The increasing frequency of natural disasters has led researchers to focus on the resilience of energy systems. Energy hubs have become efficient tools to enhance the resilience of integrated energy systems (IESs) due to their inherent flexibility and controllability. These energy hubs play prominent roles in load restoration, they can significantly enhance power system robustness against natural disasters. On the other hand, mobile energy storage systems (MESs) are powerful tools for load recovery due to their mobility and flexibility. This paper presents a resilience strategy for integrated power-gas-transportation systems that uses energy hubs and multicarrier mobile storage for operational recovery of interrupted loads. The proposed model is implemented on an integrated test system including a 33-bus distribution network, a 20-node gas network, and a transportation network. The results show that the proposed framework, in two critical hour samples, provides an improvement of 84.8 % (17:00) and 81.8 % (21:00) in the recovery of electrical loads, as well as an improvement of 44.9 % (17:00) and 28.8 % (21:00) in gas loads, and significantly prevents the outage of critical loads.

The rapid transformation of distribution networks toward smart and low-carbon operation has fundamentally altered the role of aggregation and control at the distribution level. As distributed energy resources (DERs) proliferate, conventional Virtual Power Plant (VPP) architectures, primarily designed for market participation, are increasingly unable to address the technical limitations and environmental objectives of modern distribution systems. This paper proposes a Regulated Techno-Commercial Virtual Power Plant Operator (TC-VPPO) that unifies the functions of the VPP Operator (VPPO) and the Distribution System Operator (DSO) within a single regulated decision-making framework. By design, the TC-VPPO simultaneously coordinates market transactions, enforces distribution network operational constraints, and manages demand response programs through the aggregation of flexible loads. The proposed formulation explicitly incorporates carbon emissions from controllable distributed generations (CDGs), energy storage system (ESS) degradation costs, and surplus renewable energy mitigation. These interactions are modeled using a stochastic mixed-integer linear programming (MILP) framework that rigorously accounts for voltage limits and line flow constraints across the distribution network through Polygonal Approximation, an aspect often neglected in existing VPP-based studies. Numerical simulations demonstrate that the proposed TC-VPPO architecture achieves a 76% reduction in CDG-related emissions, lowers renewable energy curtailment by 29.3%, and ensures full compliance with all network technical constraints. The results confirm that integrating techno-commercial coordination with network-aware regulation provides a robust and scalable pathway toward sustainable and reliable operation of future decentralized distribution systems.

Advanced operational strategies are necessary to optimize power systems when integrating Distributed Energy Resources into power networks. To maximize profits from decentralized generation, effective management through Virtual Power Plants is crucial, along with harnessing their full potential in the energy and carbon markets. As part of this study, recent Virtual Power Plant models are systematically reviewed and categorized, highlighting market interactions, optimization techniques, and practical implementations through case studies and pilot projects. Model development, mathematical complexity (e.g., objective function formulations, energy management, problem-solving methods, and decentralized optimization), bidding strategy optimization, and Virtual Power Plant programming approaches are included in the classification framework. Prior review articles frequently fail to categorize key research into flexibility and carbon market integration as distinct forms of market participation. An in-depth analysis of multi-objective optimization, Virtual Power Plant deployment progress, and global market outlook is provided, along with a synthesis of relevant pilot projects and real-world examples. This analysis incorporates the strengths and weaknesses of different Virtual Power Plant models and solution methods. Notably, the implementation of Virtual Power Plant is still limited in real-life settings, with most projects spanning from one day to one year. Unlike previous reviews, this study introduces a novel classification framework that explicitly incorporates flexibility and carbon market participation into the analysis of Virtual Power Plants. This approach extends beyond traditional categorizations, which are limited to energy and ancillary services, thereby providing a more comprehensive understanding of multi-market integration. By applying the Market Coupling Intensity taxonomy, the review establishes a structured pathway for assessing the progression from loosely coupled energy market participation to fully integrated frameworks involving carbon and flexibility products.

Technological advancements, environmental concerns, and improved electricity management drive the transition to smart grids. With the increasing integration of distributed energy resources (DERs), efficient management is crucial for optimizing both renewable and non-renewable energy sources. This paper presents a bi-level stochastic optimization framework to enhance virtual power plant (VPP) operations and improve grid flexibility. At the lower level, VPPs maximize profits by optimizing resource scheduling, managing demand response (DR) programs, and determining power exchanges with the distribution system operator (DSO). The upper level minimizes DSO costs while ensuring network security and reliability. A key feature is the provision of upward and downward flexibility by VPPs at both the individual resource level and in day-ahead scheduling, enabling the DSO to enhance grid stability and renewable integration. The model, formulated as a stochastic mixed-integer linear programming (MILP) problem, effectively addresses uncertainties in load demand, renewable generation, and energy prices. Simulation results demonstrate significant improvements in flexibility and economic efficiency, achieving an 89.91% reduction in excess renewable generation through targeted penalty mechanisms. These findings highlight the practical benefits of the proposed framework for VPP operators and DSOs, offering a robust strategy for managing DERs while ensuring stable and secure grid operation.

