Chapter 7: Analyzing the Impact of Variable Energy Resources on Power System Reserves
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24 POWER TRANSMISSION AND DISTRIBUTION↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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SIDSIM (Sub-Interval Distribution-function in Simulation) [SWR-17-22]
Time-series simulation is a technique used to estimate the energy delivery of a power generation system with output that changes under changing conditions. Such variable resources include photovoltaic energy systems that experience different levels of sunlight or wind farms with changing wind patterns. As distributed generation from these variable resources continues to flourish, having improved software tools to accurately measure, model, and estimate phenomena of interest to grid integration, in addition to energy output, will be crucial for safeguarding smooth integration and operation of these energy systems. Currently, time-series simulations assume conditions and power output are constant over the duration of a time-step (steady-state assumption). NREL researchers improve the accuracy with a new simulation software called SIDSIM (Sub-Interval Distribution Simulation) that replaces this steady value (solar or wind resource) with a distribution function that has a maximum value, a minimum value, and a shape to the curve within each time-step. The distribution function used for modeling varying values in the SIDSIM software is a novel equation and presents advantageous new benefits over the conventional methods. The first advantage comes in the ability to determine maximum instantaneous power within a time-step. This enables better protection of a system from overcurrent issues and it also allows for better forecasting and reservation of transmission capacity; such capabilities are essential for energy system integration. Second, a distribution function helps to determine the amount of energy loss due to “clipping” in cases where the photovoltaic direct current (DC) system capacity exceeds the inverter capacity, the AC interconnect capacity, or any other limiting component or threshold value. It improves estimates of battery throughput by allowing both charge and discharge of a battery within the duration of a time-step. Third, it provides a more refined estimate of utility-bill impacts by considering the delivered power may be higher than the site load or lower than the regular average power estimates that are made using a quasi-steady-state assumption. SEE ALSO OSTI ID 1,361,018
Walker, Andy↗
Representation of the Solar Capacity Value in the ReEDS Capacity Expansion Model: Preprint
An important emerging issue is the estimation of renewables' contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to resource variability. Reliability-based methods, particularly, effective load-carrying capacity (ELCC), are considered to be the most robust techniques for addressing this resource variability. The Regional Energy Deployment System (ReEDS) capacity expansion model and other long-term electricity capacity planning models require an approach to estimating CV for generalized PV and system configurations with low computational and data requirements. In this paper we validate treatment of solar photovoltaic (PV) capacity value by ReEDS capacity expansion model by comparing model results to literature for a range of energy penetration levels. Results from the ReEDS model are found to compare well with both comparisons--despite not being resolved at an hourly scale.
14 SOLAR ENERGY↗
Representation of Solar Capacity Value in the ReEDS Capacity Expansion Model
An important issue for electricity system operators is the estimation of renewables' capacity contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to the resource variability. Reliability-based methods, particularly assessment of the Effective Load-Carrying Capacity, are considered to be the most robust and widely-accepted techniques for addressing this resource variability. This report compares estimates of solar PV capacity value by the Regional Energy Deployment System (ReEDS) capacity expansion model against two sources. The first comparison is against values published by utilities or other entities for known electrical systems at existing solar penetration levels. The second comparison is against a time-series ELCC simulation tool for high renewable penetration scenarios in the Western Interconnection. Results from the ReEDS model are found to compare well with both comparisons, despite being resolved at a super-hourly temporal resolution. Two results are relevant for other capacity-based models that use a super-hourly resolution to model solar capacity value. First, solar capacity value should not be parameterized as a static value, but must decay with increasing penetration. This is because -- for an afternoon-peaking system -- as solar penetration increases, the system's peak net load shifts to later in the day -- when solar output is lower. Second, long-term planning models should determine system adequacy requirements in each time period in order to approximate LOLP calculations. Within the ReEDS model we resolve these issues by using a capacity value estimate that varies by time-slice. Within each time period the net load and shadow price on ReEDS's planning reserve constraint signals the relative importance of additional firm capacity.
