Edited by Stephen Polasky, University of Minnesota, St. Paul, MN, and approved November 2, 2015 (received for review May 26, 2015)
The large-scale conversion to 100% wind, water, and solar (WWS) power for all purposes (electricity, transportation, heating/cooling, and industry) is currently inhibited by a fear of grid instability and high cost due to the variability and uncertainty of wind and solar. This paper couples numerical simulation of time- and space-dependent weather with simulation of time-dependent power demand, storage, and demand response to provide low-cost solutions to the grid reliability problem with 100% penetration of WWS across all energy sectors in the continental United States between 2050 and 2055. Solutions are obtained without higher-cost stationary battery storage by prioritizing storage of heat in soil and water; cold in water and ice; and electricity in phase-change materials, pumped hydro, hydropower, and hydrogen.
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.
Worldwide, 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.
The key to this study is the development of a grid integration model (LOADMATCH). Inputs include time-dependent loads (every 30 s for 6 y); time-dependent 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 (H2); specifications of losses from storage, transmission, distribution, and maintenance; and specifications of a DR system.
CONUS loads for 2050–2055 for use in LOADMATCH are derived as follows. Annual CONUS loads are first estimated for 2050 assuming each end-use energy sector (residential, transportation, commercial, industrial) is converted to electricity and some electrolytic hydrogen after accounting for modest improvements in end-use energy efficiency (22). Annual loads in each sector are next separated into cooling and heating loads that can be met with thermal energy storage (TES), loads that can be met with hydrogen production and storage, flexible loads that can be met with DR, and inflexible loads (Table 1).
Projected 2050 CONUS load by sector and use in sector and projected percent and quantity of load for each use that is flexible and/or can be coupled with storage
Most (50–95%) air conditioning and refrigeration and most (85–95%) air heating and water heating are coupled with TES (Table 1). Cooling coupled with storage is tied to chilled water (sensible-heat) TES (STES) and ice production and melting [phase-change material (PCM)-ice] (SI Appendix, Table S1). All building air- and water-heating coupled with storage uses underground TES (UTES) in soil. UTES storage is patterned after the seasonal and short-term district heating UTES system at the Drake Landing Community, Canada (23). The fluid (e.g., glycol solution) that heats water that heats the soil and rocks is itself heated by sunlight or excess electricity.
Overall, 85% of the transportation load and 70% of the loads for industrial high temperature, chemical, and electrical processes are assumed to be flexible or produced from H2 (Table 1).
Six types of storage are treated (SI Appendix, Table S1): three for air and water heating/cooling (STES, UTES, and PCM-ice); two for electric power generation [pumped hydropower storage (PHS) and phase-change materials coupled with concentrated solar power plants (PCM-CSP)]; and one for transport or high-temperature processes (hydrogen). Hydropower (with reservoirs) is treated as an electricity source on demand, but because reservoirs can be recharged only naturally they are not treated as artificially rechargeable storage. Lithium-ion batteries are used to power battery-electric vehicles but to avoid battery degradation, not to feed power from vehicles to the grid. Batteries for stationary power storage work well in this system too. However, because they currently cost more than the other storage technologies used (24), they are prioritized lower and are found not to be necessary for a reliable system. Nevertheless, they could still be incorporated, but at higher cost, in this system.
PHS is limited to its present penetration plus preliminary and pending permits as of 2015. CSP is coupled with a PCM rather than molten salt because of the greater efficiency and lower cost of the PCM (25). The maximum charge rate of CSP storage (thus mirror collector size) can be up to a factor of 5 the maximum discharge rate of CSP steam turbines to increase CSP’s capacity factor (26). Here, the maximum CSP charge rate is ∼2.6 times the maximum discharge rate (SI Appendix, Tables S1 and S2), but more CSP turbines are used than needed solely to provide annual CONUS power to increase the discharge rate of stored CSP power during times of peak power demand.
The 2050 annual cooling and heating loads (Table 1) are distributed in LOADMATCH each 30-s time step during each month of 2050–2055 in proportion to the number of cooling- and heating-degree days, respectively, each month averaged over the United States from 1949 to 2011 (27). Hydrogen loads and flexible loads are initially spread evenly over each year. Annual 2050 and 2051 inflexible loads are scaled by the ratio of hourly to annual 2006 and 2007 CONUS-aggregated loads, respectively (SI Appendix, Figs. S2 and S3) (28) to give hourly 2050 and 2051 inflexible loads, which are then applied alternately between 2052 and 2055 and distributed evenly each 30-s time step each hour. DR allows initial flexible loads to be pushed forward in 30-s increments but by no more than 8 h in the base case, at which point they are made inflexible loads. However, sensitivity tests indicate that the system is also stable with no DR (SI Appendix, Fig. S14).
