day3 panel2b ilic - ieee · 2014-03-07 · today’s transmission grid tomorrow’s transmission...
TRANSCRIPT
The Challenges and Opportuni3es in Educa3ng Electric Energy Systems
Marija Ilic [email protected] ISGT2014 Panel on Educa3on Washington,DC 01/20/2014
Outline • Boom-‐and-‐bust cycles of electric energy systems educa3on in the United States
• Difficult ini3al condi3ons • Future electric energy systems programs—key to formula3ng and developing methods for mee3ng the grand societal challenges in energy
• Progress at CMU’s ECE
Many boom-‐and-‐bust cycles in the US electric energy educa3on
• Boom #1 : The biggest contribu3on of the 20th century –electrifica3on • Well established programs (even en3re departments on electric power
engineering—RPI). • Bust #1: Closing of power engineering programs and labs at leading
universi3es; educa3on and research on life support. • Boom #2: Restructuring of electric power industry-‐-‐-‐ economics, policy
disciplines gain recogni3on. Engineering knowledge assumed. • Bust #2: Restructuring problems –markets ``not working’’—they never
were designed nor implemented to support physics of electric power grids. • Boom #3: Energy and environment emerge as key social goals. Young
minds very excited and mo3vated to make the vision a reality. • Bust#3-‐-‐??? The biggest danger-‐-‐-‐ overwhelming complexity; change driven
by technology breakthroughs, social drivers. A very real danger of not mee3ng the expecta3ons.
•
Difficult ini3al condi3ons • For a long 3me not recognized as the key intellectual discipline; complexity of problems the same as in integrated circuits, healthcare, transporta3on, and many other complex network systems.
• Hard to a_ract the best young minds -‐ funding and ins3tu3onal encouragement lacking -‐ electric power industry has not offered the most exci3ng jobs -‐ non-‐compe33ve salaries
• ``The only industry in which it is impossible to do innova3on’’ (quote from a major venture capitalist)
• Moving forward-‐-‐Requires pa3ence and perseverance
The basic challenge
• Importance of the area; new problem; what needs fixing and why; ques3onable prac3ces
• Physical, informa3on and economic incen3ves closely aligned
• The key challenge—integrate combina3ons of technologies at value (non-‐unique designs, OK as long as G,T and D work together to add value to the system as a whole)
• Examples of value of different technologies
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It works today, but… • Increased frequency and dura3on of service interrup3on (effects measured in billions)
• Major hidden inefficiencies in today’s system (es3mated 25% economic inefficiency by FERC)
• Deploying high penetra3on renewable resources is not sustainable if the system is operated and planned as in the past (``For each 1MW of renewable power one would need .9MW of flexible storage in systems with high wind penetra3on” –clearly not sustainable)
• Long-‐term resource mix must serve long-‐term demand needs well
Must take a systems view.. • Need to define system efficiency and assess technology
deployment with this in mind; efficiency improvements of individual components do not add to the system-‐level efficiency
• Different technologies naturally lend themselves to different 3me horizon over which they bring value
• Different technologies bring different spa3al value to the system, everything else being equal
• Short-‐ and long-‐term efficiency and reliability enhancements enabled by systema3c resource management
• Framework (not a single technology) needed for mul3-‐temporal and mul3-‐spa3al decision processes (investment, maintenance, unit commitment, dispatch) and automa3on.
New systems engineering challenge • Not a best effort problem; guaranteed performance • Highly nonlinear dynamics • Complex 3me-‐space scales in network systems (milliseconds—10 years; one town to Eastern US )
• Inadequate storage • Large-‐scale op3miza3on under uncertain3es • Complex large-‐scale dynamic networks (energy and cyber) • Informa3on and energy processing intertwined • Framework required for ensuring guaranteed performance (no single method will do it!)
