Analytics and Empirics of Emission Trading

In the acadamic discussion emission trading schemes (ETS) have emerged as a political instrument to both comply to emission targets and achieve this in a cost effcient way for all participants. However, until today most of the theoretical analysis in this area has been done in a qualitative rather than a quantitive way. In our research we aim to analyze already established ETS in terms of price volatility and jumpy behaviour and, based on this, point out alternative designs. We investigate how linking of different ETS effects the market price and consider various hybrid schemes. Our analysis is based on stochastic equilibrium models and employs techniques in financial mathematics used for option pricing.

Details: Analytics and Empirics of Emission Trading

Cooperation: We will closely work together with the research groups of

  • Professor Fred Espen Benth, Centre of Mathematics for Application, Departement of Mathematics, University of Oslo, Norway
  • Dr. Luca Taschini, Grantham Research Institute, London School of Economics, UK

StoBeS

Stochastic Methods for Management and Valuation of Centralized and Decentralized Energy Storages in the Context of the Future German Energy System


As infeed of electricity from fluctuating renewable energy sources increases the balance between supply and demand in the current market is becoming increasingly difficult to establish. Here, an important role is attributed to storages which store power in times of great over-supply and release it in times of high demand. Disregarding issues of natural sciences and engineering the question of economic profitability of such storages will be discussed in the context of this project. For this purpose, we first model adequately the stochastic nature of the fluctuating infeed of wind and solar energy in particular with regard to time and spatial correlation patterns. Then, we want to link modelling approaches of financial mathematics and energy economics to obtain various applicable methods for the determination of optimal storage management, depending on economic restrictions such as available network or generation capacity