Lifetime Assurance of Energy Storage Systems · ' I ANCAJ ?U1 LAN=PEK J P E = ' I ANCAJ ?U1 LAN=PEK...
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Unrestricted © Siemens AG 2016 All rights reserved. Lifetime Assurance of Energy Storage Systems IMA: Optimization and Uncertainty Quantification in Energy and Industrial Applications Benjamin Lee, Utz Wever, Efrossini Tsouchnika [email protected] CT RDA AUC MSP-DE Munich, Germany
Transcript of Lifetime Assurance of Energy Storage Systems · ' I ANCAJ ?U1 LAN=PEK J P E = ' I ANCAJ ?U1 LAN=PEK...
Unrestricted © Siemens AG 2016 All rights reserved.
Lifetime Assurance of
Energy Storage Systems IMA: Optimization and Uncertainty Quantification in Energy and Industrial Applications
Benjamin Lee, Utz Wever, Efrossini Tsouchnika
CT RDA AUC MSP-DE
Munich, Germany
Corporate Technology
Unrestricted © Siemens AG 2016 All rights reserved.
Page 2 CT RDA AUC MSP
Research Group
Production Modeling & Simulation
Our Offer: Simulation Consulting, Concepts, Tool development, add-on tools
Topics
Commissioning
Modernization
Solutions for
production & networked systems
based on simulation technologies
Applications
Design Operation
• Test & Pre-FAT support
• Virtual commissioning • Integrated engineering of
complex systems & plants
• Validation of process & automation
Engineering
Development
• Optimal production
• Lifetime & service
• Crowd Control
Mathematical & Virtual Engineering
Uncertainty Quantification
Robust design optimization
Production aware design
Simulation-based system design
PLM tool chains & data formats (JT)
Virtual commissioning
Model Management, generation
• Mechatronic System Design
• Multiphysics
• Individualized Production
SPES
Software Plattform Embedded Systems XT
XT
Operational Excellence
Crowd Control
Hybrid analytics
Model-predictive control
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Energy Storage System Operation Decision
Demands Decisions Calculations Status
Current State
T = ti
Operation
T = ti Aging Models
Electricity Price
T = ti
Emergency
Power Demand
T = ti
Replace or
Repair?
Reliability
Estimation
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Aging and Damage Mechanisms
• High temperature
• Extreme voltage
Corrosion
• Long periods at low
charge
Sulfation
• Static operation
Stratification
• Capacity Loss
• Internal Resistance
gain
• Current Leak gain
Damage
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Aging and Damage Mechanisms
• High temperature
• Extreme voltage
Corrosion
• Long periods at low
charge
Sulfation
• Static operation
Stratification
• Capacity Loss
• Internal Resistance
gain
• Current Leak gain
Damage
Physical Models:
If the capacity falls below a
given level (usually 80%),
end of life is achieved
Judgment: Model must be
very complex (3D reaction
kinetics). Not possible within
the CPU time constraints
Data-driven Models:
Manufacturers offer charts of
cycles to failure for various
factors. Consumed lifetime
can be determined
Judgment: Simple model,
but literature claims very
reasonable results
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Cyclic – based aging estimation
10 20 30 40 50 60 70 80 90
Lif
eti
me in
Cycle
s
Depth of Discharge [%]
10 20 30 40 50 60 70 80 90
Red
ucti
on
in
Lif
e
[%/c
ycle
] ∆
L
Depth of Discharge (DoD) [%]
Well modeled by:
∆L = C1 ∙ exp (C2 ∙DoD)
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Energy Storage System Operation Decision
Demands Decisions Calculations Status
Manf.
Aging
Spec.
Current State
T = ti
Operation
T = ti Aging Models
Electricity Price
T = ti
Emergency
Power Demand
T = ti
Replace or
Repair?
Reliability
Estimation
Corporate Technology
Unrestricted © Siemens AG 2016 All rights reserved.
Page 8 CT RDA AUC MSP
Energy Storage System Operation Decision
Demands Decisions Calculations Status
Manf.
Aging
Spec.
Current State
T = ti
Operation
T = ti Aging Models
Electricity Price
T = ti
Emergency
Power Demand
T = ti
Replace or
Repair?
Reliability
Estimation
Uncertain future
demand for peak
shifting and price of
power
Error in manufacturer’s
specification and aging
models
Loads are defined over
entire ESS, but individual
cell reactions vary slightly
Delay between repair
decision and installation
Real-time decision
making on operation
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Maximize:
𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑃𝑟𝑜𝑓𝑖𝑡 = 𝐄 𝑁𝑜𝑚𝑖𝑛𝑎𝑙𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡 ∙ 𝐸𝑛𝑒𝑟𝑔𝑦𝑃𝑟𝑖𝑐𝑒𝑡 − 𝑅𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝐶𝑜𝑠𝑡𝑡 𝑡
Subject To:
𝑝𝑟 𝐸𝑚𝑒𝑟𝑔𝑒𝑛𝑐𝑦𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 ≤ 𝑆𝑡𝑎𝑡𝑒𝑡𝑖 𝑖 ≥ 99.9%,∀ 𝑡
𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑡+1𝑖 = 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑡
𝑖 − 𝐶1 ∙ 𝑒𝐶2 ∙𝐷𝑜𝐷𝑡 + 𝑁 µ, 𝜎
𝑆𝑡𝑎𝑡𝑒𝑡𝑖 ≤ 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑡
𝑖
𝑆𝑡𝑎𝑡𝑒𝑡+1𝑖 = 𝑆𝑡𝑎𝑡𝑒𝑡
𝑖 − 𝑁𝑜𝑚𝑖𝑛𝑎𝑙𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡𝑖 − 𝐸𝑚𝑒𝑟𝑔𝑒𝑛𝑐𝑦𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡
𝑖
𝑁𝑜𝑚𝑖𝑛𝑎𝑙𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡𝑖 ≤ 𝑅𝑎𝑡𝑒𝑑𝑃𝑜𝑤𝑒𝑟
𝑁𝑜𝑚𝑖𝑛𝑎𝑙𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡𝑖 = 𝑁𝑜𝑚𝑖𝑛𝑎𝑙𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡 𝐼 + 𝑁 µ, 𝜎
𝐸𝑚𝑒𝑟𝑔𝑒𝑛𝑐𝑦𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡𝑖 = 𝐸𝑚𝑒𝑟𝑔𝑒𝑛𝑐𝑦𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑡 𝐼 + 𝑁 µ, 𝜎
Probabilistic Composite Aging Model
Variation in
individual cells
Emergency Use
Model Inaccuracy
Reliability constraint
Demand based pricing
Result of Dispatching Algorithm
Size of System
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Next Steps
ESS
Generator
PV
Wind
• Dispatching algorithm
for optimization under
uncertainty with
reliability constraints
• Rare event prediction
• Efficient calculation of
expectation and
reliability
• Consideration of
additional components
