.

14 SOLAR ENERGY↗

DOE OSTI2014

Index for Wind Power Variability

Variability of large scale wind power generation is dependent on several factors: characteristics of installed wind power plants, size of the area where the plants are installed, geographic dispersion within that area and its weather regime(s). Variability can be described by ramps in power generation, i.e. changes from time period to time period. Given enough data points, it can be described with a probability density function. This approach focuses on two dimensions of variability: duration of the ramp and probability distribution. This paper proposes an index based on these two dimensions to enable comparisons and characterizations of variability under different conditions. The index is tested with real, large scale wind power generation data from several countries. Considerations while forming an index are discussed, as well as the main results regarding what the drivers of variability experienced for different data.

17 WIND ENERGY↗

DOE OSTI2025

Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

17 WIND ENERGY↗

DOE OSTI2016

Energy Storage Requirements for Achieving 50% Solar Photovoltaic Energy Penetration in California

We estimate the storage required to enable PV penetration up to 50% in California (with renewable penetration over 66%), and we quantify the complex relationships among storage, PV penetration, grid flexibility, and PV costs due to increased curtailment. We find that the storage needed depends strongly on the amount of other flexibility resources deployed. With very low-cost PV (three cents per kilowatt-hour) and a highly flexible electric power system, about 19 gigawatts of energy storage could enable 50% PV penetration with a marginal net PV levelized cost of energy (LCOE) comparable to the variable costs of future combined-cycle gas generators under carbon constraints. This system requires extensive use of flexible generation, transmission, demand response, and electrifying one quarter of the vehicle fleet in California with largely optimized charging. A less flexible system, or more expensive PV would require significantly greater amounts of storage. The amount of storage needed to support very large amounts of PV might fit within a least-cost framework driven by declining storage costs and reduced storage-duration needs due to high PV penetration.

14 SOLAR ENERGY↗