Optimization Energy Planning August 2012

1. OptimizationEnergy Planning August 2012 here

2. 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

3. Energy Planning Applications New Builds/retirements Maintenance Schedule Operating Policies Detailed by-period results

4. Plexos Applications • Long-term decision horizon • 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

5. 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

6. Simplified LT Plan Formulation for Generation Expansion • Sum for all hours and years and generators: • Minimise the following Objective Function:DFy x BuildCostg x Pmaxg x GensBuiltg,yDFy x FOMCostg x Pmaxg x (GensExistg + GensBuiltg,y)DFh x [VoLL x USEh + (SRMC) x GensLoadg,h] • Subject to: • GenLoadg,h + USEh = Demandh • GenLoadg,h <= Pmaxg x (GensExistg + GensBuiltg,y) • Integrality: GensBuiltg,y is an integer Expansion Dispatch Linkage

7. 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)

8. 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

9. 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 probabilityfor 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

10. 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

11. Projects • Wind Forecasting • Establish a centralized wind forecasting program. • To investigate how the integration of large amount of renewables impact: • Ancillary reserve requirements to back up intermittent generation. • 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. Lookup Y Lookup X