German Transmission System Operators Research Articles

Power flow forecasts at transmission grid nodes using Graph Neural Networks

The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.

Challenging natural monopolies: Assessing market power of gas transmission system operators for cross-border capacity

It is expected that several national gas markets in Europe will be merged in the near future. Such mergers provide network users transport alternatives, which may imply competition amongst transmission system operators (TSOs). TSOs are, however, regulated by default, as they are viewed to operate natural monopolies facing no effective competition. Using daily data for the German gas market over 2014–2018, this paper assesses market power for cross-border transport capacity based on the Residual Supply Index to analyse the need for regulation. To contribute to the debate on the future regulatory framework within the EU, we assess TSOs’ market power for cross-border capacity in a single merged German gas market. We find 15 German TSOs operating in seven relevant markets. In 85% of the cases, we do not find any significant market power for TSOs. In 20% of these cases, the absence of significant market power was related to the utilisation of excess storage capacity. Hence, we conclude that current regulatory constraints imposed on gas TSOs, such as tariff regulation, may be relaxed for cross-border capacity, when market mergers lead to effective inter-TSO competition.

Competition under revenue-cap regulation with efficiency benchmarking: tariff related incentives for gas transmission system operators in merged markets

In Europe, gas market mergers aim at reducing restrictions on gas wholesale markets. Market mergers also allow network users to book transport capacity at different gas transmission system operators (TSOs), which may give rise to inter-TSO competition. Our theoretical analysis reveals the incentive for TSOs, operating under a revenue-cap regulation in merged markets, to charge lower tariffs at borders where different TSOs offer capacity, compared to borders where only one TSO offers capacity. This incentive does not directly result from revenue-cap regulation but is due to efficiency benchmarking. We test this hypothesis by applying a panel data analysis to tariffs charged at German border points between 2015 and 2018. In line with our hypothesis, we find lower tariffs at those border points where network users have a choice between different TSOs. An additional sensitivity analysis differentiating between transit and meshed networks confirms this result. We conclude that German TSOs, operating in merged markets and under a revenue-cap regime with efficiency benchmarking, compete for demand at borders at which different TSOs offer capacity.

A Participatory Stakeholder Process for Evaluating Sustainable Energy Transition Scenarios

This paper presents an evaluation of four energy transition scenarios under consideration of multiple stakeholder opinions. We construct a multi-criteria group decision model that applies Value-Focused Thinking to construct a holistic objective system and Multi-Attribute Utility Theory to evaluate the energy transition scenarios. Although the individual scenario evaluations show that the opinions of the stakeholders towards a sustainable energy transition differ largely, we are able to derive three main strands of opinions among the considered stakeholders. For this, we apply a clustering technique to identify and bundle the stakeholders into three groups. This bundling of stakeholder interests enables the identification of the most important policy recommendations for a sustainable energy transition. For the case of Germany, these are to reduce GHG and pollutant emissions and at the same time enable citizens’ participation, limit the visual impact on landscapes, and ensuring internationally comparable energy-related political frameworks for the economy. For the case of a sustainable energy transition in Germany, we find that the stakeholders considered prefer either the highly ambitious climate protection scenario (Scenario B) or the Pan-European scenario (Scenario C). The reference scenario, which was developed by the German Transmission System Operators (TSOs), turns out to be relatively unpopular.

Mergers of Germany's natural gas market areas: Is transmission capacity booked efficiently?

In the past, networks of natural gas transmission system operators (TSOs) determined the gas market areas in the European Union. However, gas markets mergers introduce the possibility to book the transmission capacity of alternative TSOs. One necessary condition for competition among TSOs is the absence of restrictions in capacity booking. This paper analyses whether this holds for Germany. As German TSOs distinguish a number of capacity types to deal with network constraints, market mergers have created transport alternatives for only 32% of cross-border capacity products. In almost all cases, we find that gas transmission network users make efficient booking decisions.

Real-world operating strategy and sensitivity analysis of frequency containment reserve provision with battery energy storage systems in the german market

Large-scale battery energy storage systems (BESS) have become increasingly interesting to provide ancillary services for the electricity grid. Especially the German frequency containment reserve (FCR) market which is organized by the German transmission system operators (TSO) is attractive for BESS. Because of its importance for the system stability, FCR has to be provided continuously with 100% availability. Due to the limited energy capacity of BESS, an operating strategy is required to enable continuous FCR provision. The German TSOs have defined several requirements that BESS have to comply with as well as degrees of freedom (DoF) to enable corrective measures such as selective recharging for an operating strategy. This paper presents a sensitivity analysis of different operational parameters based on an operating strategy developed within the M5BAT (Modular Multi_Megawatt Multi-Technology Medium-Voltage Battery Energy Storage System) project. Within this project the construction and operation of a 5MW hybrid BESS with five different battery technologies is realized. As this BESS will mainly be operated in the German FCR market, it is used as reference for this paper. The developed operating strategy considers the TSO’s FCR requirements and DoF and is fully applicable to the German FCR market. Within this paper it is explained how corrective measures have to be triggered in order to comply with the requirements. A model of the M5BAT BESS, which comprises various battery technologies and an energy management system (EMS) for load allocation among multiple battery strings, is introduced. Simulations of FCR operation with measured frequency data of the year 2014 are presented. The influence of the lead time and duration of corrective measures, the EMS and the FCR requirements on compliance with the FCR requirements, amount of corrective measures, resulting energy throughput, state-of-energy (SOE) distributions and cycle numbers are investigated. The results show a significant impact of all parameters. It is found that velocity and flexibility of corrective measures (e.g. recharging) and an efficient EMS are prerequisites for FCR operation with BESS under the applicable regulation. Additionally, the results indicate that the overall benefits of FCR provision with BESS could be improved with an adjustment of the requirements (30-min-criterion).

