Power System Resilience in the Pacific Northwest Eduardo - - PowerPoint PPT Presentation
Power System Resilience in the Pacific Northwest Eduardo Cotilla-Sanchez, Ph.D., Assistant Professor School of Electrical Engineering & Computer Science College of Engineering, Oregon State University Corvallis, Oregon, USA Nov 28, 2017
Eduardo Cotilla-Sanchez, Ph.D., Assistant Professor
School of Electrical Engineering & Computer Science College of Engineering, Oregon State University Corvallis, Oregon, USA
Nov 28, 2017
Funded by the Oregon Talent Council & Department of Energy Award Number DE-OE0000780 & Electric Power Research Institute
Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies
1 Power System Protection Models
Understanding Critical Assets Blackout Risk Models
2 Cascading Failures
Building a Dynamic Cascading Failure Model Example Simulation: Poland
3 Clustering and Islanding as Resiliency Strategies
Remedial Action Schemes Automated Planning and Policy Switching Clustering
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Understanding Critical Assets Blackout Risk Models
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Understanding Critical Assets Blackout Risk Models
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Understanding Critical Assets Blackout Risk Models
Number of bus failures à 1 2
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Understanding Critical Assets Blackout Risk Models
Number of bus failures à 1 2
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Understanding Critical Assets Blackout Risk Models
Number of bus failures à 1 2
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Understanding Critical Assets Blackout Risk Models
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Building a Dynamic Cascading Failure Model Example Simulation: Poland
5 10 15 20 0.8 0.9 1 1.1 1.2
|V| time (sec.)
|V|: constant Z load
5 10 15 20 0.8 0.9 1 1.1 1.2
|V| time (sec.)
|V|: constant I load
5 10 15 20 0.8 0.9 1 1.1 1.2
|V| time (sec.)
|V|: constant P load
5 10 15 20 0.8 0.9 1 1.1 1.2
|V| time (sec.)
|V|: constant E load
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Building a Dynamic Cascading Failure Model Example Simulation: Poland
https://github.com/ecotillasanchez/cosmic.git
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Building a Dynamic Cascading Failure Model Example Simulation: Poland
150 200 250 300 350 400 450 10 10
1
Time (sec.) Number of Branch Outages
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
CHARACTERISTICS Naive and Homogenous:
Affects all loads in the network. Usually triggered by under-voltage conditions.
Adaptive and Dynamic:
Ability to select loads based on a rule. (priority, severity, electrical distance, etc.) Usually triggered by multiple conditions (UV, UF, frequency, dV
dt , dF dt ).
LS BENEFITS Direct manipulation of generation/load mismatch. Can be done in real-time. LS CHALLENGES Direct customer impact. Must be done incrementally.
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
CHARACTERISTICS Intentional creation of microgrids. Typically pre-determined based on common contingencies. Usually done based on slow-coherency, electrical distance, active/reactive power balance. BENEFITS Can isolate operational sections from affected sections. Can fully utilize DER capability to decrease impact on customers. CHALLENGES Slow calculation – usually done offline. Can cause portions of the grid to fully collapse.
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
MDPs provide well-developed theory for computational solutions to controllable and observable systems with stochastic dynamics.
S1 S3 S2 Aij, Pij,R2 Aij, Pij,R1 Aij, Pij,R3 Aij, Pij,R1 Aij, Pij,R3 A
i j
, P
i j
, R
2
MDP COMPONENTS System States (S): Time Independent Control Actions (A): Stationary or Non-Stationary Probabilistic Transition Distributions (P): Stochastic Movement Between States Rewards (R): Function of Current State
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
Policy (π) = A mapping of States (s) to Actions (a). Optimal Policy (π∗) = Policy with max Value (V ) in any given state (s). Value: V π(s) = E ∞
βtR(st)
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
i
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
1 Python/Siemens PSSe 2 Time-Domain Simulation 3 3 Different Timescales 1
1 120 s – PSSe step
2
1 10 s – Grid check
3
5 s – Emgcy. dispatch
4 Random N-2 Contingencies 5 Parallelization
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
Case Topology
39 Buses 10 Generators 19 Loads 46 Branches
N-1 Secure
Flow Limits Governor Dynamics Exciter Dynamics
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
∆P = δP δθ
δP δ |V |
(1)
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
∆Q = δQ δθ
δQ δ |V |
(2)
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
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Power System Protection Models Cascading Failures Clustering and Islanding as Resiliency Strategies Remedial Action Schemes Automated Planning and Policy Switching Clustering
Es = |Ep + jEq| (3)
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