Greenhouse Gas Mitigation Options in the U.S. Electric Sector: A ReEDS Analysis.

• Author: Sullivan, Patrick
• Position: Report

1. INTRODUCTION

   This paper, written in conjunction with EMF 24, is a discussion of the implications of various climate policy options across a set of possible technology development pathways. The model, ReEDS, on which this analysis is based focuses on the modeling of renewable energy technologies and their integration into the electric sector under each of the policy and technology futures considered. Policy options include both technology standards--mandated targets for energy generation from clean or renewable sources--and carbon caps that we can roughly compare the efficiency of. Optimistic and pessimistic technology improvement and availability futures for low carbon energy sources allow investigation of the sensitivity of the mitigation pathways to technology development

   ReEDS, in particular, brings a focus on renewable energy sources and integration to the EMF 24 questions. Renewable resource supply curves and detailed transmission representation allow us to suggest possible geographic distribution of renewable energy development and to show that greenhouse gas mitigation pathways for the electric sector can rely on a technologically and geographically diverse portfolio of investments. Similarly, the operational details and emphasis on maintaining electric reliability while integrating variable renewable resources allow us to discuss how the rest of the generating fleet responds to growth in renewable technologies under various policy options, and what renewable technologies contribute to or demand from the electric system as a whole.

2. OVERVIEW OF THE REEDS MODEL

   The Regional Energy Deployment System (ReEDS) model is a long-term, capacity-expansion model for the deployment of electric power generation technologies and transmission infrastructure throughout the contiguous United States. Developed by the National Renewable Energy Laboratory (NREL), ReEDS is designed to analyze critical issues in the electric sector, especially those issues of particular importance to integration of variable renewable resources (Short, et al. 2011). This makes ReEDS particularly well-suited to analysis of potential energy policies, such as clean energy- and renewable energy standards or restrictions on carbon emissions.

   ReEDS provides a detailed representation of electricity generation and transmission systems and specifically addresses a variety of issues related to renewable energy technologies, including accessibility and cost of transmission, regional quality of renewable resources, seasonal and diurnal load and generation profiles, variability and uncertainty of wind and solar power, and the influence of variability on the reliability of electric power provision. ReEDS addresses these issues through a highly discretized regional structure, explicit statistical treatment of the variability in wind and solar output over time, and consideration of ancillary service requirements and costs.

   A linear program optimization model, ReEDS determines potential expansion of electricity generation, storage, and transmission systems on a least-cost basis, considering load and reliability requirements, resource constraints, transmission limitations, and policy considerations. ReEDS is unique among capacity expansion models for its regional structure (see Figure 1) and statistical treatment of the impact of variability of wind and solar resources on capacity planning and dispatch. It is able to capture both the impact of resource uncertainty on system reliability and the ability of resource diversity to mitigate resource uncertainty not only because of its regional resolution, but also because it utilizes a sequential formulation: 23 optimizations performed seriatim, each reflecting a 2-year window of expansion planning. Problem coefficients can be updated between solves to capture non-linearities of system expansion: load growth; parameters reflecting integration concerns of wind and solar generators; retirements; transmission capacity changes.

   Time in ReEDS is subdivided within each 2-year period: ReEDS distinguishes four seasons, each with a representative day comprising four diurnal time-slices. There exists one additional super-peak time-slice representing the handful of hours per year with the highest load. These 17 annual time-slices enable ReEDS to capture much of the dynamics of meeting electric loads that vary throughout the day and year.

   ReEDS considers a full suite of generating technologies: hydropower, simple and combined-cycle natural gas, several varieties of coal, oil/gas steam, nuclear, wind, solar (both thermal and photovoltaic), geothermal, biopower, and a handful of electricity storage systems. Although ReEDS includes all major generator types, it has been designed primarily to address the market issues that are of the greatest significance to renewable energy technologies. As a result, renewable and carbon-free energy technologies and barriers to their adoption are a focus. Diffuse resources, such as wind and solar power, come with concerns that conventional dispatchable power plants do not have, particularly regarding transmission and variability. The model examines these issues, primarily by using a much higher level of geographic resolution than other long-term large-scale capacity expansion models: 356 different resource regions in the continental United States. These 356 resource supply regions are grouped into larger regional groupings--134 reserve-sharing groups (in ReEDS parlance, power control areas, or PCAs), states, North American Electric Reliability Council (NERC) regions (NERC 2010), and interconnects. States are also represented for the inclusion of state policies.

   Much of the data inputs to ReEDS are tied to these regions and derived from a detailed geographic information system (GIS) model/database of the wind and solar resource, transmission grid, and existing plant data (Lopez et al. 2012). The geospatial detail of renewable resources enables ReEDS to consider tradeoffs between high-quality remote resource and accessible but lower-quality alternatives as well as the benefits of dispersed wind farms or solar power facilities for reducing the integration burden through resource diversification.

   Annual electric loads and fuel price supply curves are exogenously specified to define the system boundaries for each period of the optimization. To allow for the evaluation of scenarios that might depart significantly from the base scenario, price elasticity of demand is integrated into the model and the exogenously-defined demand projection can be adjusted based on a comparison of the computed electricity price with an externally specified expected price.

   In sum, ReEDS generates scenarios that describe type and location of conventional and renewable resource development over the next few decades; transmission infrastructure expansion requirements of those installations; composition and location of generation, storage, and demand-side technologies needed to maintain system...