Operating the integrated power and gas systems necessitates solving optimal power and gas flow (OPGF) problem, which is notably challenging due to the inherent nonlinearity and complexity of both systems. This study introduces a novel approach to address the OPGF problem by integrating problem decomposition with reinforcement learning (RL)-based cutting plane techniques. The proposed strategy divides the original OPGF problem into a simplified OPGF (SOPGF) sub-problem and a power and gas flow (PGF) calculation sub-problem. In the constraint set of the SOPGF sub-problem, linear inequality constraints (i.e., cutting planes or cuts) are introduced, bounding the feasible region of the SOPGF sub-problem and thereby obtaining the optimal solution to the original OPGF problem. The cuts are determined using an RL algorithm, and the RL agent is trained to maximize the rewards estimated by the PGF calculation. To enhance the efficiency of the agent training, action selection method is utilized in the RL-based cutting plane approach. Case studies are performed to assess the proposed strategy in comparison to conventional methods across diverse test conditions, verifying that the new approach surpasses conventional approaches in both computational efficiency and feasibility.

The conventional de-load power tracking algorithm, utilizing a perturb and observe method, manifests deficiencies in terms of speed, stability, and efficacy in identifying operating points within the inverter's voltage range. In this article, the Adaptive Multi-Mode Single-Step Power Tracking (AMSPT) algorithm is introduced, showcasing rapid adaptability to varying solar irradiation conditions, while mitigating energy losses and enhancing overall operational stability. Its key innovation lies in efficiently pinpointing the operating point within the inverter's specified voltage range through a single step. Upon achieving the desired operating point, the algorithm promptly suppresses oscillatory behavior, expediting the settling process and minimizing deviations around the set-point. This article substantiates the superiority of the AMSPT algorithm over existing methods, showcasing remarkable advancements in tracking accuracy, power fluctuations, and energy discrepancies across diverse PV system case studies. Comprehensive validation through theoretical analysis, simulations, and experimental setups meticulously confirms the claimed benefits of the proposed method.

Detection of abnormal photovoltaic (PV) system operation is essential to ensure safe and uninterrupted performance. In this study, the authors present a data-driven two-stage method for PV fault detection and diagnosis (FDD). We exploit an inherent characteristic of PV systems, i.e., voltage and current changes at maximum power point (MPP) caused by faults. In the first stage, fault occurrences are detected using predefined criteria based on the MPP values. The second stage employs – characteristic curve data and a densely connected convolutional network (DenseNet) model to diagnose the fault type. The DenseNet model is rigorously trained using a very large dataset of – curves; this ensures precise and efficient fault diagnosis. We validate our approach via simulations and hardware analyses employing a PV array that initially operates normally, but then develops line-to-line faults (LLFs), open-circuit faults (OCFs), degradation faults (DFs), and partial shading faults (PSFs). We compare our DenseNet-based PV FDD model to the latest PV FDD models. The results confirmed that the new method accurately detect and diagnose PV faults.

본 논문은 중국 위진남북조대에 행해진 소신공양(燒身供養)이 희극성(戲劇性)과 공희성(供犧性)을 지닌 일종의 의례였음을 밝히고, 그 문헌 배경으로서 『묘법연화경』 「약왕보살본사품」의 소신공양 서사를 분석하는 데 목적이 있다. 먼저 「약왕보살본사품」에 등장하는 소신공양 서사에서 소신정각(燒身正覺)·소신왕생(燒身往生)·사인발심(使人發心)·요익중생(饒益衆生) 등 소신공양의 동기를 추출했고, 또한, 여기에 등장하는 소신 후 재생이라는 사유가 신체 훼손이라는 비판에서 벗어나고, 소신에 대한 두려움을 감소시키는 메커니즘으로 작용함을 확인했다.<br/> 또한, 『비구니전』, 『고승전』, 『속고승전』 등 승려 전기에 등장하는 위진남북조대 소신 승려의 사례를 분석하여, 소신공양이 공개적으로 예고하고 집단적으로 준비하며 대중 참여를 고려해 불교 재일(齋日)에 거행한 일종의 의례였음을 확인하였다. 이렇게 소신공양은 의례로서 희극성을 지닐 뿐만 아니라 고대 희생제의와 일면 유사하게 희생자, 봉납 대상, 봉헌자, 수혜자 등 공희의 성격도 지니고 있음을 확인하였다.<br/> 이상의 연구를 통해서 소신공양은 소신자 개인의 자발성과 신앙적 결단에 근거하면서도, 교단과 불교 공동체의 집단적 이익을 고려하였고, 전통적인 인신공희나 번제와 완전히 일치하지는 않지만, 희생을 통한 소신자 개인의 해탈과 공동체적 구원의 지향을 내포하고 있음을 규명하였다.

This paper introduces a novel dynamic power tracking (DPT) algorithm tailored for microinverter-based photovoltaic (PV) systems, enhancing power stability and efficiency under varying irradiance conditions. By refining the conventional voltage-step perturb and observe (P&O) approach, the algorithm minimizes power oscillations and dynamically adapts to environmental changes without reinitialization. Experimental validation confirms reliable power stabilization across various PV modules, supporting seamless integration into grid-connected systems under diverse operational scenarios.

Achieving zero carbon emissions by 2050 in South Korea necessitates significant restrictions on coal and natural gas power plants, along with increased reliance on renewable energy sources (RES). This transition drives the development of high voltage dc (HVdc) systems to efficiently transmit renewable energy. The intermittent nature of RES leads to real-time power imbalances, prompting research into HVdc systems for essential system services such as frequency control and power balancing. As RES expands, a multi-terminal dc network will be needed to enhance inter-grid power sharing, with centralized and decentralized control strategies managing power flow and grid stability.

Operating the integrated power and gas systems necessitates solving optimal power and gas flow (OPGF) problem, which is notably challenging due to the inherent nonlinearity and complexity of both systems. This study introduces a novel approach to address the OPGF problem by integrating problem decomposition with reinforcement learning (RL)-based cutting plane techniques. The proposed strategy divides the original OPGF problem into a simplified OPGF (SOPGF) sub-problem and a power and gas flow (PGF) calculation sub-problem. In the constraint set of the SOPGF sub-problem, linear inequality constraints (i.e., cutting planes or cuts) are introduced, bounding the feasible region of the SOPGF sub-problem and thereby obtaining the optimal solution to the original OPGF problem. The cuts are determined using an RL algorithm, and the RL agent is trained to maximize the rewards estimated by the PGF calculation. To enhance the efficiency of the agent training, action selection method is utilized in the RL-based cutting plane approach. Case studies are performed to assess the proposed strategy in comparison to conventional methods across diverse test conditions, verifying that the new approach surpasses conventional approaches in both computational efficiency and feasibility.