© Siemens AG 2012
Energy Sector
CAISO Technical Workshop
Market Development – Vendor Perspective
Enable market models enhancements and scale-up :
more and new modeling elements (bids, resources, services…)
more detailed, accurate models of system operations
finer time grids and larger number of time intervals
dealing with risks and future uncertainties (stochastic optimization)
While ensuring acceptable worst case performance
average or expected execution times not as important as lowering the probability of time outs
Page 2 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Motivation
Listen and seek feedback/input
Give insight in some important work behind scene
No impact on present but enabling future features
Improving experience
Better positioning to serve the Industry to manage emerging challenges
Share some ideas
Page 3 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Topics
1. Continuous Model Implementation Improvements
2. Increasing horizon length, number of time intervals and two stage stochastic optimization
3. Using Third Party Software
4. Integrated Outage Coordination
5. Review of Modeling Improvements
6. Market Simulation Potentials
Page 4 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Continuous Model Implementation Improvement
Achieving the same with “improved model”
What does “improved” mean?
Fewer constraints and variables (type 1)
Solving small systems of equations (constraints) and fewer variables to examine/price out
Fewer decisions to make in case of binary variables
Better LP relaxations/approximations (type 2)
Less branching/ searching by improved search tree pruning
Lower MIP gap and faster termination due to better lower bounds
Numerically better scaled model (type 3)
Variation of the problem parameters, e.g. avoid ε , large penalties,..
Some recent examples
Energy Sector
Page 5 Energy Automation M.Aganagic, S. Rajagopal
CAISO Technical Workshop
Startup Time and Cost Modeling
STT and STC increase with cool-off time
Industry standard approach:
Setup cool-off time counter
Increment counter each interval resource is OFF
If starting, check counter against STT/STC counter, enforce STT and account for STTC
If shutting down, reset the counter to 0
STT/C model size: 13 constraints, 7 integer variables ; in total 10-15% constraints and variables
STT/C for MSG , and longer horizons will aggravate situation further – a possible roadblock
Page 6 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
STT/C Improvement – Type1
If STT and STC curves have same cooling break points, number of constraints and variables can be reduced to 7 and 4
Being implemented for Summer release
Testing shows about 5% performance improvement on average
Page 7 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
STT/C Improvement – Type 1-3
Type 1-3 discussed for Fall Release
Page 8 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Overcoming limitations of (MI) LP approach
(MI) LP approach is commonly used
Improve to include equal Lambda property
One implemented approach uses piecewise quadratic bids and separable QP
Challenge is to overcome this problem without impacting existing bid structure or imposing data modification
Energy Sector
Page 9 Energy Automation M.Aganagic, S. Rajagopal
CAISO Technical Workshop
Increased number of time intervals
Present:
DA – 24 hours, STUC –18 15-min intervals
3-day RUC in preparation
Potential need to extended market horizons
Addressing DA market risks
Extended horizons or potentially new markets
Longer RTPD/STUC runs
End of horizon effects
Lack of insight what comes beyond market horizon
Deterministic or stochastic view
Required by some applications – ex. Outage coordination
Multi scenario stochastic optimization
Page 10 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Two Stage Stochastic Optimization
Stochastic approach to the end-of horizon problem
Total Number of Intervals = N + k* M
High Forecast [P h
%]
Market Horizon
N - intervals
Typical Forecast [P t
%]
Low Forecast [P l
%]
M - intervals
100 = P t
+ P h
+ P l
Page 11 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Non- uniform time intervals
Presently time intervals of uniform length
(1 hour, 15 and 5 min)
Longer time horizons may require coarser time intervals for farther out in the horizon
To manage problem size
To filter out forecast errors
Towards a generalized optimization engine with non-uniform time intervals
Finer time intervals around peak load periods, for example
Page 12 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Third Party Software
CPLEX presently, but we have been reviewing/ benchmarking progress of the competition
Exploring platform architectures multi-core/ multithreads and platform architectures (SMP vs NUMA)
Exploration optimal settings for CAISO markets for
CPLEX new versions
Page 13 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Integrated Outage Coordination
Currently Outage requests are accepted or denied based on reliability assessment
Current method lacks evaluation of win-win benefits of keeping Generation and Transmission resources in operation when valued the most
We can exploit ISO’s complete view of all outage information
We can provide tools to rationalize decisions in coordination
Prototype is under evaluation to validate the results of optimal coordination of outages with reliability assessment
Optimal Transmission Switching and Integrated Outage
Coordination
Page 14 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Modeling Improvements
Non-Generating Resources
Generic models to allow representations of Storage and Dispatchable
Demand Response
Allow modeling of any combination of demand response and distributed energy resources
Performance improvements allow us to reduce MIP GAP to accommodate commitment of small MW nature of NGR/DDR resources
Islanded operation of Market
Network Application already models islanding
Market Application consideration of Energy balance based on islands, and handling of LMPs in islands is achievable
Enhanced contingency modeling
Representation of Remedial Action Schemes (RAS) during contingency analysis allow screening of contingencies for preventive correction
Parallelism to accommodate a larger number of contingencies
Energy Sector
Page 15 Energy Automation M.Aganagic, S. Rajagopal
CAISO Technical Workshop
Modeling Improvements (continued)
Co-optimization of gas network and electric market
Work being done for vertical UC for fuel constraints (bid curves relative to fuel used, dynamic fuel switching, gas day models, different fuel contracts and constraints)
IFM-RUC pass integration
Similar to consolidation of LMPM and SMPM, combined IFM-RUC is doable
Market Results Validation
In the process of analyzing Market results Validation methods as a derivative of results of Optimization
Possible to provide such validation tools for ISO as well as Market Participants comparing input data and market clearing results
Energy Sector
Page 16 Energy Automation M.Aganagic, S. Rajagopal
CAISO Technical Workshop
Market Simulation Potentials
CAISO - Market Operator Training Simulator [On-going effort]
Market Operator Training Simulator constructed from two days of Production system Market Save cases;
Integrated with Grid Operator Training Simulator;
Transmission and Market type events of “What if” nature inserted into simulation to
Prepares operators for managing a wide variety of system operation scenarios
Page 17 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Market Simulation Potentials [continued]
ISO-Market Participant Training Simulator [Future Possibility]
Exact representation starting with a base line bid set
Initial operating conditions from a selected past date
Transmission simulator
Market System Simulator
Settlement
Access rights based view of results
Similar to Market Simulation, but conducted for different context
Page 18 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector
CAISO Technical Workshop
Market Simulation Potentials [continued]
Market Participant - Market Clearing Simulator [Future
Possibility]
Base case constructed from ISO published data of
PSS/E cases
Past anonymous bids
Forecast, and outages to coincide with a past day
Bids assignment to location by means of Engineering approximation and judgment and adjustments by the user
Used together with Market Participant’s own data and
ISO published data
Page 19 Energy Automation M.Aganagic, S. Rajagopal
Energy Sector