Modelling Electricity Transitions with an Agent-Based Model Jörn C. Richstein

Why ABM for my question?

Challenges in Transition Modelling of the Electricity System Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Quite a typical representation of an energy-system: input/output description + influences However, modelling the system over longer time-periods is a complex task: Abundance of exogenous uncertainties Interactions between neighboring electricity systems Influence of overlapping policy instruments

Challenges in Transition Modelling Path Dependence Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Differences in: Technology specific revenue structure Technology specific learning Institutional arrangement Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Investments in power systems are very long lived They create path dependencies, by: Technology-specific changes of prospective revenues of future investments Driving technological learning Create institutional pathways Example: German decision to invest in PV: Drove learning-by-doing, which had a global impact on prices Changed the merit order, by removing the midday peak in summer, reducing peak plant / pumping storage business case Result: multiple future equilibria Historical path dependence Non-predictability Possibilities of lock-in -> so not static efficiencies (or metrics) matter, but also dynamic metrics under uncertainty. t=0 t=1 t=2

Challenges in Transition Modelling Heterogeneous Agents & Expectations Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Electricity System CO2 RES Cong. Mgtm. Security of Supply CO2 Emissions Prices Generation Policy Fuel Prices Demand Technologies Opening the black box: Since liberalisation independent actors These actors are heterogeneous, and make independent decisions Decisions are based on expectations: Price (Fuel & Electricity) Demand Technological development Policy Competitor behaviour Traditional approach: equilibrium economics with rational expectations, that are consistent with state of system -> homogeneous beliefs An energy system in transition will often be out-of equilibrium, the adjustment processes, ie. the investments become important Investment decision Investment decision

Methodology Agent-based Modelling Methodology where actors and environment are modelled from the bottom up by algorithmic description: Naturally allows for heterogeneous agents Differing levels of detail possible for technologies and actors Data richness an option (e.g. to include locational specific meteorological data) Focus on behaviour and emergence of phenomena Informational asymmetry and multiple equilibria can be represented (L. Tesfatsion, 2006) Since ABM is a generative methodology, out-of-equilibrium processes can be represented (W. B. Arthur, 2006) Decisive for modelling slowly adjusting infrastructures in transition

EMLab Electricity Market Laboratory Energy producers are the main agents, but other types exist, which incorporate simpler behaviour: European government & national governments Banks, commodity suppliers, etc. Energy Producers make investments in power plants: Based on a per-agent merit-order forecast On a yearly basis Energy producers bid into two-country electricity system, connected by market coupling and a common CO2 market Market clearing based on stepwise load-duration curve approximation of demand

How?

EMLab Implementation Details Implemented in Java, build with Maven Uses AgentSpring (https://github.com/alfredas/AgentSpring) as an ABM Framework Split of Roles (Behaviour) and Agents Uses a graph database for data storage (Neo4j and Spring Data) Available as open source on https://github.com/EMLab/emlab Single run interfaces: Web-based Scriptable via R Statistical evaluation of Monte Carlo runs with R

Agentspring: class structure Domain Specification of ‘things’ and their properties and possible relations to other ‘things’. Role Coded behavior, to be ‘acted’ by the agents from specific agent classes Repository For interaction with the database Other Trend: to be able to incorporate various types of trends in data Util: helper classes Note: properties and methods are inherited

The short term market

Short detour: The electricity market – During Peak Load

Short detour: The electricity market: During Base Load

The medium term

EMLab CO2 market in more detail

EMLab - The carbon market

The long term

EMLab Investment in generation capacity

EMLab Investment in generation capacity

Which parts are typically ABM, which not?

Example results

EMLab Example Run – Capacity Development

EMLab Example Run – Capacity Development

EMLab Example Run – CO2 Prices

EMLab Example Run – CO2 Prices

EMLab Example Run – CO2 Prices

Thanks for listening

Literature L. Tesfatsion, Agent-based computational economics: A constructive approach to economic theory., in Agent-Based Computational Economics, vol. 2 of Handbook of computational economics, p. 831--880, North-Holland, 2006. W. B. Arthur, Chapter 32 Out-of-Equilibrium Economics and Agent-Based Modeling, in Agent-Based Computational Economics (L. Tesfatsion and K. Judd, eds.), vol. 2 of Handbook of Computational Economics, pp. 1551--1564, Elsevier, 2006. J. C. Richstein, E. Chappin, and L. D. de Vries, Impacts of the Introduction of CO2 Price Floors in a Two-Country Electricity Market Model, IAEE European PhD day at the 12th IAEE European conference, 2012. A. Chmieliauskas, E. J. L. Chappin, C. B. Davis, I. Nikolic, and G. P. J. Dijkema, New methods for analysis of systems-of-systems and policy: The power of systems theory, crowd sourcing and data management, in System of Systems (A. V. Gheorghe, ed.), ch. 5, pp. 77--98, InTech, March 2012.

EMLab Demand Approximation of the load duration curve by 20 segments Renewables have a segment specific contribution.

EMLab Short-term market Uniform price-volume auction The carbon price is found via an iteration algorithm. It is adjusted until: The carbon emissions are close to the cap (+- 5%) Carbon prize is 0 The price floor is implemented as a complementary tax If the market price is below the price floor, generators pay the price difference between the market and the price floor

Investment decision Forecasting

Forecasting Running Hour Estimation

Forecasting Cash Flow Estimation

Forecasting DCF Method