Abstract: The present disclosure provides a method and apparatus for calculating carbon emission response based on carbon emission flows. The method includes: calculating a total carbon flow rate of a power system from a carbon flow rate of each of carbon meter users, the carbon flow rate being obtained based on a carbon emission flow of the carbon meter user; determining whether the total carbon flow rate is greater than a carbon emission response threshold; and calculating, in response to determining that the total carbon flow rate is greater than the carbon emission response threshold, a target carbon emission reduction of one or more of the carbon meter users during a carbon emission response period based on target carbon emission response demand, and initiating a carbon emission response based on the target carbon emission reduction.

Abstract: Provided is a carbon emission flow calculation method for a regional integrated energy system.

Abstract: The present disclosure discloses a method and apparatus for measuring carbon emission of a district heating system, an electronic device, and a medium. The method includes: obtaining a steady carbon emission amount of a current district heating system using a pre-trained steady carbon emission flow model; obtaining a dynamic carbon emission amount of the current district heating system using a pre-trained dynamic carbon emission flow model; and counting a carbon emission amount of the current district heating system based on the steady carbon emission amount and the dynamic carbon emission amount. Therefore, the present disclosure can effectively identify carbon emission details of each link of a source, a grid, and a load of the district heating system, clarify carbon emission responsibilities on both a source side and a load side, and realize accurate measurement of carbon emission of the district heating system, which has high application value.

Abstract: The disclosure relates to a quantile probabilistic short-term power load ensemble forecasting method. The method includes: dividing historical power load data of a power system into a first data set and a second data set; performing bootstrap sampling on the first data set to generate multiple training data sets; training a neural network quantile regression model, a random forest quantile regression model and a gradient boosting regression tree regression model for the each training data set to obtain quantile forecasting models; establishing an optimization model with an objective function for minimizing the quantile loss for the second data set, and determining a weight for each of the quantile regression models, to calculate a power load ensemble forecasting model for predicting the power load in the power system.

Abstract: A multi-energy system planning method is disclosed based on security region identification. The method includes obtaining alternative planning schemes from a multi-energy system planning department; for each alternative scheme, establishing a matrix model for describing energy conversion relationships in the multi-energy system, in which the multi-energy system comprises N energy conversion elements, N being an integer greater than or equal to 1; identifying N feasible domains of the multi-energy system under N operation scenarios, in which the i-th energy conversion element is out of operating under the i-th operation scenario, and calculating a security region of the multi-energy system by intersecting the identified feasible domains under N operation scenarios; calculating a load fitness rate of each alternative scheme based on each security region; and selecting an alternative scheme with the highest load fitness rate as a target scheme for planning the multi-energy system.

Abstract: The disclosure relates to a quantile probabilistic short-term power load ensemble forecasting method. The method includes: dividing historical power load data of a power system into a first data set and a second data set; performing bootstrap sampling on the first data set to generate multiple training data sets; training a neural network quantile regression model, a random forest quantile regression model and a gradient boosting regression tree regression model for the each training data set to obtain quantile forecasting models; establishing an optimization model with an objective function for minimizing the quantile loss for the second data set, and determining a weight for each of the quantile regression models, to calculate a power load ensemble forecasting model for predicting the power load in the power system.

Abstract: The present disclosure provides a fast model generating and solving method for security-constrained power system operation simulation, which includes: obtaining information of all branches and nodes which are involved during operation simulation time period, calculating original-node impedance matrix, load shifting distribution factor original matrix and generator shifting distribution factor original matrix of all involved branches; correcting the load shifting distribution factor original matrix and the generator shifting distribution factor original matrix according to the on-off state of branches; obtaining output of each generator unit at each time period according to no-security-constraint unit commitment model, and determining overload of each branch again; solving iteratively until no branch is overloaded, and obtaining output of each generator unit at each time period under security constraint of operation simulation for the current simulation day, performing operating simulation for the rest simulat

Abstract: The present disclosure provides a fast model generating and solving method for security-constrained power system operation simulation, which includes: obtaining information of all branches and nodes which are involved during operation simulation time period, calculating original-node impedance matrix, load shifting distribution factor original matrix and generator shifting distribution factor original matrix of all involved branches; correcting the load shifting distribution factor original matrix and the generator shifting distribution factor original matrix according to the on-off state of branches; obtaining output of each generator unit at each time period according to no-security-constraint unit commitment model, and determining overload of each branch again; solving iteratively until no branch is overloaded, and obtaining output of each generator unit at each time period under security constraint of operation simulation for the current simulation day, performing operating simulation for the rest simulat