Cost-Effectiveness of Grid Energy Storage

Technologies in Current and Future U.S.

Power Systems

Omar J. Guerra

Josh Eichman, Bri-Mathias Hodge, and Jennifer Kurtz

NREL/PR-5D00-72709

November 1, 2018

2018 AIChE Annual Meeting

Pittsburgh, Pennsylvania
NATIONAL RENEWABLE ENERGY LABORATORY 2

Outline

Motivation

Methods

Results: Storage dispatch

Results: Cost reductions and 

curtailment 

Conclusions & future work
NATIONAL RENEWABLE ENERGY LABORATORY 3

Motivation: The Renewable Electricity Futures Study (RE Futures)

This study explores the implications and challenges of very high

renewable electricity generation levels—from 30% up to 90%,

focusing on 80%, of all U.S. electricity generation—in 2050. *

Storage versus percent renewable energy Curtailed electricity versus percent 

renewable energy

*Source: https://www.nrel.gov/analysis/re-futures.html
NATIONAL RENEWABLE ENERGY LABORATORY 4

Motivation: Electricity storage applications and technologies

“Hydrogen-based electricity storage covers large-scale and long-term storage applications 

(including seasonal energy storage)” (EIA, 2015)
NATIONAL RENEWABLE ENERGY LABORATORY 5

Goal:

Quantify the opportunity of utilizing flexibility from hydrogen systems to support

the electric grid.

Research Questions:

• Is there any cost reduction opportunity for hydrogen-based seasonal energy

storage in current and future U.S. power systems?

• How do the hydrogen seasonal storage technology benefits compare to the

technology costs (Cost-Effectiveness)?

Motivation: Project Overview

Scenarios

Renewables (43% & 85% renewable penetration incl. large hydro)

Sensitivities: Storage power capacity and Storage efficiency
NATIONAL RENEWABLE ENERGY LABORATORY 6

Methods: Modeling tools for the WECC power system

System to be modeled Optimization frameworks

WECC: Western Electricity Coordinating Council

ReEDS: Regional Energy Deployment System

RPS: Renewable Portfolio Standards

Renewable penetration scenarios were drawn from

the ReEDS Standard Scenarios “National RPS 80%”

Inputs: Technology costs, 

fuel cost, etc.

ReEDS

(Capacity planning model)

Grid resource mix

Plexos

(Production cost model)

Outputs: Dispatch order, 

Production cost, Value of 

storage, ..
NATIONAL RENEWABLE ENERGY LABORATORY 7

Methods: Modeling approaches for seasonal energy storage

Plexos MT: mid-term operational planning (one-year time frame based on load duration curve) 

Plexos ST: short-term operational optimal power flow (one-day optimization window (hourly 

resolution) with one day look-ahead (four-hour resolution)) 

RODeO: Revenue Operation and Device Optimization (one- or three-years optimization 

windows with hourly temporal resolution)

Currently considering 3 techniques for PLEXOS

Selected methods: “Internal model optimization” (preferred) & 

“External model optimization”

Method Pros Cons

Internal model optimization

(Plexos MT-ST decomposition)

• Easy constraint formation and 

model implementation

• Can leverage entire existing 

network model

• New feature: Must learn how 

to properly implement

External model optimization 

(Price-taker model, e.g., RODeO)

• Familiarity with model 

formulation

• Does not include explicit 

consideration for network 

constraints

Bid price or other heuristics

• Provides unique ways to 

integrate real-world decisions

• Simple implementation

• Requires significant amount of 

time and testing to select 

appropriate setpoints
NATIONAL RENEWABLE ENERGY LABORATORY 8

Methods: Modeling approaches for seasonal energy storage (cont.) 

• Target penalties are used in Plexos ST to match the daily end volume targets

• Weekly or monthly target could be explored

System configurations, technology 

assumptions, and signals (energy 

prices, Net Load, etc.)

