Do minimum trading capacities for the cross-zonal exchange of electricity lead to welfare losses?

Within flow-based market coupling, the EU's preferred method for calculating cross-border trading capacities, recent regulatory changes stipulate minimum trading capacities, so-called minRAMs which have to be provided to electricity markets. Effectively, high predicted flows on considered electricity grid elements have to be reduced to reserve a minimum of the elements' capacities for cross-zonal trading. This analysis investigates if the adjustments made to meet this criterion, in the form of augmented trading domains, lead to higher amounts of curative congestion management. To quantify the effect of increasing minRAMs on overall welfare, the markets and grids of Central Western Europe are analyzed during two representative weeks of 2016. The results show the increasing market coupling welfare is more than offset by rising congestion management costs, leading to net welfare losses. In the best case, the generation plus congestion management costs within Central Western Europe rise by 7.25% when increasing the minRAMs from the current 20%–45% and a minRAM of 70% is 6.28% more expensive compared to a minRAM of 20%. The analysis derives policy recommendations for implementing the minRAM stipulation, with a particular focus on a cost-minimizing selection of generation shift keys, in general as well as situation-dependent terms

Temporal aggregation of time series to identify typical hourly electricity system states: A systematic assessment of relevant cluster algorithms

Comprehensive numerical models are pivotal to analyze the decarbonization of electricity systems. However, increasing system complexity and limited computational resources impose restrictions to model-based analyses. One way to reduce computational burden is to use a minimum, yet representative, set of system states for model simulation. These states characterize fluctuating renewable generation and variable demand for electricity prevailing at a certain point in time. A review of possible time series aggregation techniques identifies cluster algorithms as most adequate, with k-Means and the Ward algorithm predominating. However, throughout the surveyed literature, the line of reasoning for the selection of these algorithms remains unclear. To support the electricity system modeling community in selecting an algorithm, this paper devises a systematic multi-stage evaluation approach to compare a large variety of cluster analysis configurations, differing in algorithm, cluster representation, and number of clusters. Results show that electricity demand and renewable energy generation time series can be compressed to below one percent while sustaining global characteristics of the original data. Two potent cluster configurations are identified, confirming k-Means and WARD as being prevalent. Beyond electricity market data, the methodology can be applied to various types of fundamental time-dependent input data

Temporal aggregation of time series to identify typical hourly electricity system states : A systematic assessment of relevant cluster algorithms

TEMPORAL AGGREGATION OF TIME SERIES TO IDENTIFY TYPICAL HOURLY ELECTRICITY SYSTEM STATES : A SYSTEMATIC ASSESSMENT OF RELEVANT CLUSTER ALGORITHMS Temporal aggregation of time series to identify typical hourly electricity system states : A systematic assessment of relevant cluster algorithms / Kittel, Martin (Rights reserved) (-

Learning by Doing: Insights from Power Market Modelling in Energy Economics Courses

Much of energy economics curricula involves the study of techno-economic aspects of energy systems with an increasing focus devoted to fostering an understanding of the interactions between innovative technologies and adaptive markets. As the interplay of these dynamics and their impacts on market equilibria and outcomes is quite complex, optimization models are well-suited to facilitate their study. This paper presents two exemplary model approaches and associated case studies, which can be employed to study market developments driving long-term adaptations in the portfolio of power-generation assets as well as scheduling problems of individual plant owners with a focus on assessing the impact of changing market conditions on the profitability of investments. The combination of these two modelling approaches constitutes an innovative means of facilitating students’ understanding of how individual decisions of different market stakeholders lead to welfare-maximizing market equilibria under the assumption of perfect competition. The models are discussed along with the experiences acquired employing them in various forms as project assignments. In summary, the integration of modelling exercises and assignments into the curriculum of energy economics courses has proven to be a practical means of reinforcing and broadening lecture material that is both interesting and rewarding for students