Philipp Staudt

Trading on local energy markets: A comparison of market designs and bidding strategies

Increasing renewable energy sources and innovative information and communication systems open up new challenges and opportunities to integrate distributed generation into the energy supply system. Formerly centralized energy systems need to be adapted to take full advantage of the immense potential of decentralized energy generation and smart, interconnected energy end users. We introduce a local electricity market on which prosumers and consumers of a community are able to trade electricity directly amongst each other. This local electricity market supports the local integration of renewable energy generation. It facilitates a local balance of energy supply and demand and hence reduces the need for extensive electricity transmission. We introduce, evaluate and compare two local market designs, a direct peer-to-peer market and a closed order book market, as well as two agent behaviors, zero-intelligence agents and intelligently bidding agents. We derive four scenarios by combining each market design with each agent behavior, respectively. All market scenarios offer similar economic advantages for the market participants. However, the peer-to-peer market with intelligent agents appears to be the most advantageous as it results in the lowest average overall electricity price.

A comprehensive modelling framework for demand side flexibility in smart grids

The increasing share of renewable energy generation in the electricity system comes with significant challenges, such as the volatility of renewable energy sources. To tackle those challenges, demand side management is a frequently mentioned remedy. However, measures of demand side management need a high level of flexibility to be successful. Although extensive research exists that describes, models and optimises various processes with flexible electrical demands, there is no unified notation. Additionally, most descriptions are very process-specific and cannot be generalised. In this paper, we develop a comprehensive modelling framework to mathematically describe demand side flexibility in smart grids while integrating a majority of constraints from different existing models. We provide a universally applicable modelling framework for demand side flexibility and evaluate its practicality by looking at how well Mixed-Integer Linear Program solvers are able to optimise the resulting models, if applied to artificially generated instances. From the evaluation, we derive that our model improves the performance of previous models while integrating additional flexibility characteristics.

Analysis of redispatch and transmission capacity pricing on a local electricity market setup

As the transition to more renewable energy sources progresses the regional generation clusters of wind and solar generation increase. Since the transmission capacity is limited we can observe an increasing need for redispatch measures in the German electricity grid to accommodate this development. In this paper we analyze the need and costs of redispatch measures and propose a local electricity market setup to create economic incentives for the expansion of generation capacity close to load centers. We introduce an analytical model to investigate small networks and a graph model simulation to deal with more sophisticated structures. We find that redispatch is especially relevant in networks with bottleneck transmission links and rather independent of the overall grid capacity. Furthermore, we argue that transmission capacity needs to be priced explicitly and that graph theory can help in investigating market mechanisms in electricity grids.

Using vehicle-to-grid concepts to balance redispatch needs: A case study in Germany

The transition to more sustainable energy generation challenges transmission system operators to include intermittent renewable generation as well as electric vehicles into the power system. Especially in uniform-price power markets, this results in the need for unpopular grid expansions to overcome grid congestion. We analyze the ability of the expanded German transmission grid to cope with the increasing penetration of electric vehicles. We find that uncoordinated charging is likely to overload the transmission system and therefore propose a decentralized local market mechanism to include electric vehicles into the congestion management mechanism. We calculate redispatch needs and the associated possible remuneration of electric vehicle owners. We conclude that EVs can effectively reduce the need for redispatch and receive an according compensation.

SCiBER: A new public data set of municipal building consumption

Data about the energy consumption of buildings contains valuable information which is essential for the future energy system and smart cities. However, only few researchers publish the data on which their methods and analysis is based. This lack of publicly available data sets, makes it difficult to compare strategies and results, and hinders a stronger development of the research field. Thus, this paper describes a data set of municipal energy consumption data, which is published with the objective to facilitate the comparability of research methods in the field.

Electricity and telecommunication markets: A discussion of market designs

The German electricity market is undergoing the largest transformation since its liberalization. With increasing shares of renewable generation and decreasing spot exchange prices utilities are searching for new business models. Lessons can be learned from the telecommunication industry which has been liberalized at similar times but has been more competitive and customer centric since. In this paper we review the development of both industries along the four dimensions of competition, tariff design, auction design and the quality of service. We then develop an electricity market design based on experiences from the development of the telecommunication industry.

Smart Data Selection and Reduction for Electric Vehicle Service Analytics

Battery electric vehicles (BEV) are increasingly used in mobility services such as car-sharing. A severe problem with BEV is battery degradation, leading to a reduction of the already very limited range of a BEV. Analytic models are required to determine the impact of service usage to provide guidance on how to drive and charge and also to support service tasks such as predictive maintenance. However, while the increasing number of sensor data in automotive applications allows for more fine-grained model parameterization and better predictive outcomes, in practical settings the amount of storage and transmission bandwidth is limited by technical and economical considerations. By means of a simulation-based analysis, dynamic user behavior is simulated based on real-world driving profiles parameterized by different driver characteristics and ambient conditions. We find that by using a shrinked subset of variables the required storage can be reduced considerably at low costs in terms of only slightly decreased predictive accuracy.