Future Power Systems-‐Diverse Physics
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
Customer
Customer
Generator
Transmission Operator
ISO – Market Makers FERC
Contextual complexity
Customer
XC
Distribution Operator
PUC
Demand Aggregators
Supply Aggregators
Generator Generator
Scheduling Power Traders
Some Utilities Are all Three
“Smart Grid” çè electric power grid and ICT for sustainable energy systems
Core Energy Variables
• Resource system (RS)
• Genera3on (RUs)
• Electric Energy Users (Us)
Man-‐made Grid
• Physical network connec3ng energy genera3on and consumers
• Needed to implement interac3ons
Man-‐made ICT
• Sensors • Communica3ons • Opera3ons • Decisions and control
• Protec3on • Needed to align interac3ons
Ques3onable prac3ce • Nonlinear dynamics related -‐Use of models which do not capture instability -‐All controllers are constant gain and decentralized (local) -‐Rela3vely small number of controllers -‐Poor on-‐line observability
• Time-‐space network complexity related -‐faster processes stable (theore3cal assump3on) -‐conserva3ve resource scheduling (industry) -‐-‐ weak interconnec3on -‐-‐fastest response localized
-‐-‐lack of coordinated economic scheduling -‐-‐ linear network constraints when op3mizing resource schedules -‐-‐preven3ve (the ``worst case” ) approach to guaranteed performance in abnormal condi3ons
Transforma3onal change in objec3ves of future energy systems
Today’s Transmission Grid Tomorrow’s Transmission Grid
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
DYMONDS-‐enabled Physical Grid
Future electric energy systems programs
• Must educate the next genera3on work force • Must do so in the context of, and centered in, Electrical and Computer
Engineering (ECE) • Must integrate ECE with other academic disciplines • Must also address non-‐technical issues (policy, economics) • Recent awareness of an educa3onal void, and a sense of urgency to
innovate and integrate electric energy systems educa3on, into exis3ng curricula
The burden on new leaders • Rethink how to plan, rebuild and operate an infrastructure which has been turned upside-‐down from what it used to be
• Leaders must understand – 3ϕ physics (the basic founda3ons) – Modeling of complex systems (architecture-‐dependent models,
components and their interac3ons, performance objec3ves) – Dependence of models on sensors and actuators; design for desired
system performance (defined by economic policy and engineering specifica3ons)
– Numerical methods and algorithms – IT
Objec3ves for modern electric energy systems programs
• Not only a novel educa3on, but mul3-‐disciplinary coverage across ECE and beyond
• Provide conceptual problem formula3on (understand how models, sensing, control and communica3on are different for sample systems: 1) old centralized infrastructure; (2) deregulated industry; and, (3) industry with lots of distributed sensors, controllers, intermi_ent genera3on, demand-‐side.)
• Introduce novel simulators/graphics/visualiza3on to teach these concepts.
Modern Electric Energy Systems at Carnegie Mellon
• Lots of fun; the number of graduate students is high and growing; the number of students taking classes is high and growing. Grass-‐root pressure from students.
• Students genuinely interested in careers in future energy systems (drawn to the area to serve mankind while s3ll doing engineering)
• Emphasis on systems formula3on (instead of on component physics); smart grid as an enabler.
• Much novel modeling for “transla3ng” a physical and business system and its objec3ves into the language of systems, control, sensors, signal processing, computer science and IT; power electronics-‐enabled control.
• Team-‐teaching with business and public policy faculty.
Electric Energy Systems Group (EESG) hNp://www.eesg.ece.cmu.edu
• A mul3-‐disciplinary group of researchers from across Carnegie Mellon with common interest in electric energy.
• Truly integrated educa3on and research • Interests range across technical, policy, sensing, communica3ons, compu3ng and much more; emphasis on systems aspects of the changing industry, model-‐based simula3ons and decision making/control for predictable performance.
A sample of subjects currently offered in ECE • 18-‐418 Electric Energy Processing: Fundamentals and Applica3ons • 18-‐875/19-‐633/45-‐855/45-‐856 Engineering and Economics
Problems in Future Electric Energy Systems • 18-‐618 Smart Grids and Future Electric Energy Systems • 18-‐777 Large-‐scale Dynamic Systems • Courses taught with an eye on regulatory, technological changes,
and the implica3ons of these on problem posing and possible solu3ons.
• Courses emphasize commonali3es across different electric energy systems (power systems-‐power distribu3on to homes; shipboards, aircravs and cars.
• In house sovware development to support the curriculum – (Graphical) Interac3ve Power Systems Simulator ((G)IPSYS).
• Many courses outside ECE
Closing remarks
• There exists now a highly unusual window of opportunity to introduce modern electric energy research and educa3on programs
• Obvious societal needs • CMU is a great environment
– Boundaries across disciplines fluid – Very strong disciplines needed for developing embedded intelligence (CS, security, sensor networks, signal processing)
• We will waste this opportunity without a full understanding of the
-‐poten3al of embedded IT-‐enabled intelligence in the new resources