A probabilistic approach to the estimation of regional photovoltaic power production

Forecasting the total photovoltaic (PV) power generated in the control areas of the transmission system operators (TSO) is an important step in the integration of the large amounts of PV energy into the German electricity supply system. A standard approach for evaluating the regional PV power generation from weather forecast consists in upscaling the forecast of a limited set of reference plants to the complete area. Previous studies shown that this method can lead to large errors when the set of reference plants has different characteristics or weather conditions than the set of unknown plants. In this paper, an alternative to the upscaling approach is proposed. In this method, called a probabilistic regional PV model, an average PV model with a very limited number of inputs (two module orientation angles) is used to calculate the power generation of the most frequent module orientation angles. The resulting power values are finally weighted according to their probability of occurrence to estimate the actual power generation. The implementation of this model thus only requires information on the location and peak capacity of the plant installed in a region and no PV plant measurement is necessary. The proposed method has been evaluated against the estimate of the total power generation provided by the German TSOs, which shows that an RMSE ranging from 4.2 to 4.9% can be obtained with this method using on IFS meteorological forecast. The regional power forecasted with the probabilistic approach was also compared to the day-ahead forecast disseminated by the TSO. This analysis shows that the forecast evaluated with the proposed approach has an RMSE less than 0.5% higher than the reference forecasts. This is considered a promising result given that the forecast evaluated with the probabilistic model is based on one single weather model and that – at the exception of the model calibration - no statistical post-processing method is used to optimize its performance.

Fundamentals of Using Battery Energy Storage Systems to Provide Primary Control Reserves in Germany

The application of stationary battery storage systems to German electrical grids can help with various storage services. This application requires controlling the charge and discharge power of such a system. For example, photovoltaic (PV) home storage, uninterruptible power supply, and storage systems for providing ancillary services such as primary control reserves (PCRs) represent battery applications with positive profitability. Because PCRs are essential for stabilizing grid frequency and maintaining a robust electrical grid, German transmission system operators (TSOs) released strict regulations in August 2015 for providing PCRs with battery storage systems as part of regulating the International Grid Control Cooperation (IGCC) region in Europe. These regulations focused on the permissible state of charge (SoC) of the battery during nominal and extreme conditions. The concomitant increased capacity demand oversizing may result in a significant profitability reduction, which can be attenuated only by using an optimal parameterization of the control algorithm for energy management of the storage systems. In this paper, the sizing optimization is achieved and a recommendation for a control algorithm that includes the appropriate parameters for the requirements in the German market is given. Furthermore, the storage cost is estimated, including battery aging simulations for different aging parameter sets to allow for a realistic profitability calculation.

Critical weather situations for renewable energies – Part A: Cyclone detection for wind power

A constantly increasing share of weather dependent renewable energies in Germany's power mix poses new challenges concerning grid management and security of energy supply. An evaluation of the three year period from 2012 to 2014 reveals, that 60% of days with largest errors in the day-ahead wind power forecasts for Germany are linked to cyclones and troughs traversing the North Sea, the Baltic Sea or Germany. A cyclone detection algorithm has been developed to automatically indicate these critical weather situations. The algorithm is based on Numerical Weather Prediction model forecasts of mean sea level pressure. The cyclone detection is used to design an automated weather information tool for end-users such as Transmission System Operators (TSOs). For 2014, it identified a critical weather development in 38% of all days. The root mean square error of day-ahead wind power forecasts increased by 1% of installed capacity during these periods. A real time application of the tool is being implemented in order to support a sustainable and save integration of the increasing wind power production. It will then be provided to, and will be tested by, three German TSOs with the purpose of an operative usage to guarantee the security of supply.

The Benefit of Coordinating Congestion Management in Germany

The management of congestion within the German electricity transmission network has become more important during the last years. This emerging relevance is caused by the increase of renewable generation and the partial phaseout of nuclear power plants. Both developments yield a change in the transmission flow pattern and thus the need for congestion management. Currently, four German transmission system operators (TSOs) are in charge of managing congestion using curative methods, particularly re-dispatch of power plants. However, the existence of four TSOs within Germany induces the question whether coordination between them in managing national congestion would be beneficial. To address this issue, we apply a generalized Nash equilibrium model to analyze different degrees of coordination, covering the German electricity market with a detailed representation of the generation and network structure. Our results indicate that the costs of congestion management decrease in a rising degree of coordination as TSOs take into account congestion in other operators' zones. Total costs are highest in case each TSO is solely responsible for its own zone, and lowest if one integrated entity is in charge of mitigating congestion. We conclude that, in a setup with multiple TSOs, inducing coordination, for instance through a common market, has the potential of lowering the overall costs of congestion management.