As global energy demand and warming increase, there is a need to transition to sustainable and renewable energy sources. Integrating different systems to create a hybrid renewable system enhances the overall adoption and deployment of renewable energy resources. Given the intermittent nature of solar and wind, energy storage systems are combined with these renewable energy sources, to optimize the quantity of clean energy used. Thus, various optimization strategies have been developed for the integration and operation of these hybrid renewable energy systems. Existing studies have either reviewed hybrid renewable energy systems or energy storage systems, however, these studies ignored energy storage systems integrated with hybrid renewable energy systems. This study offers a comprehensive analysis of the optimization methods used in hybrid renewable energy systems (HRES) integrated with energy storage systems (ESS). We examined the optimization models used in the integration of HRES and ESS, their objectives, and the common constraints. Based on our review, capacity and CO₂ emissions constraints were frequently used in hybrid optimization techniques that are effective approaches for integrating HRES and ESS. This research supports the move towards sustainable, clean energy solutions by combining an analysis of energy storage techniques with the optimization of hybrid renewable energy systems.

As the world faces pressing climate and energy challenges, Artificial Intelligence is proven as a transformative force in advancing renewable energy systems. This study reviews the current and future applications of Artificial Intelligence in renewable energy, highlighting its transformative role in enhancing the efficiency, reliability, and scalability of renewable energy systems. The study draws from over 400 recent publications, selected based on their relevance to Artificial Intelligence and renewable energy systems. We discuss the use of Artificial Intelligence techniques including machine learning, deep learning, and reinforcement learning models for optimizing energy production, forecasting demand, predictive maintenance, and managing decentralized energy systems. Emerging fields such as quantum machine learning and Artificial Intelligence-augmented reality are also considered because of their potential to transform energy infrastructures. The survey reviews significant innovations in wind and solar energy, energy storage, and smart grid technologies, focusing on how Artificial Intelligence addresses challenges like intermittency and variability. Furthermore, we discuss the importance of big data, the Internet of Things, and real-time analytics in advancing Artificial Intelligence models, along with the evolving landscape of Artificial Intelligence-driven policy and market modeling for renewable energy adoption. Real-world case studies, like Google’s collaboration with DeepMind for optimizing wind energy output and Australia’s National Electricity Market integrating Artificial Intelligence for grid stability, underscore the practical impact of Artificial Intelligence in renewable energy. This paper highlights challenges that are hindering Artificial Intelligence adoption in renewable energy systems and offers recommendations for improving the available technology to maximize Artificial Intelligence’s potential in promoting sustainable energy and addressing climate change.

In the power system, the application of the signals transmitted from the sensors to the controller can significantly improve the dynamic performance. However, cyber-attacks and time delays exist widely in the communication network, which has a considerable effect on stability, operation, and control. In this paper, an H_ load frequency control (LFC) scheme for multi-area power systems under cyber-attacks and time-varying delays is investigated. First, false date injection attacks (FDIA), as well as denial of service (DoS) attacks, are viewed as two typical cyber-attacks and introduced into the multi-area power system. Then, the state-space equations of the LFC power system under these two attacks and time-varying delays are analyzed and modeled. Furthermore, by using Lyapunov stability theory and linear matrix inequality (LMI) method, an H_ LFC scheme for attacked and delayed power systems is developed. The purpose of the proposed controller is to guarantee the closed-loop system exponentially mean-square stable with prescribed H_ performance. Finally, a three-area LFC system is employed to demonstrate the effectiveness of the proposed mechanism. The simulation results verified that the control performance of an attacked and delayed system can be improved significantly via the designed controller.

This paper provides a comprehensive review of the research progress, current state-of-the-art, and future research directions of energy storage systems. With the widespread adoption of renewable energy sources such as wind and solar power, the discourse around energy storage is primarily focused on three main aspects: battery storage technology, electricity-to-gas technology for increasing renewable energy consumption, and optimal configuration technology. The paper employs a visualization tool (CiteSpace) to analyze the existing works of literature and conducts an in-depth examination of the energy storage research hotspots in areas such as electrochemical energy storage, hydrogen storage, and optimal system configuration. It presents a detailed overview of common energy storage models and configuration methods. Based on the reviewed articles, the future development of energy storage will be more oriented toward the study of power characteristics and frequency characteristics, with more focus on the stability effects brought by transient shocks. This review article compiles and assesses various energy storage technologies for reference and future research.

There are lots of cyber-attack, especially false data injection attacks, in modern power systems. This attack can circumvent traditional residual-based detection methods, and destroy the integrity of control information, thus hindering the stability of the power system. In this paper, a novel Spatio-temporal detection mechanism is proposed to evaluate and locate false data injection attacks. In the proposed method, temporal correlation and spatial correlation are analyzed by cubature Kalman filter and Gaussian process regression, respectively, to capture the dynamic features of state vectors. Then, a deep convolutional neural network is trained to depict the functional relationship between Spatio-temporal correlation functions and the output, which is set as the detection indicator to access whether the power system under attack or not. Furthermore, the performance of the proposed mechanism is evaluated with comprehensive numerical simulation on IEEE 39-bus test system. The results of the case studies showed that the proposed method can achieve 99.84%-100% accuracy.