Tom Trapp defended his PhD thesis on April 23, 2015. His thesis addressed designing minimum cost shipboard integrated electric power (IEP) systems that meet survivability requirements. He formulated the IEP design problem as a network optimization that finds the lowest cost network topology that satisfies operational and casualty constraints. His approach models electrical power flow, cooling flow, and their interdependencies. It accounts for multiple operating conditions and casualties. The demonstration showed his approach designing a survivable IEP for a sample ship with generators, motors, pumps and stored energy, minimizing cost while meeting survivability specifications.
2. Committee Chair & Thesis Advisor
Franz Hover Assoc. Prof. MechE MIT
Committee Members
Joe Harbour Prof. of Pract. MechE MIT
Jim Kirtley Prof. ElecE MIT
Mark Thomas CAPT USN (retired)
Ed Zivi Prof. SystemsE USNA
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3. Background & Problem Statement
Contributions
Previous Work
Network Optimization / IEP Modeling
Demonstrations
Conclusions & Recommendations
3
4. Background & Problem Statement
Contributions
Previous Work
Network Optimization / IEP Modeling
Demonstrations
Conclusions & Recommendations
4
7. Shipboard IEP design for survivability
is a complex, iterative process that does
not guarantee a minimum cost solution.
The process is not well matched for the
Pareto-optimal tradeoff process used to
support cost-as-an-independent-
variable (CAIV) DoD policy.
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8. Background & Problem Statement
Contributions
Previous Work
Network Optimization / IEP Modeling
Demonstrations
Conclusions & Recommendations
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10. Electrical & Cooling Interdependence
Detailed Modeling
Flexibility to add
domains
Fischer Panda
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11. Integral design of minimum
cost stored energy
Accounting for casualty &
operating conditions
Outback Power
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12. 1. Direct Design of Minimum Cost
Survivable IEP
2. Detailed Modeling of Interdependent
Domains with Flexibility to Add
Domains
3. Stored Energy Design at Minimum
Cost
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13. Background & Problem Statement
Contributions
Previous Work
Network Optimization / IEP Modeling
Demonstrations
Conclusions & Recommendations
13
14. Petry & Rumberg – Electrical Distribution
Topology
Doerry – Survivability, QoS, Definitions
– Zonal Elec. Distribution (ZED)
– Integrated Power System
Chalfant – Breaker-and-a-Half Topology
NAVSEA – Acquisition Strategy
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15. Cramer, Zivi, Sudhoff – IEP Dynamic
Modeling & Survivability Metrics
Chan - Stored Energy Allocation
Marden, Chryssosstomidis – Model of
Purdue MVDC plant
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16. Labbé – Network Synthesis NSMCF
Woungang – Survivable Mesh
Networks
Grover – Survivable Mesh-Based
Transport Networks
Taylor – Convex Optimization of Power
Systems
16
17. Background & Problem Statement
Contributions
Previous Work
Network Optimization / IEP Modeling
Demonstrations
Conclusions & Recommendations
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21. Laws
Conservation:
Sum of flow in and out of node = supply or
demand (Continuity)
Direction:
If flows have physical restrictions
Capacity:
Flow cannot exceed limitations of edges
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23. Capacity relates to the flow limitation of the
roadway
Speed limit, Number of lanes
Rate of goods/time
Cost relates to how much it costs to transport on
the roadway
Mileage, Tolls, Wear & Tear
$/flowrate
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26. Can optimize anything that can be
modeled as flow:
Commodity Distribution
Transportation (Rail, Airlines, etc.)
Financial Investment
Electrical Power Flow
Cooling Mass Flow
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28. Now minimize cost of roadways. Account for
contingencies.
1. Minimize Capacity (instead of flow cost).
2. Roadway (1,2) could be lost sometime in future
3. Two unique set of constraints – (1) for normal
operations, (2) for the loss of roadway (1,2).
4. Find the minimum capacity (roadway limitation)
needed.
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31. Specification: Survive the loss of all roadways,
any one roadway at a time (M-1 survivability).
Make a unique set of constraints for each loss
condition.
Let the capacity variable Uij take the value of the
largest of all flow conditions.
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36. Example 1: Network flow cost minimization
Example 2: Design a minimum cost network, with
contingencies for loss of roadways.
Requires unique variables for each casualty
Design output is minimum cost survivable network.
