Search Results (14)

The regulation of voltage in transmission networks is becoming increasingly complex due to the dynamic behavior of modern power systems and the growing penetration of renewable generation. This study presents a comparative analysis of three artificial intelligence approaches—Deep Q-Learning (DQL), Genetic Algorithms (GAs), and Particle Swarm Optimization (PSO)—for training agents capable of performing autonomous voltage control. A unified neural architecture was implemented and tested on the IEEE 30-bus system, where the agent was tasked with adjusting reactive power set points and transformer tap positions to maintain voltages within secure operating limits under a range of load conditions and contingencies. The experiments were carried out using the GridCal simulation environment, and performance was assessed through multiple indicators, including convergence rate, action efficiency, and cumulative reward. Quantitative results demonstrate that PSO achieved 3% higher cumulative rewards compared to GA and 5% higher than DQL, while requiring 8% fewer actions to stabilize the system. GA showed intermediate performance with 6% faster initial convergence than DQL but 4% more variable results than PSO. DQL demonstrated consistent learning progression throughout training, though it required approximately 12% more episodes to achieve similar performance levels. The quasi-dynamic validation confirmed PSO’s advantages over conventional AVR-based strategies, achieving voltage stabilization approximately 15% faster. These findings underscore the potential of neuroevolutionary algorithms as competitive alternatives for advanced voltage regulation in smart grids and point to promising research avenues such as topology optimization, hybrid metaheuristics, and federated learning for scalable deployment in distributed power systems. Full article

Cities host over 50% of the world’s population and account for nearly 75% of the world’s energy consumption and 80% of the global greenhouse gas emissions. Consequently, ensuring a smart way to organize cities is paramount for the quality of life and efficiency of resource use, with emphasis on the use and management of energy, under the context of the energy trilemma, where the objectives of sustainability, security, and affordability need to be balanced. Electrification associated with the use of renewable energy generation is increasingly seen as the most efficient way to reduce the impact of energy use on GHG emissions and natural resource depletion. Electrification poses significant challenges to the development and management of the electrical infrastructure, requiring the deployment of Smart Grids, which emerge as a key development of Smart Cities. Our review targets the intersection between Smart Cities and Smart Grids. Several key components of a Smart City in the context of Smart Grids are reviewed, including elements such as metering, IoT, renewable energy sources and other distributed energy resources, grid monitoring, artificial intelligence, electric vehicles, or buildings. Case studies and pilots are reviewed, and metrics concerning existing deployments are identified. A portfolio of 16 solutions that may contribute to bringing Smart Grid solutions to the level of the city or urban settings is identified, as well as 11 gaps existing for effective and efficient deployment. We place these solutions in the context of the energy trilemma and of the Smart Grid Architecture Model. We posit that depending on the characteristics of the urban setting, including size, location, geography, a mix of economic activities, or topology, the most appropriate set of solutions can be identified, and an indicative roadmap can be built. Full article

With increasing power system complexity and distributed energy penetration, traditional voltage control methods struggle with dynamic changes and complex conditions. While existing deep reinforcement learning (DRL) methods have advanced grid control, challenges persist in leveraging topological features and ensuring computational efficiency. To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. The method uses GCNs to extract higher-order topological features of the power grid, enhancing the state representation capability, while the SAC optimizes the load shedding strategy in continuous action space, dynamically adjusting the control scheme to balance load shedding costs and voltage stability. Results from the simulation of the IEEE 39-bus system indicate that the proposed method significantly reduces the amount of load shedding, improves voltage recovery levels, and demonstrates strong control performance and robustness when dealing with complex disturbances and topological changes. This study provides an innovative solution to voltage control problems in smart grids. Full article

This study examines the impact of data integrity attacks on Federated Learning (FL) for load forecasting in smart grid systems, where privacy-sensitive data require robust management. While FL provides a privacy-preserving approach to distributed model training, it remains susceptible to attacks like data poisoning, which can impair model performance. We compare Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL), using line, ring and bus topologies, under adversarial conditions. Employing a three-layer Artificial Neural Network (ANN) with substation-level datasets ($AP{E}_{hourly},PJM{E}_{hourly},$ and $COME{D}_{hourly}$), we evaluate the system’s resilience in the absence of anomaly detection. Results indicate that DFL significantly outperforms CFL in attack resistance, achieving Mean Absolute Percentage Errors (MAPEs) of 0.48%, 4.29% and 0.702% across datasets, compared to the CFL MAPEs of 6.07%, 18.49% and 10.19%. This demonstrates the potential of DFL as a resilient, secure solution for load forecasting in smart grids, minimizing dependence on anomaly detection to maintain data integrity. Full article

