This report is an authoritative assessment of the science of climate change, with a focus on the United States. It represents the second of two volumes of the Fourth National Climate Assessment, mandated by the Global Change Research Act of 1990.

This report summarizes the methodology and models developed to assess the risk to energy delivery from the potential loss of underground gas storage (UGS) facilities located within the United States. The U.S. has a total of 418 existing storage fields, of which 390 are currently active. The models estimate the impacts of a disruption of each of the active UGS facilities on their owners/operators, including (1) local distribution companies (LDCs), (2) directly connected transporting pipelines and thus on the customers in downstream States, and (3) third-party entities and thus on contracted customers expecting the gas shipment. Impacts are measured across all natural gas customer classes. For the electric sector, impacts are quantified in terms of natural gas-fired electric generation capacity potentially affected from the loss of a UGS facility. For the purpose of calculating the overall supply risk, the overall consequence of the disruption of an UGS facility across all customer classes is expressed in terms of the number of expected equivalent residential customer outages per year, which combines the unit business interruption cost per customer class and the estimated number of affected natural gas customers with estimated probabilities of UGS disruptions. All models and analyses are based on publicly available data. The report presents a set of findings and recommendations in terms of data, further analyses, regulatory requirements and standards, and needs to improve gas/electric industry coordination for electric reliability.

Although U.S. helium demand has remained relatively flat since 2009, exports of helium have increased significantly since then, driven primarily by demand for electronic and semiconductor manufacturing in Asia. In the midst of this global demand shift, the Helium Act dictates a new procedure for pricing and distributing the gas through a reserve that historically functioned as a loose “oligarchy.” The new procedure requires prices to be determined by the open market through auctions and a survey of market prices, as opposed to increasing prices according to the consumer price index. Response to these changes has caused temporary shortages, price increases, and a significant increase in the development of the helium extraction technologies used to produce helium from formerly marginal sources. Technologies are being developed and refined to extract helium from formerly low-yielding natural gas fields containing much lower amounts of helium than the previously considered economic threshold of 0.3%. Combining these transformative policies with the potential for new and significant global supplies from Qatar, Algeria, and Russia could lead to new and unforeseen market behaviors and reactions from global helium markets. The objective of the project is to analyze the global helium markets.

In addition to providing energy to end-consumers, power system operators are also responsible for ensuring system reliability. To this end, power markets maintain an array of ancillary services to ensure it is always possible to balance the supply and demand for energy in real-time. A subset of these ancillary services are commonly procured through market-based mechanisms: namely, Regulation, Spinning, and Non-spinning Reserves.

As part of efforts to position the United States as a leader in clean energy technology production, the U. S. Department of Energy (DOE) issued two Critical Materials Strategy reports, which assessed 16 materials on the basis of their importance to clean energy development and their supply risk (U.S. Department of Energy (DOE), 2010, DOE, 2011). To understand the implications for clean energy of disruptions in supplies of critical materials, it is important to understand supply chain dynamics from mining to final product production. As a case study of critical material supply chains, we focus on the supply of two rare earth metals, neodymium (Nd) and dysprosium (Dy), for permanent magnets used in wind turbines, electric vehicles and other applications. We introduce GCMat, a dynamic agent-based model that includes interacting agents at five supply chain stages consisting of mining, metal refining, magnet production, final product production and demand. Agents throughout the supply chain make pricing, production and inventory management decisions. Deposit developers choose which deposits to develop based on market conditions and detailed data on 57 rare earth deposits. Wind turbine and electric vehicle producers choose from a set of possible production technologies that require different amounts of rare earths. We ran the model under a baseline scenario and four alternative scenarios with different demand and production technology inputs. Model results from 2010 to 2013 fit well with historical data. Projections through 2025 show a number of possible future price, demand, and supply trajectories. For each scenario, we highlight reasons for turning points under market conditions, for differences between Nd and Dy markets, and for differences between scenarios. Because GCMat can model causal dynamics and provide fine-grain representation of agents and their decisions, it provides explanations for turning points under market conditions that are not otherwise available from other modeling approaches. Our baseline projections show very different behaviors for Nd and Dy prices. Nd prices continue to drop and remain low even at the end of our simulation period as new capacity comes online and leads to a market in which production capacity outpaces demand. Dy price movements, on the other hand, change directions several times with several key turning points related to inventory behaviors of particular agents in the supply chain and asymmetric supply and demand trends. Scenario analyses show the impact of stronger demand growth for rare earths, and in particular finds that Nd price impacts are significantly delayed as compared to Dy. This is explained by the substantial excess production capacity for Nd in the early simulation years that keeps prices down. Scenarios that explore the impact of reducing the Dy content of magnets show the intricate interdependencies of these two markets as price trends for both rare earths reverse directions – reducing the Dy content of magnets reduces Dy demand, which drives down Dy prices and translates into lower magnet prices. This in turn raises the demand for magnets and therefore the demand for Nd and eventually drives up the Nd price.

