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: 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: Provided are method and device for adjusting a power flow based on operation constraints. The method includes: establishing a power flow adjusting model comprising an objective function and constraints; acquiring active power and reactive power of each node by using a two-stage optimization of active and reactive powers. In this method, the power flow adjusting model is divided into the active power optimization sub-model and the reactive power optimization sub-model. The active power optimization sub-model, where the linearized power flow constraint and the tie line section active power constraint are considered, may be regarded as quadratic programming. The reactive power flow optimization sub-model is solved on the basis of the active sub-model, and the AC power flow constraint, the grid voltage range constraint, and the pilot bus voltage setting value constraint are considered.

Abstract: The present disclosure provides a method for planning a distribution network with reliability constraints based on a feeder corridor, including determining installation states of respective elements in the distribution network; determining an objective function, the objective function being an objective function of minimizing a total investment cost of the distribution network; obtaining fault-isolation-and-load-transfer time and fault recovery time in a case where the feeder segment of each feeder line that is contained in each feeder corridor fails; determining constraint conditions including reliability constraints; building a distribution network planning model according to the objective function and the constraints; and solving the distribution network planning model built to obtain optimal solutions as planning states and reliability indexes to plan the distribution network.

Abstract: The disclosure provides a stochastic look-ahead dispatch method for power system based on Newton method, belonging to power system dispatch technologies. The disclosure analyzes historical data of wind power output, and uses statistical or fitting software to perform Gaussian mixture model fitting. A dispatch model with chance constraints is established for system parameters. Newton method is to solve quantiles of random variables obeying Gaussian mixture model, so that chance constraints are transformed into deterministic linear constraints, thus transforming original problem to convex optimization problem with linear constraints. Finally, the model is solved to obtain look-ahead dispatch. The disclosure employs Newton method to transform chance constraints containing risk level and random variables into deterministic linear constraints, which effectively improves model solution efficiency, and provides reasonable dispatch for decision makers.

Abstract: The disclosure provides a method, an apparatus, and a storage medium for controlling heating system. The method includes: establishing an objective function and constraints for estimating system parameters of the heating system, in which the heating system includes nodes, pipelines and equivalent branches, the equivalent branch is configured to represent a heating resource or a heating load in the heating system, the system parameters include a resistance coefficient of each of the pipelines and equivalent branches, and a heat dissipation coefficient of each of the pipelines; solving the objective function based on the constraints to obtain the system parameters; modeling the heating system based on the obtained system parameters to obtain control parameters of the heating system; and controlling the heating system based on the control parameters.

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.

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: The present disclosure provides a scheduling method for a power system based on flexible HVDC (high-voltage direct current) and a pumped storage power station, which belongs to a field of power system control technologies. The method is applicable in a power system having a flexible HVDC system and a pumped storage power station. By establishing a scheduling model for the power system, which contains an objective function and multiple constraints, and solving the scheduling model, a capability of the pumped storage power station is used to adjust the unstable output of the renewable energy power generator and stabilize fluctuant of the renewable energy power generation, such that a power incoming into a load center presents a stable ladder pattern and an optimal scheduling scheme can be obtained.

Abstract: The disclosure provides a stochastic dynamical unit commitment method for power system based on solving quantiles via Newton method, belonging to power system technologies. The method establishes a unit commitment model with chance constraints for power system parameters. Quantiles of random variables obeying mixed Gaussian distribution is solved by Newton method, and chance constraints are transformed into deterministic linear constraints, so that original problem is transformed into mixed integer linear optimization problem. Finally, the model is solved to obtain on-off strategy and active power plan of units. The disclosure employs Newton method to transform chance constraints containing risk level and random variables into deterministic mixed integer linear constraints, which effectively improves the model solution efficiency, eliminates conservative nature of conventional robust unit commitment, provides reasonable dispatch basis for decision makers.

Abstract: A method for calculating a voltage stability margin of a power system considering electric-gas system coupling is provided. The method includes: establishing constraint equations for stable and secure operation of an electric-gas coupling system; establishing a continuous energy flow model of the electric-gas coupling system using a load margin index ? based on a correlation between an electric load of the power system and a natural gas load of the natural gas system; setting inequality constraints for the stable and secure operation of the electric-gas coupling system based on the limits of pressure and gas supply amount of the natural gas system; and solving the energy flow equation established based on the constraints and the continuous energy flow model to obtain the voltage stability margin of the power system considering electric-gas system coupling.

