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The rise of electric vehicles (EVs) has initiated a significant transformation in both the transportation and energy sectors. With the increasing adoption of EVs, their interaction with the power grid is becoming more critical. A notable and innovative concept emerging in this context is Vehicle-to-Home (V2H) operations, which utilize the battery storage capabilities of EVs to meet residential energy demands. Our research provides a way of economically evaluating V2H operations under various environmental conditions including pricing, seasonal differences, and EV operations. The proposed model aids in understanding the optimal operation of V2H and identifying the factors that encourage its adoption. Furthermore, optimizing V2H use can promote renewable energy utilization, providing an additional solution to address its intermittent nature. The findings highlight the potential of V2H operations to contribute to more economically efficient energy systems, provided that supportive policies and adaptive technologies are in place. Full article

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG). Full article

The current study proposes a strategy for sensing fault detection in the secondary control of an isolated Microgrid based on a high-order Sliding Mode Robust Observers design. The proposed strategy’s main objective is to support future diagnostic and fault tolerance systems in handling these extreme situations. The proposal is based on a generation system and a waste management system. Four test scenarios were generated in a typical Microgrid to validate the designed strategy, including two Battery Energy Storage Systems in parallel, linear, and non-linear loads. The scenarios included normal grid operation and three types of sensing faults (abrupt, incipient, and random) directly affecting the secondary control of a hierarchical control strategy. The results showed that the proposed strategy could provide a real-time decision for detection and reduce the occurrence of false alarms in this process. The effectiveness of the fault detection strategy was verified and tested by digital simulation in Matlab/Simulink R2023b. Full article

The integrated gate-commutated thyristor (IGCT) has the advantages of high voltage, high current, high reliability, and low manufacturing costs and has the potential to replace thyristor devices in the field of high-voltage direct current (HVDC) transmission. Over time, the development and manufacture of IGCT devices, drivers, and valve bodies have gradually matured, but the modeling and control technology of HVDC systems based on IGCT needs further research. This review aims to discuss the research status of key technologies of HVDC system modeling and control based on the IGCT in recent years, including the development of HVDC systems and the application potential of the IGCT, the efficient simulation and modeling technology of the IGCT HVDC system, and the key problems of HVDC system control technology based on the IGCT. At the same time, according to the author’s point of view, the existing problems and difficulties are extracted, and the next development ideas are clarified. Full article

Emerging data processing techniques brought back into attention the HF range communication as an interesting alternative to third-party solutions for IoT applications, such as data transmission in distributed energy production facilities. The physical size of HF antennas, often comparable to the surrounding objects, require in situ radiation measurements resulting in site-customized antenna design and positioning, and consequently in a higher reliability of such HF grid communications. Drone-borne measuring systems are already known as a flexible solution, but are mostly restricted to higher frequency ranges where full-wave, wide-band probes are feasible. In this work, we propose to use an electrically small, folded dipole as a probe for drone-borne measurements on HF antennas. We also propose a calibration approach for the effects related to the near-field zone, and to the drone body proximity; corrections on these two effects are the key methodological steps. We show that despite a realized gain figure in the order of −20 dBi, such a probe can provide stable results for near-field measurements, even at input power levels as low as 1 mW. Compared to other similar approaches, our configuration provides a wider frequency band of operation, higher stability in terms of pattern diagram, and a lower cost. Full article

The increasing use of renewable energies places new challenges on the balance of the electricity system between demand and supply, due to the intermittent nature of renewable energy resources. However, through frequency regulation (FR) services, owners of battery storage systems can become an essential part of the future smart grids. We propose a thorough first study on the use of batteries associated with base stations (BSs) of a cellular network, to participate in ancillary services with respect to FR services, via an auction system. Trade-offs must be made among the number of participating BSs, the degradation of their batteries and the revenues generated by FR participation. We propose a large-scale mathematical programming model to identify the best participation periods from the perspective of a cellular network operator. The objective is to maximize profit while considering the aging of the batteries following their usage to stabilize the electrical grid. Experiments are conducted with data sets from different real data sources. They not only demonstrate the effectiveness of the optimization model in terms of the selection of BSs participating in ancillary services and providing extra revenues to cellular network operators, but also show the feasibility of ancillary services being provided to cellular network operators. Full article