The increasing integration of electric vehicles (EVs) and vehicle-to-grid (V2G) technology introduces significant operational challenges in modern power systems, mainly due to the high uncertainty of renewable generation and rapidly varying demand profiles. Conventional energy management approaches are often based on simplified system models or centralized optimization schemes, which struggle to capture system dynamics in real time and scale effectively under highly stochastic and large-scale operating conditions. However, most existing studies still lack a unified framework that can simultaneously provide accurate prediction of uncertain renewable generation, real-time system representation, and adaptive decentralized decision-making for large-scale EV coordination. To address these limitations, this paper proposes an integrated framework combining Digital Twin Technology (DTT), Deep Neural Networks (DNN), and Multi-Agent Reinforcement Learning (MARL) for real-time EV charging and discharging optimization. This integration enables coordinated prediction, decision support, and distributed control within a data-driven framework, improving adaptability compared to conventional standalone approaches. In a Smart Neighborhood scenario, interconnected residential microgrids host multiple electric vehicles that operate as autonomous agents using a Q-learning-based strategy. Within this framework, EVs dynamically adapt their behavior by charging during renewable energy surplus, discharging during energy deficits, and remaining idle when no beneficial action exists, enabling efficient local energy management. Simulation results demonstrate that the proposed approach improves energy utilization efficiency, reduces battery degradation, and enhances overall system performance while increasing economic benefits for both EV owners and grid operators.

The variability of renewable energy sources (RES) presents a challenge concerning the stability and operational efficiency of microgrids. This study suggests a Digital Twin-based framework encompassing deep learning and reinforcement learning (RL) techniques to improve the overall energy forecasting potential and optimization of battery management. Among the machine learning models tested, Deep Neural Network (DNN) turned out to be the most accurate and computationally efficient. When combined with RL, this allows the charging-discharging operation of batteries to be dynamically managed, thus maximizing energy efficiency and battery life. Through the implementation of this framework, microgrids will receive more reliable support via sustainable energy utilization and scalable support for intelligent energy management.

The increasing frequency of extreme weather events presents critical challenges to the resilience of energy distribution systems and the operation of energy management systems (EMS). Traditional model-based optimization approaches rely heavily on accurate forecasts and detailed system models, which can be difficult to obtain under high uncertainty. To address this limitation, this study proposes a data-driven EMS framework based on deep reinforcement learning (DRL), which learns robust coordination strategies for residential energy resources directly from stochastic environments. The framework is designed to optimize energy coordination in residential buildings during both normal and extreme operating conditions. The proposed framework combines renewable generation, mobile and stationary energy storage, and employs a sequential decision-making model to adapt its actions in real time based on system dynamics. The DRL agent is trained using event-adversarial scenarios to enhance robustness and resilience in unpredictable environments. Using real household data from Queensland, Australia, we validate the agent's ability to maintain load support, minimize energy not supplied, and improve renewable generation self-consumption. Compared to deterministic optimization methods, the DRL-based approach achieves comparable performance with faster response, requiring minimal user intervention. This makes it highly suitable for real-time EMS deployment in weather-vulnerable urban areas. The proposed method offers a scalable and intelligent solution to enhance the resilience, sustainability, and energy efficiency of residential systems in cities facing uncertain and extreme climatic conditions.

In modern smart energy systems, which are critical cyber-physical infrastructures, False Data Injection Attacks (FDIAs) remain a significant threat to state estimation. The transition toward Energy Communities (ECs) further increases vulnerability, as EC-driven smart grids are persistently exposed to Byzantine attacks originating from within the ECs, designed to cause sabotage. These attacks can result in misclassification within stealthy FDIA detection, thereby undermining system reliability. Dependence on a central server or third party for establishing trust is rendered infeasible under such conditions, as centralized architectures introduce additional risks, including the creation of a single point of failure (SPoF) capable of halting the learning process. To address these challenges, a resilient committee-based federated learning framework has been introduced, employing transformers to enhance robustness. Multi-head self-attention (MHSA) transformers are applied to improve local FDIA detection in each EC, while a committee-based mechanism is utilized to select honest ECs. Selection strategies are proposed for both non-attack environments and fully untrusted EC-driven smart grids. Various forms of Byzantine attack are examined and compared with alternative federation methods on IEEE 33-Bus and a real-world distribution system. Results show that the proposed framework effectively detects stealthy FDIAs and remains resilient against malicious ECs aiming for market gain or disruption. Strategies I and II achieved accuracies of 0.962 and 0.998 in a trusted environment, while counterintuitive outcomes were observed in the fully untrusted case.

