Abstract
Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability. To solve the conflicting interests over the two timescales, this article: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as 'active' actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.

Abstract
In an era characterized by extensive use of and reliance on information and communications technology (ICT), cyber-physical power systems (CPPSs) have emerged as a critical integral of modern power infrastructures, providing vital energy sources to consumers, communities, and industries worldwide. The integration of ICT in these systems, while beneficial, introduces a rapidly evolving range of cybersecurity challenges that significantly threaten their confidentiality, integrity, and availability. To address this, our article offers a comprehensive and timely survey of the current landscape of cyber vulnerabilities in CPPS, reflecting the latest developments in the field up to the present. This includes an in-depth analysis of the diverse types of cyber threats to CPPS and their potential consequences, underscoring the necessity for a broad, multidisciplinary approach. Our review is distinguished by its thoroughness and timeliness, covering recent research to offer one of the most current overviews of cybersecurity in CPPSs. We adopt a holistic perspective, integrating technical, societal, environmental, and policy implications, thereby providing a more comprehensive understanding of cybersecurity in CPPSs. We delve into the complexities of cyberattacks, exploring sophisticated, targeted attacks alongside common threats, and emphasize the dynamic nature of cyber threats, providing insights into their evolution and future trends. Additionally, our review highlights critical yet often overlooked challenges, such as system visibility and standardization in security protocols, arguing their significance in enhancing CPPS resilience. Furthermore, our work gives special attention to the aspects of restoration and recovery postcyberattack, an area less emphasized in the existing literature. Through this comprehensive overview of the current state and evolving challenges of CPPS security, our article serves as an indispensable resource for research, practice, and policymaking dedicated to safeguarding the safety, reliability, and resilience of ICT-empowered energy systems.

Abstract
AbstractRenewable energy-based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long-term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen-based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting-edge methodologies, such as machine learning-based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy (EMS) methodologies for hydrogen-based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Abstract
In recent years, the energy landscape has undergone significant transformations, characterized by the integration of renewable energy sources, smart grids, and the proliferation of IoT-enabled devices. As a result, the efficient management of energy resources has become paramount, requiring advanced methodologies in load forecasting and clustering. This article presents an enhanced methodology for short-term load forecasting that focuses on load consumption profile recognition within a smart building environment. The methodology is designed to analyze and identify recurring load consumption profiles and measures of sensors, thereby enhancing load consumption profile recognition capabilities within the smart building context. The interaction between single and grouped datasets is explored to enhance the accuracy and interpretability of predictions, contributing to optimized energy consumption and providing valuable information for demand response programs. The default forecasting methods used in the methodology are artificial neural networks and k-nearest neighbors. For comparing results and evaluating the proposed approach, XGBoost is also employed. The dataset is selected from a specific database, and the clustering method, partitioning type, is applied with k-means. The results, validated with error evaluation models and statistics, reveal the advantages of the proposed approach, especially with three clusters, where the results achieved by the Artificial Neural Network are the best. The clustering process, particularly the partitioning type, demonstrates a strong capability in improving load forecasting in smart buildings and helps understand load consumption patterns and achieve energy savings. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
In collaborative energy communities, optimizing energy costs is a critical aspect of sustainable management. This article explores the potential benefits of applying clustering algorithms to vary retail tariffs monthly, aiming to reduce energy bills for the community as a whole. The article compares a traditional approach of applying the same tariff to all community members throughout the year with a novel approach of dynamically changing tariffs based on monthly clustering results. A case study is conducted, wherein energy bill costs per month are analyzed under different tariff scenarios utilizing k -means clustering. Results indicate that the proposed approach yields promising reductions in energy costs, up to 8.76% (1170.18 EUR) improvement compared to the traditional method. The study contributes valuable insights into the practical application of clustering in energy community management and highlights the potential for significant cost savings through dynamic tariff adjustments.

Abstract
Electric vehicles (EVs) are key to a sustainable future, but extending battery life is essential to reduce costs and environmental impact. Thus, this paper presents the development of an Adaptive Nonlinear Predictive Model (ANLPM), integrated with a Third Order Generalized Integrator (TOGI) flux observer, which enhances induced torque estimation and stator reactance in Permanent Magnet Synchronous Motor (PMSM) systems. The model employs a Sequential Quadratic Programming (SQP) algorithm, ensuring numerical stability and efficiency within the Model Predictive Control (MPC) framework to handle nonlinear constraints effectively. Moreover, simulation results demonstrate that the ANLPM significantly outperforms classical Adaptive Linear Predictive Models (ALPM), Seven-Dimensional LPM (SDLPM), and Proportional-Integral (PI) control strategies. It achieves marked reductions in battery discharge current and energy consumption rates. Therefore, simulation comparisons, across different scenarios, show that ANLPM reduces battery discharge current by 3% over ALPM and 44.7% over PI, while cutting energy consumption by 12.2% and 28.2%, and decreasing parallel battery cells by 14.2% and 28%, respectively. Under high temperatures, ANLPM cuts battery consumption by 45.3% and reduces cells by 43.7% compared to SDLPM, highlighting its efficiency in managing energy and extending battery life in EVs.