How much wind and solar are needed to realize emissions benefits from storage?

Naga Srujana Goteti1*, Eric Hittinger2, Eric Williams1

Supplementary Information

1.1 Summary of Storage Services in the United States

Global Energy Storage Database developed by Sandia National Laboratories, supported by Department of Energy, US is an open source, up-to date information on grid connected energy storage projects [1]. It has 22 detailed categories of storage services- energy arbitrage being one of the services. Apart from energy arbitrage, we categorized the other 21 types of storage services into 7 major types illustrated in the Fig. 1. Those are storage for residential purposes, reserve capacities, for integrating renewables to grid (given as renewable energy support), ramping, power quality, power backup, frequency regulation, and demand response. Overall, out of 24 GW of storage capacity in the US, 21 GW provide arbitrage services.

Fig. 1 Total energy storage capacities of different services offered by storage facilities in the US. Y-axis represents the different services provided by the storage, and X-axis represents the total capacities of these services in MW.

1.2 Summary of data sources used in economic dispatch model is shown in Table 4.

Table 1 Summary of data sources used in economic dispatch model. *-Detailed data sources of fuel costs are given in Table 1 in the main text.

Parameters Database SourcesElectricity load demand Real-time market data available from NYISO and

MISO [2, 3]Power plants data eGrid database [4]Variable O&M cost at plant level EIA [5]Carbon Tax EPA[6]Fuel costs EIA Electricity database*

Hourly wind variation Eastern Wind Integration Dataset [7]Hourly solar variation Eastern Solar Integration Dataset [8]

1.3 Variations in Wind and Solar energy

The average hourly variations of the wind and solar energy in MISO region across the 30 potential sites chosen from Wind Integration National Database (WIND) Toolkit [7]and Eastern Solar Integration Database [8] by NREL respectively is as shown in the Fig. 2.

The screenshot of the potential sites of wind energy on the map as seen on the NREL Wind Prospector interface based on the Eastern Wind Dataset, [9] is shown in the Fig. 3. Out of all the points, 30 potential locations, 2 from each state under the Midcontinent ISO (MISO) are chosen. Most of the locations have capacity factor of wind greater than 0.4.

Fig. 2 Average Variability of the Wind and Solar Energy across 15 potential sites chosen in the MISO region. The variability is shown for a sample 1kW capacity system to understand the system output/kW/hour given in kWh/hour.

Fig. 3 . Screenshot of the potential sites of wind energy on the map as seen on the NREL Wind Prospector interface, based on the Eastern Wind Integration Dataset. The pink shaded region indicates the states under Midcontinent ISO (MISO). The color gradient of the dots indicates the capacity factor of the wind power plants- Green being the lowest (0.032) and red being the highest (0.472). The average capacity factor of most of the sites in MISO is 0.4.

1.4 Emissions factors of storage with addition of solar and wind

Fig. 4 Change in emissions per delivered electricity from storage with the addition of wind/solar energy on the grid in the Midcontinent ISO (MISO). CO2eq. emissions/MWh decrease as wind/solar are added to the grid and for

slower charging rates.

References

1. Sandia National Laboratories, and Strategen Consulting: DOE Global Energy Database. US Department of Energy (2017). http://www.sandia.gov/ess/doe-global-energy-storage-database/ (accessed on 12- Nov 2016)

2. MISO: Historical Regional Forecast and Actual Load- Summary, Market Reports. MISO (2015), https://www.misoenergy.org/Library/MarketReports/Pages/MarketReports.aspx (accessed on 18- June 2016)

3. NYISO: Integrated Real-Time Load Data, NYSO (2015), http://www.nyiso.com/public/markets_operations/market_data/load_data/index.jsp (accessed on 18- June 2016)

4. US Environmental Protection Agency (EPA).: Emissions & Generation Resource Integrated Database (eGrid), US EPA (2014), https://www.epa.gov/energy/egrid (accessed on 3- Apr 2017)

5. US Energy Information Administration (EIA): Cost and Performance Characteristics of New Generating Technologies, Annual Energy Outlook 2017. EIA (2017), https://www.eia.gov/outlooks/aeo/assumptions/pdf/table_8.2.pdf, (accessed on 10- Aug 2017)

6. US EPA.: The Social Cost of Carbon, Environmental Protection Agency (2016), https://19january2017snapshot.epa.gov/climatechange/social-cost-carbon_.html

7. Draxl, C., Clifton, A., Hodge, B.-M., McCaa, J.: The Wind Integration National Dataset (WIND) Toolkit. Appl. Energy. 151, 355–366 (2015). doi:10.1016/j.apenergy.2015.03.121

8. National Renewable Energy Laboratory (NREL): Solar Power Data for Integration Studies. NREL (2006), http://www.nrel.gov/grid/solar-power-data.html (accessed on 20 April 2017)

9. Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa.: Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit, (2015)