Static Grid Equivalent Models Based on Artificial Neural Networks

Abstract

Power systems are rapidly and significantly changing due to the increasing penetration of distributed energy resources (DERs) and the rapid growth of widespread grid interconnections. An increasing number of grid operators is thus interested in the reduced equivalent representation of a large, interconnected power system to reduce the amount of required computational resources and data exchange, e.g., between grid operators. However, state-of-the-art grid equivalents become more and more inapplicable since they are analytically calculated for one specific grid state. They cannot properly be adapted to grid state changes and the behavior of the increasingly used controllers, such as reactive power controllers of DERs. Therefore, we propose an innovative grid equivalent based on artificial neural networks (ANN) which overcomes the drawbacks of the state-of-the-art grid equivalents as follows: 1) Using supervised ANNs with feedforward and recurrent architectures, power systems can be equivalently represented adaptively and thus more accurately. 2) A feature selection method identifies the elements in the grid with high sensitivity on the boundary enabling a reduction of grid data required for the ANN-based equivalent.3) To guarantee data confidentiality and cybersecurity, an additional unsupervised ANN, an Autoencoder, is used for obfuscation of the data which is required for the proposed grid equivalent to be exchanged among grid operators, while the relevant information of the original data is preserved, maintaining the estimation accuracy. Our ANN-based approach is analyzed and evaluated with two German benchmark grids and representative scenarios. The simulation results demonstrate that the proposed ANN-based grid equivalent outperforms the state-of-the-art radial equivalent independent method. INDEX TERMS grid equivalent, feedforward neural networks, recurrent neural networks, Autoencoder

Application of Data-Driven Tuned Fuzzy Inference System for Static Equivalencing of Power Systems with High Penetration of Renewable Energy

“…To overcome the economic and technical challenges faced by independent power grids and achieve economic operation by sharing unevenly distributed resources and infrastructure, and bilateral electric power trading, power system interconnection becomes a common practice around the world [2,3]. Interconnection among power systems managed and controlled by di erent operators along with the increasing penetration of renewable energy-based generation brings about new challenges into the power system, such as increased complexity, uncertainty, interarea oscillation, and increased di culty to real-time power system security evaluation and control [4][5][6][7][8].…”

“…erefore, the updated equivalent model of external systems together with detailed model of internal system is required to perform any kind of power system analysis in the internal system [8,11]. e reduced power system with the external equivalent has some advantages, such as fast online computational performance and better database maintenance [20][21][22][23].…”

“…To ensure the simplified equivalent system, maintain the static characteristics of the original system. e power flow of the internal system before and after the equivalence remains unchanged [8,25].…”

“…e aforementioned conventional grid equivalencing techniques require not only the knowledge of complete topology and network parameters of the external system but also recalculation of the grid equivalent networks and power injections every time the grid state changes in one of the connected systems [8,23,25,37], which is not practical for modern power systems dominated by various renewable energy based generators (REGs) with frequent variations [15,16,32].…”

“…e goal of adaptive grid equivalencing is to take into account the impact of the external generation and load variations and other disturbances on the study of internal power systems as accurately as possible [8]. For modern power systems with intermittent generation because of renewable energy resources (RERs) and varying load demand, unless the external equivalent is updated frequently, it leads to misleading security assessment results [10].…”