Although the smart grid, equipped with situational awareness and contextual understanding, represents the future of energy management and offers flexible, extensible, and adaptable intelligent grid services, it still shares similarities with traditional systems. For instance, the control performance of the DC (Direct Current) bus voltage will continue to be adversely affected by various uncertain interference factors in the future smart grid. In practice, this often leads to challenges, as inverters typically operate at high frequencies when connected to the grid. Therefore, the ability to effectively suppress fluctuations in DC bus voltage and mitigate their impact, as well as enhance the dynamic performance of the system, will be one of the key indicators for evaluating the upcoming smart grid. Consequently, this paper proposes DC-link Voltage Control using a two-stage Extended State Observer (ESO)-Cascaded Topology Structure in an LCL (Inductive-Capacitive-Inductive) Filtered Photovoltaic Grid-Connected Inverter based on Padé Approximation and Improved Active Disturbance Rejection Control. Results from both simulations and experiments demonstrate that the proposed algorithm performs effectively and is capable of suppressing fluctuations. Full article

Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. However, the current literature yields a lack of methods for efficient fault prediction and fast reconfiguration. To achieve this goal, this paper builds on DEN2DE, an adaptable routing and reconfiguration solution potentially applicable to SGs, and investigates its potential extension with AI-based fault prediction using real-world datasets and randomly generated topologies based on the IEEE 123 Node Test Feeder. The study applies models based on Machine Learning (ML) and Deep Learning (DL) techniques, specifically evaluating Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and Artificial Neural Network (ANN) as a DL method, evaluating each for accuracy, precision, and recall. Results indicate that the RF model with Recursive Feature Elimination (RFECV) achieves 94.28% precision and 81.05% recall, surpassing SVM (precision 89.32%, recall 6.95%) and ANN (precision 72.17%, recall 13.49%) in fault detection accuracy and reliability. Full article

This paper introduces a new hybrid DC–DC converter with enhanced voltage gain and synchronized multiple output capabilities, specifically tailored for smart grid applications. The proposed converter is based on the integration of non-isolated Zeta and Mahafzah converters, comprising a single controlled switch, two diodes, three inductors, and two coupling capacitors. The primary objective of this novel hybrid converter is to improve voltage gain as compared to conventional Zeta and Mahafzah topologies. By achieving higher voltage gain at lower duty cycles, the converter effectively reduces voltage stress on semiconductor switches and output diodes, thereby enhancing overall performance and reliability. A comprehensive examination of the hybrid converter’s operating principle is presented, along with detailed calculations of duty cycle and switching losses. The paper also explores the converter’s application in smart grids, specifically in the context of renewable energy systems and electric vehicles. Two distinct scenarios are analyzed to evaluate the converter’s efficacy. Firstly, the converter is assessed as a DC–DC converter for renewable energy systems, highlighting its relevance in sustainable energy applications. Secondly, the converter is evaluated as an electric vehicle adapter, showcasing its potential in the transportation sector. To validate the converter’s performance, extensive simulations are carried out using MATLAB/SIMULINK with parameters set at 25 kW, 200 V, and 130 A. The simulation results demonstrate the converter’s ability to efficiently supply multiple loads with opposing energy flows, making it a promising technology for optimized grid management and energy distribution. Moreover, the paper investigates the total harmonic distortion (THD) of the grid current, focusing on its impact in smart grid environments. Notably, the new hybrid converter topology achieves a THD of 21.11% for the grid current, indicating its ability to effectively mitigate harmonics and improve power quality. Overall, this research introduces a cutting-edge hybrid DC–DC converter that enhances voltage gain and synchronizes multiple outputs, specifically catering to the requirements of smart grid applications. The findings underscore the converter’s potential to significantly contribute to the advancement of efficient and resilient power conversion technologies for smart grids, enabling seamless integration of renewable energy systems and electric vehicles into the grid. Full article

The Internet of Things (IoT) has assumed a pivotal role in the advancement of communication technology and in our daily lives. However, an IoT system such as a smart grid with poorly designed topology and weak security protocols might be vulnerable to cybercrimes. Exploits may arise from sensor data interception en route to the intended consumer within an IoT system. The increasing integration of electronic devices interconnected via the internet has galvanized the acceptance of this technology. Nonetheless, as the number of users of this technology surges, there must be an aligned concern to ensure that security measures are diligently enforced within IoT communication systems, such as in smart homes, smart cities, smart factories, smart hospitals, and smart grids. This research addresses security lacunae in the topology and configuration of IoT energy monitoring systems using post-quantum cryptographic techniques. We propose tailored implementations of the Rivest–Shamir–Adleman (RSA), N-th degree Truncated Polynomial Ring Units (NTRU), and a suite of cryptographic primitives based on Module Learning With Rounding (Saber) as post-quantum cryptographic candidate algorithms for IoT devices. These aim to secure publisher–subscriber end-to-end communication in energy system monitoring. Additionally, we offer a comparative analysis of these tailored implementations on low-resource devices, such as the Raspberry Pi, during data transmission using the Message Queuing Telemetry Transport (MQTT) protocol. Results indicate that the customized implementation of NTRU outperforms both SABER and RSA in terms of CPU and memory usage, while Light SABER emerges as the front-runner when considering encryption and decryption delays. Full article

