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At least 487 records · Page 27

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DOE OSTI2016

An Improved Stochastic Unit Commitment Formulation to Accommodate Wind Uncertainty

The United States targets to supply 20% of its electricity generation using wind energy by 2030. The expansion of renewable resources, especially weather-based resources such as wind, creates more uncertainty and variability in the operation of the power grid. New methods and approaches in electricity market operations are needed to efficiently manage the continuing increase in variability and uncertainty caused by expanding intermittent wind. This paper proposes an improved stochastic programming approach for incorporating wind uncertainty into energy markets. The proposed formulation improves the two-stage stochastic unit commitment problem by introducing a dynamic decision making approach similar to a multi-stage formulation in the presence of wind power scenarios which are not well represented by a scenario tree. The numerical results present up to 1%-2% decrease in operational costs compared to the two-stage stochastic unit commitment formulation.

Electricity markets↗

DOE OSTI2016

Investigating Power System Primary and Secondary Reserve Interaction under High Wind Power Penetration

Power system frequency needs to be maintained close to its nominal value at all times to successfully balance load and generation and maintain system reliability. Adequate primary frequency response and secondary frequency response are the primary forces to correct an energy imbalance at the second-to-minute level. As wind energy becomes a larger portion of the world's energy portfolio, there is an increased need for wind to provide frequency response. This paper addresses one of the major concerns about using wind for frequency regulation: the unknown factor of the interaction between primary and secondary reserves. The lack of a commercially available tool to model this has limited the energy industry's understanding of when the depletion of primary reserves will impact the performance of secondary response or vice versa. This paper investigates the issue by developing a multi-area frequency response integration tool with combined primary and secondary capabilities. The simulation is conducted in close coordination with economical energy scheduling scenarios to ensure credible simulation results.

17 WIND ENERGY↗

DOE OSTI2021

Integrating Cambium Marginal Costs into Electric Sector Decisions: Opportunities to Integrate Cambium Marginal Cost Data into Berkeley Lab Analysis and Technical Assistance

NREL’s Cambium tool generates forward-looking simulations of marginal wholesale electricity costs associated with NREL’s Standard Scenarios. The scenarios include growing shares of variable renewable energy (VRE, i.e. wind and solar), among other power sector assumptions, between 2018 and 2050. The tool’s primary output—hourly costs at more than 130 balancing areas—could serve as public and transparent data source that supports electric-sector decision-making processes across the U.S. Berkeley Lab conducts a large range of analyses that use historical and forward-looking wholesale electricity prices to inform electric-sector decisions. In this report, Berkeley Lab uses its expertise to evaluate the Cambium cost data. We compare Cambium data with historical wholesale prices for the year 2018 and other modeled prices for the year 2030. We then present eight case studies in which Berkeley Lab researchers use Cambium data to replicate previous analyses based on other price datasets. We describe where primary findings and underlying key price dynamics align or differ, and highlight possible novel insights from the Cambium data. Finally, we qualitatively evaluate the suitability of Cambium costs in ten additional Berkeley Lab studies, though a direct comparison with alternative price data was not feasible at this time. The goal is to inform how electric-sector decision-makers and DOE program offices may be able to use this cohesive dataset, and to highlight what improvements to Cambium may make it even more useful.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Hydropower Role in a Power Grid with a High Proportion of Variable Renewables

The importance of hydropower increases as the power grid evolves with the higher variable renewable contribution. As conventional thermal power plants are retired, the importance of hydropower contribution increases to balance the variability of solar and wind generation. However, reservoir water resources are constrained by multiple constraints, and variability of water inflow to the reservoirs creates limitations to dam water releases for power grid needs. Coordination of multiple tools, including water resources modeling, ecological modeling, and technical and economic power grid modeling, informs dam water releases. Better representation of ecological constraints and other water use details in the suit of power grid models are required to identify the hydropower flexibility to provide multiple power grid services. Impacts of water planning and hydropower operation changes to a power grid are better understood by production cost model simulations with technical and economic data, including unserved energy, reserve failures, transmission congestion to serve load, and local marginal price increases. Similarly, PCM simulation of current and future projected power grid information, including LMP, and carbon emission rates, informs short-term water release plans and long-term investments for power plant upgrades for technical and ecological needs. Hydropower operations of several case studies are simulated to understand operation patterns, revenue, water release decisions in multiple time scales (month, day, minutes), hydropower contracts scheduling, and water variability impacts.