The variability of active and reactive power in electric loads, influenced by voltage and frequency fluctuations, presents significant challenges in the AC optimal power flow (AC-OPF) of islanded microgrids (IMGs). These fluctuations affect power demand, which, in turn, impacts system voltage and frequency, creating a complex interdependence that must be carefully addressed in the AC-OPF formulation. To tackle this issue, this study proposes a novel mixed-integer nonlinear programming (MINLP) model for AC-OPF in droop-based IMGs, incorporating voltage- and frequency-dependent loads (VFDL) while considering various technical constraints. To enhance computational efficiency and ensure solution optimality, the MINLP problem is transformed into a mixed-integer linear programming model through linearization techniques and approximations, making it solvable with commercial optimization solvers. The proposed model is implemented in the open-source Pyomo optimization framework and solved using Gurobi. The effectiveness of the proposed model is validated on an enhanced IEEE 33-bus benchmark system. The results indicate that including VFDLs significantly influences system operation, particularly in cost minimization and voltage profile.

In this paper, we propose a bilevel optimization framework to model competition among multiple renewable integrated energy hubs within an interconnected local electricity and heat market. The upper-level problem focuses on the strategic profit maximization behavior of each energy hub using bidding, while at the lower-level, the market-clearing problem allocates the available resources through social welfare maximization. Each energy hub has several components including combined heat and power units (CHP), heat pumps, boilers, renewables, electrical and thermal energy storage systems. These energy hubs are locally active market participants that strive to maximize their profits while meeting their own energy needs. A scenario-based method is developed to enable robust decisions in the face of the utmost uncertainty regarding prices and loads. The developed bilevel model is solvable in any KKT transformation and iterative methods. Finally, the extensive case studies aim to understand the influence of renewable integration, increased reserve storage, different competition scenarios, levels of uncertainty, and grid constraints on energy hub operations and market performance. The results demonstrate that strategic participation in the local market by the energy hubs leads to substantial profits for them compared to non-strategic participation. However, this strategic behavior introduces a 'Price of Anarchy, 'resulting in a slight reduction in overall social welfare compared to a perfectly competitive baseline. This framework provides valuable insights for policymakers, market designers, and hub operators seeking to build efficient and sustainable local energy systems.

With the shift toward smarter and more sustainable energy communities, optimized operation of Semi-Transparent Photovoltaic (STPV) greenhouses in arid climates like Qatar requires integrated management of energy dispatch, battery lifetime, and crop-specific thermal control. Ambient temperature significantly affects crop cultivation and directly influences the long-term performance and lifespan of battery energy storage systems (BESS), which undergo sudden and frequent charging/discharging events. To address these challenges, this paper proposes an interval-analysis-based optimization model formulated as mixed integer linear programming (MILP) to maximize the daily profit of STPV greenhouses. The model incorporates comprehensive BESS management constraints to extend battery lifespan and includes detailed modeling of electricity generation from STPV walls and roofs. Moreover, a novel thermal model is proposed that accounts for thermal inertia and the effects of solar irradiance on greenhouse internal temperature to represent realistic greenhouse conditions integrated with inverter-based HVAC. In addition, a novel agricultural temperature stability index (ATSI) is introduced, combining statistical measures with crop-specific agricultural requirements, and can be customized according to the requirements of different crops. Daily environmental and technical input profiles from Qatar are used to enhance the realism of the model. Simulations were performed across multiple cases and crop types, including sensitivity analyses on key input parameters. Furthermore, the model's implications for BESS lifespan and longterm economic performance were evaluated. Results indicate that, for tomato cultivation, SoC-aware operation reduced BESS degradation cost by up to 6.6% from $170.12 to $158.83, although grid transaction cost increased from 0.5224 k$ to 0.8327 k$, highlighting the trade-off between battery lifetime preservation and short-term operating cost. Moreover, the inverter-based HVAC improved crop-specific thermal stability by achieving an ATSI of 0.964, compared with 0.833 for conventional thermostat-based HVAC.

