In many climate change mitigation scenarios, integrated assessment models of the energy and climate systems rely heavily on renewable energy technologies with variable and uncertain generation, such as wind and solar PV, to achieve substantial decarbonization of the electricity sector. However, these models often include very little temporal resolution and thus have difficulty in representing the integration costs that arise from mismatches between electricity supply and demand. The global integrated assessment model, MESSAGE, has been updated to explicitly model the trade-offs between variable renewable energy (VRE) deployment and its impacts on the electricity system, including the implications for electricity curtailment, backup capacity, and system flexibility. These impacts have been parameterized using a reduced-form approach, which allows VRE integration impacts to be quantified on a regional basis. In addition, thermoelectric technologies were updated to include two modes of operation, baseload and flexible, to better account for the cost, efficiency, and availability penalties associated with flexible operation. In this paper, the modeling approach used in MESSAGE is explained and the implications for VRE deployment in mitigation scenarios are assessed. Three important stylized facts associated with integrating high VRE shares are successfully reproduced by our modeling approach: (1) the significant reduction in the utilization of non-VRE power plants; (2) the diminishing role for traditional baseload generators, such as nuclear and coal, and the transition to more flexible technologies; and (3) the importance of electricity storage and hydrogen electrolysis in facilitating the deployment of VRE.

This paper summarizes initial steps to improving the robustness and accuracy of global renewable resource and techno-economic assessments for use in integrated assessment models. We outline a method to construct country-level wind resource supply curves, delineated by resource quality and other parameters. Using mesoscale reanalysis data, we generate estimates for wind quality, both terrestrial and offshore, across the globe. Because not all land or water area is suitable for development, appropriate database layers provide exclusions to reduce the total resource to its technical potential. We expand upon estimates from related studies by: using a globally consistent data source of uniquely detailed wind speed characterizations; assuming a non-constant coefficient of performance for adjusting power curves for altitude; categorizing the distance from resource sites to the electric power grid; and characterizing offshore exclusions on the basis of sea ice concentrations. The product, then, is technical potential by country, classified by resource quality as determined by net capacity factor. Additional classifications dimensions are available, including distance to transmission networks for terrestrial wind and distance to shore and water depth for offshore. We estimate the total global wind generation potential of 560 PWh for terrestrial wind with 90% of resource classified as low-to-mid quality, and 315 PWh for offshore wind with 67% classified as mid-to-high quality. These estimates are based on 3.5 MW composite wind turbines with 90 m hub heights, 0.95 availability, 90% array efficiency, and 5 MW/km2 deployment density in non-excluded areas. We compare the underlying technical assumption and results with other global assessments.

Mitigation-Process Integrated Assessment Models (MP-IAMs) are used to analyze long-term transformation pathways of the energy system required to achieve stringent climate change mitigation targets. Due to their substantial temporal and spatial aggregation, IAMs cannot explicitly represent all detailed challenges of integrating the variable renewable energies (VRE) wind and solar in power systems, but rather rely on parameterized modeling approaches. In the ADVANCE project, six international modeling teams have developed new approaches to improve the representation of power sector dynamics and VRE integration in IAMs. In this study, we qualitatively and quantitatively evaluate the last years' modeling progress and study the impact of VRE integration modeling on VRE deployment in IAM scenarios. For a comprehensive and transparent qualitative evaluation, we first develop a framework of 18 features of power sector dynamics and VRE integration. We then apply this framework to the newly-developed modeling approaches to derive a detailed map of strengths and limitations of the different approaches. For the quantitative evaluation, we compare the IAMs to the detailed hourly-resolution power sector model REMIX. We find that the new modeling approaches manage to represent a large number of features of the power sector, and the numerical results are in reasonable agreement with those derived from the detailed power sector model. Updating the power sector representation and the cost and resources of wind and solar substantially increased wind and solar shares across models: Under a carbon price of 30$/tCO2 in 2020 (increasing by 5% per year), the model-average cost-minimizing VRE share over the period 2050–2100 is 62% of electricity generation, 24%-points higher than with the old model version.

