Station power control method, device and equipment and storage medium

Abstract

The invention provides a station power control method and device, equipment and a storage medium. The method comprises the steps of obtaining a scheduling instruction; sending a scheduling instruction to an administrator terminal; receiving an operation mode instruction sent by the administrator terminal; obtaining a controlled power station group or an optimization target and a constraint condition corresponding to a controlled power station according to the operation mode instruction; updating a preset optimization model according to the optimization target and the constraint condition; acquiring real-time operation data of the controlled power station group or the controlled power stations; inputting the real-time operation data and the corresponding target power into the updated optimization model to obtain a to-be-processed power instruction of each controlled power station; performing data preprocessing on the to-be-processed power instruction of each controlled power station to obtain an actual power instruction of each controlled power station; and respectively sending the actual power instruction of each controlled power station to each controlled power station. The regulation and control mode is more flexible, and the scheduling mode can be flexibly switched according to the scheduling mode of the power grid.

Description

Technical Field

[0001] The present application relates to the field of coordinated operation of power multi-energy complementarity, and particularly to a method, device, equipment and storage medium for controlling the power of a power station yard. Background Art

[0002] The large-scale base project of wind, light, thermal and energy storage mainly includes a large-scale and intensive construction project of a wind farm, a photovoltaic power station, a thermal power plant and an energy storage power station. The large-scale base project of wind, light, thermal and energy storage is a new thing, and there is no mature case in its regulation mode and system device.

[0003] Currently, for the most widely used new energy power station yard control system, the adopted dispatching mode is that the grid dispatching center formulates a dispatching plan according to information such as power prediction and cleared electricity price sent by each power station, and issues a dispatching instruction to each wind farm, photovoltaic power station, etc. Then, the regulation system or device of each power station yard controls the output of each generating unit, and finally makes the output at the grid connection point of the power station yard reach the grid dispatching target value.

[0004] However, since the specific dispatching mode after completion cannot be determined during the initial construction of the large-scale base project, using the conventional power station yard control system will result in an inflexible regulation mode and the problem that the dispatching mode cannot be flexibly switched according to the dispatching mode of the power grid. Summary of the Invention

[0005] The present application provides a method, device, equipment and storage medium for controlling the power of a power station yard to solve the technical problem that the existing technology has an inflexible regulation mode and cannot flexibly switch the regulation mode according to the dispatching mode of the power grid.

[0006] In a first aspect, the present application provides a method for controlling the power of a power station yard, including:

[0007] Obtain a dispatching instruction;

[0008] Send the dispatching instruction to an administrator terminal so that the administrator terminal generates an operation mode instruction in response to an input operation of an administrator according to the dispatching instruction; wherein the operation mode instruction includes a separate control instruction and a combined control instruction, wherein the separate control instruction includes a plurality of controlled power stations and a target power corresponding to each controlled power station, and wherein the combined control instruction includes a group of controlled power stations and a target power corresponding to the group of controlled power stations;

[0009] Receive the operation mode instruction sent by the administrator terminal;

[0010] Analyze the operation mode instruction to obtain a combined control instruction, and obtain an optimization target and constraint conditions corresponding to the group of controlled power stations according to the combined control instruction;

[0011] Update a preset optimization model according to the optimization target and constraint conditions;

[0012] Obtain the real-time operation data of each controlled power station in the controlled power station group;

[0013] Input the real-time operation data and the target power corresponding to the controlled power station group into the updated optimization model to obtain the to-be-processed power commands of each controlled power station in the controlled power station group;

[0014] Perform data preprocessing on the to-be-processed power commands of each controlled power station to obtain the actual power commands of each controlled power station;

[0015] Send the actual power commands of each controlled power station to each controlled power station in the controlled power station group respectively;

[0016] Parse the operation mode command to obtain the separate control command, and for each controlled power station in the separate control command, perform the following steps:

[0017] Obtain the optimization objective and constraint conditions corresponding to the controlled power station;

[0018] Update the preset optimization model according to the optimization objective and constraint conditions;

[0019] Obtain the real-time operation data of the controlled power station;

[0020] Input the real-time operation data of the controlled power station and the target power corresponding to the controlled power station into the updated optimization model to obtain the to-be-processed power command of the controlled power station;

[0021] Perform data preprocessing on the to-be-processed power command of the controlled power station to obtain the actual power command of the controlled power station;

[0022] Send the actual power command to the controlled power station.

