This study addresses the greatest concern facing the large-scale integration of wind, water, and solar (WWS) into a power grid: the high cost of avoiding load loss caused by WWS variability and uncertainty. It uses a new grid integration model and finds low-cost, no-load-loss, nonunique solutions to this problem on electrification of all US energy sectors (electricity, transportation, heating/cooling, and industry) while accounting for wind and solar time series data from a 3D global weather model that simulates extreme events and competition among wind turbines for available kinetic energy. Solutions are obtained by prioritizing storage for heat (in soil and water); cold (in ice and water); and electricity (in phase-change materials, pumped hydro, hydropower, and hydrogen), and using demand response. No natural gas, biofuels, nuclear power, or stationary batteries are needed. The resulting 2050-2055 US electricity social cost for a full system is much less than for fossil fuels. These results hold for many conditions, suggesting that low-cost, reliable 100% WWS systems should work many places worldwide.energy security | climate change | grid stability | renewable energy | energy cost W orldwide, the development of wind, water, and solar (WWS) energy is expanding rapidly because it is sustainable, clean, safe, widely available, and, in many cases, already economical. However, utilities and grid operators often argue that today's power systems cannot accommodate significant variable wind and solar supplies without failure (1). Several studies have addressed some of the grid reliability issues with high WWS penetrations (2-21), but no study has analyzed a system that provides the maximum possible long-term environmental and social benefits, namely supplying all energy end uses with only WWS power (no natural gas, biofuels, or nuclear power), with no load loss at reasonable cost. This paper fills this gap. It describes the ability of WWS installations, determined consistently over each of the 48 contiguous United States (CONUS) and with wind and solar power output predicted in time and space with a 3D climate/weather model, accounting for extreme variability, to provide time-dependent load reliably and at low cost when combined with storage and demand response (DR) for the period 2050-2055, when a 100% WWS world may exist. Materials and MethodsThe key to this study is the development of a grid integration model (LOADMATCH). Inputs include time-dependent loads (every 30 s for 6 y); timedependent intermittent wind and solar resources (every 30 s for 6 y) predicted with a 3D global climate/weather model; time-dependent hydropower, geothermal, tidal, and wave resources; capacities and maximum charge/ discharge rates of several types of storage technologies, including hydrogen (H 2 ); specifications of losses from storage, transmission, distribution, and maintenance; and specifications of a DR system. Loads and Storage. CONUS loads for 2050-2055 for use in LOADMATCH are derived as follows. Annual CONUS loads are first e...

We are especially grateful to Walter Short who first envisioned and developed the WinDS and ReEDS models. We also thank the NREL analysts who provided input on the technology costs, assumptions, and methodologies in ReEDS, including

Wind and solar PV generation data for the entire contiguous US are calculated, on the basis of 32 years of weather data with temporal resolution of one hour and spatial resolution of 40×40 km 2 , assuming site-suitability-based as well as stochastic wind and solar PV capacity distributions throughout the country. These data are used to investigate a fully renewable electricity system, resting primarily upon wind and solar PV power. We find that the seasonal optimal mix of wind and solar PV comes at around 80% solar PV share, owing to the US summer load peak. By picking this mix, long-term storage requirements can be more than halved compared to a wind only mix. The daily optimal mix lies at about 80% wind share due to the nightly gap in solar PV production. Picking this mix instead of solar only reduces backup energy needs by about 50%. Furthermore, we calculate shifts in FERC (Federal Energy Regulatory Commission)-level LCOE (Levelized Costs Of Electricity) for wind and solar PV due to their differing resource quality and fluctuation patterns. LCOE vary by up to 35% due to regional conditions, and LCOE-optimal mixes turn out to largely follow resource quality. A transmission network enhancement among FERC regions is constructed to transfer high penetrations of solar and wind across FERC boundaries, based on a novel least-cost optimization approach.

This study explores various scenarios and flexibility mechanisms to integrate high penetrations of renewable energy into the United States (US) power grid. A linear programming model-POWER-is constructed and used to (1) quantify flexibility cost-benefits of geographic aggregation, renewable overgeneration, storage, and flexible electric vehicle charging, and (2) compare pathways to a fully renewable electricity system. Geographic aggregation provides the largest flexibility benefit with ~5-50% cost savings, but each region's contribution to the aggregate renewable portfolio standard (RPS) target is disproportionate, suggesting the need for regional-and-resource-specific RPS targets. Electric vehicle charging yields a lower levelized system cost, revealing the benefits of demand-side flexibility. However, existing demand response price structures may need adjustment to encourage optimal flexible load in highly renewable systems. Two scenarios with RPS targets from 20% to 100% for the US (peak load ~729GW) and California (peak load ~62GW) find each RPS target feasible from a planning perspective, but with 2x the cost and 3x the overgeneration at a 100% versus 80% RPS target. Emission reduction cost savings for the aggregated US system with an 80% versus 20% RPS target are roughly $200 billion/year, outweighing the $80 billion/year cost for the same RPS range.

AcknowledgmentsWe gratefully acknowledge the many people whose efforts contributed to this report. The ReEDS modeling and analysis team at the National Renewable Energy Laboratory (NREL) was active in developing and testing the ReEDS model v.2018. We also acknowledge the vast number of current and past NREL employees on and beyond the ReEDS team who have participated in data and model development, testing, and analysis. We are especially grateful to Walter Short who first envisioned and developed the Wind Deployment System (WinDS) and ReEDS models. We thank for their comments and improvements on successive versions of this report. Finally, we are grateful to all those who helped sponsor ReEDS model development and analysis, particularly supporters from the U.S. Department of Energy (DOE) but also others who have funded our work over the years.