3 results about "Power sector" patented technology

This application discloses a reliable AI scheduling method, system, and computer equipment for microgrids. It includes: conditional independence testing of multi-source data; constructing a candidate causal graph among variables, constrained by prior knowledge in the power sector; calculating the differences in causal effects caused by fluctuations in the current scheduling strategy; performing counterfactual reasoning verification based on different differentiated scheduling strategies; and scoring the current scheduling strategy on causal interpretability based on causal effect differences, counterfactual verification results, and link integrity, followed by AI scheduling. This application reveals the causal interaction mechanism between variables, and the counterfactual reasoning that changes in variables lead to changes in scheduling results, improving the interpretability of the scheduling strategy. It can trace the causal driving factors of each scheduling instruction, significantly reducing the rate of human intervention. Prior knowledge in the power sector ensures safe and compliant scheduling, making the results more reliable. It solves the problem of dependence on human intervention in the application of AI scheduling in microgrids.

This disclosure relates to the power sector and discloses an adaptive grid-connected control method, system, device, and medium for adapting to phase angle abrupt changes. The method includes: establishing a full-state oscillator model to generate the internal potential of the photovoltaic-storage grid-connected power source; establishing a phase angle adaptive model to correct the internal potential of the photovoltaic-storage grid-connected power source; and establishing a current inner-loop control model to control the stable operation of the photovoltaic-storage grid-connected power source based on the corrected internal potential. This disclosure enables photovoltaic energy storage systems to provide stable synchronous grid-connected characteristics under large phase angle abrupt changes, and allows microgrids containing a very high proportion of new energy sources to achieve flexible and reliable grid-connected / off-grid stable operation.

This invention discloses a large-model-driven multi-objective photovoltaic-storage distribution network control method under extreme weather conditions, belonging to the field of distribution network control technology. The method includes: collecting real-time electrical data and extreme weather data from the distribution network; identifying topology change events and generating structured natural language scene descriptions; generating a dynamic reward function with four objectives—voltage stability, frequency recovery, etc.—adapted to the scenario using a lightweight large-language model fine-tuned with power sector corpora; extracting global high-dimensional features using a topology and feature dual-attention graph convolutional network; using a vector concatenated from features and reward weights as state input, generating multi-device collaborative control instructions through a near-end policy optimization algorithm, and outputting the instructions after verification. This invention achieves dynamic perception of the distribution network topology, dynamic adaptation of control objectives, and collaborative optimization control of multiple devices under extreme weather conditions, comprehensively improving the operational resilience and renewable energy utilization efficiency of high-proportion renewable energy distribution networks under extreme weather conditions.