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

Business case analysis of hybrid systems consisting of battery storage and power-to-heat on the German energy market

Declining prices on frequency containment reserve (FCR) markets endanger the profitability of battery energy storage systems (BESS). BESS combined with power-to-heat units could improve the economics both by supplying higher power rates on FCR markets and by converting excessive power into heat. Two cases were investigated with a techno-economic model using primary operation and market data of 2018/2019. The system amortises after 12 years with a net present value of two million € operating on the FCR market. No improvement was realized by additional arbitrage trading. Taxes, levies and charges frameworks are crucial for the economic success of hybrid systems

Potentials and Technical Requirements for the Provision of Ancillary Services in Future Power Systems with Distributed Energy Resources

A decentralized supply of electrical power based on renewable energies paves the way to a sustainable power supply without nuclear energy and without the emission of greenhouse gases. This energy transition (Energiewende) entails challenges regarding the provision of Ancillary Services (AS), associated with intermittent in-feed of Distributed Energy Resources (DER) into the distribution grids. In this paper, the demand, potentials, and technical requirements for AS provision in Germany, especially in the state of Lower Saxony, are discussed. These aspects are considered from multiple perspectives across all voltage levels. Beginning with a steady state analysis that focuses on the transmission grid, an expected increment in voltage violations and line congestions is revealed. Counteracting the resulting technical limit violations requires consideration of distribution grid flexibilities among others. To address this emerging demand, the potentials for the provision of AS by components in the distribution grids are identified. However, technical concepts are also required to exploit the potential, as DER in-feed has significant impact on the functionality of conventional protection systems. The analysis in this paper indicates the need for development of concepts to provide AS in the distribution grid and detailed technical requirements within a holistic simulative approach

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

Load Recognition in Hardware-Based Low Voltage Distribution Grids using Convolutional Neural Networks

Due to climate targets of the German government, the share of renewable energy in the power grid will be increased and the number of grid participants connected to the low voltage level of the power grid will rise. This leads to new requirements in voltage control, especially in low voltage distribution grids. In order to achieve a stable power grid in future, further development of control strategies is necessary. In this paper, a load recognition concept, which was tested on simulative data in previous work, is further developed to reduce simulation effort. Additionally, the concept is adapted for real hardware influences and active grid participants complicating the recognition task. Thus, the main contribution of this study is the successful application of the methodology within a hardware-based test grid containing a charging electric vehicle. Using a convolutional neural network in a time series classification setting, the recognition rates in this use-case exceeded 99 % while benefiting from an asymmetric charging behavior. Due to these promising results, future voltage control strategies could be supported based on gained information through integration of the presented concept

Voltage-Based Heat Pump Recognition in Low Voltage Distribution Grids with Convolutional Neural Networks

The increasing power generation by renewable energy plants in low voltage level leads to the need for further development of strategies for grid voltage stabilization. For this, there is the idea to gain information from the local grid environment of an inverter’s grid connection point by recognition and classification of electric loads based on the grid voltage to contribute to adaptive voltage control. This is solved by convolutional neural networks (CNNs) using a systematic training data generation, starting with power profiles and ending with scaled and noisy data. Hence, the proposed methodology achieves the goal without much simulation effort. Furthermore, it is shown that the CNNs can recognize a particular heat pump within realistic grid situations with an average accuracy of ca. 86%, while the accuracy is highly correlated to the distance of the measurement point to the transformer and the load to be recognized

Reactive Power Control of a Converter in a Hardware-Based Environment Using Deep Reinforcement Learning

Due to the increasing penetration of the power grid with renewable, distributed energy resources, new strategies for voltage stabilization in low voltage distribution grids must be developed. One approach to autonomous voltage control is to apply reinforcement learning (RL) for reactive power injection by converters. In this work, to implement a secure test environment including real hardware influences for such intelligent algorithms, a power hardware-in-the-loop (PHIL) approach is used to combine a virtually simulated grid with real hardware devices to emulate as realistic grid states as possible. The PHIL environment is validated through the identification of system limits and analysis of deviations to a software model of the test grid. Finally, an adaptive volt–var control algorithm using RL is implemented to control reactive power injection of a real converter within the test environment. Despite facing more difficult conditions in the hardware than in the software environment, the algorithm is successfully integrated to control the voltage at a grid connection point in a low voltage grid. Thus, the proposed study underlines the potential to use RL in the voltage stabilization of future power grids

Grid-in-the-Loop Environment for Stability Investigation of Converter-Dominated Distribution Grids

The effective modeling of power distribution grid components has become extremely challenging especially considering the increase of power electronics interface based distributed energy resources (DER). The aim behind is, to optimize the accuracy of the models to precisely evaluate the component response and stability of the system. For this purpose, based on the fundamentals of power hardware-in-the-loop simulation, grid-in-the-loop (GIL) environment is proposed in this study to evaluate the grid stability due to integration of DER. A complete low-voltage (LV) distribution grid is emulated along with active grid participants. Additionally, to study the impact at high voltage levels the emulated grid is firmly synchronized with a medium-voltage (MV) simulated grid. An initial case-study is also performed to demonstrate the interesting dynamic behavior at coupling nodes, that otherwise may not have been depicted in simulation studies. Thus, the approach eases up the need for time-intensive detailed modeling of distribution grid participants and provides a setting to test actual components in grid-connected states. If required, the results can be used to replicate the behavior for simulation studies as an alternate to detailed component models

Analysis of taxation and framework conditions for hybrid power plants consisting of battery storage and power-to-heat providing frequency containment reserve in selected European countries

Flexible energy plants are one of the key requirements for future energy systems with high levels of fluctuating renewable energy. In the course of the transition to sustainable energy systems, regulatory frameworks and tax systems should promote carbon-reduced flexible power plants in a timely manner. This paper considers hybrid systems consisting of battery energy storage systems (BESS) and Power-to-Heat (PtH) modules which can contribute to a more flexible energy system by providing Frequency Containment Reserve (FCR). Contrary to many papers, this contribution explicitly focuses on taxes for FCR providing power plants, which are incurred annually or based on energy consumption. Additionally, regulatory frameworks are investigated, meaning requirements for FCR provision and conditions for energy trading. The effects of these factors on the economic efficiency of hybrid power plants providing FCR are analysed. The regulatory framework conditions and tax systems from three countries are analysed: Germany, France and Austria. For each of these countries four scenarios are simulated in which the net present values (NPV) are calculated considering the corresponding national tax systems and framework conditions. Additionally, operational strategies using the degrees of freedom (DoF) are examined regarding their influence on the economic performance. The comparison shows a huge influence of taxes on the profitability of the hybrid system. Framework conditions mostly play a minor role in this context. Compared to a benchmark scenario with uniform framework conditions and without taxes, on average the NPV decreases more rapidly considering taxes (− 107 k€ in France to − 710 k€ in Austria) than considering country specific framework conditions (− 122 k€ in France to − 308 k€ in Austria). Since framework conditions mostly determine the size of the battery capacity, they primarily affect the investment costs. Additionally, the longer the time slices and the earlier the gate closure is, the more often the hybrid systems violate requirements for FCR provision