Abstract:
A security and economy coordinated automatic voltage control method based on a cooperative game theory is provided. The method includes: establishing a multi-objective reactive voltage optimizing model of a power system; resolving the multi-objective reactive voltage optimizing model into an economy model and a security model; solving the economy model and the security model based on the cooperative game theory to obtain the automatic voltage control instruction; and performing an automatic voltage control for the power system according to the automatic voltage control instruction.
Abstract:
A method and a device for navigating an electric vehicle in charging are provided. The method comprises: S1, obtaining a navigation area, wherein the navigation area comprises a plurality of charging stations; S2, receiving a charging request from an electric vehicle in the navigation area; S3, obtaining a plurality of first time periods according to the electric vehicle and the plurality of charging stations; S4, selecting a minimum first time period from the plurality of first time periods; and S5, navigating the electric vehicle to a charging station corresponding to the minimum first time period.
Abstract:
A method for obtaining a three-phase power flow of a power distribution network and a device for obtaining a three-phase power flow of a power distribution network are provided. The method comprises steps of: selecting a three-phase power transformer with an ungrounded neutral connection in the power distribution network; correcting a three-phase admittance matrix of the three-phase power transformer; and applying the three-phase admittance matrix to a preset algorithm to obtain a three-phase power flow of the power distribution network.
Abstract:
Method and system for charging electric vehicles in an aggregation is provided. The method includes: obtaining a plurality of first charge power curves of a plurality of electric vehicles in the aggregation; obtaining a coordinating information of each of the plurality of electric vehicles from the plurality of first charge power curves; obtaining a first feedback charge power curve of each of the plurality of electric vehicles from the coordinating information and a charging cost curve of each of the plurality of electric vehicles; judging whether the first feedback charge power curve is same with the first charge power curve of each of the plurality of electric vehicles; if yes, charging each of the plurality of electric vehicles in accordance with the first charge power curve.
Abstract:
The present invention relates to a method of assessing dynamic flexibility for a virtual power plant, which belongs to the technical field of operating and controlling a power system. The method equals a virtual power plant to an equivalent energy storage device and an equivalent generator and decouples a network constraint condition between the two types of devices through a Robust optimization method. Subsequently, by using a two-stage Robust optimization algorithm, parameters of the equivalent energy storage device and the equivalent generator are calculated and finally accurate depiction is realized on adjusting ability of a distributed resource, so as to provide a scientific decision basis for the virtual power plant to participate in grid control, such that it has a great value in an actual application.
Abstract:
A power grid reactive voltage control model training method. The method comprises: establishing a power grid simulation model; establishing a reactive voltage optimization model, according to a power grid reactive voltage control target; building interactive training environment based on Adversarial Markov Decision Process, in combination with the power grid simulation model and the reactive voltage optimization model; training the power grid reactive voltage control model through a joint adversarial training algorithm; and transferring the trained power grid reactive voltage control model to an online system. The power grid reactive voltage control model trained by using the method according to the present disclosure has transferability as compared with the traditional method, and may be directly used for online power grid reactive voltage control.
Abstract:
The disclosure provides a method for planning a power distribution network, an apparatus for planning a power distribution network, and a storage medium. The method includes: establishing a model for planning the power distribution network, the model including a target function and constraints, the target function for minimizing a cost of the power distribution network when branches and nodes are installed into the power distribution network, the nodes including transformers and substations, the constraints including a power balance constraint of the power distribution network, a power constraint of the branches, a power constraint of the transformers, a radial operation constraint of the power distribution network, a fault constraint, a calculation constraint of indices of a reliability, a constraint of the indices of the reliability, and a logic constraint; and solving the model to determine whether the branches and the nodes are installed into the power distribution network.
Abstract:
A power grid reactive voltage control method and control system based on two-stage deep reinforcement learning, comprising steps of: building interactive training environment based on Markov decision process, according to a regional power grid simulation model and a reactive voltage optimization model; training a reactive voltage control model offline by using a SAC algorithm, in the interactive training environment based on Markov decision process; deploying the reactive voltage control model to a regional power grid online system; and acquiring operating state information of the regional power grid, updating the reactive voltage control model, and generating an optimal reactive voltage control policy. As compared with the existing power grid optimizing method based on reinforcement learning, the online control training according to the present disclosure has costs and safety hazards greatly reduced, and is more suitable for deployment in an actual power system.
Abstract:
The disclosure provides a method, an apparatus, and a storage medium for controlling a heating system in a combined heat and power system. The method includes: establishing a load flow model of the heating system, in which the heating system includes pipelines and nodes; the nodes include loads and heating sources; the load flow model includes an objective function and constraints; the objective function for maximizing and minimizing an inlet water temperature of each load or each source; solving the load flow model to obtain an upper limit and a lower limit of the inlet water temperature of each load or each source; and controlling the inlet water temperature of each load or each source based on the upper limit and the lower limit of the inlet water temperature of each load or each source.
Abstract:
The present disclosure proposes a decomposition-coordination voltage control method for wind power to be transmitted to a nearby area via flexible DC. The method includes: initializing parameters; sending the parameters to wind power farms; for each of the wind power farms, establishing a voltage control optimization sub-model; solving the voltage control optimization sub-model to obtain a first optimal result; for the control center, establishing a voltage control optimization main model; solving the voltage control optimization main model to obtain a second optimal result; calculating a determination index based on the first optimal result and the second optimal result; and determining whether the determination index is convergent to an admissible value, if no, updating the parameters and returning to establishing the voltage control optimization sub-model.