To maximize the environmental and social benefits of energy production and use, all 2050 loads are supplied only with WWS technologies: onshore and offshore wind turbines, rooftop photovoltaic (PV) systems, utility PV plants, CSP plants, geothermal plants, hydropower plants, tidal devices, wave devices, and solar collectors for heating fluid. SI Appendix, Table S2 provides the proposed CONUS 2050 installed capacities and capital costs of each generator type, both of which are preestimated state-by-state based on resource and load constraints, to provide 2050 all-purpose end-use power in each state (22).
The state-by-state wind and solar installations are input here into the Gas, Aerosol, Transport, Radiation-General Circulation, Mesoscale, and Ocean Model (GATOR-GCMOM), a 3D global climate/weather model (29, 30) (SI Appendix, Section S1). Wind turbines are placed near each of 42,000 existing US turbines (31, 32). Utility PV and CSP (SI Appendix, Section S1.I) are sited in deserts or low-latitude regions of states where they exist. Rooftop PV is placed in urban areas. The model predicts time-dependent winds, accounting for competition among wind turbines for limited kinetic energy at the 100-m hub height of turbines (SI Appendix, Section S1.H). It also calculates direct and diffuse solar and infrared radiation accounting for time-varying gases, aerosols, and clouds and the cooling of underlying surfaces by all PV and CSP during energy extraction. It further calculates heat release to the air during electricity use. Modeled solar and wind resources are aggregated spatially to obtain CONUS totals each 30-s time step from 2050 to 2055.
The 2050–2055 loads and intermittent resources described above are input into LOADMATCH, which prioritizes load matching, to determine whether and at what cost supply can match load.
When more instantaneous WWS electricity supply is available than needed for current inflexible plus flexible electricity loads during a time step, both loads are met immediately with the supply. Excess supply then goes first to fill non-UTES storage up to the storage limit, then to produce H2 up to its storage limit, then to fill UTES storage up to its storage limit, and last to shedding.
When instantaneous WWS electricity supply exceeds inflexible load (including H2 load not met from storage) but is less than inflexible plus flexible load during a time step, inflexible plus flexible load up to WWS supply is first satisfied with supply, and the remaining flexible load is pushed to the next 30-s time step. Any flexible load not satisfied during the previous 8 h is converted to inflexible load that is immediately satisfied first with current instantaneous supply, then with stored electricity, and last with hydropower.
When instantaneous WWS electricity supply is lower than inflexible load for a given time step, the difference is made up first from stored electricity and then from hydropower, which is used only as a last resort.
All instantaneous heat from non-CSP solar-thermal collectors first satisfies instantaneous heat load. Any excess then goes into UTES. WWS electricity can also increase UTES, but only when all of the following constraints are met: instantaneous WWS electricity supply exceeds inflexible plus flexible electricity load; all non-UTES storage is filled; and H2 storage is filled. Although UTES is not an efficient way to store excess electricity, it is more efficient than simply shedding the excess.
When instantaneous heat load exceeds instantaneous solar-thermal collector heat, the excess load is drawn from UTES. If UTES is depleted, the energy for meeting the heat load is drawn first from current WWS electricity, then in order from stored PCM-CSP electricity, PHS, and hydropower.
Hydrogen demand each time step is first met with stored hydrogen. If hydrogen storage is depleted, the remaining demand is met with electrolysis using current electricity. Hydrogen is produced, compressed, and added to storage when more electricity is available than can be put into non-UTES storage.
The model assumes a short- and long-distance transmission (T&D) system that carries power from distributed and centralized WWS generators to storage and load centers. Costs of and power losses during T&D are accounted for (Table 2, footnote), but power flows through individual lines or substations are not explicitly modeled. The model also accounts for storage costs and power losses during charging/discharging (SI Appendix, Table S1).
Summary of energy loads met, losses, energy supplies, changes in storage, and costs during the base 6-y (52,548-h) simulation
LOADMATCH is run first with a 30-s time step for 6 y, using the parameters in Table 1 and SI Appendix, Tables S1 and S2 and time-dependent wind and solar resources derived from GATOR-GCMOM. An ensemble of 19 additional simulations with different time series of wind, solar, and load inputs is also run to test the model’s robustness (SI Appendix, Sections S1.K and S1.M). The GATOR-GCMOM simulations account for extraction of and competition for kinetic energy by wind turbines (Fig. 1). The power extracted among all onshore plus offshore turbines when accounting for competition among ∼489,809 5-MW onshore plus offshore CONUS turbines (SI Appendix, Table S2) is ∼0.828 TW (Fig. 1A), giving a wind capacity factor of ∼33.81%, vs. ∼36.95% when competition is ignored. Thus, competition among turbines reduces aggregate power output by ∼0.0769 TW (Fig. 1B), or ∼8.5%, and peak wind speeds averaged over 400- × 400-km regions by up to ∼1 m/s.
