The merit-order effect: a detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany

The German feed-in support of electricity generation from renewable energy sources has led to high growth rates of the supported technologies. Critics state that the costs for consumers are too high. An important aspect to be considered in the discussion is the price effect created by renewable electricity generation. This paper seeks to analyse the impact of privileged renewable electricity generation on the electricity market in Germany. The central aspect to be analysed is the impact of renewable electricity generation on spot market prices. The results generated by an agent-based simulation platform indicate that the financial volume of the price reduction is considerable. In the short run, this gives rise to a distributional effect which creates savings for the demand side by reducing generator profits. In the case of the year 2006, the volume of the merit-order effect exceeds the volume of the net support payments for renewable electricity generation which have to be paid by consumers. --

Agent-based simulation of electricity markets: a literature review

Liberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets. --

Feed-in Tariffs and Quotas for Renewable Energy in Europe

Regenerative Energie, Elektrizität, Stromtarif, Förderung regenerativer Energien, EU-Staaten, Renewable energy, Electricity, Electricity price, Renewable energy policy, EU countries

An Iterative Procedure for the Estimation of Drift and Diffusion Coefficients of Langevin Processes

A general method is proposed which allows one to estimate drift and diffusion coefficients of a stochastic process governed by a Langevin equation. It extends a previously devised approach [R. Friedrich et al., Physics Letters A 271, 217 (2000)], which requires sufficiently high sampling rates. The analysis is based on an iterative procedure minimizing the Kullback-Leibler distance between measured and estimated two time joint probability distributions of the process.Comment: 4 pages, 5 figure

Finite sampling interval effects in Kramers-Moyal analysis

Large sampling intervals can affect reconstruction of Kramers-Moyal coefficients from data. A new method, which is direct, non-stochastic and exact up to numerical accuracy, can estimate these finite-time effects. For the first time, exact finite-time effects are described analytically for special cases; biologically inspired numerical examples are also worked through numerically. The approach developed here will permit better evaluation of Langevin or Fokker-Planck based models from data with large sampling intervals. It can also be used to predict the sampling intervals for which finite-time effects become significant.Comment: Preprin

Datengetriebene Modelle stochastischer dynamischer Systeme - am Beispiel hydrodynamischer Turbulenz

This thesis deals with the development of data driven models for stochastic dynamical systems based on measured time sequences. Reconstruction of deterministic dynamical systems from time series is a well established technique, whereas only very few methods exist in the case of a nonlinear stochastic setting. The need for such algorithms arises in a variety of fields, such as physics, biology, economics, or meteorology. First we derive a method to construct Fokker-Planck- and Langevin-equations from time series, if all relevant dynamical variables are measured. The latter assumption, however, is unrealistic in many applications. Therefore we discuss, whether it is possible to embed a scalar time series in a stochastic setting and, if so, how to choose the correct parameters. This leads us to novel time series predictors for Markovian-processes. The performance of these algorithms is demonstrated by numerical examples. In the second part of this work we apply the techniques developed in the first section to hydrodynamic turbulence and the problem of predicting the fluctuations in wind energy production. For a hydrodynamic flow with pronounced coherent structures, nonlinear phase space methods prove to have significantly higher predictive power within such structures than ordinary linear schemes. This knowledge is then applied to time series of atmospheric surface wind velocities and of the power output of a wind turbine. In these cases as well, strong turbulent fluctuations are clearly better predicted by nonlinear methods than by linear ones. This fact has relevant applications for the control of rotor blades of wind turbines as well as for the regulation of conventional power supplies

Economic and Non-Economic Barriers and Drivers for the Uptake of Renewables

This is the final versionLarge scale renewables raise new challenges and provide new opportunities across electricity systems. This paper considers the barriers faced by large scale renewables in electricity systems in Sub-Saharan Africa and South Asia. We review the current state of knowledge in relation to grid-connected renewables. This paper then explores key issues in electricity system structure, the main challenges to the uptake of renewables, and the various existing fiscal and policy approaches to encouraging renewables. The authors also highlight possible ways moving forward to ensure more widespread renewables deployment. This research was supported by the UK Department for International Development’s Energy and Economic Growth Applied Research Programme.The Applied Research Programme on Energy and Economic Growth (EEG) is led by Oxford Policy Management in partnership with the Center for Effective Global Action and the Energy Institute @ Haas at the University of California, Berkeley. The programme is funded by the UK Government, through UK Aid

Renewable energy sources to secure the base load in electricity supply. Summary

The share of renewable energies in Germany\u27s electricity supply has been increasing at an impressive rate in recent years: it already amounts to over 20 %, about half of which comes from fluctuating sources - mainly wind power and photovoltaics. In the long term (by 2050), the goal is to achieve almost full supply with renewable energies. This makes it clear that the system of electricity supply will be subject to an upheaval of historic proportions in the coming decades. The TAB report addresses the question of how the base load in the electricity supply can continue to be secured under these conditions. This question can only be addressed in a system perspective that encompasses all levels: from generation to transport and distribution to the consumption of electricity. Therefore, the question expands to how a secure supply can be organised as a whole. It is becoming apparent that the electricity system must be able to react much more flexibly than before to different feed-in and demand situations. Options for increasing flexibility exist in many areas: > Increasing the efficiency of the grids > Increasing the flexibility of the conventional power plant fleet and its mode of operation > Greater orientation of electricity production from renewable energies to demand > load management, and not least the construction of additional storage facilities. In all of these fields of action, the TAB report identifies options for action in which the public sector and the energy policy actors in the executive and legislative branches can contribute to the success of the upcoming transformation of the electricity supply by shaping the framework conditions