Transcript Slide 1

Optimization
Energy Planning
here
August 2012
About Plexos
• Plexos is the software used to produce the Integrated Resource Planning.
• PLEXOS is a MIP-based next-generation energy market simulation and
optimization software
• Co-optimization architecture is based on the Ph.D. work of Glenn Drayton*
• Advanced Mixed Integer Programming (MIP) is the core algorithm of the
simulation and optimization
• Foundation for the mathematical formulation of the New Zealand,
Australia, and Singapore energy and spinning reserve markets
• PLEXOS licensed in United States, Europe, Asia-Pacific, Russia, and Africa
(17 countries, more than 100 sites) PLEXOS 4.0 first released in 2000
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Energy Planning Applications
LT Plan – Optimal investment
New Builds/retirements
PASA – Optimal reserve share
Maintenance Schedule
MT – Resource Allocation
Operating Policies
ST – Chronological
Unit Commitment
Detailed by-period results
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Plexos Applications
– Long-term decision horizon
Decision
horizon
second
Operational Planning
Operational
minute
hour
day
week
month
year
Planning
3 years 20 years
– Long-term studies with decision variables impacting across all the
horizon, e.g.
•
Object function minimizes NPV of:
– Cost of new builds
– Cost of retirements
– Fixed operating costs
– Variable operating costs
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LT-Objective
Two types of costs
Capital costs C(x):
Cost of new generator builds
Cost of transmission expansion
Cost of generator retirements
Production costs P(x):
Cost of operating the system with any given set of
existing and new builds and transmission network
Notional cost of unserved energy
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Simplified LT Plan Formulation for Generation
Expansion
• Sum for all hours and years and generators:
• Minimise the following Objective Function:
Expansion
DFy x BuildCostg x Pmaxg x GensBuiltg,y
DFy x FOMCostg x Pmaxg x (GensExistg +
Dispatch
GensBuiltg,y)
DFh x [VoLL x USEh + (SRMC) x GensLoadg,h]
• Subject to:
Linkage
• GenLoadg,h + USEh = Demandh
• GenLoadg,h <= Pmaxg x (GensExistg +
GensBuiltg,y)
• Integrality: GensBuiltg,y is an integer
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Problem Reduction
• Aggregate the LDC to reduce the number of
variables (under LT Plan)
• Reduce the number of LDC blocks
• Change the period over which the LDC blocks are
calculated (from weeks to month, for example)
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Stochastic process in PLEXOS
• To model input values as stochastic drivers
• Define stochastic Variables in Variable class
• Specify stochastic characteristics
• Specify number of iterations for stochastic
sampling and simulation
• Properties of the Stochastic object
• Assign Variables to stochastic drivers
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Stochastic process in PLEXOS
• Define a Variable object in Variable class
• Define stochastic characteristics
• Two methods to define stochastic Variables
• Exogenous sampling: user-defined profile
samples (with assigned probability for each
sample)
• Endogenous sampling: user-defined expected
profile that will be scaled up and down by
random samples with random numbers with
user-specified distribution
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Example of Stochastic process in IRP
• Stochastic modeling for wind generation
• Process
• Define “Wind Stochastics” in Variables class
• Define Stochastic distribution of the Variable as
•Hourly sampling
•Variable.Profile = Wind Generation Profile
(wind.csv)
•Error Std Dev = 30%
•Max Value = 50, Min Value = 0
• Run a Model with the Variable, report sample details
and compare the solution with the base case
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Projects
• Wind Forecasting
 Establish a centralized wind forecasting
 To investigate how the integration
of large amount of renewables impact:
Lookup Y
program.
 Ancillary reserve requirements
to back up intermittent generation.
Lookup X
 The overall system load volatility given large amount of renewables.
 Generators ramping requirements to circumvent stochastic generation.
 System emissions: how the system’s emissions profile is impacted if stations are
cycled.
 Cost of cycling: a determined of the cost of cycling.
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