The proliferation of electronic loads has led to a substantial increase in harmonic emissions within low-voltage distribution networks. The accurate estimation of the expected levels of harmonics in a network is a daunting task for network operators. Stochastic-based harmonic estimation models can offer a comprehensive assessment of the expected levels of harmonics in the presence of existing and future loads, including electric vehicles and smart-grid-enabled devices. Such models offer a valuable tool for network operators to assess the potential impact of harmonics on future networks and to create sustainable design solutions to meet the increasing demand for electricity while achieving net zero targets. However, several variables associated with these estimations models involve a level of uncertainty due to their stochastic nature, leading to inaccuracies in the estimations. This paper aims to provide a more realistic estimate of these uncertainties in order to improve the outcomes of harmonic estimation models for the development of sustainable distribution networks. Full article

Managing costs in microgrids presents a formidable challenge due to the intricate blend of renewable and non-renewable energy sources that underpin their power generation. Ensuring seamless integration of microgrids with the national grid is pivotal for continuous load demand satisfaction and adherence to liberalized energy market mandates. To address this challenge, this paper introduces a new optimization technique for the Cost Management and Operation System (CMOS) of multi-source microgrids through a smart management unit. The cornerstone of this unit is the Egyptian Stray Dog Optimization (ESDO) algorithm, meticulously designed to optimize operational costs in line with load demands, energy cost dynamics, and generation proficiencies. Rigorous testing of the proposed system was conducted on a multi-resource microgrid using MATLAB, encompassing various operational scenarios. The simulation outcomes consistently highlighted the unit’s capability to achieve optimal cost-efficiency. Comparative analysis with other optimization techniques, particularly Particle Swarm Optimization (PSO), demonstrated the superior performance of the Egyptian Stray Dog algorithm, underscoring its potential as a leading solution in this domain. Full article

This paper reviews the use of game theory tools to study the operation and design of modern power grids. The contribution of this work is to summarize the literature to highlight the versatile solution capability of game theory by focusing on the interconnected objectives of energy trading and energy management. This review was conducted with a focus on various applications in energy systems, including general energy markets, micro grids (MGs), virtual power plants (VPP), electric vehicles (EVs), and smart homes, and explores how game theory can summarize the solutions for pricing, bidding, demand side management, and resource optimization. A key finding is the suitability of game theory for modeling decentralized energy systems where strategic incentives can lead to outcomes that benefit both individuals and society. It also discusses the limitations, challenges, and potential benefits of game theory in complex power systems. This study provides researchers and policy makers with a comprehensive overview of current research and insights into the potential of game theory to shape the future of energy systems. Full article

To address the potential threat to the power grid industry posed by quantum computers and ensure the security of bidirectional communication in smart grids, it is imperative to develop quantum-safe authentication protocols. This paper proposes a semi-quantum bidirectional authentication protocol between a control center (CC) and a neighboring gateway (NG). This method uses single photons to facilitate communication between the CC and the NG. Security analysis demonstrates that the protocol can effectively resist common attack methods, including double CNOT attacks, impersonation attacks, interception-measurement-retransmission attacks, and entanglement-measurement attacks. Comparisons with other protocols reveal that this protocol has significant advantages, making it more appealing and practical for real-world applications. Finally, by simulating the protocol on the IBM quantum simulator, this protocol not only validates the theoretical framework but also confirms the practical feasibility of the protocol. Full article

In the power grid, inspection robots enhance operational efficiency and safety by inspecting power lines for information sharing and interaction. Edge computing improves computational efficiency by positioning resources close to the data source, supporting real-time fault detection and line monitoring. However, large data volumes and high latency pose challenges. Existing offloading strategies often neglect task divisibility and priority, resulting in low efficiency and poor system performance. This paper constructs a power grid inspection offloading scenario using Python 3.11.2 to study and improve various offloading strategies. Implementing a game-theory-based distributed computation offloading strategy, simulation analysis reveals issues with high latency and low resource utilization. To address these, an improved game-theory-based strategy is proposed, optimizing task allocation and priority settings. By integrating local and edge computing resources, resource utilization is enhanced, and latency is significantly reduced. Simulations show that the improved strategy lowers communication latency, enhances system performance, and increases resource utilization in the power grid inspection context, offering valuable insights for related research. Full article

