In this work, we developed an optimization approach to analyze the amount of energy storage and renewable generation required for 100% clean energy target. Given locations of renewable generation, we solve an optimization problem to find the amount of solar PV and wind resources at each site and to size energy storage to balance a utility’s demand. Case studies are conducted using historical weather and demand data from a medium-sized utility in New Mexico, USA.

1. Sandia National Laboratories is a multimission laboratory managed and
operated by National Technology & Engineering Solutions of Sandia, LLC,
a wholly owned subsidiary of Honeywell International Inc., for the U.S.
Department of Energy’s National Nuclear Security Administration under
contract DE-NA0003525.
SAND No. ____________
Case Studies
Energy Storage Planning for Clean EnergyTarget
David A. Copp1, Tu A. Nguyen2, RobbThomson3, Raymond H. Byrne2, Babu R. Chalamala2
1 University of California at Irvine, CA 2 Sandia National Laboratories, NM 3 Retired Fellow at NIST, MD
Email: dcopp@uci.edu, tunguy@sandia.gov, robbm@toast.net, rhbyrne@sandia.gov, bchalam@sandia.gov
Abstract
In this work, we developed an optimization approach to analyze the amount of energy storage and renewable generation
required for 100% clean energy target. Given locations of renewable generation, we solve an optimization problem to find
the amount of solar PV and wind resources at each site and to size energy storage to balance a utility’s demand. Case
studies are conducted using historical weather and demand data from a medium-sized utility in New Mexico, USA.
Methodology
A mathematical optimization
problem is formulated the
objective of minimizing energy
resource sizes while ensuring
that the utility's demand is met at
all times.
The constraints of this problem
include:
• Storage constraints: state
of charge constraints.
• Power balance constraint:
load must be met.
• Curtailment constraint:
renewable curtailment must be
limited.
• Interconnection
constraint: the energy
exchanged with other regions
must be limited.
Formulation
Conclusions
In this work, energy storage and renewable resources planning for clean energy target have been studied. Specifically, the
contributions include: 1. An optimization approach for sizing required renewable resources and energy storage to balance
the demand of a utility with 100% renewable generation, 2. Results from case studies for a utility in New Mexico.
Case Studies – Results
Objective function:
Storage and renewable constraints:
• Case studies are conducted using
different historical demand profiles and
weather data from 2016 to 2018. In each
case, we analyze different trade
scenarios.
Power balance constraint:
Acknowledgements
Curtailment and interconnection constraints:
Image Credit: NREL
New Mexico Solar Resource Map New Mexico Wind Resource Map
Case Description
1 2018 demand and TMY data
2 2017 demand and weather data
3 2016 demand and weather data
4 Case 1 with only solar resources
5 Case 1 with only wind resources
Key takeaways:
• PV+ES need is dominating against wind.
• Larger ES to accommodate wind vs PV.
• Energy exchange leads to less resources
Funding provided by US DOE Energy Storage Program managed by Dr. Imre Gyuk of the DOE Office of Electricity.
1021119

2. Sandia National Laboratories is a multimission laboratory managed and
operated by National Technology & Engineering Solutions of Sandia, LLC,
a wholly owned subsidiary of Honeywell International Inc., for the U.S.
Department of Energy’s National Nuclear Security Administration under
contract DE-NA0003525.
SAND No. ____________
References
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Incorporating Technology-specific Nonlinear Models,” in IEEE Transactions on Power Systems, vol. 34, no. 5,
pp. 3706 – 3715, April 2019.
[2] Byrne, R. H., Nguyen, T. A., Copp, D. A., Chalamala, B. R., & Gyuk, I, “Energy Management and
Optimization Methods for Grid Energy Storage Systems,” in IEEE Access, vol. 6, pp. 13231-13260, 2018.
[3] T. A. Nguyen, D. A. Copp, R. H. Byrne, “Stacking Revenue of Energy Storage System from Resilience, T&D
Deferral and Arbitrage,” accepted for the 2019 IEEE Power and Energy Society General Meeting, Aug 2019,
Atlanta, GA.
[4] D. A. Copp, T. A. Nguyen, and R. H. Byrne, “Adaptive Model Predictive Control for Real-time Dispatch of
Energy Storage systems,” accepted for the 2019 IEEE American Control Conference, Jul 2019, Philadelphia, PA.
[5] A. Ingalalli, A. Luna, V. Durvasulu, T. Hansen, R. Tonkoski, D. A. Copp, T. A. Nguyen, “Energy Storage
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and Energy Society General Meeting, Aug 2019, Atlanta, GA.
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General Meeting, Aug 2018, Portland, OR. (Selected for Best Paper Session in Power System Planning, Operation,
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[10] T. A. Nguyen, R. H. Byrne, B. R. Chalamala and I. Gyuk, “Maximizing The Revenue of Energy Storage
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Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM 2018), June 2018,
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the German Secondary Regulation Market,” in the proceedings of the 2018 IEEE Transmission and Distribution
Conference and Exposition, Apr 2018, Denver, CO.
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Symposium, Morgan Town, WV, 2017, pp. 1-7.