Smart Grid Research Articles

Multi-Agent Deep Reinforcement Learning for Blockchain-Based Energy Trading in Decentralized Electric Vehicle Charger-Sharing Networks

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency.

Survey of Reliability Challenges and Assessment in Power Grids with High Penetration of Inverter-Based Resources

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research.

Advancing system of systems engineering using intangible value logic measurements from intellectual capital thinking approach

This study explores the integration of intangible value logic measurements derived from the Intellectual Capital Thinking (ICT) approach to advance System of Systems Engineering (SoSE). Focusing on demand-side electricity management, the research illuminates the challenges and opportunities in capturing intangible value creation within engineering systems. By delving into human behaviour-driven demand intricacies and recognising intangible resources among stakeholders, the study unveils the potential for innovation in demand management strategies. Through the analysis of three interacting value logics—shop, chain, and network—the research elucidates their impact on system performance. Addressing the practical application gap, the study introduces ICT as a strategic lens to enhance performance through non-monetary values. Qualitative and quantitative exploration showcases ICT's ability to propel system dynamics, offering crucial insights for sustainable smart grid systems. By emphasising the significance of understanding intangible measures in engineering systems, this research contributes to innovation and performance improvement in SoSE.

Narratives, expectations, and policy criteria for a democratic and socially engaging energy transition

Energy transition policies can be translated into narratives about how energy systems should change (e.g., towards a centralised or decentralised system). These narratives tend to reflect expectations, priorities, and perceptions on feasibility and the social acceptability of different policy options, as well as long-term goals and trade-offs, all of which influence policy criteria. Taking as its case study Portugal and the implementation of European directives there, this study aims to characterise energy transition narratives (e.g. a swift transformation to renewables) and interrelated policy criteria (e.g., participation of local communities), focusing on expectations for a socially engaging and democratic energy transition. The analysis builds on the results of a Delphi survey with 10 expert stakeholders, a citizens’ survey (n=500), and a workshop with 19 participants. It identifies the most relevant criteria to stakeholders, as well as the importance of different underlying expectations, meanings, and attitudes shaping narratives about energy system futures. The findings indicate that criteria interrelated to narratives which highlight a promise of democratic energy governance may be less important for energy transition policies, and therefore undermine energy democracy goals. The conclusion highlights suggestions for policy and future research more likely to foster sociopolitical acceptance.

Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review

As outlined by the International Energy Agency, 44% of carbon emissions in 2021 were attributed to electricity and heat generation. Under this critical scenario, the power industry has adopted technologies promoting sustainability in the form of smart grids, microgrids, and renewable energy. To overcome the technical challenges associated with these emerging approaches and to preserve the stability and reliability of the power system, integrating advanced digital technologies such as Digital Twins (DTs) and Artificial Intelligence (AI) is crucial. While existing research has explored DTs and AI in power systems separately, an overarching review of their combined, synergetic application in sustainable power systems is lacking. Hence, in this work, a comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The main results of this review analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity. The prominent utilisation of deep learning, supervised learning, reinforcement learning, and swarm intelligence techniques was identified as mainly constrained to power system operations and maintenance functions, along with the potential for more sophisticated AI techniques in computer vision, natural language processing, and smart robotics. This review also discovered sustainability-related objectives addressed by AI-powered DTs in power systems, encompassing renewable energy integration and energy efficiency, while encouraging the investigation of more direct efforts on sustainable power systems.

