dddams-based real-time assessment and control of · pdf file• was the root cause a...

1/44
DDDAMS-based Real-time Assessment and
Control of Electric-Microgrids
Nurcin Celik, PI
Assistant Professor Simulation & Optimization Research Lab (SIMLab)
Department of Industrial Engineering Center for Computational Sciences
University of Miami
Team: A.E. Thanos, M. Bastani, H. Damgacioglu, and D.E. Moore
DDDAS Program PI Meeting Program Director: Dr. Frederica Darema
January 2016

2/44 Outline
Motivation
Proposed DDDAMS Framework Measurements and Data Collection A1: Fault Detection and Isolation Agent-based Simulation of MGs A2: MG Control Design Selection A3: Multi-Objective Optimization
DDDAMS for Interruptible Load Management Experiments and Results
Conclusion and Future Work

3/44
USA and Canada, August 2003: Affected 55 million people
Italy, September 2003: Affected 55 million people
Java and Bali, August 2005: Affected 100 million people
Brazil and Paraguay, November 2009: Affected 87 million people
India, July 2012: Affected 670 million people
0
0.5
1
1.5
2
2.5
3 Direct Effect Loss Earning
Indirect Effect Loss Earning
Industry and Residents
Government
Power Industry
Total Economic Impact
Economic Losses (in Billion $)
Motivation for Power Grid Resilience

4/44 Motivation for a Safety-critical Air Force Base
Several questions arise in case of a power outage that affects an AF Base:
How should a real-time diagnosis and forensics analysis be performed automatically? Was the root cause a technical incident or a power contingency? Did it occur because of an accidental failure or malicious and possibly ongoing attack? A wide spread disturbance or just a localized outage of a few minutes? How should the AFB microgrid respond to this abnormality (or catastrophe)? What actions should be taken to secure the AFB power supply?
Quick responsive and corrective actions are needed via autonomous control for System Resilience

5/44 Challenges in Power Dispatch
about power networks large # of variables, nonlinearities, and uncertainties operate at various scales resources =>more distributed generation =>more intermittent system => more conducive to demand-response
about dispatch control
changing demands very large range for the solution space Intense and time-critical information exchange significant burden on computational resources (processing of massive datasets)
To produce electricity economically, environmentally, reliably, and securely
Objectives : Min Cost & Emissions Constraints: Power Balance

7/44 DDDAMS Framework
Control Configuration
Decision Variables Number of Replications
for Designs Evaluation of Designs
Database Power Price
Power Provision
Microgrid Topology
Electric Microgrid
Temperature [oF]
Electrical Parameters
Wind Speed [m/s]
Solar Radiation [W/m2]
Sensors in the Real System Wind Speed [WS-201]
Temperature [T-200A]
Solar Radiation [CMP-11]
Electrical Sensors
Algorithm 1: Fault Detection & Isolation
Sensory Data
Algorithm 2: MG Control Design Selection
Algorithm 3: Multi-objective Optimization
Sensory Data and Microgrid Topology
Industrial Load
Residential Load
Commercial Load
Grid 20 kV
WT 1
PV 25
PV 1
MT
400 V 1
2 3
4
5
6
7 8
9
10
11
12
13 14
15 16
FC
M2: Batteries
M3: Solar Generators
M1: Demand
M6: Computational Resource
Availability
M5: Diesel Generators
M4: Wind Turbines
Agent-based Simulation
State Information
Data from sensors Data from main grid Data from modules and algorithms
Investigates new algorithms and instrumentation methods for RT data acquisition and timely control
Self-configuring fidelity formation and adaptation
=> Information needs to be updated wisely in the model for savings in computing and networking resources
Estimation of system states under uncertainty
Modular modeling

