With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-loop framework that integrates forecasting, strategy, and feedback for coordinated decision-making. The Prediction Agent learns statistical patterns of price spreads to generate distributional forecasts, directional probabilities, and extreme-value indicators; the Strategy Agent adaptively maps these signals into executable bidding ratios through a hybrid mechanism; and the Feedback Agent incorporates settlement results for performance evaluation, CVaR-based risk control, and preference-driven optimization. These agents form a dynamic “forecast–strategy–feedback” loop enabling self-improving trading. Experimental results show that Joint achieves a monthly profit of 146,933.46 CNY with strong classification performance (Precision = 53.25%, Recall = 40.45%, AA = 56.05%, SWA = 57.36%), and the complete model in ablation experiments reaches 157,746.64 CNY, demonstrating the indispensable contributions of each component and confirming its robustness and practical value in volatile electricity spot markets.

The escalating penetration of distributed photovoltaic (PV) systems has intensified grid-connected voltage violations, posing severe challenges to the stability of distribution networks. This paper first investigates the mechanisms of voltage violations at 35 kV substations and 380 V consumer-side terminals under high-penetration scenarios. It is demonstrated that PV integration elevates line voltage, with the voltage profile at any given node being governed by the equivalent net load—defined as the offset between total demand and PV generation—downstream of that node. Subsequently, the impacts of critical operating conditions, including PV penetration levels, line impedance, and dynamic meteorological variations, are quantitatively analyzed. Simulation results characterize voltage fluctuation patterns under diverse variables, such as varying PV outputs, line parameters, and interconnection points, thereby validating the theoretical derivation. Finally, an integrated management strategy, coupling coordinated reactor compensation with voltage-source inverter (VSI) control, is proposed. Simulation results across multi-dimensional complex scenarios verify the effectiveness of the proposed strategy in suppressing voltage violations and enhancing grid resilience.

The escalating penetration of distributed photovoltaic (PV) systems has intensified grid-connected voltage violations, posing severe challenges to the stability of distribution networks. This paper first investigates the mechanisms of voltage violations at 35 kV substations and 380 V consumer-side terminals under high-penetration scenarios. It is demonstrated that PV integration elevates line voltage, with the voltage profile at any given node being governed by the equivalent net load—defined as the offset between total demand and PV generation—downstream of that node. Subsequently, the impacts of critical operating conditions, including PV penetration levels, line impedance, and dynamic meteorological variations, are quantitatively analyzed. Simulation results characterize voltage fluctuation patterns under diverse variables, such as varying PV outputs, line parameters, and interconnection points, thereby validating the theoretical derivation. Finally, an integrated management strategy, coupling coordinated reactor compensation with voltage-source inverter (VSI) control, is proposed. Simulation results across multi-dimensional complex scenarios verify the effectiveness of the proposed strategy in suppressing voltage violations and enhancing grid resilience.

Ultrafast physical random bit generators (PRBGs) are essential components for modern applications in secure communication, quantum cryptography, and artificial intelligence. While optical chaos-based PRBGs offer high-speed capabilities, conventional systems often rely on discrete components that suffer from system complexity and environmental instability. This paper proposes and experimentally demonstrates a robust, integrated solution using a two-section mutual injection DFB laser. The device was fabricated using the reconstruction equivalent chirp (REC) technique, which provides precise control over grating phase variation while utilizing low-cost, high-volume fabrication methods.The laser sections, each measuring 450 m in length, were designed with a free-running wavelength difference of 0.3 nm to ensure a flat optical spectrum and enhanced chaotic dynamics. By optimizing the bias currents, we achieved a chaos RF bandwidth of 20.1 GHz. Notably, the resulting chaotic signal lacks time-delayed signatures, which simplifies the randomness extraction process. To generate random bits, the chaotic waveform was sampled by an 8-bit analog-to-digital converter at 100 Gb/s. Following post-processing through delay-subtracting and the extraction of the four least significant bits (4-LSBs), we realized a total physical random bit rate of 400 Gb/s. The randomness of the generated sequence was successfully verified using the NIST SP 800-22 statistical test suite. This approach offers a compact, energy-efficient, and high-performance integrated chaotic source suitable for secure communication and high-performance computation.

To address the issues of thermal non-uniformity and insulation aging of converter transformers operating under long-term high electric field and high-temperature conditions in ultra-high-voltage direct current (UHVDC) transmission systems, this paper investigates the temperature field distribution characteristics of converter transformers based on electro–thermal–mechanical multiphysics coupling. By establishing a full-scale multiphysics simulation model of a ±800 kV converter transformer, the interactions among the electric field, temperature field, and mechanical stress field are comprehensively considered. The temperature gradient distribution and hotspot formation mechanisms within the valve-side winding and the lead-out structure are revealed. The results show that the internal temperature distribution of the converter transformer is non-uniform, resulting in a nonlinear distribution of material parameters in oil-paper insulation, which significantly affects the insulation performance. The research findings provide a theoretical basis and engineering reference for the structural optimization and thermal stability improvement of the main insulation system of converter transformers.