Stochastic equilibrium modeling: The impact of uncertainty on the - PowerPoint PPT Presentation

- Oslo Centre of Research on Environmentally friendly Energy Stochastic equilibrium modeling: The impact of uncertainty on the European energy market Rolf Golombek EcoMod2016 Lisbon July 6-8, 2016 Stiftelsen Frischsenteret for samfunnsøkonomisk forskning Ragnar Frisch Centre for Economic Research www.frisch.uio.no

Uncertainty, several decision makers and market equilibrium • Several studies on uncertainty: – Informal policy exercise: scenario analysis – Formal analysis: one decision maker and/or stochastic price path independent of decisions • Basic economics: prices and quantities are interrelated – If demand for one good increases, the price of this good will be affect, and also all other prices will be affected • With no uncertainty: standard general equilibrium theory • With uncertainty: What to do when there are many agents and markets ? Frisch Centre

Contribution • Guide to transform a deterministic model with several agents and markets to a stochastic model • No programming of a stochastic solution algorithm is necessary • Case: Large equilibrium model for the European energy markets (LIBEMOD) • Framework for stochastic equilibrium modeling. Should not be mixed with scenario analysis; informal policy exercise where no agents make decisions under uncertainty • Why not use Monte Carlo simulations? Frisch Centre

Monte Carlo Simulations • Draw a value from a distribution • Put it into the deterministic model. Get an output • Repeat and find the average values • Each time agents falsely assume they know the future for sure – Investment will differ across scenarios ! • In economics: theory under certainty versus theory under uncertainty. Two different behvior of agents Frisch Centre

Modeling uncertainty • Future states – scenarios s • Each scenario is assigned a probability q • Some decisions are taken under uncertainty (investment), others are not: Like a two-period model • Seemingly complicated way of modeling uncertainty, but effecient in transforming a deterministic model to a truly stochastic model. • Our method to model uncertainty: Make one choice (investment) for each scenario s (Monte Carlo). But: Because you do not know which scenario that will materialize, you have to make the same decision for all scenarios (!) Frisch Centre

Simple model of uncertainty – 2 periods • Each possible future state has an assigned probability • Period 1: Each producer has to determine investment in energy capacity K before the uncertainty is resolved – There is a cost of investment, but no cost of production • Period 2: First, the uncertainty is resolved. Next, producer determines how much to produce, given the capacity and the price facing the producer (Standard deterministic decision problem) Frisch Centre

Investment decision problem of the producer (Period 1)     max q p K cK s s s s  s S   s.t. for all . K K s S s    • foc: q p q c s s s s ~    • Define: s s q s      for all • Rewrite foc: . p c s S s s ~   • Can show: 0 E • Number of equations: s+1 (Deterministic model: 1 equation) • In period 2 there are s standard equilibrim equations; one for each scenario s (Deterministic model: 1 equilibrium equation) • Transformation guide Frisch Centre

Numerical deterministic model of the Western European Energy Market - LIBEMOD • 16 countries • 7 energy goods • 4 types of energy users (in each country; demand 7 energy goods) • 18 electricity technologies • Investment, extraction/production, trade and consumption • Investment (if profitable) – Capacity of new power plants – Capacity of international electricity lines – Capacity of international natural gas pipes • Determines all prices, quantities and CO2 emissions • Transform deterministic LIBEMOD model to stochastic LIBEMOD model using the developed guide Frisch Centre

Uncertainty in LIBEMOD – Scenarios for 2030 • Scenarios: Uncertain economic development – growth rates, future oil and coal prices; 10 scenarios for 2030 • Investors know today (2000) the probability of each 2030 scenario and the corresponding equilibrium prices (rational expectations) • Investors decide investment today (2000) under uncerainty. Build capacities (electricity plants and international transmission capacity) for 2030. • Uncertainty is revealed in 2030. Then standard general equilibrium determination of prices and quantities Frisch Centre

Investment (GW or mtoe) under economic uncertainty 10 scenarios in 2030 Gas Electricity Total power Coal power Wind power transmissio transmissio capacity capacity capacity n n Expected 157 5 365 304 9 values Stochastic 154 16 358 250 31 Monte Carlo w. average 157 19 354 250 28 minimum 130 3 238 54 0 maximum 173 146 432 367 89 Frisch Centre

Main contribution and findings • Simple way to transform a deterministic model to a stochastic model • Stochastic model vs. using expected values: rather large differences • Stochastic model vs. Monte Carlo average: small differences for most aggregate variables, but large differences for some disaggregate variables • Extensions – Multi-period models; learing by partitioning the set of scenarios – Irreversibilities – Risk-aversion (decisions may be taken by risk-averse managers) Frisch Centre