An electric power system planning operation device against occurrence of a disaster includes: a disaster information selection unit that selects disaster information data of a disaster type to be handled; a disaster countermeasure candidate calculation unit that calculates a disaster countermeasure candidate, system maintenance countermeasure candidate data, and system operation countermeasure candidate data; a reliability economic efficiency index value calculation unit that calculates a reliability economic efficiency index value for the disaster countermeasure candidate calculation result, system data that is information about the configuration of an electric power system, damage cost data that is information about the amount of damage at the time of an assumed disaster type, and reliability economic efficiency index data that is information indexing a relation between reliability and economic efficiency; and a disaster countermeasure decision unit that decides a disaster countermeasure by using the reliability economic efficiency index value data.

An impedance matching method for a CLC branch of a low-frequency resonance suppression device is provided, which includes: establishing an equivalent circuit model for a power supply system with a low-frequency resonance suppression device joined in; obtaining, according to a target low-frequency harmonic frequency band and the equivalent circuit model, a constraint required by the power supply system for suppressing low-frequency harmonics; constructing an objective function based on a low-frequency harmonic suppression rate in a bus of the power supply system; obtaining a multi-constraint objective optimization function of CLC branch impedance based on the constraint and the objective function; and solving the multi-constraint objective optimization function through an improved harmony search algorithm to obtain an impedance parameter of the CLC branch. The present disclosure can ensure a low-frequency harmonic suppression effect for the power supply system.

Disclosed are an abnormality diagnosis method and apparatus for photovoltaic power generation, a computer device and a storage medium. The method comprises acquiring operation data of a photovoltaic station, generating a photovoltaic power generation capacity prediction model by training a preset neural network based on the operation data of the photovoltaic station; determining an abnormal photovoltaic station by performing daily power generation capacity fluctuation evaluation and power generation efficiency evaluation on a plurality of photovoltaic stations using the photovoltaic power generation capacity prediction model; and generating a diagnosis result of the photovoltaic power generation abnormality by comparing the operation data corresponding to the abnormal photovoltaic station with an abnormality condition. According to the method, accurate evaluation of a photovoltaic power generation operation situation and accurate determining of the photovoltaic power generation abnormality are realized, and a brand new perspective is provided for operation management of the photovoltaic station.

A method for characterizing power quality events in an electrical system includes deriving electrical measurement data for at least one first virtual meter in an electrical system from (a) electrical measurement data from or derived from energy-related signals captured by at least one first IED in the electrical system, and (b) electrical measurement data from or derived from energy-related signals captured by at least one second IED in the electrical system. In embodiments, the at least one first IED is installed at a first metering point in the electrical system, the at least one second IED is installed at a second metering point in the electrical system, and the at least one first virtual meter is derived or located at a third metering point in the electrical system. The derived electrical measurement data may be used to generate or update a dynamic tolerance curve associated with the third metering point.

A method for forecasting power output of a target site. The method includes normalizing power output data for the target site, at least in part, on an installed capacity. The normalized power output data is transformed to yield transformed normalized power output data. A temporal module fits a temporal model to model input data for the target site. The model input data corresponds to normalized power output data or transformed normalized power output data. A copula model is fit for the target site, based, at least in part, on at least one residual value. Each residual value is determined based, at least in part on a selected fitted temporal model for each target site.

A building energy system includes equipment configured to consume, store, or discharge one or more energy resources purchased from a utility supplier. At least one of the energy resources is subject to a demand charge. The system further includes a controller configured to determine an optimal allocation of the energy resources across the equipment over a demand charge period. The controller includes a stochastic optimizer configured to obtain representative loads and rates for the building or campus for each of a plurality of scenarios, generate a first objective function comprising a cost of purchasing the energy resources over a portion of the demand charge period, and perform a first optimization to determine a peak demand target for the optimal allocation of the energy resources. The peak demand target minimizes a risk attribute of the first objective function over the plurality of the scenarios.

A system and method for optimizing power grid operations and enhancing the life of switching components therein is provided. Current meteorological information of a region of operation of the power grid is collected during operation thereof, along with historical meteorological data of the region. A plurality of prediction models are executed using the current meteorological information and/or the historical meteorological data and a meteorological parameter of the region is forecast by selectively combining outputs of at least some of the executed prediction models, the meteorological parameter being a parameter that causes a renewable energy source in the power grid to generate power. The forecasted meteorological parameter is compensated with physical models and the historical meteorological data, and optimal switching operations of switching components in the power grid are computed based on the compensated forecasted meteorological parameter, with the switching components being controlled based on the computed optimal switching operations.

A new energy linkage charging system and a storage medium are provided. The system includes a charging interface configured to charge an electrical vehicle with an electrical energy through a converter and a third circuit in response to receiving a charging instruction; a power storage module configured to store the electrical energy and deliver the electrical energy to the charging interface; a power grid charging module and a power generating module configured to deliver the electrical energy to the charging interface and the power storage module; a control module configured to determine a power storage instruction based on power storage module information, and determine the charging instruction and send the charging instruction to the charging interface based on a charging request in response to obtaining the charging request; and an interaction module in communication connection with the control module and configured to obtain the charging request.

A control method, apparatus, device, medium, and product for an integrated storage-charging station are disclosed. The method includes: sending predicted regulation data for a control period to an integrated storage-charging station, the predicted regulation data being generated based on a load prediction model corresponding to the integrated storage-charging station; receiving reported regulation data sent by the integrated storage-charging station, the reported regulation data being generated based on the predicted regulation data; generating a control instruction corresponding to the integrated storage-charging station based on winning regulation data, the control instruction carrying final regulation data corresponding to the integrated storage-charging station, and the final regulation data being obtained after splitting the winning regulation data based on the regulation data reported by the integrated storage-charging station; and issuing a corresponding control instruction to the integrated storage-charging station, for instructing the integrated storage-charging station to regulate its own charging and discharging power.

A load management system integrates comparator-based neutral sensing, machine learning prediction, and relay control into a single integrated AC board requiring no additional wiring. A highspeed comparator circuit detects grid failures in sub millisecond timeframes, providing clean data to a temporal convolutional network that predicts load requirements 24 hours in advance with integration of external data sources such as weather and time of use pricing. The system automatically manages 120V and 240V circuits during grid transitions, learning from user override patterns to continuously improve performance. A mobile application provides real-time monitoring and control. The integration of low-latency sensing with predictive machine learning enables performance improvements exceeding 40% in battery runtime compared to conventional systems, while reducing installation time and cost.