Federated Deep Learning Model for False Data Injection Attack Detection in Cyber Physical Power Systems

Cyber-physical power systems (CPPS) integrate information and communication technology into conventional electric power systems to facilitate bidirectional communication of information and electric power between users and power grids. Despite its benefits, the open communication environment of CPPS is vulnerable to various security attacks. This paper proposes a federated deep learning-based architecture to detect false data injection attacks (FDIAs) in CPPS. The proposed work offers a strong, decentralized alternative with the ability to boost detection accuracy while maintaining data privacy, presenting a significant opportunity for real-world applications in the smart grid. This framework combines state-of-the-art machine learning and deep learning models, which are used in both centralized and federated learning configurations, to boost the detection of false data injection attacks in cyber-physical power systems. In particular, the research uses a multi-stage detection framework that combines several models, including classic machine learning classifiers like Random Forest and ExtraTrees Classifiers, and deep learning architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results demonstrate that Bidirectional GRU and LSTM models with attention layers in a federated learning setup achieve superior performance, with accuracy approaching 99.8%. This approach enhances both detection accuracy and data privacy, offering a robust solution for FDIA detection in real-world smart grid applications.

Hybrid Triboelectric‐Electromagnetic‐Electric Field Energy Harvester for Simultaneous Wind and Electric Field Energy Capture in High‐Voltage Transmission System

AbstractWith the development of smart grids, efficient condition monitoring of high voltage transmission system has become crucial, necessitating reliable power supplies for distributed sensors. Traditional energy harvesters often focus on either internal or external sources, limiting overall efficiency. This study introduces a triboelectric‐electromagnetic‐electric field hybrid energy harvester (TEE‐HEH) that synergistically integrates triboelectric nanogenerators (TENGs), electromagnetic generators (EMGs), and electric field energy harvesters (EEHs) to simultaneously capture electric field and wind energy. Electric field energy is harvested via displacement currents between transmission lines and the ground, while TENGs and EMGs efficiently capture low‐ and high‐speed wind energy, respectively, enabling broadband harvesting (2.3–10 m s−1). The synergistic combination of TENG, EMG, and EEH within the TEE‐HEH leads to significantly enhanced energy capture efficiency from multiple sources. At a wind speed of 5 m s−1, a transmission line voltage of 25 kV, and a distance of 1.5 m, the TEE‐HEH achieved peak power outputs of 18.5 mW (TENG), 262 mW (EMG), and 1.85 mW (EEH), demonstrating enhanced energy collection efficiency. An environmental monitoring system has been powered, demonstrating the TEE‐HEH's practicality for dual‐source energy harvesting in smart grid applications.

Distributed Optimization on Matrix‐Weighted Networks

ABSTRACTThis article is intended to tackle the optimization problems for continuous‐time first‐order and second‐order multi‐agent systems (MASs) operating over matrix‐weighted networks. A matrix‐weighted network serves as a powerful framework to model the interdependence among agents' multidimensional states, providing an effective approach to analyze smart grids, intelligent transportation systems, and so forth. Our optimization objective is to facilitate the convergence of all agents toward the optimal value of a global cost function, which is formed by a sum of local cost functions. To achieve this goal, distributed optimization algorithms based on Hessian matrix and gradient information are constructed. Additionally, an edge‐based event‐triggered mechanism is utilized to avoid communicating with all neighbors at the time of event triggering. It is proved that this mechanism theoretically excludes Zeno behavior. The results show that the proposed algorithms ensure that the agents can achieve the optimization goal while reducing energy consumption. Finally, an application is presented to substantiate the theoretical results.

Analysis of the Potential Carbon Emission Reduction Through Interaction Between New Energy Vehicles and Smart Grids

Abstract: This paper analyzes the potential for carbon emission reduction through the interaction between new energy vehicles and smart grids, discussing how the optimization of energy efficiency and balancing of grid loads can be achieved through Vehicle-to-Grid (V2G) technology. The interaction between new energy vehicles and smart grids can enhance energy utilization efficiency, optimize grid management, and reduce reliance on fossil fuels by leveraging renewable energy sources, thereby achieving the goal of carbon emission reduction. However, this process faces multiple challenges including technological integration, cost-effectiveness, policy support, and societal acceptance. The paper proposes a series of strategic recommendations, including promoting technological standardization, enhancing policy and economic incentives, and increasing public awareness and participation, to support the effective interaction between new energy vehicles and smart grids, and to achieve long-term carbon emission reduction goals.