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Price Formation and Grid Operation Impacts from Variable Renewable Energy Resources
Increasing amounts of Variable Renewable Energy Resources (VREs) impact electricity markets and their operation. VREs are intermittent, zero marginal cost resources that tend to displace emissions-intensive generators in electricity dispatch, reducing emissions, but impacting price formation, revenue sufficiency, reliability, and market power mitigation processes of electricity markets. But VREs are not the only factor that affects operational and financial challenges in electricity markets. Declining natural gas prices, changing resource mixes, as well as different electricity market designs and regulatory policies all factor into the challenges both electricity market participants and operators face in today’s electricity markets. With this in-depth examination of electricity markets and related literature review, we aim to inform on key challenges of market design and operation for successful integration of large amounts of zero marginal cost resources. We’ve identified several areas, including VREs impact on price formation, revenue sufficiency, reliability, market power monitoring and mitigation, as well as how state-level incentives and market design impact VREs and these challenges. With each key challenge, we survey the literature to answer the question: To what extent is the problem, and how has it evolved over time? We first conduct a thorough review of ongoing challenges in electricity markets to understand the problem and review the empirical literature to capture important findings on how VREs, specifically, impact the problem. From this review, we highlight metrics that are important to understanding VRE integration and how market designs and outcomes are evolving with increasing levels of VREs. We propose several empirical models for future research to determine the impact of VREs on these identified challenges.
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Emerging Best Practices for Electric Utility Planning with Climate Variability: A Resource for Utilities and Regulators
This report is intended to support electric utilities and regulators as they work together to increase the climate resilience of the power system in the United States. It focuses on best practice methods and examples of conducting utility forecasting with climate change (Section 2) as well as three overlapping types of electric utility planning for climate change: resource planning (Section 3), asset planning (Section 4), and contingency planning (Section 5). This report also addresses data development and access (Section 6), emerging pathways for planning including decision-making under deep uncertainty and storylines approaches (Section 7), regulatory considerations (Section 8), and coordination and innovative funding and cost sharing approaches to planning for climate variability (Section 9).
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Chapter 4: Multi-Dimensional, Multi-Scale Modeling and Algorithms for Integrating Variable Energy Resources in Power Networks: Challenges and Opportunities
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Relating solar resource and its variability to weather and climate across the northwestern United States
Not Available
Hinkelman, Laura M.↗
Strategies and Decision Support Systems for Integrating Variable Energy Resources in Control Centers for Reliable Grid Operations. Executive Summary
This is the executive summary for a report that provides findings from the field regarding the best ways in which to guide operational strategies, business processes and control room tools to support the integration of renewable energy into electrical grids.
best practices↗
Strategies and Decision Support Systems for Integrating Variable Energy Resources in Control Centers for Reliable Grid Operations
This report provides findings from the field regarding the best ways in which to guide operational strategies, business processes and control room tools to support the integration of renewable energy into electrical grids.
grid↗
Phasor-Based Control for Scalable Integration of Variable Energy Resources
We propose an innovative framework termed phasor-based control (PBC) to facilitate the integration of heterogeneous and intermittent distributed energy resources (DER) on the electric grid. PBC presents a unified approach that is agnostic to optimization criteria and to the particular characteristics of participating resources. It is enabled by synchronized, high-precision voltage phasor measurements that allow stating control objectives in grid-specific, rather than resource-specific, terms. We present qualitative justification and examine the general feasibility of this control approach, including the behavior of candidate control algorithms in simulation. Initial results suggest that PBC has significant potential to support stable and resilient grid operations in the presence of arbitrarily high penetrations of DER.
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Deploying Intra-hour Uncertainty Analysis Tools to ABB’s GridView - CRADA 445
The quickly-changing generation resource mix in the US grid, with large additions of variable resources, retirements of traditional thermal generation, distributed generation and demand response, are creating new challenges on the traditional operation of both the generation and transmission systems. The ability to perform intra-hour high fidelity production cost modeling (PCM) is needed to allow grid operators and grid planners to integrate high penetration (more than 50%) of variable energy resources, and to make prompt well informed decisions in market operations and planning. In the last decade, PNNL has developed several stand-alone tools to enable grid operators and planners to understand the impact of high variable generation on their systems. These tools have been used is studies such as: (1) Evaluation the benefits of WECC balancing authorities coordination under high variable generation penetration , (2) Benefits of Energy Imbalance Market in the North West Power Pool and (3) Duke Energy and NV Energy solar integration studies. ABB’s GridView is a widely used commercial PCM tool. It is the tool used by WECC and their stakeholders to develop the WECC PCM planning model on bi-annual basis. This proposal is focused on the integrating of PNNL intra-hour uncertainty analysis tools to GridView.
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Wind energy variability and links to regional and synoptic scale weather
The accurate characterization of seasonal and inter-annual site-level wind energy variability is essential during wind project development. Understanding the features and probability of low-wind years is of particular interest to developers and financers. However, a dearth of long-term, hub-height wind observations makes these characterizations challenging, and thus techniques to improve these characterizations are of great value. To improve resource characterization, we explicitly link wind resource variability (at hub-height, and at specific sites) to regional and synoptic scale wind regimes. Our approach involves statistical clustering of high-resolution modeled wind data, and is applied to California for a period covering 1980–2015. With this approach, we investigate the unique meteorological patterns driving low and high wind years at five separate wind project sites. We also find wind regime changes over the 36-year period consistent with global warming: wind regimes associated with anomalously hot summer days increased at half a day per year and stagnant conditions increased at one-third days per year. Despite these changes, the average annual resource potential remained constant at all project sites. Additionally, we identify correlations between climate modes and wind regime frequency, a linkage valuable for resource characterization and forecasting. Our general approach can be applied in any location and may benefit many aspects of wind energy resource evaluation and forecasting.