RODeO

(Price-Taker model)

Plexos ST

System configurations, 

technology assumptions, 

Plexos MT

Plexos ST

End volume targets 

(daily)

Plexos MT-ST decomposition Price-taker approach
NATIONAL RENEWABLE ENERGY LABORATORY 9

Methods: Renewable penetration scenarios (2018-2050 time frame)

Examined several years to understand 

how the system is operating.

Target Year

Renewable 

Penetration 

(with large 

hydro)

Total Load 

(TWh)

2018 43% 746

2050 85% 1128

• Renewable penetrations: 43% and 85% driven by wind and solar
NATIONAL RENEWABLE ENERGY LABORATORY 10

Methods: Configuration of the storage device and scenarios 

Storage device:

• 500 MW storage with 3 months of storage duration,

• 40% roundtrip efficiency (hydrogen-based storage),

• located in P10 region

Scenario Storage device and power system Modeling approach

2018 no-storage • No seasonal storage

• WECC 2018 • Plexos MT-ST

2018 MT-ST (5% I. V.)

• Seasonal storage @ 5% initial 

volume

• WECC 2018

• Plexos MT-ST

2050 no-storage • No seasonal storage

• WECC 2050 • Plexos MT-ST

2050 MT-ST (5% I. V.)

• Seasonal storage @ 5% initial 

volume

• WECC 2050

• Plexos MT-ST

2050 MT-ST (9.6% I. V)

• Seasonal storage @ 9.6% initial 

volume (based on RODeO)

• WECC 2050

• Plexos MT-ST

2050 RODeO-ST (9.6% I. V.)

• Seasonal storage @ 9.6% initial 

volume (based on RODeO)

• WECC 2050

• RODeO-Plexos ST (energy 

price signal and daily targets)
NATIONAL RENEWABLE ENERGY LABORATORY 11

Results: Price-taker dispatch based on energy price signal

• Using 3-year time frame in RODeO helps to eliminate end-effects.

RODeO dispatch based on P10 energy price 

signal and three years time frame 

P10 energy price signal for WECC 2050
NATIONAL RENEWABLE ENERGY LABORATORY 12

Results: Dispatch for Plexos MT-ST & RODeO-Plexos ST (WECC 2050)

• Plexos MT and RODeO optimally dispatches the storage seasonally for WECC 2050.

• There are no seasonal shifts but just diurnal storage dispatches for WECC 2018 (not shown

here).

Plexos MT-ST dispatch RODeO and Plexos ST dispatch based on P10 

energy price signal and daily targets
NATIONAL RENEWABLE ENERGY LABORATORY 13

Results: Seasonal dispatch for the RODeO-Plexos ST approach
NATIONAL RENEWABLE ENERGY LABORATORY 14

Results: Cost reductions and curtailment for each scenario

• Fuel and Start & Shutdown costs are drivers of cost reductions (regardless of the

scenario).

• PV curtailment is reduced regardless of the scenario, while wind curtailment increases or

decreases depending on the scenario. This is expected given the daily and seasonal

patterns of solar generation profiles and the randomness of wind generation.

Cost reductions for each scenario Wind and Solar curtailment (% of wind or 

solar generation) for each scenario

I. V. =Initial Volume
NATIONAL RENEWABLE ENERGY LABORATORY 15

Conclusions & future work

Conclusions

• There are cost reduction opportunities for seasonal energy storage in the WECC 2050

power system (61% VRE penetration).

• The Plexos MT-ST modeling approach provides higher cost reductions than the RODeOPlexos approach.

• Seasonal energy storage reduces solar PV curtailment, while wind curtailment increases

or decreases depending on the scenario.

Future work

• Determine methodology for probing system impact of storage: (1) remove storage

from the ReEDS grid mix, then add back in or (2) remove power generation capacity.

• Develop cost-benefit analysis: Cost reduction versus capital and operating cost for the

seasonal storage device.
www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Thank you!
NATIONAL RENEWABLE ENERGY LABORATORY 17

Methods: Modeling of hydrogen-based seasonal energy storage in Plexos

Pumped-storage hydroelectric (PSH) power station object is 

used to model hydrogen production and storage devices.