This paper provides an overview of an early attempt at developing a simulation model on a solid-state transformer (SST) based on input-serial and output-parallel (ISOP) topology. The proposed SST is designed as a base for a smart grid (SG). The paper provides a theoretical review of the power converters under consideration, as well as their control techniques. Further, the paper presents a simulation model of the proposed concept with a PLECS circuit simulator. The proposed simulation model examines bidirectional energy flow control between the medium-voltage AC grid and DC smart grid, while evaluating power flow efficiency and qualitative indicators of the AC grid. After the completion of design verification and electrical properties analysis by the PLECS simulation models, the synthesis offers recommendations on the optimal layout of the proposed SST topology for smart grid application. Full article

Traditionally, electric power systems are subject to uncertainties related to equipment availability, topological changes, faults, disturbances, behaviour of load, etc. In particular, the dissemination of distributed generation (DG), especially those based on renewable sources, has introduced new challenges to power systems, adding further randomness to the management of this segment. In this context, stochastic analysis could support planners and operators in a more appropriate manner than traditional deterministic analysis, since the former is able to properly model the power system uncertainties. The objective of this work is to present recent achievements of one of the most important techniques for stochastic analysis, the Monte Carlo Method (MCM), to study the technical and operational aspects of electric networks with DG. Besides covering the DG topic itself, this paper also addresses emerging themes related to smart grids and new technologies, such as electric vehicles, storage, demand response, and electrothermal hybrid systems. This review encompasses more than 90 recent articles, arranged according to the MCM application and the type of analysis of power systems. The majority of the papers reviewed apply the MCM within stochastic optimization, indicating a possible trend. Full article

The smart grid is an unprecedented opportunity to shift the current energy industry into a new era of a modernized network where the power generation, transmission, and distribution are intelligently, responsively, and cooperatively managed through a bi-directional automation system. Although the domains of smart grid applications and technologies vary in functions and forms, they generally share common potentials such as intelligent energy curtailment, efficient integration of Demand Response, Distributed Renewable Generation, and Energy Storage. This paper presents a comprehensive review categorically on the recent advances and previous research developments of the smart grid paradigm over the last two decades. The main intent of the study is to provide an application-focused survey where every category and sub-category herein are thoroughly and independently investigated. The preamble of the paper highlights the concept and the structure of the smart grids. The work presented intensively and extensively reviews the recent advances on the energy data management in smart grids, pricing modalities in a modernized power grid, and the predominant components of the smart grid. The paper thoroughly enumerates the recent advances in the area of network reliability. On the other hand, the reliance on smart cities on advanced communication infrastructure promotes more concerns regarding data integrity. Therefore, the paper dedicates a sub-section to highlight the challenges and the state-of-the-art of cybersecurity. Furthermore, highlighting the emerging developments in the pricing mechanisms concludes the review. Full article

The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design’s implementation, formulation of parameters, and promising test results indicated the potential for real-life applications. Full article

The realization of the Smart Grid vision will change the way of producing and distributing electrical energy. It paves the road for end-users to become pro-active in the distribution system and, equipped with renewable energy generators such as a photovoltaic panel, to become a so called “prosumer”. The prosumer is engaged in both energy production and consumption. Prosumers’ energy can be transmitted and exchanged as a commodity between end-users, disrupting the traditional utility model. The appeal of such scenario lies in the engagement of the end user, in facilitating the introduction and optimization of renewables, and in engaging the end-user in its energy management. To facilitate the transition to a prosumers’ governed grid, we propose a novel strategy for optimizing decentralized energy exchange in digitalized power grids, i.e., the Smart Grid. The strategy considers prosumer’s involvement, energy loss of delivery, network topology, and physical constraints of distribution networks. To evaluate the solution, we build a simulation program and design three meaningful evaluation cases according to different energy flow patterns. The simulation results indicate that, compared to traditional power distribution system, the maximum reduction of energy loss, energy costs, energy provided by the electric utility based using the proposed strategy can reach $51\%$ , $66\%$ , $97.5\%$ , depending on the strategy. Moreover, the proportion of energy self-satisfaction approaches reaches $98\%$ . Full article

A novel artificial cobweb routing algorithm (ACRA) for routing the tree-type physical topology of a low-voltage distribution network in a smart grid is proposed and analyzed in this paper. The establishment, maintenance and reconstruction of the route are presented. The artificial cobweb routing algorithm is shown to have broad general applicability for power line communication. To provide a theoretical foundation for further research, the communication delay of the network is calculated accurately. Simulation analysis of the communication delay and throughputs, which were based on Opnet14.5, demonstrate the accuracy of the theoretical calculation. For the performance evaluation of ACRA, a test-bed that includes PLC nodes with the ACRA is set up in a noisy environment. Experimental results show the feasibility of the ACRA algorithm. These indicate that ACRA is effective for guaranteeing Quality of Service (QoS) and reliability in power line communication. Full article