8/44 Collection of Data from Various Sources
Database
Voltages, Current, Real and Reactive Power Injections
Power Systems Test Case Archive University of Washington
Sub-networks Split of the IEEE-30
Power Systems Literature Rakpenthai et al. 2005.
Atmospheric Science Data Center (NASA)/
Weather Underground Weather Profiles and Temperature
Wind Integration Datasets National Renewable Energy Laboratory (NREL)
Electrical Sensors
Wind Speed
CMP-11 Pyranometer
IEEE-30 Bus System Data
Solar Irradiance
Power generation/ Energy consumption from the AF, NREL, and EIA
Tyndall AFB map Energy consumption in the base List of AF bases with renewables Structure of the electricity network in base Voltages and angles for buses Power losses in branches Apparent power for the transformers
Effective control of microgrid systems requires all-embracing acquisition of data about major system components
Fluctuating demand profiles, power generation (conventional /renewable), differences in power planning technologies, costs and availabilities of primary energy resources, transmission/distribution capacities etc.
AFB Site Visits

9/44
4 sources of power generation within the microgrid
Diesel Generation Solar Generation Wind Generation Wave Generation
Wind Generation
Wave Generation
Solar Generation
Main Grid Provision
Considered Air Force Base Microgrid
3 types of demand Critical Priority Non-Critical
5 feeders 1 Critical 2 Priority 2 Non-Critical
Type of demand does not necessarily match the type of
feeder (priority demands in critical feeder)
Substation
Diesel Generation
+ Main Grid

10/44 Components of DDDAMS Framework
Number of Replications for Designs
Evaluation of Designs
M2: Batteries
M1: Demand
Availability
M4: Wind Turbines
State Information
A1: Fault Detection and Isolation Agent-based Simulation A2: MG Control Design Selection A3: Multi-objective Optimization
Initial system state is used for the appropriate control design
Detects system abnormalities

11/44
Measured Variable Event Possible Fault
Voltage
Increased Voltage Decreased Voltage Neutral Voltage Negative Voltage
Load-shedding Short-circuit/Motor Start-up Ground-fault Sensor Fault
Current Overcurrent Differential Current Negative Current
Overload/Short Circuit Short Circuit Sensor Fault
Impedance Low Impedance Abnormal Ratio X/R Short-circuit Short-circuit/ Sensor Fault
Frequency Low Frequency High Frequency Increased Load/Sensor Fault Loss of Load/Sensor Fault
Phase Angle Phase Angle Change Short-circuit/Sensor Fault
Temperature Increased Temperature Decreased Temperature Negative Temperature
Overload Short-circuit Sensor Fault
Power Active Power in Zero-Sequence Component Change of Direction of Power Flow Short-circuit/Sensor Fault Ground-fault/Sensor Fault
Speed Change of Oil Flow in Transformer Overload/Short-circuit Solar Irradiance Abnormal Solar Irradiance Sensor Fault Wind Speed Abnormal Wind Speed Sensor Fault
Events that may be triggered in the DDDAMS Framework:
A1: Fault Detection and Isolation (FDI)

12/44
Number of Replications for Designs
Evaluation of Designs
M2: Batteries
M1: Demand
Availability
M4: Wind Turbines
State Information
Data from sensors Data from main grid Data from modules and algorithms
A1: Fault Detection and Isolation Agent-based Simulation A2: MG Control Design Selection A3: Multi-objective Optimization
Simulation and A2 are linked and operate in synch
Components of DDDAMS Framework
Sensory Data

13/44 Agent-based Simulation

14/44
Device/System Information Sensed Information Derived
Generator
Magnitude of voltage (V) Magnitude of current (Amps) Environmental measurements
(temperature, solar irradiance, wind speed, etc.)
Power output (kW) Fuel used (gallons) Emission produced (lbs.)
PV Array Solar irradiance (W/m2)
Temperature (oF) Power output (kW) Purchase cost
Wind Turbine Wind speed (m/s) Power output (kW) Purchase cost ($)
Wave Generator Wave speed (m/s) Wave period (s) Power output (kW) Purchase cost($)
Load Magnitude of voltage (V) Phase of voltage (o) Current consumption (kW)
Power consumed (kW)
Power Network Electricity price ($/k

dddams-based real-time assessment and control of · pdf file• was the root cause a...

Documents