environmental↗

DOE OSTI2024

Hydropower Flexibility and Environmental Tradeoffs Analysis

The importance of hydropower increases as the power grid evolves with the higher variable renewable contribution. As conventional thermal power plants are retired, the importance of hydropower contribution increases to balance the variability of solar and wind generation. However, reservoir water resources are constrained by multiple constraints, and variability of water inflow to the reservoirs creates limitations to dam water releases for power grid needs. Coordinating multiple tools, including water resources, ecological, and technical and economic power grid modeling, informs dam water releases. The case study, the Columbia River Basin multipurpose reservoir project, is operated for hydropower production and many other purposes considering the aquatic habitat of the river basin. Specifically, the river basin fish population is a vital element for the tribal community of the river basin. We integrated a production cost model, a water resource model, and decades of tribal knowledge to analyze the fish-friendly way of operating Columbia hydropower scheduling and grid impacts. We measure power grid impacts for various water resources planning scenarios in terms of total system operating cost, system reliability indicators, changes in wind and solar generation and curtailments, local marginal prices, and revenue for hydropower producers. The study results inform reservoir operating rules decisions from hydropower power producers, system operators, other water users, tribes, environmentalists, and other stakeholders.

Columbia River↗

DOE OSTI2012

Effect of Large Scale Transmission Limitations on Renewable Energy Load Matching for Western U.S.: Preprint

Based on the available geographically dispersed data for the Western U.S. (excluding Alaska), we analyze to what extent the geographic diversity of these resources can offset their variability. Without energy storage and assuming unlimited energy flows between regions, wind and PV can meet up to 80% of loads in Western U.S. while less than 10% of the generated power is curtailed. Limiting hourlyenergy flows by the aggregated transmission line carrying capacities decreases the fraction of the load that can be met with wind and PV generation to approximately 70%.

14 SOLAR ENERGY↗

DOE OSTI2022

Building energy efficiency and load flexibility optimization using phase change materials under futuristic grid scenario

This work investigates the energy savings and load flexibility capacity of phase change material (PCM) integrated into building envelope under a future energy generation scenario, where 80% of the energy load comes from renewable sources. Using community-scale modeling, we first determine the net thermal storage required to fully manage the demand variability at a utility grid, and then using whole-building simulations we optimize operating parameters - such as PCM thickness, latent heat, interior setpoint profile, PCM distribution, and heat transfer coefficient - to determine the optimal conditions required for maximum energy reduction and load flexibility without compromising occupants' thermal comfort. The optimal PCM-integrated envelope proposed in this study can provide annual load flexibility up to 33.6% and annual energy savings up to 10.8% in a lightweight residential building located in Baltimore, MD. This study is relevant given the increasing contributions of renewable energy in the total energy generation mix, leading to a significant time-imbalance between peak energy demand and peak energy production. While thermal energy storage using PCM is a recognized technique, there is no known prior study dedicated to examining the thermal performance of PCM-integrated envelope under future energy generation scenarios. This study bridges the research gap by investigating a practical way to implement PCMs in the buildings and maximize the energy efficiency as well as load flexibility related benefits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Renewable electricity capacity planning with uncertainty at multiple scales

Abstract We formulate and compare optimization models of investment in renewable generation using a suite of social planning models that compute optimal generation capacity investments for a hydro-dominated electricity system where inflow uncertainty results in a risk of energy shortage. The models optimize the expected cost of capacity expansion and operation allowing for investments in hydro, geothermal, solar, wind, and thermal plant, as well as battery storage for smoothing load profiles. A novel feature is the integration of uncertain seasonal hydroelectric energy supply and short-term variability in renewable supply in a two-stage stochastic programming framework. The models are applied to data from the New Zealand electricity system and used to estimate the costs of moving to a 100% renewable electricity system by 2035. We also explore the outcomes obtained when applying different forms of CO 2 constraint that limit respectively non-renewable capacity, non-renewable generation, and CO 2 emissions on average, almost surely, or in a chance-constrained setting, and show how our models can be used to investigate the merits of a proposed pumped-hydro scheme in New Zealand’s South Island.

Ferris, Michael C.↗

DOE OSTI2017

Integrating biomass quality variability in stochastic supply chain modeling and optimization for large-scale biofuel production

The production of biofuels using second-generation feedstocks has been recognized as an important alternative source of sustainable energy and its demand is expected to increase due to regulations such as the Renewable Fuel Standard. However, the pathway to biofuel industry maturity faces unique, unaddressed challenges. Here, to address this challenges, this article presents an optimization model which quantifies and controls the impact of biomass quality variability on supply chain related decisions and technology selection. We propose a two-stage stochastic programming model and associated efficient solution procedures for solving large-scale problems to (1) better represent the random nature of the biomass quality (defined by moisture and ash contents) in supply chain modeling, and (2) assess the impact of these uncertainties on the supply chain design and planning. The proposed model is then applied to a case study in the state of Tennessee. Results show that high moisture and ash contents negatively impact the unit delivery cost since poor biomass quality requires the addition of quality control activities. Experimental results indicate that supply chain cost could increase as much as 27%–31% when biomass quality is poor. We assess the impact of the biomass quality on the topological supply chain. Our case study indicates that biomass quality impacts supply chain costs; thus, it is important to consider the impact of biomass quality in supply chain design and management decisions.

