Amir badkoubeh, Ph.D., P.Eng., · 2019. 7. 11. · Amir badkoubeh, Ph.D., P.Eng.,...
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Amir badkoubeh, Ph.D., P.Eng.,
Cognitive Systems Inc. Founder & President
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PIMS Workshop on Mathematical Sciences and Clean Energy Applications, UBC, May 2019.
Cognitive Systems Inc.
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Outline:
• Technological and social drivers in the clean energy
• Smart Grid ( moving from central to distributed energy systems),
• Decision making-Algorithm to improve efficiency,
• Improve Quality of Service,
• Safety concerns for equipment and system level,
• A non-intrusive condition monitoring,
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Conventional Energy Systems:
Electro-mechanical Devices (Generators)
Energy Sources
Load(Converts Electricity into different forms of work)
Transmission Line
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Future Electric Energy Systems:
Electro-mechanical Devices (Generators)
Energy Sources
Load(Converts Electricity into different forms of work)
Transmission Network
Electro-mechanical
Device
Photo-voltaic Device
Energy Sources
Demand Response
PHEVs
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Technological and social drives in energy systems:
• Multiple objective (reliable, efficiency and environmental)
• Non-homogenous and non-utility-owned resources
• Renewable resources and demand response
• Technology drivers: Cost-effective ICT, GPS synchronized wide-area measurement systems (WAMS).
• Emergence of electricity market,
• Technologies for plug-&-play deployment
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Increasing presence of renewable energy resources which are environmentally attractive with fast rate of response but Intermittent .
3 major questions for reliability and efficiency:
1) Better Prediction of Intermittent Resources
2) More efficient utilization of intermittent resources
3) More reliable operation of intermittent resources
Clean Energy drives and concerns:
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Moving from Central to Distributed Socio-Ecological Systems [1]:
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The changing role of decision making:
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Today’s Power Grid
(centralized objective subject to
many constraints (externalities)
``Smart Grid”
(multi-layered interactive
coordination of objectives)
Deliver supply to meet given demand Deliver power to support supply and
demand schedules in which both supply
and demand have costs assigned
Deliver power assuming a predefined tariff Deliver electricity at QoS determined by
the customers willingness to pay
Deliver power subject to predefined CO2
constraint
Deliver power defined by users’
willingness to pay for CO2
Deliver supply and demand subject to
transmission congestion
Schedule supply, demand and transmission
capacity (supply, demand and transmission
costs assigned); transmission at value
Use storage to balance fast varying supply
and demand
Build storage according to customers
willingness to pay for being connected to a
stable grid
Build new transmission lines for forecast
demand
Build new transmission lines to serve
customers according to their ex ante
(longer-term) contracts for service
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ICT
ICTICT
ICT (Sensors, Communications, Control & Decision)
“Smart Grid” electric power grid
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What is the minimum number of measurement and a sufficient (accurate but not complex) model?
Measurement and Modelling:
ICT
ICT
ICT
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Interaction Variable:
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A means of going from very coarse to granular model and back.
framework for relating engineering design, financial & environmentalobjectives.
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Decision-Making Algorithm: Efficient Utilization
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Economic Dispatching (ED):
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Given a mixture of energy resources, how to determine the output of individual energy resources so that:(1) power supply always balances demand (2) total generation cost is minimized.
Economic Dispatch (ED): Choose output levels from conventional power plants to meet the “net load” at minimum cost [2].
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Model Predictive Control (MPC)[2]:
At each step, a finite-horizon optimal control problem is solved but only one step is implemented.
Markove Model to predict wind, ad demand).
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Numerical Example (New):
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load data from New York Independent System Operator, available online at
http://www.nyiso.com/public/market_data/load_data.jsp
IEEE RT Model
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Elastic demand that respond to time-varying price.
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Numerical Example (New) [3,4]:
Capacity Saving
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Optimal Control of Plug-in –Electric Vehiles: Fast v.s. Smart Charging (Rotering, 2009)
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Decision-Making Algorithm: Improve the Quality of Service (QoS)
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Example: Flatness Systems for Automated Control Generation (AGC)
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AGC is a system for adjusting the power
output of multiple generators in response to changes in the load.