Aligned with the objectives of the energy transition, the increased penetration levels of electric vehicles as part of the electrification of economy, especially within the framework of local energy communities and distributed energy resources, are crucial in shaping sustainable and decentralized energy systems. This work aims to assess the impact of escalating electric vehicles’ deployment on sustainable local energy community-based low-voltage distribution networks. Through comparative analyses across various levels of electric vehicle integration, employing different charging strategies and system management approaches, the research highlights the critical role of active system management instruments such as smart grid monitoring and active network management tools, which significantly enhance the proactive management capabilities of distribution system operators. The findings demonstrate that increased electric vehicle penetration rates intensify load violations, which strategic electric vehicle charging management can significantly mitigate, underscoring the necessity of load management strategies in alleviating grid stress in the context assessed. This study highlights the enhanced outcomes derived from active system management strategies which foster collaboration among distribution system operators, demand aggregators, and local energy communities’ managers within a local flexibility market framework. The results of the analysis illustrate that this proactive and cooperative approach boosts system flexibility and effectively averts severe grid events, which otherwise would likely occur. The findings reveal the need for an evolution towards more predictive and proactive system management in electricity distribution, emphasizing the significant benefits of fostering robust partnerships among actors to ensure grid stability amid rising electric vehicle integration. Full article

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits. Full article

The increasing number of electric vehicles (EVs) necessitates the installation of more charging stations. The challenge of managing these grid-connected charging stations leads to a multi-objective optimal control problem where station profitability, user preferences, grid requirements and stability should be optimized. However, it is challenging to determine the optimal charging/discharging EV schedule, since the controller should exploit fluctuations in the electricity prices, available renewable resources and available stored energy of other vehicles and cope with the uncertainty of EV arrival/departure scheduling. In addition, the growing number of connected vehicles results in a complex state and action vectors, making it difficult for centralized and single-agent controllers to handle the problem. In this paper, we propose a novel Multi-Agent and distributed Reinforcement Learning (MARL) framework that tackles the challenges mentioned above, producing controllers that achieve high performance levels under diverse conditions. In the proposed distributed framework, each charging spot makes its own charging/discharging decisions toward a cumulative cost reduction without sharing any type of private information, such as the arrival/departure time of a vehicle and its state of charge, addressing the problem of cost minimization and user satisfaction. The framework significantly improves the scalability and sample efficiency of the underlying Deep Deterministic Policy Gradient (DDPG) algorithm. Extensive numerical studies and simulations demonstrate the efficacy of the proposed approach compared with Rule-Based Controllers (RBCs) and well-established, state-of-the-art centralized RL (Reinforcement Learning) algorithms, offering performance improvements of up to 25% and 20% in reducing the energy cost and increasing user satisfaction, respectively. Full article

The construction of China’s Xiongan New Area aims to create a smart city characterized by green, low-carbon, intelligent information, livability, business-friendliness, and harmony between humans and nature, with energy Internet services as a crucial foundation. Using macro-environmental (PEST), situational (SWOT) analyses and ANP analysis, this research explores the sustainability of Xiongan’s energy Internet system. The findings reveal that economic factors are particularly significant, with “abundance and easy extraction of resources” being the primary strength (12.25%). The most pronounced weakness is “insufficient integration of the Internet with energy”, a social factor (52.60%). Opportunities are mainly economic, with “strong financial support” as the primary driver (46.58%). Technological barriers, such as “monopolistic practices hindering progress”, are the chief threat (38.73%). This comprehensive analysis forms the basis for proposing targeted sustainable development strategies for Xiongan’s energy Internet system, offering valuable insights for similar initiatives elsewhere. Full article