The National Center for Atmospheric Research and the National Renewable Energy Laboratory (NREL) collaborated to develop a method to assess the interannual variability of wind and solar power over the contiguous United States under current and projected future climate conditions, for use with NREL's Regional Energy Deployment System (ReEDS) model. The team leveraged a reanalysis-derived database to estimate the wind and solar power resources and their interannual variability under current climate conditions (1985-2005). Then, a projected future climate database for the time range of 2040-69 was derived on the basis of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) simulations driven by free-running atmosphere-ocean general circulation models. To compare current and future climate variability, the team developed a baseline by decomposing the current climate reanalysis database into self-organizing maps (SOMs) to determine the predominant modes of variability. The current climate patterns found were compared with those of an NARCCAP-based future climate scenario, and the CRCM-CCSM combination was chosen to describe the future climate scenario. The future climate scenarios' data were projected onto the Climate Four Dimensional Data Assimilation reanalysis SOMs. The projected future climate database was then created by resampling the reanalysis on the basis of the frequency of occurrence of the future SOM patterns, adjusting for the differences in magnitude of the wind speed or solar irradiance between the current and future climate conditions. Comparison of the changes in the frequency of occurrence of the SOM modes between current and future climate conditions indicates that the annual mean wind speed and solar irradiance could be expected to change by up to 10% (increasing or decreasing regionally).

This report is one of several products resulting from an initial effort to provide a consistent set of technology cost and performance data and to define a conceptual and consistent scenario framework that can be used in the National Renewable Energy Laboratory's (NREL's) future analyses. The long-term objective of this effort is to identify a range of possible futures of the U.S. electricity sector in which to consider specific energy system issues through (1) defining a set of prospective scenarios that bound ranges of key technology, market, and policy assumptions and (2) assessing these scenarios in NREL's market models to understand the range of resulting outcomes, including energy technology deployment and production, energy prices, and carbon dioxide (CO2) emissions.

Our purpose is to develop a methodology to quantify the impact of climate change on electric loads in the United States. We perform simple linear regression, assisted by geospatial smoothing, on paired temperature and load time-series to estimate the heating- and coolinginduced sensitivity to temperature across 300 transmission zones and 16 seasonal and diurnal time periods. The estimated load sensitivities can be coupled with climate scenarios to quantify the potential impact of climate change on load, with a primary application being long-term electricity scenarios. The method allows regional and seasonal differences in climate and load response to be reflected in the electricity scenarios. While the immediate product of this analysis was designed to mesh with the spatial and temporal resolution of a specific electricity model to enable climate change scenarios and analysis with that model, we also propose that the process could be applied for other models and purposes.

The electric power sector both affects and is affected by climate change. Numerous studies highlight the potential of the power sector to reduce greenhouse gas emissions. Yet fewer studies have explored the physical impacts of climate change on the power sector. The present analysis examines how projected rising temperatures affect the demand for and supply of electricity. We apply a common set of temperature projections to three well-known electric sector models in the United States: the US version of the Global Change Assessment Model (GCAM-USA), the Regional Electricity Deployment System model (ReEDS), and the Integrated Planning Model (IPM®). Incorporating the effects of rising temperatures from a control scenario without emission mitigation into the models raises electricity demand by 1.6 to 6.5 % in 2050 with similar changes in emissions. The increase in system costs in the reference scenario to meet this additional demand is comparable to the change in system costs associated with decreasing power sector emissions by approximately 50 % in 2050. This result underscores the importance of adequately incorporating the effects of long-run temperature change in climate policy analysis.

We introduce a new method for incorporating short-term temporal variability of both power demand and VRE (variable renewables) into long-term energy-economy models: the RLDC approach. The core of the implementation is a representation of RLDCs (residual load duration curves), which change endogenously depending on the share and mix of VRE. The approach captures major VRE integration challenges and the energy system's response to growing VRE shares without a considerable increase of numerical complexity. The approach also allows for an endogenous representation of power-to-gas storage and the simultaneous optimization of long-term investment and short-term dispatch decisions of non-VRE plants. As an example, we apply the RLDC approach to REMIND-D, a long-term energy-economy model of Germany, which was based on the global model REMIND-R 1.2. Representing variability results in significantly more non-VRE capacity and reduces the generation of VRE in 2050 by about one-third in baseline and ambitious mitigation scenarios. Explicit modeling of variability increases mitigation costs by about one fifth, but power-to-gas storage can alleviate this increase by one third. Implementing the RLDC approach in a long-term energy-economy model would allow improving the robustness and credibility of scenarios results, such as mitigation costs estimates and the role of VRE.

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.

We apply a U.S. electric-sector capacity-expansion and dispatch model to assess possible implications—changes in generation mix, system cost, CO2 emissions, distribution of renewable energy deployment—of a set of potential greenhouse gas mitigation policy options over a range of technology projections. The model used, ReEDS, provides unique spatial and temporal detail to ensure electric-sys-tem constraints of reliable load provision are maintained throughout the system’s transformation.