A system is differential flat if we can
define the system inputs and states
based on a so-called “flat” output and a
finite number of its differentiations.
(A tool to transform a nonlinear system to linear control problem).
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Reliable Operation (Safety & Protection)
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Harmonic Resonance & Su-Synchronous Harmonics
o Harmonic resonance problem (transformer destroyed by the resonance of specific harmonic);
o Sub-synchronous resonance (SSR) between turbine shafts and series capacitor banks (long transmission lines).
Peculiar safety challenges at the system level:
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IEEE 14-Bus System
Safety Problem Caused by Harmonic Resonance [5,6]
System-dependent; disturbance-dependent
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Wind Gust
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Nonlinear Load connected to Bus 3
Harmonic Propagation for the 5th harmonics. The Percentage of Harmonic Voltage to Normal Voltage at each Bus
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Harmonic Source at Bus 6
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Harmonic Propagation for the 5th harmonics. The Percentage of Harmonic Voltage to Normal Voltage at each Bus
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Harmonic Source at Bus 8
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Harmonic Propagation for the 5th harmonics. The Percentage of Harmonic Voltage to Normal Voltage at each Bus
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Reliable Operation (Safety & Protection)
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Fast Dynamics Matter! For more details contact [email protected]
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A Data-Driven Solution for Fast Dynamics:
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Non-Intrusive Harmonic Monitoring:
Output Voltage
Harmonic Distortions
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Non-Intrusive Health (Condition) Monitoring:
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Calculated Machine Torque
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Non-Intrusive Health (Condition) Monitoring:
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• Bearing faults;
• Blade problems;
• Low efficiency, heat effect;
• Gearbox/transmission problems;
• Unbalance/misalignment shaft;
• Rotor/stator faults like cracked rotor;
• Electrical (Distortions, Current imbalance);
• Loose windings, foundation, connections/contactors;
• Various Vibrations (e.g. cavitation, Stick-Slip, loos foundation, etc.).
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Non-Intrusive Health (Condition) Monitoring:
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Conclusion:• Moving from a central system to a distributed systems,
• Challenges and Opportunities for Mathematicians (decision making),
• Peculiar case of sub-harmonics and the fast dynamics,
• Non-Intrusive Condition monition and data-driven modelling (as a part of servoc offered by Cognitive Systems to improve machinery reliability).
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Thank You!
www.cognitvesystems.caE: [email protected]
T: 4034379780
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Reference:
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[1] Elinor Astrom, A General Framework for Analyzing Sustainability of Social-Ecological Systems, Science 24 Jul 2009.
[2] L. Xe, M. Illic, Model Predictive Dispatch in Electric Energy Systems with Intermittent Resources, 2009.
[3] N. Abdel‐Karim and M. Ilic,"Short Term Wind Speed Prediction by Finite and Infinite Impulse Response Filters: A StateSpace Model Representation Using Discrete Markov Process", IEEE PowerTech Conference, Romania June 2009[4] J. Joo and M.D. Ilic, “A Multi‐Layered Adaptive Load Management (ALM) system: information exchange betweenmarket participants for efficient and reliable energy use,” IEEE PES Transmission and DistributionConference.
[5] E. Allen, et al., Effects of torsional dynamics on nonlinear generator control, IEEE Transactions on Control Systems Technology.
[6] D. Jeltsema, J.M.A. Scherpen, Multidomainmodeling of nonlinear network and systems, Control Systems Magazine, 2009.
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Harmonic Dampening:
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Model Predictive Control:
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New Technical Problems:
• The energy system, including its communication and control, does not readily enable choice and mulit-participant information exchange and processing for aligning [often] conflicting goals.
• It is essential to design intelligence for T&D operations to align these goals an concequently to make the most out of availbe resources while simultaneously offering robust and afforbale quality of service.
• New flexible energy processing equipment will also be needed to handle increasing variety and bandwith of many participants requests.
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