Decentralized Energy Supply and Electricity Market Structures

Small decentralized power generation units (DG) are politically promoted because of their potential to reduce GHG-emissions and the existing dependency on fossil fuels. A long term goal of this promotion should be the creation of a level playing field for DG and conventional power generation. Due to the impact of DG on the electricity grid infrastructure, future regulation should consider the costs and benefits of the integration of decentralized energy generation units. Without an adequate consideration, the overall costs of the electricity generation system will be unnecessarily high. The present paper analyses, based on detailed modelling of decentralized demand and supply as well as of the overall system, the marginal costs or savings resulting from decentralized production. Thereby particular focus is laid on taking adequately into account the stochasticity both of energy demand and energy supply. An efficient grid pricing system should then remunerate long-term grid cost savings to operators of decentralized energy production or/and charge long-term additional grid costs to these operators. With detailed models of decentralized demand and supply as well as the overall system, the marginal costs or savings resulting from decentralized production are determined and their dependency on characteristics of the grid and of the decentralized supply are discussed

Efficient and Robust Machine Learning for Real-World Systems

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e.\ the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems

Do Benefits from Dynamic Tariffing Rise? Welfare Effects of Real-Time Pricing Under Carbon-Tax-Induced Variable Renewable Energy Supply

Common intuition holds that retail real-time pricing (RTP) of electricity demand should become more beneficial in markets with high variable renewable energy (VRE) supply mainly due to increased price volatility. Using German market data, we test this intuition by simulating long-run electricity market equilibria with carbon-tax-induced VRE investment and real-time price responsive and nonresponsive consumption behavior. We find that the potential welfare gains from RTP are only partially explained by price volatility and are rather driven by opposing wholesale price effects caused by the technology portfolio changes from carbon taxation. Consequently, annual benefits from RTP actually change nonmonotonously with the carbon tax level, implying that increasing RTP at relatively high VRE shares can be both less and much more beneficial than without VRE supply. Nonetheless, as zero marginal cost supply becomes abundant with VRE entry, allocative efficiency increasingly depends on exposing more and more consumers to RTP