Using the IMAGE/TIMER (The Targets IMage Energy Regional) long-term integrated assessment model, this paper explores the regional and global potential of offshore wind to contribute to global electricity production. We develop long-term cost supply curve for offshore wind, a representation of the potential suitable for inclusion in global integrated assessment models. For this, we combine available data on resource potential and cost estimates to estimate regional and global characteristics of offshore wind electricity generation. We find that for 2050, a baseline scenario would include about 4% of the total electricity production based on offshore wind. The findings also show that in most regions, technical potential is not a limiting factor. In some regions, that have a seriously constrained resource base for onshore wind, offshore wind could provide a key source of renewable energy, including South-East Asia, Indonesia and Brazil.

This paper examines a range of technological and regulatory approaches to reducing greenhouse gas (GHG) emissions. Availability of new technologies will control how the economy and energy infrastructure respond to any future climate policies. How such policies interact with other types of environmental regulations will also influence the best options for meeting emissions goals. To investigate these effects, the ADAGE model is used to examine policy impacts for several climate and technology scenarios, focusing on key factors such as emissions, technology deployment, energy prices and macroeconomic indicators. In general, the simulations indicate that reductions in GHG emissions can be accomplished with limited economic adjustments, although the impacts depend on both the regulatory approaches used and the future availability of new low-carbon technologies.

This paper introduces a methodology for incorporating metrics for electric-sector reliability into a global Integrated Assessment Model. Using load, resource availability, and system dispatch data with high temporal resolution, we designed a set of reduced-form constraints that guide investment and usage decisions among power plants in IIASA's MESSAGE model. The analysis examines how such reliability metrics impact modeled system build-out, including in scenarios with greenhouse gas (GHG) limits. Scenarios show how carefully chosen model constraints can allow a flexible approach to treating integrations concerns of variable renewable technologies into the electric sector in a high-level energy model.

This paper addresses the issue of how to account for short‐term temporal variability of renewable energy sources and power demand in long‐term climate change mitigation scenarios in energy‐economic models. An approach that captures in a stylized way the major challenges to the integration of variable renewable energy sources into power systems has been developed. As a first application this approach has been introduced to REMIND‐D, a hybrid energy‐economy model of Germany. An approximation of the residual load duration curve is implemented. The approximating function endogenously changes depending on the penetration and mix of variable renewable power. The approach can thus be used to account for variability and correlations between different sources of renewable supply and power demand within the intertemporal optimization of long‐term (energy system) investment decisions in climate change mitigation scenarios. Moreover, additional constraints are introduced to account for flexibility requirements concerning load‐ following and ancillary services. The parameterization is validated with MICOES a highly resolved dispatch model. Model results show that significant changes are induced when the new residual load duration curve methodology is implemented. With variability, scenarios show that the German power sector is no longer fully decarbonized because natural gas combined‐cycle plants are built to complement renewable energy generation. The mitigation costs increase by about 20% compared to a model version in which variability is not taken into account. JEL classification code: Q42, Q54

Energy system modeling can be intentionally or unintentionally misused by decision-makers. This report describes how both can be minimized through careful use of models and thorough understanding of their underlying approaches and assumptions. The analysis summarized here assesses the impact that model and data choices have on forecasting energy systems by comparing seven different electric-sector models. This analysis was coordinated by the Renewable Energy and Efficiency Modeling Analysis Partnership (REMAP), a collaboration among governmental, academic, and nongovernmental participants.

This paper analyzes potential impacts of proposed national renewable electricity standard (RES) legislation. An RES is a mandate requiring certain electricity retailers to provide a minimum share of their electricity sales from qualifying renewable power generation. The analysis focuses on draft bills introduced individually by Senator Jeff Bingaman and Representative Edward Markey, and jointly by Representative Henry Waxman and Markey. The analysis uses NREL's Regional Energy Deployment System (ReEDS) model to evaluate the impacts of the proposed RES requirements on the U.S. energy sector in four scenarios.

Rapid expansion of wind power in the electricity sector is raising questions about how wind resource variability might affect the capacity value of wind farms at high levels of penetration. Electricity storage, with the capability to shift wind energy from periods of low demand to peak times and to smooth fluctuations in output, may have a role in bolstering the value of wind power at levels of penetration envisioned by a new Department of Energy report ('20% Wind by 2030, Increasing Wind Energy's Contribution to U.S. Electricity Supply'). This paper quantifies the value storage can add to wind. The analysis was done employing the Regional Energy Deployment System (ReEDS) model, formerly known as the Wind Deployment System (WinDS) model. ReEDS was used to estimate the cost and development path associated with 20% penetration of wind in the report. ReEDS differs from the WinDS model primarily in that the model has been modified to include the capability to build and use three storage technologies: pumped-hydroelectric storage (PHS), compressed-air energy storage (CAES), and batteries. To assess the value of these storage technologies, two pairs of scenarios were run: business-as-usual, with and without storage; 20% wind energy by 2030, with and without storage. This paper presents the results from those model runs.