Dealing with Uncertainty in Energy Systems Models

Overview Intro: SATIM UNEP Project – SATIM-MC MAPS Project – SATIM-SP

ERC’s Bread n Butter Model: SATIM (South African TIMES Model) Deterministic Least Cost Planning Model (Similar to Model used for IRP/IEP) Uncertainty affecting operation/short-term decisions (dispatch): – Unpredictable hourly fluctuation in wind regime, load – Unpredictable chances of a large thermal unit breaking down – Dealt with using outside model (LOLP calculator/Dispatch Model) Uncertainty affecting medium to long term decisions (investment): – Demand: Economic Growth, technology and fuel costs, Behaviour – Supply: Technology and fuel costs – This is normally dealt with using scenarios

UNEP: SATIM-MC Projecting South African CO 2 emissions to 2050 The Model : Demographics Economy Fuels SATIM energy model Least cost energy mix UNCERTAINTY Technology GHG Emissions Projection Monte Carlo algorithm SATIM Expert Elicitation Combined Elicited Distributions Resulting GHG Emissions Projection + Full Story

Some of the Distributions: Global Prices – Results of 108 runs of Imaclim-W Coal 2010 $/ton Gas 2010 $/MMBtu Oil 2010 $/bbl Without Mitigation With Mitigation (2 deg)

Other Distributions: GDP/Coal price From Expert Elicitations Coal Price Supply Curves Range R/ton Cumul. Mt GDP growth GDP Growth for 10 Samples

Stochastic TIMES Analysis of hedging strategies

Stochastic TIMES (cont.) TIMES offers the possibility of doing stochastic programming with recourse on the following parameters: – Capacity limits (which can be used to allow/disallow techs/fuels with different costs/prices) – Cumulative limits on flows (reserves) – Seasonal availabilities (useful for hydro: dry year) – Damage Costs of emissions – Demand Projections (growth) Can be used to construct up to 5 stages, with a large number of states of the world Objective function can also be altered: – Linearised expected utility criterion (where risk/variance is added to the cost) – MiniMax – least regret (Savage criterion, when likelihoods are not well known)

MAPS: SATIM-SP Use some of the distribution data from the UNEP project to analyse some hedging strategies: – Nuclear Programme, given uncertainty about: Growth Gas Price Nuclear costs – Other mitigation policies/targets, given uncertainty about: Uncertainties listed above + Damage costs? Global CO2 prices?

IRP Update Experiment Start with Big Gas scenario (Cheap gas – No Nuclear) Gas Price starts at reference step 5 ( R/GJ dropping to 45 R/GJ by 2035) Gas Price Stays High Gas Price Drops Probability of Low Price GasInstalled Nuclear Capacity %4.8 GW 25%3.36 GW 50%2.7 GW 75%0.3 GW (~0 GW) 100%0 Prelim Results