Embed Size (px)
Citation preview
Dr. Pramod KhargonekarProfessorUniversity of Florida
Pramod P. Khargonekar
Department of Electrical and Computer Engineering
Smart Grid and Integration of Renewable Energy
Impact of the Southeast in the World's Renewable Energy Future
SEC SymposiumAtlanta, GA
February 12, 2013
AcknowledgementsJoint work with E. Bitar (Cornell), K. Poolla & P. Varaiya (Berkeley), E. Bayenes (Valladolid), R. Rajgopal (Stanford), M. Fang (Florida), …
Support from NSF and Florida Energy Systems Consortium
Electric Grid - Background
Smart Grid
Renewable Generation
Grid Integration
Storage
Future Directions
Outline
Focus on system operations, not on specific hardware technologies
High voltage transmission network is a mesh network
Distribution networks are largely radial networks
(Socio-economic-technical) Dynamic system with phenomena at many time-scales
Milliseconds, seconds, minutes, hours, days, months, and years
Geographically distributed yet tightly interconnected
Electric energy storage is very expensive and nearly impossible
Energy produced must equal energy consumed on a second-by-second basis – power balance
A complex hierarchical distributed control system has evolved over the years to ensure stability and
performance of the large scale networked power system
Electric Grid Characteristics
Power BalancePower balance – balance power generation and consumption on a second-by-second basis
Main Approach: adjust supply to meet demand with reliability
Natural uncertainty in consumption [load]
Use of reserve capacity to manage uncertainty and contingencies
Day-ahead – hourly schedules, one day ahead
Real-time – 5 minute schedules, 15 minutes ahead
Automatic generation control using system frequency
Deregulation of the electricity sector – unique mix of engineering and economics
Smart Grid =Power Grid + Sensors +
Communications+ Computation + Control
Source: DOE
Smart Grid is a Vision
“a stronger, smarter, more efficient electricity infrastructure that will
encourage growth in renewable energy sources, empower consumers to reduce their energy use, and lay the foundation
for sustained, long-term economic expansion”
Steven Chu, U.S. Energy Secretary, 2009
Renewable Electricity Production
Hydro
Biomass
Geothermal
Wind
Solar
Source: EPRI
Mismatch Between Production and Consumption
Missing Link: Transmission Capacity
US Wind Capacity
US PV Installation
Source: IREC USA
Renewable Electricity Generation
Source: EIA
Power output varies in all time frames:
Annual
Seasonal
Daily
Hours
Minutes
Seconds
Variability of Wind and SolarSource: CAISO
Intermittency, uncontrollability, and uncertainty - principal causes of difficulty at the operational
level in integration of wind and solar into the grid.
Source: Integration of Renewable Resources at 20% RPS, CAISO, 2010
Large ramps, up and down,pose particular difficulties
Uncertainty – reliable predictions of power output are hard, particularly day ahead
Uncontrollability – power output cannot be controlled as desired
Intermittency – even if we could predict perfectly, the power output is inherently variable
Variable Generation captures all three aspects into a single phrase
Variability – Three Distinct Issues
Regulatory guidelines require periodic assessment and multi-year planning to ensure sufficient generation capacity to meet demand
LOLP - 1 day in 10 years criterion (p=0.9997)
Resource Adequacy (RA) requirements; capacity markets
Particularly challenging in deregulated markets
Capacity credit
Nameplate capacity fraction for meeting RA requirements
Several probabilistic analysis techniques for CC calculation
What is the capacity credit of VG (wind)?
~PJM, MISO – 13%, NYISO – 10% (summer), SPP – 10%, E.ON – 8%, …
Could be even less at deep penetration
What are the impacts on power system planning?
How much traditional generation can be displaced by VG?
What happens at deep penetration of VG?
Capacity Credit
Better forecasts are extremely valuable
Prediction error decreases with horizon
Forecast of wind velocity or solar flux – numerical weather prediction models
Conversion of wind velocity or solar flux into electric power
Use of neural networks and statistical forecasting methods
Natural opportunity for nonlinear estimation methods enabled by sensors and communications
Event detection methodologies to predict the timing and magnitude of ramp events
ForecastingB
otte
rud e
t al, 2
010
CA
ISO
Renewable IntegrationMany large scale studies have been conducted in the last few years
Eastern Wind Integration and Transmission Study (EWITS)
Western Wind and Solar Integration Study (WWSIS)
CAISO Integration Studies
Integration of Variable Generation Task Force (IVGTF)
NREL Studies, European projects, …
General conclusions: With sufficient transmission capacity, we can go up to 20-30% renewable electricity with significant impacts on power systems operations – implications for:
Markets, reserves, balancing areas, flexibility of production stack, fast ramping resources, storage, adjustable demand, ….
This is the focus of our work
Large simulation studies to estimate the impact of 20% and 33% RPS
Load following reserves:
2,292 MW in 2006 - 3,207 MW in 2012 - 4,423 MW in 2020
Up regulation reserves
277 MW in 2006 - 512 MW in 2012 - 1,135 MW in 2020
Questions:
Is there a more rigorous method for estimating the additional reserves?