17 WIND ENERGY↗
The future of wind energy in California: Future projections with the Variable-Resolution CESM
Shifting wind patterns are an expected consequence of global climate change, with direct implications for wind energy production. However, wind is notoriously difficult to predict, and significant uncertainty remains in our understanding of climate change impacts on existing wind generation capacity. Historical and future wind climatology and associated capacity factors at five wind turbine sites in California are examined. Historical (1980–2000) and mid-century (2030–2050) simulations were produced using the Variable-Resolution Community Earth System Model (VR-CESM) to understand how these wind generation sites are expected to be impacted by climate change. A high-resolution statistically downscaled WRF product provided by DNV GL, reanalysis datasets MERRA-2, CFSR, NARR, and observational data were used for model validation and comparison. These projections suggest that wind power generation capacity throughout the state is expected to increase during the summer, and decrease during fall and winter, based on significant changes at several wind farm sites. Finally, this study improves the characterization of uncertainty around the magnitude and variability in space and time of California's wind resources in the near future, and also enhances our understanding of the physical mechanisms related to the trends in wind resource variability.
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Bumble bee colony growth and reproduction depend on local flower dominance and natural habitat area in the surrounding landscape
Conservation measures for bees often focus on increasing the diversity and abundance of floral resources. But it has not been clear if observed benefits of floral enhancements result from greater population growth, which is critical for the long-term success of conservation, or from mobile foragers aggregating in high-resource locations. Experimental evidence is only beginning to emerge in favor of the former mechanism and it is not well-established how different aspects of floral resources affect population growth. For example, bumble bee colonies may benefit from greater overall floral abundance, richness, or relative dominance of resource species. Because bumble bees are highly mobile, resource variability in the surrounding landscape is also important for colonies and may mediate local-scale effects. We experimentally assessed the growth and reproduction of bumble bee colonies (Bombus impatiens) deployed in grasslands in different local- and landscape-scale resource environments. Furthermore, we found that floral dominance, rather than the overall abundance or richness of floral resources, was the most important local factor for colony growth and reproduction. This may reflect more efficient foraging on a few numerically dominant and abundant resource species. Local- and landscape-scale predictor variables had interacting effects on colony growth and reproduction, suggesting that foraging distance depends on where in the landscape efficiently used resources are located. Our results provide further evidence that conservation strategies aimed at enhancing floral resources can increase bumble bee population growth. However, the most effective form of floral enhancement may vary among bee species.
60 APPLIED LIFE SCIENCES↗
Why Are We Talking About Capacity Markets?
Revenue sufficiency or 'missing money' concerns in wholesale electricity markets are important because they could lead to resource (or capacity) adequacy shortfalls. Capacity markets or other capacity-based payments are among the proposed solutions to remedy these challenges. This presentation provides a high-level overview of the importance of and process for ensuring resource adequacy, and then discusses considerations for capacity markets under futures with high penetrations of variable resources such as wind and solar.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Resource Analysis of NP Kunta Solar Park Site (White Paper)
India has set a target of 175 GW of renewable energy (RE) capacity by 2022 and 450 GW by 2030. Flexibility is key for efficient integration of renewables. The modern-day RE plants are grid-friendly and can also provide this flexibility. A pilot to demonstrate this flexibility by implementing automatic generation control (AGC) at a solar plant is being done by the U.S. Agency for International Development (USAID) under USAID’s Greening the Grid (GTG) Program and Renewable Integration & Sustainable Energy (RISE) initiative. This paper presents the resource variability analysis of the 250-MW NP Kunta solar plant site where the AGC pilot project is being implemented. This paper also demonstrates the use of publicly available resource quality data, which can be utilized by various stakeholders to better understand the variability of any existing or potential RE site in India and possibly increase confidence in decisions or help to understand the impacts that can be expected.
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Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates
Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating independent surrogates for the mean and standard deviation of each output with respect to the inflow realizations. A global sensitivity analysis shows that the turbulent inflow realization has a bigger impact on the total distribution of equivalent fatigue loads than the shear coefficient or yaw miss-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertainty models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces. In conclusion, the surrogates are a way to obtain power and load estimation under site specific characteristics without sharing the proprietary aeroelastic design.
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