As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.

A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor

To mitigate the fast-growing demand of electrical energy, the use of renewable energy resources, e.g., solar and wind, can offer an environmentally friendly and sustainable solution. Due to their intermittent nature, the grid connected operation of renewable energy resources provides a better performance compared to the standalone operation. However, the massive penetration of power electronic inverter/converter-interfaced renewable resources in power systems introduces new issues, such as voltage and frequency instabilities, because of their inherent low inertia properties. As a consequence, these issues may lead to serious problems, such as system blackouts. Therefore, there is an immediate demand to solve these new issues and ensure the normal performance of the power system with the large penetration of renewable energy resources. To achieve this, grid connected inverters/converters are designed to address these problems and behave as synchronous generators, which is possible with grid forming (GFM) inverters/converters concepts. This paper reviews the recent advancement of GFM converters for solving emerging issues related to the renewable rich power grids. It also provides a comprehensive review on frequency deviations and power system stability issues in low-inertia systems and recent advancements in control methods for harmonic mitigation. It is expected that this paper will help the research community to enhance the technology further to solve the challenges in renewable rich power grids.

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State of the Art of the Techniques for Grid Forming Inverters to Solve the Challenges of Renewable Rich Power Grids

False data injection (FDI) attacks commonly target smart grids. Using the tools that are now available for detecting incorrect data, it is not possible to identify FDI attacks. One way that can be used to identify FDI attacks is machine learning. The purpose of this study is to analyse each of the six supervised learning (SVM-FS) hybrid techniques using the six different boosting and feature selection (FS) methodologies. A dataset from the smart grid is utilised in the process of determining the applicability of various technologies. Comparisons of detection strategies are made based on how accurately each one can identify different kinds of threats. The performance of classification algorithms that are used to detect FDI assaults is improved by the application of supervised learning and hybrid methods in a simulated exercise.

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Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models

Ensuring a reliable and uninterrupted supply of electricity is crucial for sustaining modern and advanced societies. Traditionally, power systems analysis was mostly dependent on formal commercial software, mathematical models produced via a mix of data analysis, control theory, and statistical methods. As power grids continue to grow and the need for more efficient and sustainable energy systems arises, attention has shifted towards incorporating artificial intelligence (AI) into traditional power grid systems, making their upgrade imperative. AI-based prediction and forecasting techniques are now being utilized to improve power production, transmission, and distribution to industrial and residential consumers. This paradigm shift is driven by the development of new methods and technologies. These technologies enable faster and more accurate fault prediction and detection, leading to quicker and more effective fault removal. Therefore, incorporating AI in modern power grids is critical for ensuring their resilience, efficiency, and sustainability, ultimately contributing to a cleaner and greener energy future. This paper focuses on integrating artificial intelligence (AI) in modern power generation grids, particularly in the fourth industrial revolution (4IR) context. With the increasing complexity and demand for more efficient and reliable power systems, AI has emerged as a possible approach to solve these difficulties. For this purpose, real-time data are collected from the user side, and internal and external grid faults occurred during a time period of three years. Specifically, this research delves into using state-of-the-art machine learning hybrid models at end-user locations for fault prediction and detection in electricity grids. In this study, hybrid models with convolution neural networks (CNN) have been developed, such as CNN-RNN, CNN-GRU, and CNN-LSTM. These approaches are used to explore how these models can automatically identify and diagnose faults in real-time, leading to faster and more effective fault detection and removal with minimum losses. By leveraging AI technology, modern power grids can become more resilient, efficient, and sustainable, ultimately contributing to a cleaner and greener energy future.

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Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models

DC motors are widely used in various household appliances and industrial control applications. The accurate information about the motor parameters is required for mathematical modelling, controller design and its analysis because complete information about these parameters are most oftenly not provided by the manufacturer. The input voltage and output speed is considered as available/measured data for estimation process and an error minimization objective function is formulated using sum of square error (SSE). The Rao-1, a metaphor-less algorithm is used to estimate permanent magnet direct current (PMDC) motor parameters, it is evident from parameter trajectories and convergence curve that Rao-1 is efficient for calculating motor parameters with minimum possible value of error.

PMDC Motor Parameter Estimation Using Rao-1 Algorithm

Driven by inertial demand from the grid, the virtual synchronous generators (VSGs) are widely utilized in distributed generation systems. However, harmonic sources in the distributed generation systems with high grid impedance will cause grid voltage distortion. Distorted voltage greatly affects the power quality of VSG. Moreover, it is difficult to suppress different types of harmonics (such as sub-synchronous harmonics and non-integer high-frequency harmonics) by one conventional solution in VSG-based grid-connected system. To solve the problem, this paper proposes a magnitude-reshaping strategy to increase the output impedance in all harmonic-frequency-bands, thereby suppressing the harmonics. The magnitude-reshaping strategy consists of a notch filter and harmonic regulator. The notch filter extracts harmonic components of grid current, whereas the harmonic regulator increases the equivalent harmonic impedance. The equivalent harmonic circuit of reshaped VSG-based system is equivalent to open circuit. Therefore, the power quality of grid-connected current can be guaranteed. To analyze the performance of the magnitude-reshaping method, frequency-coupling impedance model of VSG is established. Furthermore, Comparing with the conventional harmonic suppression methods, the frequency response characteristics of the equivalent harmonic impedance are analyzed. Finally, the effectiveness of proposed control strategy is validated by experiment results.

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Wen Yang

Papers

Magnitude-Reshaping Strategy for Harmonic Suppression of VSG-Based Inverter Under Weak Grid