Ignacio Gil

Abstract
The effective management of shared resources within energy communities poses a significant challenge, particularly when balancing renewable energy generation and fluctuating demand. This paper introduces a novel optimization framework that integrates people flow data, modeled using the Social Force Model (SFM), with energy management strategies to enhance the efficiency and sustainability of energy communities. By combining SFM with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the framework addresses multi-objective optimization problems, including minimizing energy costs, reducing user waiting times, and maximizing renewable energy utilization. The study employs synthesized data to simulate an energy community with shared facilities such as electric vehicle (EV) charging stations, communal kitchens, and laundry rooms. Results demonstrate the frameworks ability to align energy generation with resource demand, reducing peak loads and improving user satisfaction. The optimization model effectively incorporates real-time behavioral dynamics, showcasing significant improvements in renewable energy utilization-reaching up to 88% for EV charging stations-and cost reductions across various scenarios. This research pioneers the integration of people flow modeling into energy optimization, providing a robust tool for managing the complexities of energy communities.

Abstract
Reliability analysis of large power networks requires accurate aggregate models of low voltage (LV) networks to allow for reasonable calculation complexity and to prevent long computational times. However, commonly used lumped load models neglect the differences in spatial distribution of demand, type of phase-connection of served customers and implemented protection system components (e.g., single-pole vs three-pole). This paper proposes a novel use of state enumeration (SE) and Monte Carlo simulation (MCS) techniques to formulate more accurate LV network reliability equivalents. The combined SE and MCS method is illustrated using a generic suburban LV test network, which is realistically represented by a reduced number of system states. This approach allows for a much faster and more accurate reliability assessments, where further reduction of system states results in a single-component equivalent reliability model with the same unavailability as the original LV network. Both mean values and probability distributions of standard reliability indices are calculated, where errors associated with the use of single-line models, as opposed to more detailed three-phase models, are quantified.

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.