09 BIOMASS FUELS↗

DOE OSTI2023

Chapter 4: "Waste"-to-Energy for Decarbonization - Transforming Nut Shells Into Carbon-Negative Electricity

This chapter presents a study demonstrating waste pistachio nut shells as a renewable feedstock for climate-friendly electricity generation via industrial gasification technology. The study includes biomass feedstock characterization (i.e., pistachio waste critical material attributes), process variability (i.e., bulk material handling), and overall operational reliability and conversion performance through extended testing. Additionally, techno-economic analysis (TEA) and life cycle assessment (LCA) were performed to assess the economic feasibility and environmental impact of the technology to transform agricultural waste to biopower. For processing pistachio waste material, among critical material attributes, fines content in the biomass (<1/4") had the largest potential to reduce the operating time of the gasifiers due to plugging. Pelletizing fines and co-feeding them with the mixed pistachio waste increased the average feed density, feed rate, and biochar production. Compared to pine wood chips, mixed pistachio waste yielded higher biochar quantity but slightly reduced quality. In general, a systematic Quality by Design methodology is the preferred approach for designing preprocessing and material conveyance systems, where a downstream technology (end user) for the produced intermediate is specified at the outset. TEA results show that the biochar production rate and selling price had an overwhelming impact on the modeled Minimum Electricity Selling Price (MESP), which ranged from 35.5 to 39.9 cents/kWh for the cases studied (16 h/day operational basis). Moreover, LCA results show that the valorization of pistachio shells for biopower generation is a "carbon negative" process that can help decarbonize the U.S. electricity grid. The specific carbon intensity was -0.29 to -0.71 kg CO2e/kWh, compared to 0.45 kg CO2e/kWh for the average U.S. electricity mix. Biochar production from pistachio waste as a potential means for carbon sequestration was a significant driver for the LCA. The highly stable biochar permanently sequesters a considerable fraction of biochar carbon in the ground, more than enough to offset the life cycle emissions, and can be a complementary climate change mitigation strategy.

bio-char↗

DOE OSTI2021

Dynamic Building Load Control to Facilitate High Penetration of Solar Photovoltaic Generation (Final Technical Report)

Solar photovoltaic (PV) resources are the most common form of distributed generation in residential and commercial customer premises within electric distribution networks. A higher penetration of PV generation in distribution circuits will impose challenges on maintaining service voltages within the range of industry standards, power quality, and power flow. Buildings consume 74% of the electricity produced in the United States, and a significant portion of the building load is dispatchable, making them responsive to electrical grid needs. Oak Ridge National Laboratory—in collaboration with Southern Company; the University of Tennessee, Knoxville; and the Georgia Institute of Technology—is examining the PV integration issues in distribution-level electrical grids and developing integrated demand-side control and communication systems to enable responsive loads. The proposed responsive loads mechanism performs renewable generation following to increase the penetration of solar PV within each feeder. The specific objectives of this project are to (1) examine distribution-level PV integration scenarios to understand requirements, (2) undertake an end-to-end simulation-based design of a distributed control strategy of loads geographically near the PV generation asset to minimize the effect on the distribution feeder, (3) deploy and demonstrate the control technology developed in partnership with utilities, and (4) perform a scalability analysis at the utility scale. This 3-year integrated project aims to develop, demonstrate, and validate demand-side control technology to enable increased the penetration of renewables while mitigating challenges that arise due to their intermittency. Activities in Budget Period (BP) 1 focused on a literature review and the formal design of a control system for integrating local distribution with generation and loads. The team used modeling and simulation to evaluate the impact of varying buildings loads, variable PV generation, and power flow dynamics on the distribution circuit. The dynamic models developed in BP 1 were used in BP 2 to develop a model-based control design and a test bed. The test bed has enabled the simulation-based testing and comparison of different control designs and formulations applied to different configurations of the distribution grid, PVs, and building loads. The control approaches developed in BP 2 were implemented in BP 3 in the form of hardware deployed at the Central Baptist Church (CBC) in Knoxville, Tennessee, for testing and evaluation. The outcome of this project was the development and demonstration of open-source, low-cost, low-touch sensing and control retrofits to distributed PV generation and building loads that, in a coordinated fashion, provide the load-shaping response needed to integrate high levels of renewable penetration. This research addresses the target metrics by dynamically controlling a load with solar generation variability to minimize the extent of two-way power flow, enhance reliability, facilitate high PV penetration (>100% of peak load in a line segment), and generate scalable software and hardware solutions adaptable to any penetration levels. The research and development activities are focused and designed to be impactful within the relevant 2020 targets time frame.An accurate open-source integration simulation framework for end-to-end control design was developed and deployed at the CBC facility for testing and evaluation. This final report provides a detailed review of the technical results achieved during this 3-year integrated project. A novel spectral analysis of PV data is demonstrated to derive the requirements of the control design. A detailed simulation-based analysis of PV integration at increasing penetration levels is presented using 1 year of PV data to demonstrate the impact on the distribution circuits. Two different control strategies were developed and demonstrated via simulation to track variable PV generation with adaptive load dispatch. The report concludes with a summary of accomplishments and recommendations for a path forward.