[0023] Optionally, in the method as described above, the sending the scheduling command to the administrator terminal includes: performing approval verification on the scheduling command to generate a verification result; if the verification result is verification passed, then send the scheduling command to the administrator terminal.

[0024] Optionally, in the method as described above, the performing data preprocessing on the to-be-processed power commands of each controlled power station to obtain the actual power commands of each controlled power station includes: performing data verification on the to-be-processed power commands of each controlled power station to obtain the depolarized power commands of each controlled power station; performing amplitude limiting and speed limiting processing on the depolarized power commands of each controlled power station to obtain the actual power commands of each controlled power station.

[0025] Optionally, for the method described above, the scheduling instruction is a scheduling instruction for an integrated wind-solar-thermal-energy storage power plant.

[0026] Optionally, for the method described above, if the combined control instruction or the individual control instruction includes a thermal power plant, the constraint conditions include the upper and lower limits of the thermal power plant output and the ramp rate constraint of the thermal power plant:

[0027] Among them, the formula for the upper and lower limits of the thermal power plant output is:

[0028]

[0029] In the formula, Pg i,t represents the power generation power of the i-th thermal power plant at time t; represents the upper limit of the power generation power of the i-th thermal power plant at time t; represents the lower limit of the power generation power of the i-th thermal power plant at time t;

[0030] Among them, the formula for the ramp rate constraint of the thermal power plant is:

[0031] -P i down ≤Pg i,t -Pg i,t-1 ≤P i up

[0032] In the formula, P i down represents the maximum downhill rate of the i-th thermal power plant between two adjacent moments; P i up represents the maximum uphill rate of the i-th thermal power plant between two adjacent moments;

[0033] Correspondingly, obtaining the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the upper limit of the power generation power, the lower limit of the power generation power, the maximum downhill rate between two adjacent moments, and the maximum uphill rate between two adjacent moments of each thermal power plant; correspondingly, obtaining the real-time operation data of the controlled power station includes: obtaining the upper limit of the power generation power, the lower limit of the power generation power, the maximum downhill rate between two adjacent moments, and the maximum uphill rate between two adjacent moments of each thermal power plant.

[0034] Optionally, for the method described above, if the combined control instruction or the individual control instruction includes a wind farm, the constraint conditions include the upper and lower limits of the wind power.

[0035] Among them, the formula for the upper and lower limits of the wind power is:

[0036]

[0037] In the formula, Pwi,t is the power generation of the i-th wind farm at time t; is the upper limit of the power generation of the i-th wind farm at time t;

[0038] Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the upper and lower limits of the wind power of each wind farm; correspondingly, the obtaining of the real-time operation data of the controlled power station includes: obtaining the upper and lower limits of the wind power of each wind farm.

[0039] Optionally, in the method as described above, if the combined control instruction or the individual control instruction includes a photovoltaic power station, the constraint condition includes a photovoltaic power upper and lower limit constraint;

[0040] Among them, the formula for the photovoltaic power upper and lower limit constraint is:

[0041]

[0042] In the formula, Ps i,t is the power generation of the i-th photovoltaic power station at time t; is the upper limit of the power generation of the i-th photovoltaic power station at time t;

[0043] Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the upper and lower limits of the photovoltaic power of each photovoltaic power station; correspondingly, the obtaining of the real-time operation data of the controlled power station includes: obtaining the upper and lower limits of the photovoltaic power of each photovoltaic power station.

[0044] Optionally, in the method as described above, if the combined control instruction or the individual control instruction includes an energy storage power station, the constraint condition includes an energy storage power constraint and an energy storage battery state of health constraint;

[0045] Among them, the formula for the energy storage power constraint is:

[0046]

[0047]

[0048] In the formula, Pessc i,t is the charging power of the i-th energy storage power station at time t; is the maximum value of the charging power of the i-th energy storage power station at time t; is the minimum value of the charging power of the i-th energy storage power station at time t; Pessd i,t is the discharging power of the i-th energy storage power station at time t; is the maximum value of the discharging power of the i-th energy storage power station at time t; is the minimum value of the discharging power of the i-th energy storage power station at time t;

[0049] Among them, the formula for the constraint of the state of health of the energy storage battery is:

[0050]

