The increasing complexity of modern power systems necessitates innovative approaches to enhance smart grid efficiency. This paper explores the integration of multi-agent systems (MAS) and machine learning (ML) to optimize decision-making processes within smart grids. MAS, characterized by decentralized control and autonomous agents, facilitate dynamic interactions among grid components, improving adaptability and resilience. By employing machine learning algorithms, agents can analyze vast datasets generated by grid operations, enabling predictive maintenance, demand forecasting, and real-time optimization of energy distribution. This study presents a framework that leverages MAS and ML to address critical challenges in smart grid management, such as load balancing, fault detection, and renewable energy integration. Results from simulations demonstrate significant improvements in operational efficiency, reliability, and sustainability. The findings highlight the potential of combining MAS with ML to create a more responsive and efficient smart grid infrastructure.

As the demand for renewable energy continues to rise, decentralized energy markets have emerged as a promising solution to empower small-scale producers, such as residential solar energy generators. This paper explores the design and implementation of incentive mechanisms that facilitate direct energy transactions between producers and consumers within a decentralized framework. We analyze the key components of these mechanisms, including dynamic pricing, reward systems, and risk-sharing strategies, and compare them to traditional centralized trading models. Through a quantitative analysis of potential revenue outcomes for small producers in both systems, we demonstrate that decentralized markets can enhance profitability and encourage greater participation in renewable energy production. Additionally, we discuss the role of blockchain technology in ensuring transparency, security, and efficiency in these markets. Our findings suggest that welldesigned incentive mechanisms are crucial for the success of decentralized energy markets and offer significant benefits for both producers and consumers, paving the way for a more sustainable and resilient energy future.

As the global climate security situation becomes increasingly severe, the development of renewable energy has become an important strategic direction for global energy transformation and addressing climate risks. Terminal energy electrification is a key measure to accelerate this strategic shift from the energy consumption end. By increasing the proportion of clean energy power supply in terminal energy consumption, it can reduce dependence on fossil energy, improve energy utilization efficiency, reduce environmental pollution, and promote sustainable development. Under the "dual carbon" goal, China proposes to build a clean, low-carbon, safe and efficient energy system, accelerate the construction of a new power system with new energy as the main body, and promote the large-scale optimization of clean power resources. However, China's power structure, which is dominated by thermal power, is heavily dependent on coal, and energy resources and power demand are inversely distributed. More than 80% of energy resources are distributed in the western and northern regions, while more than 70% of energy consumption is concentrated in the eastern and central regions. Against this background, the country has adopted macro-adjustment measures such as "west-to-east power transmission" and "north-to-south coal transportation" to optimize energy allocation and promote energy supply and demand balance. At the same time, the western and northern regions have borne many negative externalities of energy production transferred from the eastern and central regions, such as coal-fired power generation, carbon emissions in coal production, environmental pollution, and health impacts. In addition, for resource-based cities and high-carbon industry clusters that are highly dependent on fossil energy, In the short term, terminal energy electrification will increase the burden on economic income, energy security, reemployment, electricity costs, etc., and thus cause new regional social equity issues. Therefore, in the context of China accelerating the high-quality development of new energy in the new era and large-scale layout of renewable energy base construction, it is of great practical significance to focus on the three dilemmas of energy "security, equity, and ecology" and study and identify the spatiotemporal pattern characteristics and key influencing factors of the coupled coordinated development of regional terminal energy electrification and social equity.

Risk management in power systems has become an essential component of ensuring the reliability, stability, and economic efficiency of modern electrical grids. As power systems become increasingly complex and vulnerable to both natural and man-made disruptions, accurate and effective risk measurement techniques are crucial for mitigating potential threats and minimizing system failures. This paper provides a comprehensive review of risk measurement methodologies used in the context of power system risk management. It explores various quantitative techniques, including probabilistic risk assessment, fault tree analysis, and Monte Carlo simulations, and examines their application in evaluating system vulnerabilities, identifying potential hazards, and optimizing risk mitigation strategies. Furthermore, we discuss the integration of these methods with modern power system planning, operation, and control strategies, particularly in light of evolving renewable energy sources and distributed generation technologies. By focusing on both theoretical and practical aspects, this paper offers valuable insights for enhancing the robustness of power systems in the face of growing risks and uncertainties.

With the increasing complexity and volatility of modern power markets, high-frequency trading (HFT) has emerged as a promising approach to enhance market efficiency and liquidity. This paper proposes a high-frequency trading methodology and system tailored for power market transactions. The approach leverages advanced algorithmic techniques, including predictive modeling, real-time data analytics, and adaptive decision-making processes, to capitalize on micro-level price movements and optimize trade execution. We introduce a novel system architecture that integrates market data feeds, trade execution modules, and risk management tools, enabling rapid responses to market dynamics and improving overall trading performance. Through simulations and case studies, we demonstrate the effectiveness of this method in capturing arbitrage opportunities, reducing transaction costs, and enhancing market stability. The proposed HFT framework offers a potential solution to address the growing challenges in power markets, particularly in terms of price discovery, liquidity provision, and market efficiency.

Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, existing models struggle to effectively capture dependencies from multiple perspectives, limiting their ability to model complex data. To address this gap, we propose the Multi-view Fuzzy Graph Attention Network (MFGAT), a novel framework that constructs and aggregates multi-view information using a specially designed Transformation Block. This block dynamically transforms data from multiple aspects and aggregates the resulting representations via a weighted sum mechanism, enabling comprehensive multi-view modeling. The aggregated information is fed into FGAT to enhance fuzzy graph convolutions. Additionally, we introduce a simple yet effective learnable global pooling mechanism for improved graph-level understanding. Extensive experiments on graph classification tasks demonstrate that MFGAT outperforms state-of-the-art baselines, underscoring its effectiveness and versatility.