14 SOLAR ENERGY↗

DOE OSTI2019

Economic analysis of a nuclear hybrid energy system in a stochastic environment including wind turbines in an electricity grid

The electricity market in the U.S. is moving toward an energy mix including more variable renewable energy (VRE) sources like wind and solar. This increasing VRE penetration is altering the profile of the net electricity demand. Normally, flexible generators (e.g. gas turbines) are used to absorb fluctuations in the electricity demand. Therefore, an increase in VRE also leads to an increase in installed capacity for such flexible generators which, in turn, may increase the price of electricity. Nuclear-Renewable Hybrid Energy Systems (N-R HES) are seen as a solution to this problem. The N-R HES contains a nuclear power plant electrically and/or thermally linked to an industrial process (IP). This paper demonstrates a new methodology and presents the corresponding RAVEN/Modelica-based software framework to evaluate the financial performance of N-R HES. The novelty of the method is that it explicitly incorporates the stochastic nature of N-R HES inputs such as electricity demand and wind speeds. The method is applied to a generic (rather than region-specific) N-R HES. The analysis shows that under the right market conditions, economic benefits can be achieved by adding a suitable IP to the base-load generator. In particular, there exists an optimal relationship between the IP capacity and the demand. Furthermore, global trends indicate VRE penetration raises the cost of electricity due to the added volatility, an issue only mitigated when operating with high profit margins for the IP.

97 MATHEMATICS AND COMPUTING↗

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 OSTI2020

DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation

Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysis using parallel Cloudenabled implementations with < 1 minute computational cycle times.

14 SOLAR ENERGY↗

DOE OSTI2018

Valuing Energy Efficiency and Distributed Energy Resources in the Built Environment: Preprint

Traditionally, analytical frameworks that underpin building energy policy have focused on the need to reduce annual energy consumption by assessing the cost and efficiency performance of energy-consuming technologies. Investments in distributed energy resources (DERs) such as photovoltaics have generally been evaluated independently, but also on a cost and performance basis. However, buildings are rapidly evolving to incorporate technologies that not just consume energy, but also generate, store, and control energy loads. Concurrently, the electric grid is becoming more dynamic and variable due to the increased proportion electricity production from variable renewable sources. To that end, building energy policy analytical frameworks should be updated to include the interactions between energy efficiency and DERs as well as potential value streams for the grid and building owner that technologies or packages of technologies might offer. As buildings with DERs and controllable loads are able to provide grid services or optimize operations based on electricity rates and grid conditions, the temporal and spatial nature of these technologies should also be incorporated into design decisions. To explore these questions further, the National Renewable Energy Laboratory will use a series of building and electric grid models to simulate the design and operation of a neighborhood of buildings under various electricity rates, grid conditions, and potential compensation scenarios. Scenarios of time-sensitive values for future grid services will also be constructed for use in the analysis. New metrics that can be used in building energy policies will also be explored to capture the responsiveness or flexibility of high efficiency buildings with DERs under various electric grid conditions and future scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Capacity Market Model Considering Flexible Resource Requirements: Preprint

17 WIND ENERGY↗

DOE OSTI2018

Valuing Energy Efficiency and Distributed Energy Resources in the Built Environment

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Wet waste-to-energy resources in the United States

Waste-to-energy (WTE) technologies present an opportunity to recycle organic waste material into renewable energy while offsetting disposal and environmental costs. A key challenge to ensuring economic and environmental viability of WTE is understanding the variability of individual WTE resource characteristics, including their location, amount, and quality. The main objective of this study is to estimate the wet WTE resource potential in the United States and illustrate its geographic distribution. The wet resources considered in this study are wastewater sludge, animal manure, food waste, and FOG (fats, oils, and greases). This study is the first to achieve results below national level, at the finest geographic resolution. Our analysis indicates that about 566 teragrams (Tg) of wet WTE resources are generated annually in the United States. This amount corresponds to about 1 exajoule (EJ), which is sufficient to displace about 18% of the 2015 U.S. on-highway diesel consumption on an energy basis. About half of this potential is generated by animal manure.

waste-to-energy, WTE, water resource, wastewater s↗