Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning

Due to the increasing penetration of renewable energies in lower voltage level, there is a need to develop new control strategies to stabilize the grid voltage. For this, an approach using deep learning to recognize electric loads in voltage profiles is presented. This is based on the idea to classify loads in the local grid environment of an inverter’s grid connection point to provide information for adaptive control strategies. The proposed concept uses power profiles to systematically generate training data. During hyper-parameter optimizations, multi-layer perceptron (MLP) and convolutional neural networks (CNN) are trained, validated, and evaluated to determine the best task configurations. The approach is demonstrated on the example recognition of two electric vehicles. Finally, the influence of the distance in a test grid from the transformer and the active load to the measurement point, respectively, onto the recognition accuracy is investigated. A larger distance between the inverter and the transformer improved the recognition, while a larger distance between the inverter and active loads decreased the accuracy. The developed concept shows promising results in the simulation environment for adaptive voltage control

Integration von Wasserstoffenergiespeichern - Nutzen für Stromnetze?

Der Beitrag gibt einen Überblick über die Anforderungen an eine netzorientierte Integration von Wasserstoffenergiespeichern und -komponenten in das Stromnetz. Vor dem Hintergrund einer allgemeinen Definition von Wasserstoffenergiespeichern und möglichen Komponenten werden der zukünftige Wasserstoffbedarf, Elektrolyseleistung und Speicherkapazität vorgestellt, der in verschiedenen aktuellen Gesamtsystemstudien mit Ziel der Klimaneutralität im Jahr 2045 bestimmt wurde. Durch den angestrebten beschleunigten Ausbau erneuerbarer Erzeugungskapazität ergibt sich weiterer Netzausbaubedarf zusätzlich zu dem Ausbaubedarf durch bisherige Netzengpässen. Elektrolyseanlagen könnten also bereits im heutigen Stromnetz zur verbesserten Integration von EE-Anlagen eingesetzt werden. Derzeit bestehen allerdings kaum Anreize für netzdienliche Allokation und Betrieb von Elektrolyse bzw. Power-to-Gas Anlagen. Als Praxisbeispiele werden zwei mögliche Standorte für Wasserstoffanlagen aus zwei aktuellen Forschungsprojekten HyCavMobil und dem Innovationslabor Wasserstoffregion Nordwest (H2-ReNoWe) vorgestellt. Anhand von Stromnetzmodellen wird die Integration von Elektrolyseanlagen an diesen Standorten im derzeitigen Hoch- bzw. Höchstspannungsnetz untersucht

Technical and economic analysis of curative actions in distribution networks utilizing battery energy storage systems

Renewable energy generation curtailment increases due to more frequently occurring congestions in power system operation. Post-contingency curative congestion management actions can reduce the necessity of renewable energy curtailment by enabling a more efficient utilization of transmission capacities. In this research, the potential of curative actions to substitute renewable energy curtailment is studied considering technical and economic criteria. Therefore, a novel pricing methodology for the market-based provision of curative actions is introduced. The method is based on the security constraint optimal power flow technique. Simulations are carried out on a modified version of the IEEE 14-bus network and a real-world 110 kV distribution network. Battery energy storage systems are implemented as an exemplary technology to provide curative actions. The developed method achieves a positive power system impact by reducing operational costs and maximizing renewable energy integration. Also, novel business models for merchant-owned battery energy storage systems are unveiled. The provision of curative actions further proves to be competitive to established battery storage applications. Additionally, results of different grid expansion scenarios of the 110 kV network reveal the need to coordinate power system planning and operation more extensively in the future

Simulation of Incidental Distribution Generation Curtailment to Maximize the Integration of Renewable Energy Generation in Power Systems

Power system security is increasingly endangered due to novel power flow situations caused by the growing integration of distributed generation. Consequently, grid operators are forced to request the curtailment of distributed generators to ensure the compliance with operational limits more often. This research proposes a framework to simulate the incidental amount of renewable energy curtailment based on load flow analysis of the network. Real data from a 110 kV distribution network located in Germany are used to validate the proposed framework by implementing best practice curtailment approaches. Furthermore, novel operational concepts are investigated to improve the practical implementation of distributed generation curtailment. Specifically, smaller curtailment level increments, coordinated selection methods, and an extension of the n-1 security criterion are analyzed. Moreover, combinations of these concepts are considered to depict interdependencies between several operational aspects. The results quantify the potential of the proposed concepts to improve established grid operation practices by minimizing distributed generation curtailment and, thus, maximizing power system integration of renewable energies. In particular, the extension of the n-1 criterion offers significant potential to reduce curtailment by up to 94.8% through a more efficient utilization of grid capacities

Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings

This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The developed mathmatical description of the disaggregation problem using a state changes matrix belongs to the group of non-event based methods for energy disaggregation. This work includes the development of an objective function in the power domain and the description of position and velocity of each particle in a high dimensional state space. For the particle swarm optimization, four adaptions have been applied to improve the results of disaggregation, increase the robustness of the optimizer regarding local optima and reduce the computational time. The adaptions are varying movement constants, shaking of particles, framing and an early stopping criterion. In this work we use two unlabelled power datasets with a granularity of 1 s. Therefore, the results are validated in the power domain in which good results regarding multiple error measures like root mean squared error or the percentage energy error can be shown.Comment: 10 pages, 13 figures, 3 table

A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings

This paper presents a new algorithm to extract device profiles fully unsupervised from three phases reactive and active aggregate power measurements. The extracted device profiles are applied for the disaggregation of the aggregate power measurements using particle swarm optimization. Finally, this paper provides a new approach for short term power predictions using the disaggregation data. For this purpose, a state changes forecast for every device is carried out by an artificial neural network and converted into a power prediction afterwards by reconstructing the power regarding the state changes and the device profiles. The forecast horizon is 15 minutes. To demonstrate the developed approaches, three phase reactive and active aggregate power measurements of a multi-tenant commercial building are used. The granularity of data is 1 s. In this work, 52 device profiles are extracted from the aggregate power data. The disaggregation shows a very accurate reconstruction of the measured power with a percentage energy error of approximately 1 %. The developed indirect power prediction method applied to the measured power data outperforms two persistence forecasts and an artificial neural network, which is designed for 24h-day-ahead power predictions working in the power domain.Comment: 15 pages, 14 figures, 4 table

Generating worst-case scenarios by randomly distributing loads for risk assessment in low voltage residential electricity grids

In order to assess the capacity of low voltage electricity grids different grid operation cases are usually analyzed. These cases are used to identify weaknesses in the grid, evaluate the risks involved and subsequently facilitate the integration of new loads such as electric vehicles or heat pumps which are joining these grids in an increasing degree. This study suggests a random load allocation algorithm to create realistic worst-case scenarios for grid operation without the need for historical load data or reverting to load profiles. This is achieved by distributing loads asymmetrically across all three phases so that they comply with grid codes and burden the local transformer moderately. In this way, a multitude of feasible load scenarios is generated and evaluated. A metric is proposed to select those scenarios which lead to a critical operation state of the grid. The generated worst-case scenarios can be used to evaluate the potential capacity and risks of integrating new consumers into grids. This is demonstrated in a use case where electric vehicles are integrated into the investigated grid at half of all connection points. The Analysis shows that the grid is additionally stressed and the reinforcement of cables or charge management would be required to facilitate the safe operation of the grid with additional loads

Predicting Renewable Curtailment in Distribution Grids Using Neural Networks

The growing integration of renewable energies into electricity grids leads to an increase of grid congestions. One countermeasure is the curtailment of renewable energies, which has the disadvantage of wasting energy. Forecasting congestion provides valuable information for grid operators to prepare and instruct countermeasures to reduce these energy losses. This paper presents a novel approach for congestion prediction in distribution grids (i.e. up to 110 kV) considering the n-1 security criterion. For this, our method considers node injections and power flow and combines three artificial neural network models. The analysis of study results shows that the implemented neural networks within the presented approach perform better than naive forecasts models. In the case of vertical power flow, the artificial neural networks also show better results than comparable parametric models: average values of the mean absolute errors relative to the parametric models range from 0.89 to 0.21. A high level of accuracy can be achieved for the neural network that predicts the loading of grid components with a F1 score of 0.92. Further, also with a F1 score of 0.92, this model shows higher accuracy for the distribution grid components than for those of the transmission grid, which achieve a F1 score of 0.84. The presented approaches show good potential to support grid operators in congestion management