(A) Difference in GATOR-GCMOM modeled (at 4° × 5° horizontal resolution) 100-m wind speed, averaged over 6 y, due to extracting kinetic energy from the wind by ∼335,400 onshore and ∼154,400 offshore 5-MW wind turbines placed state by state in the CONUS. (B) Loss in total power extracted by the turbines due to the competition for kinetic energy among them in A.
Table 2, Figs. 2–4, and SI Appendix, Figs. S4–S6 summarize results from the baseline LOADMATCH simulation. Zero load loss occurs for the base case (Table 2 and Fig. 2) and all sensitivity cases (SI Appendix, Table S3 and Figs. S7–S19). For the base case, ∼11% of all WWS energy potentially available is lost during transmission, distribution, maintenance downtime, and storage. Zero electricity shedding occurs because all excess electricity goes into either hydrogen production or storage. Some excess solar heat is shed when UTES storage is full (Table 2). Energy summed over all storage at the end of the simulation slightly exceeds that at the beginning (Table 2).
(A) Six-year (72-mo) time series comparison of modeled CONUS-aggregated power generation vs. load plus losses plus changes in storage plus shedding. (B) Breakdown of power generation for the same period. (C) Breakdown of load plus losses plus changes in storage plus shedding. (D) Breakdown of changes in storage.
(A) Time series comparison each hour of modeled CONUS-aggregated power generation vs. load plus losses plus changes in storage plus shedding for July 1–4, 2052. (B) Breakdown of power generation for the same period. (C) Breakdown of load plus losses plus changes in storage plus shedding.
(A) Time series comparison each hour of modeled CONUS-aggregated power generation vs. load plus losses plus changes in storage plus shedding for January 14–17, 2055. (B) Breakdown of power generation for the same period. (C) Breakdown of load plus losses plus changes in storage plus shedding.
Figs. 2–4 and SI Appendix, Figs. S4–S6 indicate supply exactly matches load plus losses and changes in storage at all times. Solar and wind are complementary seasonally (Fig. 2) and diurnally (Figs. 3 and 4 and SI Appendix, Figs. S4–S6). Seasonally, CONUS-aggregated wind peaks during winter; solar peaks during summer. Daily, wind peaks at night and is often lowest when solar is greatest during the day.
Collected solar heat is added to UTES during summers and removed primarily during winters. Conversely, electricity is drawn from storage other than UTES during summers to provide peaking electricity. Hydropower is used only sporadically and only when other storage is depleted.
The 2050 delivered social (business plus health and climate) cost of all WWS including grid integration (electricity and heat generation, long-distance transmission, storage, and H2) to power all energy sectors of CONUS is ∼11.37 (8.5–15.4) ¢/kWh in 2013 dollars (Table 2). This social cost is not directly comparable with the future conventional electricity cost, which does not integrate transportation, heating/cooling, or industry energy costs. However, subtracting the costs of H2 used in transportation and industry, transmission of electricity producing hydrogen, and UTES (used for thermal loads) gives a rough WWS electric system cost of ∼10.6 (8.25–14.1) ¢/kWh. This cost is lower than the projected social (business plus externality) cost of electricity in a conventional CONUS grid in 2050 of 27.6 (17.2–54.4) ¢/kWh, where 10.6 (8.73–13.4) ¢/kWh is the business cost and ∼17.0 (8.5–41) ¢/kWh is the 2050 health and climate cost, all in 2013 dollars (22). Thus, whereas the 2050 business costs of WWS and conventional electricity are similar, the social (overall) cost of WWS is 40% that of conventional electricity. Because WWS requires zero fuel cost, whereas conventional fuel costs rise over time, long-term WWS costs should stay less than conventional fuel costs.
In sum, an all-sector WWS energy economy can run with no load loss over at least 6 y, at low cost. As discussed in SI Appendix, Section S1.L, this zero load loss exceeds electric-utility-industry standards for reliability. The key elements are as follows: (i) UTES to store heat and electricity converted to heat; (ii) PCM-CSP to store heat for later electricity use; (iii) pumped hydropower to store electricity for later use; (iv) H2 to convert electricity to motion and heat; (v) ice and water to convert electricity to later cooling or heating; (vi) hydropower as last-resort electricity storage; and (vii) DR. These results hold over a wide range of conditions (e.g., storage charge/discharge rates, capacities, and efficiencies; long-distance transmission need; hours of DR; quantity of solar thermal) (SI Appendix, Table S3 and Figs. S7–S19), suggesting that this approach can lead to low-cost, reliable, 100% WWS systems many places worldwide.
The authors received no external support for this work.
Author contributions: M.Z.J. designed research; M.Z.J. and M.A.D. performed research; M.Z.J., M.A.D., M.A.C., and B.A.F. contributed analytic tools; M.Z.J., M.A.D., and M.A.C. analyzed data; and M.Z.J., M.A.D., M.A.C., and B.A.F. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data available upon request (from M.Z.J.).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1510028112/-/DCSupplemental.