Scalability and Replicability Analysis in Smart Grid Demonstration Projects: Lessons Learned and Future Needs

This paper compares various approaches to the scalability and replicability analysis (SRA) of smart grid pilot projects, highlighting the need for a comprehensive SRA methodology as called for by the European Commission and International Energy Agency. This study addresses the need for a standardized SRA methodology and explores how three EU-funded projects—Platone, EUniversal, and IElectrix—adapted the general guidelines developed by the BRIDGE initiative. These guidelines provide recommendations for developing a comprehensive large-scale deployment analysis. The results show that while the guidelines are usable and flexible, project-specific conditions and data availability limitations—particularly in regulatory and technical analysis—can pose challenges. Some key recommendations to overcome these and facilitate future applications are identified. These include defining SRA methodologies and securing data-sharing agreements early. The lack of standardized approaches for presenting SRA results hampers cross-project comparison. Thus, creating an open-use case repository and updating the BRIDGE guidelines with more detailed examples, benchmarks, and reference networks is recommended. Additionally, linking SRA with cost–benefit analysis (CBA) is suggested in order to evaluate the commercial viability of smart grid solutions. The paper concludes that while the BRIDGE guidelines have proven to be fit for purpose, further developments are needed to facilitate their practical application in real-world projects.

Analysis of New Energy Generation Technology Based on the "Dual Carbon" Goal

With the increasingly severe global climate change issues, achieving the "dual carbon" goal has become a common pursuit for China and the world. The development of new energy power generation technology is of great significance for reducing greenhouse gas emissions and optimizing energy structure. This article first outlines the background and significance of the "dual carbon" goal, and analyzes its driving effect on new energy power generation technology. Subsequently, this paper discusses in detail several mainstream new energy power generation technologies, including wind energy, solar energy, biomass energy, and geothermal energy, and systematically analyzes their development status and challenges. In the part of technological innovation, this paper focuses on the application and innovation trend of smart grid technology, energy storage technology and power electronics technology in the field of new energy power generation. In addition, this paper also evaluates the economy of new energy power generation technology, including cost-benefit analysis and the impact of policy environment. Finally, this paper summarizes the research results and puts forward the direction and suggestions for future research.

AI for Climate Action: Leveraging Artificial Intelligence to Address Climate Change Challenges

This comprehensive article explores the transformative role of Artificial Intelligence (AI) in addressing the global climate crisis, focusing on its applications in climate modeling, renewable energy optimization, and disaster response. Through an extensive literature review, case studies, and expert interviews, we examine how AI technologies are revolutionizing climate prediction accuracy, enhancing data processing capabilities, and enabling sophisticated climate scenario simulations. The article delves into AI's contributions to renewable energy forecasting, smart grid management, and energy storage optimization, highlighting its potential to accelerate the transition to sustainable energy systems. We also investigate AI-driven approaches to disaster response and resilience, including early warning systems, resource allocation during climate-related disasters, and post-disaster recovery planning. While acknowledging the significant advancements AI brings to climate action, this study also addresses the challenges and limitations, such as data quality issues, ethical considerations, and technical barriers. Looking ahead, we discuss emerging AI technologies, their integration with other solutions, and the policy implications for effective climate change mitigation and adaptation. This research underscores the critical role of AI in combating climate change, while emphasizing the need for responsible development and deployment of these technologies to ensure a sustainable and resilient future.