A novel agent-based model, the Electricity Market Complex Adaptive System (EMCAS) model, is designed to study market restructuring and the impact of new technologies on the power grid. The agent-based approach captures the complex interactions between the physical infrastructure and the economic behaviors of various agents operating in an electricity market. The electric power system model consists of power generating plants, transmission lines, and load centers. The electric power market is composed of generating company agents who bid capacity and prices into power pools administered by an Independent System Operator (ISO). The ISO agent balances supply and demand for day-ahead markets. EMCAS also simulates real-time market operation to account for the uncertainties in day-ahead forecasts and availability of generating units. This paper describes the model, its implementation, and its use to address questions of congestion management, price forecasting, market design, and market power.

This report describes the work conducted in support of the Eastern Interconnection States’ Planning Council (EISPC) Energy Zones Study and the development of the Energy Zones Mapping Tool performed by a team of experts from three National Laboratories. The multi-laboratory effort was led by Argonne National Laboratory (Argonne), in collaboration with the National Renewable Energy Laboratory (NREL) and Oak Ridge National Laboratory (ORNL). In June 2009, the U.S. Department of Energy (DOE) and the National Energy Technology Laboratory published Funding Opportunity Announcement FOA-0000068, which invited applications for interconnection-level analysis and planning. In December 2009, the Eastern Interconnection Planning Collaborative (EIPC) and the EISPC were selected as two award recipients for the Eastern Interconnection. Subsequently, in 2010, DOE issued Research Call RC-BM-2010 to DOE’s Federal Laboratories to provide research support and assistance to FOA-0000068 awardees on a variety of key subjects. Argonne was selected as the lead laboratory to provide support to EISPC in developing a methodology and a mapping tool for identifying potential clean energy zones in the Eastern Interconnection. In developing the EISPC Energy Zones Mapping Tool (EZ Mapping Tool), Argonne, NREL, and ORNL closely collaborated with the EISPC Energy Zones Work Group which coordinated the work on the Energy Zones Study. The main product of the Energy Zones Study is the EZ Mapping Tool, which is a web-based decision support system that allows users to locate areas with high suitability for clean power generation in the U.S. portion of the Eastern Interconnection. The mapping tool includes 9 clean (low- or no-carbon) energy resource categories and 29 types of clean energy technologies. The EZ Mapping Tool contains an extensive geographic information system database and allows the user to apply a flexible modeling approach for the identification and analysis of potential energy zones and is publicly available at http://eispctools.anl.gov. In addition to enabling EISPC members and other stakeholders to identify areas with a high concentration of clean energy resources that could provide significant power generation in the future, another objective of the study was to promote open and transparent collaboration among state-level energy planning and regulatory agencies and to foster consistent and coordinated direction for regional and interconnection-level electricity analyses and planning. Funding for the project was provided by DOE’s Office of Electricity Delivery and Energy Reliability (DOE/OE) under the American Recovery and Reinvestment Act. Page

Considering usage context attributes in choice modeling has been shown to be important when product performance highly depends on the usage context. To build a reliable choice model, it is critical to first understand the relationship between usage context attributes and customer profile attributes, then to identify the market segmentation characterized by both sets of attributes, and finally to construct a choice model by integrating data from multiple sources. This is a complex procedure especially when a large number of customer attributes are potentially influential to the product choice. Using the hybrid electric vehicle (HEV) as an example, this paper presents a systematic procedure and the associated data analysis techniques for implementing each of the above steps. Usage context and customer profile attributes extracted from both National Household Travel Survey (NHTS) and Vehicle Quality Survey (VQS) data are first analyzed to understand the relationship between usage context attributes and customer profile attributes. Next the principal component analysis is utilized to identify the key characteristics of hybrid vehicle drivers, and to determine the market segmentations of HEV and the critical attributes to include in choice models. Before the two sets of data are combined for choice modeling, statistical analysis is used to test the compatibility of the two datasets. A pooled choice model created by incorporating usage context attributes illustrates the benefits of context-based choice modeling using data from multiple sources. Even though NHTS and VQS have been used in the literature to study transportation patterns and vehicle quality ratings, respectively, this work is the first to explore how they may be used together to benefit the study of customer preference for HEVs.

