Kaveri K. Iychettira

Simulating climate and energy policy with agent-based modelling: The Energy Modelling Laboratory (EMLab)

We present an approach to simulate climate and energy policy for the EU, using a flexible and modular agent-based modelling approach and a toolbox, called the Energy Modelling Laboratory (EMLab). The paper shortly reviews core challenges and approaches for modelling climate and energy policy in light of the energy transition. Afterwards, we present an agent-based model of investment in power generation that has addressed a variety of European energy policy questions. We describe the development of a flexible model core as well as modules on carbon and renewables policies, capacity mechanisms, investment behaviour and representation of intermittent renewables. We present an overview of modelling results, ongoing projects, a case study on current reforms of the EU ETS, and we show their relevance in the EU context.

Cross-border effects of capacity mechanisms

This paper uses an agent-based model of interconnected electricity market to analyze the cross-border impact of a capacity market and a strategic reserve on consumer costs and on investment in generation capacity in the affected markets. We find that both capacity mechanisms can improve investment signals and cause positive cross border effects. The interconnected energy-only market could free ride on the additional capacity and may deter investment in this market. If a capacity market and a strategic reserve are implemented in neighboring regions, there appears to be a negative spillover effect on the strategic reserve due to the capacity market.

Harmonising RES-E support schemes using design elements

In this paper, we present a new lens to study harmonization of RES-E support schemes. We collect data from existing RES-E support scheme designs, break up RES-E policies into their design elements, and relate each design element or policy characteristic to policy objectives. Based on these relations, which are complex, we use the decision tree learning method in order to understand exactly how relevant each design element is to a certain policy objective. With this knowledge it is possible to understand which attributes of an RES-E scheme must be harmonized or co-ordinated at the EU level and which ones could be left to the discretion of the member states.