Abstract: The present disclosure relates to a method, an apparatus and a storage medium for controlling a combined heat and power system, belonging to the field of power system technologies. The method discloses: establishing a decision model, the decision model including an objective function aiming to minimize a total cost of the first generators and the second generators, and constraints with respect to the first generators, the second generators and the heating exchange stations; solving the decision model to acquire operation states of the first generators, operation states of the second generators, and operations states of the heating exchange stations; and controlling the combined heat and power system, based on the operation states of the first generators, the operation states of the second generators, and the operations states of the heating exchange stations.

Abstract: The disclosure provides a stochastic look-ahead dispatch method for power system based on Newton method, belonging to power system dispatch technologies. The disclosure analyzes historical data of wind power output, and uses statistical or fitting software to perform Gaussian mixture model fitting. A dispatch model with chances constraints is established for system parameters. Newton method is to solve quantiles of random variables obeying Gaussian mixture model, so that chances constraints are transformed into deterministic linear constraints, thus transforming original problem to convex optimization problem with linear constraints. Finally, the model is solved to obtain look-ahead dispatch. The disclosure employs Newton method to transform chance constraints containing risk level and random variables into deterministic linear constraints, which effectively improves model solution efficiency, eliminates conservative nature of conventional robust, provides reasonable dispatch for decision makers.

Abstract: The disclosure provides a stochastic dynamical unit commitment method for power system based on solving quantiles via Newton method, belonging to power system technologies. The method establishes a unit commitment model with chance constraints for power system parameters. Quantiles of random variables obeying mixed Gaussian distribution is solved by Newton method, and chance constraints are transformed into deterministic linear constraints, so that original problem is transformed into mixed integer linear optimization problem. Finally, the model is solved to obtain on-off strategy and active power plan of units. The disclosure employs Newton method to transform chance constraints containing risk level and random variables into deterministic mixed integer linear constraints, which effectively improves the model solution efficiency, eliminates conservative nature of conventional robust unit commitment, provides reasonable dispatch basis for decision makers.

Abstract: A state estimation method for a heat supply network in a steady state based on a bilateral equivalent model is provided. The method includes: establishing the bilateral equivalent model based on a mass flow rate in each supply branch of the heating network, a mass flow rate in each return branch of the heating network, a mass flow rate in each connecting branch of the heating network, a pressure and a temperature of each node in the heating network, wherein each heat source is configured as a connecting branch and each heat load is configured as a connecting branch; and repeatedly performing a state estimation on the heating network based on the bilateral equivalent model, until a coverage state estimation result is acquired.

Abstract: A method for calculating control parameters of a heating supply power of a heating network, pertaining to the technical field of operation and control of a power system containing multiple types of energy. The method: establishing a heating network simulation model that simulates a thermal dynamic process of the heating network; starting an upward simulation based on the heating network simulation model to obtain first control parameters from a set of up adjustment amounts; starting a downward simulation based on the heating network simulation model, to obtain second control parameters from a set of down adjustment amounts.

Abstract: An intra-day rolling scheduling method for an integrated heat and electricity system is provided. The method includes: establishing an objective function for scheduling of the integrated heat and electricity system, the objective function aiming to make operating costs of the integrated heat and electricity system to be a minimum; establishing constraints for a steady-state safe operation of the integrated heat and electricity system; and solving the objective function based on the constraints by an interior point method, to obtain an active power and a heating power of each combined heat and power unit, an active power of each thermal power unit, a heating power of each heat pump, and an active power consumed by each circulating pump, as an intra-day rolling scheduling scheme of the integrated heat and electricity system.

Abstract: A method for estimating a state of a combined heat and power system is provided. The method include: establishing an objective function; establishing constraints under a steady-state operating stage; converting the objective function and the constraints by utilizing a Lagrangian multiplier to obtain a Lagrange function; obtaining a steady-state estimation result of the combined heat and power system based on the Lagrange function; calculating an energy transmission delay produced by each pipe; establishing a dynamic constraint of each pipe based on the steady-state estimation result and the energy transmission delay; converting the objective function, the constraints, and the dynamic constraint by utilizing the Lagrangian multiplier to update the Lagrange function; obtaining a dynamic-state estimation result of the combined heat and power system during a dynamic-state operating stage of the combined heat and power system based on the updated Lagrange function.

Abstract: A reactive power optimization method for integrated transmission and distribution networks related to a field of operation and control technology of an electric power system is provided. The reactive power optimization method includes: establishing a reactive power optimization model for a transmission and distribution network consisting of a transmission network and a plurality of distribution networks, in which the reactive power optimization model includes an objective function and a plurality of constraints; performing a second order cone relaxation on a non-convex constraint of a plurality of distribution network constraints of the plurality of constraints; and solving the reactive power optimization model by using a generalized Benders decomposition method so as to control each generator in the transmission network and each generator in the plurality of distribution networks.