EMLab-generation
Overview

Emile Chappin
March 6, 2013

Outline

Overview
Purpose
Why this is different
Policies
Agent-based modelling
Modelling framework: AgentSpring
Further reading
Team

Overview

EMLab-Generation is an agent-based model of two interconnected electricity markets. These may be two countries, such as the Netherlands and Germany, or two groups of countries, for instance the UK and Continental Europe. The two markets are linked by an interconnector. Various power companies are modelled as the main agents. They bid into a power and they invest based on the NPV of prospective power plant investments. Power companies invest in various technologies. Load is represented as a load-duration curve divided into segments, thus reducing the simulation time and enabling Monte-Carlo simulations. Various energy and climate policies are implemented.

Purpose

The main purpose is to explore the long-term effects of interacting energy and climate policies by means of a simulation model of power companies investing in generation capacity. With this model, we study the influence of policy on investment in the electricity market in order to explicate possible effects of current and alternative/additional policies on the various sector goals, i.e. renewables targets, CO2 emission targets, security of supply and affordability. The methodology, agent-based modelling, allows for a different set of assumptions different as to the mainstream models for such questions: this model can explore heterogeneity of actors, consequences of imperfect expectations and investment behaviour outside of ideal conditions.

Why is this different?

EMLab-Generation offers an entirely new way of modelling policy effects in electricity markets. It may provide insight in actor and system behaviour over time, i.e. investment cycles, abatement cycles, delayed responses, effects of uncertainty and risk upon investment decisions. It is possible to simulate cross-border effects of national policies, interactions between policies and capacity mechanisms. We see the need for agent-based modelling in addition to existing approaches in order to explore the various types of insights that can be gained with each of these paradigms, and to confront each model with other assumptions.

Policies

Various energy and climate policies are implemented. The EU ETS is modelled according to the EU 2050 roadmap. An optional price floor is modelled as a complementary tax (as implemented in the UK) in either one or both countries. A gradually increasing, optional CO2 tax is implemented and exogenous subsidized renewable investors that invest according to renewables targets, which are a linear extrapolation of the national renewable energy action plans. A scenario is implemented that contains demand and fuel prices modelled as stochastic trends based on Matthes (2010).

Agent-Based Modelling

In an agent-based model, actors are modelled as individual agents who make autonomous decisions. In that sense, energy producers are modelled as separate agents and each of those decides on their own investments. Agents interact though markets and therefore, their decisions influence each other over time. For example, an investment decision is based on expected power prices, which are, in turn, affected by past investments. All decisions together determine the system-wide developments and performance. For example, electricity prices are influenced by the bids of individual players in the market, which are based on their generation portfolio.

Modelling framework: AgentSpring

The model uses an open source Agent-Based Modelling platform AgentSpring, which enables a web-based user interface, runs on Linux, Mac and Windows, and headless mode for large-scale simulations. This enables to look at and discuss individual runs (see figure above), but also to look at multiple runs. These Monte-Carlo simulations are a step towards capturing a larger degree of uncertainty (e.g. fuel price developments) in the model that is a key characteristic of the development of a power system.

More on AgentSpring can be found on this website.

Further reading

Model: The model is open source and can be accessed online.

Factsheet: A factsheet of the core features.

Documentation: A technical report with the formal documentation.

Scientific publication with an overview of all modelling work: Chappin, E. J. L., L. J. de Vries, J. Richstein, P. Bhaghwat, K. Iychettira, and S. Khan. Simulating climate and energy policy with agent-based modelling: the energy modelling laboratory (EMLab). Environmental Modelling & Software, 96:421-431, 2017. doi: 10.1016/j.envsoft.2017.07.009. Other articles describing model results and related work:

• Bhagwat, P. C., J. C. Richstein, E. J. L. Chappin, K. K. Iychettira, and L. J. de Vries. Crossborder effects of capacity mechanisms in interconnected power systems. Utilities Policy, 46: 33-47, 2017. ISSN 0957-1787. doi: http://dx.doi.org/10.1016/j.jup.2017.03.005.
• Bhagwat, P. C., K. K. Iychettira, J. C. Richstein, E. J. L. Chappin, and L. J. D. Vries. The effectiveness of capacity markets in the presence of a high portfolio share of renewable energy sources. Utilities Policy, 2017. doi: 10.1016/j.jup.2017.09.003.
• Bhagwat, P. C., A. Marchesellia, J. C. Richstein, E. J. Chappin, and L. J. D. Vries. An analysis of a forward capacity market with long-term contracts. Energy Policy, 111:257-267, 2017. doi: 10.1016/j.enpol.2017.09.037.
• Bhagwat, P. C., J. C. Richstein, E. J. L. . Chappin, and L. J. de Vries. The effectiveness of a strategic reserve in the presence of a high portfolio share of renewable energy sources. Utilities Policy, 39:13 - 28, 2016. ISSN 0957-1787. doi: http://dx.doi.org/10.1016/j.jup.2016.01.006.
• Richstein, J. C., E. J. L. Chappin, and L. J. de Vries. Adjusting the CO2 cap to subsidised RES generation: Can co2 prices be decoupled from renewable policy? Applied Energy, 156:693-702, 2015. doi: 10.1016/j.apenergy.2015.07.024.
• Richstein, J. C., E. J. L. Chappin, and L. J. de Vries. The market (in-)stability reserve for eu carbon emission trading: Why it may fail and how to improve it. Utilities Policy, 35:1-18, 2015. doi: 10.1016/j.jup.2015.05.002.
• Richstein, J. C., E. J. L. Chappin, and L. J. de Vries. Cross-border electricity market effects due to price caps in an emission trading system: An agent-based approach. Energy Policy, 71: 139-158, August 2014 2014. doi: 10.1016/j.enpol.2014.03.037.
• Chmieliauskas, A., E. Chappin, C. Davis, I. Nikolic, and G. Dijkema. New methods in analysis and design of policy instruments. In Gheorghe, A. V., editor, System of Systems. Intech, 2012.
• Bollinger, A., C. Davis, R. Evins, E. Chappin, and I. Nikolic. Multi-model ecologies for shaping future energy systems: design patterns and development paths. Renewable and Sustainable Energy Reviews, 2018, accepted

Team

Emile Chappin	Laurens de Vries	Joern Richstein	Pradyumna Bhagwat
Contact

Overview of the model structure

Modelled market clearing process

Overview of the investment algorithm