A system refers to actual weather data made publicly available by a first institution, and creates a model that uses a value of a weather element for each section as an input and uses a value of a renewable energy power generation amount of the area as an output based on the actual value of the weather element calculated for each section, and the actual value of the renewable energy power generation amount of the area. The system refers to weather prediction data made publicly available by the second institution, and calculates an actual value of the weather element regarding each of the plurality of sections including the area based on a prediction value of the weather element for each segment in the corresponding section, and calculates a prediction value of the renewable energy power generation amount based on the prediction value of the weather element for each section.

A method for automatic adjustment of a power grid operation mode based on reinforcement learning is provided. An expert system for automatic adjustment is designed, which relies on the control sequence of thermal power units, enabling automatic decision-making for power grid operation mode adjustment. A sensitivity matrix is extracted from the historical operating data of the power grid, from which a foundational thermal power unit control sequence is derived. An overload control strategy for lines within the expert system is devised. A reinforcement learning model optimizes the thermal power unit control sequence, which refines the foundational thermal power unit control sequence and provides the expert system with the optimized control sequence for automatic decision-making in power grid operation mode adjustment. This method offers a solution to balancing and absorption challenges brought about by fluctuations on both the supply and demand sides in high-proportion renewable energy power systems.

Disclosed are a method and system for verifying a low-frequency oscillation damping control effect of a governor power system stabilizer (GPSS), relating to the technical field of power systems. The method includes: selecting a wiring working condition for a fault-free disconnection test, and constructing a unit test working condition; performing the fault-free disconnection test on an outgoing line of a power plant, and collecting an electromagnetic power fluctuation recording curve; and comparing low-frequency oscillation damping effects under different GPSS settings, evaluating the effects, and performing a cyclic test. A success rate of the test is increased, safety of the test is improved, and the overall stability and anti-oscillation capacity of the system are improved in the disclosure.

Renewable energy wheeling distribution systems and methods include providing a model for performing green electricity optimization on at least one electricity consumption site based on a genetic algorithm and gradient descent to generate, based on an objective according to electricity generation parameters and at least one electricity consumption parameter, at least one wheeling solution including matching relationships of green electricity between the at least one electricity consumption site and electricity generation sites, as well as the proportion for allocating the green electricity within the matching relationships; repeatedly performing the green electricity optimization on a specific electricity consumption site based on renewable energy objectives to generate wheeling solutions; and calculating, based on the plurality of wheeling solutions and cost parameters, a renewable energy marginal cost as a ratio of a variation of electricity purchase expenses to an increment of green electricity corresponding to adjacent two of the objectives.

A control method includes performing multi-level classification processing on distributed energy resources to obtain corresponding power supply equipment, power storage equipment, and hybrid equipment, analyzing and processing power distribution data and load data in the smart grid to obtain a power distribution demand of the smart grid, and dividing the smart grid into different division areas, determining corresponding power supply equipment, power storage equipment, or hybrid equipment in accordance with the power distribution demand and the division areas, and performing storage regulation and control on the corresponding power supply equipment, power storage equipment, or hybrid equipment, and if it is determined that the power supply equipment, power storage equipment, or hybrid equipment has a manner of multi-resource participation in the storage regulation and control, adding different multi-resource participation pricing strategies for power supply equipment, power storage equipment, or hybrid equipment in the different division areas.

A method for optimising the consumption of an installation includes, carried out before a specified period, implementing a disaggregation method, so as to predict, for each appliance, an expected individual consumption profile, predicting an expected renewable production profile by the renewable energy source, defining first optimised individual consumption profiles for the appliances, making it possible to maximise a use of renewable electrical energy, and the second step of controlling the appliances during the specified period, by using the first optimised individual consumption profiles.

A charging pile coordination system, method, and medium, the system including: a user terminal provided with a first positioning unit, a plurality of charging piles, an environmental monitoring unit, and a processor. The processor is configured to: for each of the plurality of charging piles, obtain a power generation parameter; determine a predicted efficiency and a confidence level for the predicted efficiency based on the environmental feature and the power generation parameter; determine an amount of power available based on the predicted efficiency, the confidence level for the predicted efficiency, and an amount of power remaining; determine a preferred charging scheduling parameter based on the amount of power available, a charging pile position, and the charging demand; and generate a first scheduling instruction, a second scheduling instruction, and notification information and send them to the plurality of charging piles, the environmental monitoring unit, and the user terminal, respectively.

A method and system for probabilistic solar generation forecasting under rapidly changing weather conditions integrate copula theory with an extreme gradient tree boosting (XGBoost) classifier to enhance forecast accuracy. Historical weather data is partitioned into meteorological clusters, and bivariate copulas analyze spatiotemporal correlations to select optimal features. Multivariable Vine and Gaussian copulas model variable dependencies, with an XGBoost classifier dynamically selecting the optimal copula based on real-time weather conditions. Synthetic weather data, generated using the selected copula, captures uncertainties and is applied to a trained XGBoost regression tree to produce probabilistic forecasts. The method achieves up to 60% higher accuracy than conventional models under non-sunny conditions, leveraging Gaussian Kernel Density Estimation and Huber loss for robustness. The system supports real-time grid operations, offering reliable solar power predictions for diverse weather scenarios, validated with real-world data from multiple global locations.

An AI-based platform for enabling intelligent orchestration and management of at least one operating process is provided herein. The AI-based platform includes an artificial intelligence system that is configured to generate a prediction of an energy pattern associated with the at least one operating process. The AI-based platform is also configured to manage the at least one operating process based on the prediction of the energy pattern.

Described herein is a method for determining a presence of anomalous conditions in a switching apparatus installed in an electric line of an electric power distribution grid. The method includes a sequence of steps for adjusting a lumped-parameter model describing, for each electric phase, the behavior of the switching apparatus during the opening maneuvers of the switching apparatus. Simulation values provided by the lumped-parameter model are used for calculating estimation values indicative of the amounts of arc energy released by the breaking components of the switching apparatus during the opening maneuvers of the switching apparatus.