“Non-Simultaneous Multicommodity Flow”
NSMCF
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39. 1. Flow Continuity
a. Electrical Power
b. Cooling Mass Flow
2. Fixed & Variable Cost
a. Fixed cost (of installation, etc.)
b. Variable cost (of material purchase)
3. Stored Energy
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40. 4. Constitutive Relationships
a. Cooling mass flow vs. Electrical Heat Load
b. Pump electrical power vs. Cooling mass flow
5. Casualty & Operating Conditions
a. Unique flow variables for each condition
b. “Sets” of constraints
6. Global Capacity Rollup
a. Min Edge Capacity = Max Edge Flows
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43. • Fixed Cost - The cost of existence of an edge in
the network
– Design
– Installation
– Maintenance
• Variable Cost – cost that varies with capacity
– Material cost
• Fixed cost is generally much higher than
variable cost
43
44. • Modeling objectives
– Allow the solver the option to delete the edge
using a binary variable
– When the solver deletes the edge, make sure
both fixed and variable costs are zeroed in the
solution
44
45. Minimize:
a12 x F12 + c12 x U12 + … … + a56 x F56 + c56 x
U56
Subject To:
U12 ≤ M x a12
aij = binary variable “on” or “off” aij = {0 or 1}
Fij = fixed cost of edge (i,j)
cij = variable cost (per unit capacity of edge (i,j))
Uij = capacity of edge (i,j)
M = very large number
GENERATOR
1 2
3
6
1
PUMP
2
5
COOLER
611
1
2
3
4
525
1
2
456
7
45
46. • Modeling objectives:
– Should only be used in emergency or in
specific instances. Otherwise, use a
conventional power source
– When turned on, the power flow will be
captured by the power flow capacity variable
– The capacity will be the minimum power flow
required for the design
– Multiply capacity by time = minimum energy
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47. Minimize:
a12 x F12 + … + c56 x U56 + Σ (ζij x fk
ij)
ζij = “cost” of stored energy power flow (large number)
fk
ij = stored energy power flow (casualty & op condition
k)
If ζij is large enough, stored energy will only be used
when all normal power is lost and demand is not met.
Uij will equal the minimum power flow
required.
Otherwise, all fk
ij = 0 and stored energy not required
GENERATOR
1 2
5
PUMP MOTOR
6
GENERATOR
12 13
14 15
16
PU
1
18
STORED ENERGY23
PUMP
2 3
45
COOLER
611
1
PUMP
114
1516
COOLER
1720
21
23
1
4
5
7
13
14
15
16
17
18
19
20
25 26
1
2
3
456
7
8
14
15
16
17
18
1920 21
24
25
27
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57. Minimize:
Sum of capacity costs (fixed & variable - both domains)
Subject to (for all casualty & operating conditions):
Continuity both domains
Capacity/flow min-max
Constitutive relationships
Electrical heat load vs. cooling mass flow
Pump mass flow vs. electrical power
Edge “choosing” for both domains
Using binary variables
Casualty & operating conditions
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75. • University of Wisconsin–
Milwaukee 88 BSEE
• Navy Nuclear Propulsion
Program 89-95
• MIT 95-98 NavEng, SM
ME
• Navy EDO 95-
• MIT 09-15 –PhD ME
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76. Shipboard Small scale
IEP Domain interdependent
Survivable Reserve capacity
Network Linear programming
Optimization Minimize cost
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82. Cost/unit Flow Capacity
Continuity
Edge
Capacities
N nodes
M edges
K casualties
e – electrical
t - thermal
H cooled
S powered
e – electrical
t – thermal
d, g – demand
cooling, powering
Constitutive relationships 82
90. Electrical
Capacity
“Rollup”
Pij is the maximum power flow edge (i,j)
experiences during all casualty and
operating conditions
pij
k are unique variables for each casop.
Pij are global variables (no k)
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91. Cooling
Capacity
“Rollup”
Qlm is the maximum mass flow edge (l,m)
experiences during all casualty and
operating conditions
qlm
k are unique variables for each casop.
Qlm are global variables (no k)
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93. Fixed Costs
aij & blm
Are binary variables
aij,blm Î {0,1}
Pij £ Maij
Qlm £ Mblm
M is a large number
Large enough to allow
P, Q to take values needed
To meet power, flow requirements
If edge associated with them is necessary
F, G are fixed costs associated with the
Existence of the edge
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94. Stored
Energy
ζ is a large number
Penalizes the solver for
Using power flow from node
n. ζ is sufficiently large
Such that solver will only use
When all normal power is lost
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