Are there techniques to reduce the need for these additional reserves?
Will/should renewable producers be required to provide their own reserves?
California Results
Wind power is taken to be a discrete-time stochastic process
Normalized to name-plate capacity:
CDF of the wind power process:
Time averaged CDF:
Wind Power Model
Two settlement market
Wind producer offers a contract for constant power C in the DAM at price p
Imbalance prices in the real-time market: q shortfall price, l excess price
Imbalance prices q and l are taken to be random variables while p is assumed to be known
Market Model
Contract, CWind power w
Forward day-ahead market Real-time market
power
time1 T
p (q, l)
Profit Function
Energy shortfall
Energy excess
Assumptions: (1) Wind power producer is a price taker
(2) Wind power and prices are uncorrelated
Optimal Policy is a Quantile Policy
Storage
Source: Electrical Storage Association
Topic of intense research worldwideWill have a major impact on Renewables and electric transport
Energy Storage Model
Constraints
Stored energy dissipation - aInjection and extraction efficiency – hinj, hext
Same two settlement market as before
At each time instant, we can choose to inject energy into or extract energy from the storage device to maximize the net profit:
Optimal Contract with Storage
S = net injection = Pinj - PextStorage operation policy = g
Admissible policies: all feedback policies that depend on the past values of w and e
Lemma: Greedy policy, g*, is optimal.
Theorem: J(g*, C) is concave in C.
Theorem: Optimal profit is a concave, monotone non-decreasing function of the storage size .
Results
J
Consider a collection of geographically dispersed VG producers.
Intuition: Averaging can reduce variability
Questions:
Can a group of wind power producers increase their collective profits by aggregating and offering their power output as a single entity?
What profit sharing policy will ensure that the individual producers cooperate?
Benefits of Aggregation
Bayens et al. CDC’2011
Current: adjust the generation to meet random demand
Future: adjust demand to meet random generation
Flexible Demand: heating, air-conditioning, refrigeration, water heaters, EVs, …
These are energy consumers, not power consumers
Questions:
How can we optimize aggregate and optimize flexibility of large numbers of individual flexible loads?
How can sensing and communications be used for distributed control of flexible loads?
What incentive and pricing mechanisms will be effective in getting consumers to participate in adjustable demand programs?
How can these distributed resources be integrated into power system operations with large RG penetration?
Paradigm Change
Scenario:
Large numbers of solar, wind, CHP, and micro-generators in the distribution system
Adjustable demand, electric vehicles
Storage
Sensing, communications, computing, control (SG)
Questions:
What is the optimal, scalable, control and communications architecture to control such a large scale distributed power system?
How can we do this while respecting the legacy centralized grid and minimize the need for additional reserves?
What level of renewable penetration can be achieved in such a distributed scenario?
Answer:
GRIP: Grid with Intelligent Periphery
Coordinated aggregation & control using smart grid sensing, communications, computation, and control
Distributed Renewable Generation
Bakken et al, SmartGridCom’2011
E. Bitar, A. Giani, R. Rajagopal, D. Varagnolo, P. P. Khargonekar, K. Poolla, P. P. Varaiya, “Optimal Contracts for Wind Power Producers in Electricity Markets,” Proc. 50th IEEE Conference on Decision and Control, pp. 1919-1926, 2010.
E. Bitar, R. Rajagopal, P. P. Khargonekar, and K. Poolla, “Optimal Bidding Strategies for Wind Power Producers: the Role of Reserve Margins and Energy Storage,” Proc. American Control Conference, pp. , June 2011.
E. Bitar, P. P. Khargonekar, and K. Poolla, “Systems and Control Opportunities in Smart Grid and Renewable Integration,” Proc. 2011 IFAC World Congress, Milan, ITALY.
D. Bakken, A. Bose, K. M. Chandy, P. P. Khargonekar, A. Kuh, S. Low, A. von Meier, K. Poolla, P. P. Varaiya, and F. Wu, “GRIP – Grids with Intelligent Periphery: Control Architectures for Grid2050,” Proc. IEEE SmartGridComm, 2011.
A. Giani, E. Bitar, M. Garcia, M. McQueen, P. P. Khargonekar, and K. Poolla, “Smart Grid Data Integrity Attacks: Characterizations and Countermeasures,” Proc. IEEE SmartGridComm, 2011
E. Baeyens, E. Bitar, P. P. Khargonekar, and K. Poolla, “Wind Energy Aggregation: A Coalitional Game Approach,” Proc. IEEE Conference on Decision and Control, 2011.
E. Bitar, K. Poolla, P. P. Khargonekar, R. Rajgopal, P. Varaiya, and F. Wu, “Selling Random Energy,” Hawaii International Conference on Systems Science, 2012.
E. Bitar, R. Rajagopal, P. P. Khargonekar, K. R. Poolla, and P. Varaiya, “Bringing Wind Energy to Market,” submitted for publication to IEEE Transactions on Power Systems.
Our Publications
Dr. Pramod KhargonekarProfessorUniversity of Florida