Enhancing Smart Grid Stability through a Hybrid Biometric Pattern Recognition and Long Short-Term Memory Approach

The stability of power grids is critical in ensuring the consistent and efficient delivery of electricity. However, traditional predictive models often fall short in addressing grid data's intricate and ever-changing nature, making it challenging to maintain grid reliability. This paper introduces a novel hybrid approach that combines Biometric Pattern Recognition (BPR) with Long Short-Term Memory (LSTM) networks to enhance the prediction of smart grid stability. This approach employs BPR techniques to extract essential features from smart grid data by leveraging the pattern recognition capabilities typically used in biometric systems. These techniques are particularly effective in identifying and isolating the most relevant patterns within the complex datasets generated by smart grids. On the other hand, the LSTM-based model is designed to handle the temporal dependencies and nonlinear patterns characteristic of grid data. LSTMs, known for their proficiency in time-series analysis, are well-suited for capturing the sequential nature of grid data, enabling more accurate predictions over time. Integrating BPR and LSTM in this hybrid model addresses several limitations in existing predictive methods. By combining the strengths of both techniques, the model enhances the accuracy of predictions and improves the overall reliability of grid stability assessments. Extensive experiments were conducted using real-world datasets to validate the effectiveness of the proposed hybrid model. The results demonstrate a significant improvement in prediction accuracy, with the BPR-LSTM model achieving a 98.25% increase in accuracy compared to traditional prediction methods. This improvement underscores the potential of the BPR-LSTM hybrid approach to play a pivotal role in advancing the stability and reliability of smart grid systems.

Adaptive electronic relay for smart grid based on self-healing protection.

The protection system is crucial for grid stability and safeguarding essential components, including generators, transformers, transmission systems, and power connections. The smart grid system increases the flexibility and complexity of the power system, making fault detection and isolation the primary challenges for the protection system. This paper presents an optimal protection solution using an adaptive electronic relay to enhance reliability and enable self-healing. The proposed protection algorithm quickly detects faults and automatically isolates them from the rest of the healthy system in 25ms. The relay operation algorithm has been validated using MATLAB SIMULINK software. The results confirm the effectiveness of the proposed smart electronic relay in various sections of the smart grid system, including transformers, transmission and distribution.

Mixed Thermal and Renewable Energy Generation Optimization in Non-Interconnected Regions via Boolean Mapping

Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide optimum exploration–exploitation trade-offs at the expense of computational complexity. To ameliorate this requirement, which is extremely expensive in non-interconnected renewable systems, radically different approaches based on enhanced priority schemes and Boolean encoding/decoding have to take place. This compilation encompasses various mappings that convert multi-valued clausal forms into Boolean expressions with equivalent satisfiability. Avoiding any need to introduce prior parameter settings, the solution utilizes state-of-the-art advancements in the field of artificial intelligence models, namely Boolean mapping. It allows for the efficient identification of the optimal configuration of a non-convex system with binary and discontinuous dynamics in the fewest possible trials, providing impressive performance. In this way, Boolean mapping becomes capable of providing global optimum solutions to unit commitment utilizing fully tractable procedures without deteriorating the computational time. The results, considering a non-interconnected power system, show that the proposed model based on artificial intelligence presents advantageous performance in terms of generating cost and complexity. This is particularly important in isolated networks, where even a-not-so great deviation between production and consumption may reflect as a major disturbance in terms of frequency and voltage.

HMLB: Holonic multi-agent approach for preventive controllers load-balancing in SDN-enabled smart grid

Smart grid networks present advantages like improving reliability, security, scalability, etc. However, designing an efficient communication infrastructure for smart grid networks is a great challenge. This is because of its dependency on proprietary protocols and specific vendors. Software-defined-enabled smart grid (SDN-SG) tackles this problem by incorporating diverse protocols and standards including open source platforms. One of the most important questions in Software-defined Networking (SDN) is the controller placement problem being NP-Hard in nature. Therefore, the predominant goal of this paper is to diminish the time complexity by modeling the controller placement problem based on the holonic multi-agent system. The hierarchical structure of a holonic organization improves the computational complexity through the divide and conquer mechanism. Such an idea also decreases the distributed controllers' synchronization overhead which is an issue in the realm of SDN. On the other hand, the proper functioning of the smart grid has a strict dependency on time-critical services. Accordingly, the controller placement is supposed to be a Quality of Service-aware (QoS-aware) one. Also, intermittent topology changes in the smart grid and the occasional joining and leaving of members result in an unsteady traffic pattern and dynamicity of controller load. This research is a pioneer in providing a QoS-aware and dynamic controller placement mechanism for SDN-SG. Experimental results certify the preponderance of the approach over similar ones concerning computational complexity, packet loss, controllers’ synchronization overhead, and also load-balancing overhead.

