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

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

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

Power Hardware-in-the-Loop: Response of power components in real-time grid simulation environment

With increasing changes in the contemporary energy system, it becomes essential to test the autonomous control strategies for distributed energy resources in a controlled environment to investigate power grid stability. Power hardware-in-the-loop (PHIL) concept is an efficient approach for such evaluations in which a virtually simulated power grid is interfaced to a real hardware device. This strongly coupled software-hardware system introduces obstacles that need attention for smooth operation of the laboratory setup to validate robust control algorithms for decentralized grids. This paper presents a novel methodology and its implementation to develop a test-bench for a real-time PHIL simulation of a typical power distribution grid to study the dynamic behavior of the real power components in connection with the simulated grid. The application of hybrid simulation in a single software environment is realized to model the power grid which obviates the need to simulate the complete grid with a lower discretized sample-time. As an outcome, an environment is established interconnecting the virtual model to the real-world devices. The inaccuracies linked to the power components are examined at length and consequently a suitable compensation strategy is devised to improve the performance of the hardware under test (HUT). Finally, the compensation strategy is also validated through a simulation scenario

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

Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning

The increasing penetration of the power grid with renewable distributed generation causes significant voltage fluctuations. Providing reactive power helps balancing the voltage in the grid. This paper proposes a novel adaptive volt-var control algorithm on the basis of Deep reinforcement learning. The learning agent is an online-learning deep deterministic policy gradient that is applicable under real-time conditions in smart inverters for reactive power management. The algorithm only uses input data from the grid connection point of the inverter itself; thus, no additional communication devices are needed and it can be applied individually to any inverter in the grid. The proposed volt-var control is successfully simulated at various grid connection points in a 21-bus low-voltage distribution test feeder. The resulting voltage behavior is analyzed and a systematic voltage reduction is observed both in a static grid environment and a dynamic environment. The proposed algorithm enables flexible adaption to changing environments through continuous exploration during the learning process and, thus, contributes to a decentralized, automated voltage control in future power grids