We analyze the vehicle usage and consumer profile attributes extracted from both National Household Travel Survey and Vehicle Quality Survey data to understand the impact of vehicle usage upon consumers choices of hybrid electric vehicles in the US. In addition, the key characteristics of hybrid vehicle drivers are identified to determine the market segmentations of hybrid electric vehicles and the critical attributes to include in the choice model. After a compatibility test of two datasets, a pooled choice model combining both data sources illustrates the significant influences of vehicle usage upon consumers' choices of hybrid electric vehicles. Even though the data-bases have in the past been used independently to study travel behavior and vehicle quality ratings, here we use them together. (C) 2011 Elsevier Ltd. All rights reserved.

This paper discusses the environmental effects of incorporating wind energy into the electric power system. We present a detailed emissions analysis based on comprehensive modeling of power system operations with unit commitment and economic dispatch for different wind penetration levels. First, by minimizing cost, the unit commitment model decides which thermal power plants will be utilized based on a wind power forecast, and then, the economic dispatch model dictates the level of production for each unit as a function of the realized wind power generation. Finally, knowing the power production from each power plant, the emissions are calculated. The emissions model incorporates the effects of both cycling and start-ups of thermal power plants in analyzing emissions from an electric power system with increasing levels of wind power. Our results for the power system in the state of Illinois show significant emissions effects from increased cycling and particularly start-ups of thermal power plants. However, we conclude that as the wind power penetration increases, pollutant emissions decrease overall due to the replacement of fossil fuels.

Achieving commercial building energy-efficiency targets strongly depends on the dynamics among the various market participants and how those dynamics are impacted by different physical and institutional constraints. We develop a Commercial Buildings Sector Agent-based Model (CoBAM) prototype that considers different types of decision makers and buildings - in different subsectors. For the simulations, we use data and characteristics of reference buildings developed by the U.S. Department of Energy to populate the model. These data, which address the ecology of buildings, change over time as a function of investment decisions regarding maintenance levels and technology adoption. During the simulations, the decision makers interact and decide on courses of action relevant to the buildings stock (adopting energy-efficiency measures, building new buildings, changing existing buildings, and demolishing buildings that no longer fit the needs or priorities of the owners). As the different actions of decision makers occur, the existing buildings stock is modified, sending crucial information cues back to the decision makers. These cues influence their future courses of action. The paper presents the prototype model and initial simulation results.

We present a proactive energy management framework that integrates predictive dynamic building models and day-ahead forecasts of disturbances affecting efficiency and costs. This enables an efficient management of re- sources and an accurate prediction of the daily electricity demand profile. The strategy is based on the on-line solu- tion of mixed-integer nonlinear programming problems. The framework is able to integrate forecasts of weather conditions, fuel prices, heat gains, and utility demands. We claim that a large adoption level of this proactive tech- nology can improve the predictability of the overall elec- tricity demand at high-level power grid operations.

This document contains a summary of the main findings from our full report entitled 'Wind Power Forecasting: State-of-the-Art 2009'. The aims of this document are to provide guidelines and a quick overview of the current state-of-the-art in wind power forecasting (WPF) and to point out lines of research in the future development of forecasting systems.

Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and uncertainty in wind power and to more efficiently operate power systems with large wind power penetrations. Moreover, in a market environment, the wind power contribution to the generation portofolio becomes important in determining the daily and hourly prices, as variations in the estimated wind power will influence the clearing prices for both energy and operating reserves. With the increasing penetration of wind power, WPF is quickly becoming an important topic for the electric power industry. System operators (SOs), generating companies (GENCOs), and regulators all support efforts to develop better, more reliable and accurate forecasting models. Wind farm owners and operators also benefit from better wind power prediction to support competitive participation in electricity markets against more stable and dispatchable energy sources. In general, WPF can be used for a number of purposes, such as: generation and transmission maintenance planning, determination of operating reserve requirements, unit commitment, economic dispatch, energy storage optimization (e.g., pumped hydro storage), and energy trading. The objective of this report is to review and analyze state-of-the-art WPF models and their application to power systems operations. We first give a detailed description of the methodologies underlying state-of-the-art WPF models. We then look at how WPF can be integrated into power system operations, with specific focus on the unit commitment problem.