Advanced parameter estimation for lithium-ion battery model using the information sharing group teaching optimization algorithm

Accurate battery modeling is crucial for optimizing the performance and safety of Lithium-ion batteries (LiBs), particularly in applications such as electric vehicles and smart grids. This paper introduces the Information Sharing Group Teaching Optimization Algorithm (ISGTOA), a novel human-based metaheuristic algorithm designed to estimate the 21 parameters of third-order Equivalent Circuit Model (ECM) for LiBs. Unlike existing methods, ISGTOA effectively manages the increased complexity of a model through advanced group teaching strategies and sophisticated information-sharing mechanisms inspired by classroom dynamics. We validated the effectiveness of ISGTOA using two distinct datasets—High Dynamic Profile (HDP) and Urban Dynamometer Driving Schedule (UDDS)—across various LiB chemistries (NCA and NMC) and temperature conditions (−5 °C, 25 °C and 45 °C). The results demonstrate that ISGTOA achieves superior parameter estimation accuracy and convergence speed compared to established and recent optimization methods such as TLBO, TLSBO, AISA, MTBO, and DTBO. Specifically, ISGTOA attained a minimum RMSE of 8.357 mV with rapid convergence and high stability in the HDP dataset and minimized RMSE to 10 mV within ten iterations at both 25 °C and 45 °C in the UDDS dataset. Additionally, ISGTOA exhibited high efficiency (up to 97.75 %) under varying thermal environments. This work not only introduces a novel algorithm for complex battery modeling but also sets the stage for future advancements in real-time battery management systems, emphasizing the importance of robust, versatile, and efficient parameter estimation techniques.

An Mn-Enriched Interfacial Layer for Reversible Aqueous Mn Metal Batteries.

Aqueous manganese metal batteries have emerged as promising candidates for stationary storage due to their natural abundance, safety, and high energy density. However, the high chemical reactivity and sluggish migration kinetics of the Mn metal anode induce a severe hydrogen evolution reaction (HER) and dendrite formation, respectively. The situation deteriorates in the low-concentration electrolyte especially. Here, we propose a novel approach to construct an Mn-enriched interfacial layer (Mn@MIL) on the Mn metal anode surface to address these challenges simultaneously. The Mn@MIL acts as a physical barrier to not only suppress HER but also accelerate the Mn2+ diffusion kinetics through the Mn2+ saturated interfacial layer to inhibit dendrite growth. Therefore, in the low-concentration electrolyte (1 M MnCl2), the Mn||Mn symmetric cells and Mn||V2O5 full cells with high mass loading demonstrate promising cycling stability with minimal polarization and parasitic reactions, making them more suitable for practical applications in a smart grid.

Energy Transition Policy of the Mexican Electricity Sector: A Representation in Terms of the Theory of Change

Energy transition in the electricity sector is central to limiting global warming to 1.5 °C. Countries around the world rely on official planning documents, such as strategies, special programs, and action plans, to guide these efforts. However, there are few systematic approaches and tools that can help policy makers determine comprehensiveness, coherence, consistency, and coordination when designing these planning documents. This represents a risk to meet energy transition and climate change goals if the relationships of policies and actions included in these documents are not addressed properly. To this end, this paper proposes an approach to model hierarchical relationships using the theory of change, while causal relationships are allocated using policy makers’ experience (yet can also be exogenously allocated using other models). As a case study, this work investigates Mexico’s energy transition policy of the electricity sector over the 2013–2018 period. The results show that Mexico’s official planning documents lack a good design since most policies and actions are not articulated in the right sequence and privilege clean centralized and distributed generation and energy efficiency, while others related to the modernization of electrical grids and climate and environmental policies are barely included.