Siemens Presentation-Market Optimization Algorithm and Modeling

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CAISO Market Optimization

Algorithm & Modeling Capabilities

Technical Workshop

Folsom, CA

© 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

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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

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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

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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

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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

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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

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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

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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

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