This paper addresses the challenge of achieving both sustainability and resilience in renewable-powered reconfigurable microgrids (RMGs) while managing computational burdens. The Green Independence Performance Index (GIPI) is introduced as a novel resilience metric designed to maximize microgrid independence from the utility grid while avoiding renewable energy curtailment. A multi-objective, nonlinear, and non-convex optimization model is developed to simultaneously minimize total operational costs and maximize GIPI. To enhance tractability and guarantee solution optimality, the problem is reformulated as a mixed-integer linear programming (MILP) model using piecewise linearization and approximation techniques. To further reduce computational burden, a custom matheuristic algorithm is introduced, which synergistically combines classical MILP optimization with a neighborhood-structure-based heuristic. Numerical experiments on the enhanced IEEE 33-bus benchmark system demonstrate that the proposed GIPI eliminates renewable energy curtailment, unlike the IPI which curtailed 13.58% of photovoltaic and 25.09% of wind generation, while the proposed matheuristic reduces computational time from 1,629.74 s (CPLEX) to only 103.32 s and maintains near-optimal operating costs.

Managing Local Multi-Carrier Energy Communities (LMCECs) has become increasingly complex due to the need to balance sustainability, flexibility, and economic performance in modern energy systems. This challenge is further compounded by uncertainties in energy supply and demand, necessitating advanced optimization approaches. To address this, a robust optimization model has been developed to enable LMCECs to effectively participate in programs emphasizing flexibility, self-sufficiency, and environmental sustainability. The model incorporates electrical flexibility constraints to enhance practical applicability and allows the LMCEC manager to adopt emissions limits recommended by upstream energy networks, promoting environmentally conscious operations. By prioritizing self-sufficiency, the model not only strengthens the resilience of LMCECs but also improves their operational efficiency. Results demonstrate the model's effectiveness in handling uncertainties while minimizing operational costs, achieving an average optimal self-sufficiency rate of 76.36 %. This represents a significant step forward in advancing sustainable and resilient energy management practices. Moreover, a comparison between the robust optimization approach and both the deterministic and Distributionally Robust Chance-Constrained (DRCC) methods highlights the superior performance of the proposed robust optimization under worst-case scenarios.

Smart grids have advanced communication technologies that make them vulnerable to cyber-attacks. This paper investigates a new method called a supervised convolutional neural network (CNN)-based system state estimator, which consists of two systems: a data validation model and a fault detection model. In this research, heat graphs are used as a tool for visualizing numerical data and a method for preprocessing information in training the CNN model. On the other hand, one emerging tool in artificial intelligence is generative adversarial network (GAN), which is a deep learning method that can bypass intrusion detection systems by generating fake examples. In response, this paper uses both common examples and examples generated by GAN for training and evaluation of the presented system. The proposed method considers the role of fake data injection (FDI) in smart networks with the aim of increasing the accuracy of the proposed model and then focuses on the impact of combined FDI and denial of service (DoS) attacks on smart networks. In this paper, two complex cyber-attack scenarios are investigated. The proposed model is capable of effectively detecting both attacks, as evidenced by the simulation results for varying attack intensities, which achieved a validation accuracy of 99.51 in scenario I and 99.32, 99.59, and 99.50 in scenario II. Moreover, the results indicate that using the examples generated by GAN helps the data validation model to increase its accuracy against cyber-attacks and the fault detection model quickly identifies the desired fault and notifies the system operators.

Pre-fault dynamic security assessment (DSA) is essential for the safe operation of power systems. Pre-fault DSA methods that utilize deep learning techniques have been successfully implemented and have shown promising results. However, these methods face challenges in real power systems, such as unknown faults and the increasing integration of power electronics-based units. Adding these units changes the system dynamics and introduces new stability problems, such as the loss of synchronism that current methods cannot analyze. In practical applications, new faults may arise that are not present in the training database, which can decrease the accuracy of the online DSA model. To tackle these challenges, this paper introduces a new dynamic security index that considers the effects of loss of synchronism in power electronics-based units on DSA. Also, a graph convolutional network (GCN)-based model is developed to improve DSA accuracy by incorporating the topological information of the power system in the form of an adjacency matrix. To address the issue of unknown faults, this paper uses transfer learning based on full fine-tuning to adapt a pre-trained GCN model to a different but related unknown fault. This approach eliminates the need for a large number of labeled examples for new faults and ensures efficient transfer of the model to new faults with a small database. Case studies are conducted on a modified IEEE 39-bus system to investigate the impact of power electronics-based units' penetration on dynamic security and the model's ability to transfer knowledge for unknown faults. The results from various evaluation indicators demonstrate the effectiveness of the proposed model.

With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model.