We discuss examples of implementations of agent-based simulation models in several European countries. Agent-based modeling and simulation (ABMS) is increasingly used as a tool to analyze power markets around the world and test the robustness of electricity markets and their underlying rules. The diversity of market designs, the complexities in their configurations with multitudes of participants, and their coupling with the underlying infrastructure require simulation approaches that allow a better representation of these real-world constraints. The power of ABMS tools lies in the flexibility they provide to merge and address these requirements in a single modeling framework.

In this paper we give an overview of the Electricity Market Complex Adaptive System (EMCAS) model. EMCAS uses the agent-based modeling and simula- tion (ABMS) technique to model the market participants in electricity markets as different agents with different strategies, risk preferences, and objectives. The complex operations of an electricity market can be simulated across several time horizons from day-ahead scheduling to long- term expansion planning. The methodology used in the model is discussed and a central European case is utilized to illustrate how EMCAS can be used to analyze a power system's operation under various assumptions. The results show the effectiveness of the model, and how the ABMS approach allows the testing of different market conditions.

The objective of this study is to analyze possible long-term development options of the Zambian electric power system in the period up to 2015. The analysis involved the hydro operations studies of the Zambezi river basin and the systems planning studies for the least-cost generation expansion planning. Two well-known and widely accepted computer models were used in the analysis: PC-VALORAGUA model for the hydro operations and optimization studies and the WASP-III Plus model for the optimization of long-term system development. The WASP-III Plus model is a part of the Argonne National Laboratory's Energy and Power Evaluation Model (ENPEP). The analysis was conducted in close collaboration with the Zambia Electricity Supply Corporation (ZESCO). On the initiative from The World Bank, the sponsor of the study, ZESCO formed a team of experts that participated in the analysis and were trained in the use of computer models. Both models were transferred to ZESCO free of charge and installed on several computers in the ZESCO corporate offices in Lusaka. In September-October 1995, two members of the ZESCO National Team participated in a 4-week training course at Argonne National Laboratory near Chicago, U.S.A., focusing on the long-term system expansion planning using the WASP and VALORAGUA models. The hydropower operations studies were performed for the whole Zambezi river basin, including the full installation of the Kariba power station, and the Cahora Bassa hydro power station in Mozambique. The analysis also included possible future projects such as Itezhi-Tezhi, Kafue Gorge Lower, and Batoka Gorge power stations. As hydropower operations studies served to determine the operational characteristics of the existing and future hydro power plants, it was necessary to simulate the whole Zambezi river basin in order to take into account all interactions and mutual influences between the hydro power plants. In addition, it allowed for the optimization of reservoir management and optimization of hydro cascades, resulting in the better utilization of available hydro potential. Numerous analyses were performed for different stages of system development. These include system configurations that correspond to years 1997, 2001, 2015 and 2020. Additional simulations were performed in order to determine the operational parameters of the three existing hydro power stations Victoria Falls, Kariba, and Kafue Gorge Upper, that correspond to the situation before and after their rehabilitation. The rehabilitation works for these three major power stations, that would bring their operational parameters and availability back to the design level, are planned to be carried out in the period until 2000. The main results of the hydro operations studies are presented in Table ES-1. These results correspond to VALORAGUA simulations of system configurations in the years 2001 and 2015. The minimum, average, and maximum electricity generation is based on the simulation of monthly water inflows that correspond to the chronological series of unregulated water inflows at each hydro profile in the period from April 1961 to March 1990. The recommended hydrology dataset provided in the Hydrology Report of the SADC Energy Project AAA 3.8 was used for this study.

Nuclear energy has the potential to play an important role in the future energy system as a large-scale source of hydrogen without greenhouse gas emissions. Thus far, economic studies of nuclear hydrogen tend to focus on the levelized cost of hydrogen without accounting for the risks and uncertainties that potential investors would face. We present a financial model based on real options theory to assess the profitability of different nuclear hydrogen production technologies in evolving electricity and hydrogen markets. The model uses Monte Carlo simulations to represent uncertainty in future hydrogen and electricity prices. It computes the expected value and the distribution of discounted profits from nuclear hydrogen production plants. Moreover, the model quantifies the value of the option to switch between hydrogen and electricity production, depending on what is more profitable to sell. We use the model to analyze the market viability of four potential nuclear hydrogen technologies and conclude that flexibility in output product is likely to add significant economic value for an investor in nuclear hydrogen. This should be taken into account in the development phase of nuclear hydrogen technologies.