[0051] In the formula, SOC i,t is the average state of charge of the battery of the i-th energy storage power station at time t; is the maximum value of the average state of charge of the battery of the i-th energy storage power station at time t; is the minimum value of the average state of charge of the battery of the i-th energy storage power station at time t;

[0052] Correspondingly, obtaining the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the maximum charging power, minimum charging power, maximum discharging power, minimum discharging power, maximum average state of charge of the battery, and minimum average state of charge of the battery of each energy storage power station; Correspondingly, obtaining the real-time operation data of the controlled power station includes: obtaining the maximum charging power, minimum charging power, maximum discharging power, minimum discharging power, maximum average state of charge of the battery, and minimum average state of charge of the battery of each energy storage power station.

[0053] Optionally, for the method as described above, the optimization objective corresponding to the combined control instruction is to minimize the sum of the scheduling instruction tracking deviation and the costs of each controlled power station in the combined control instruction;

[0054] Among them, the formula for the optimization objective is:

[0055] minf c = E + θ g Cg + θ w Cw + θ s Cs + θ ess Cess

[0056] In the formula, θ g , θ w , θ s , θ ess are binary variables, indicating whether the thermal power plant, wind farm, photovoltaic power station, and energy storage power station participate in the combined control. When it is 0, it means that the corresponding power station does not participate in the combined control. When it is 1, it means that the corresponding power station participates in the combined control; E is the scheduling instruction tracking deviation; Cg is the operation and startup cost of the thermal power plant; Cw is the cost of wind power curtailment; Cs is the cost of photovoltaic power curtailment; Cess is the operation cost of the energy storage;

[0057] Among them, the formula for the scheduling instruction tracking deviation is:

[0058]

[0059] Wherein, n is the total number of thermal power plants, wind farms, photovoltaic power stations or energy storage power stations that are independently controlled; Pcmd i,t represents the dispatching instruction value of the i-th power station that is independently controlled at time t; Pact i,t is a decision variable, representing the actual output of the i-th power station at time t; Pl i,t represents the line loss of the i-th power station at time t;

[0060] Among them, the formula for the operation and start-up cost of the thermal power plant is:

[0061]

[0062] Wherein, Tg represents the operation duration of the thermal power plant; ng represents the total number of controlled thermal power plants; a i Pg i,t 2 +b i Pg i,t +c i represents the operation cost of the i-th thermal power plant, a i 、b i 、c i are the operation cost function coefficients of the i-th thermal power plant, Pg i,t is a decision variable, representing the output of the i-th thermal power plant at time t; q i,t is a binary variable, representing the start-up state of the i-th unit at time t. When it is 0, it means the unit is shut down; when it is 1, it means the unit is operating; is the start-up cost of the i-th thermal power plant;

[0063] Among them, the formula for the wind power curtailment cost is:

[0064]

[0065] Wherein, Tw represents the operation duration of the wind farm; nw represents the total number of controlled wind farms; λw is the wind power curtailment cost factor; is the maximum power generation of the i-th wind farm at time t; Pw i,t is a decision variable, representing the power generation of the i-th wind farm at time t;

[0066] Among them, the formula for the photovoltaic power curtailment cost is:

[0067]

[0068] Wherein, Ts represents the operation duration of the photovoltaic power station; ns represents the total number of controlled photovoltaic power stations; λs is the photovoltaic power curtailment cost factor; is the maximum power generation of the i-th photovoltaic power station at time t; Ps i,tis a decision variable representing the power generation power of the i-th photovoltaic power station at time t;

[0069] Among them, the formula for the energy storage operation cost is:

[0070]

[0071] In the formula, Te represents the operation duration of the energy storage power station; ne represents the total number of controlled energy storage power stations; λe is the operation cost factor of the energy storage power station; SOC i,t is the average battery state of charge of the i-th energy storage power station at time t; is the maximum value of the average battery state of charge of the i-th energy storage power station at time t; is the minimum value of the average battery state of charge of the i-th energy storage power station at time t; Pess i,t is a decision variable representing the power of the i-th energy storage power station at time t. When it is positive, it represents power generation. At this time, Pess i,t = Pessd i,t When it is negative, it represents charging. At this time, Pess i,t = -Pessc i,t Pessc i,t is the charging power of the i-th energy storage power station at time t, and Pessd i,t is the discharging power of the i-th energy storage power station at time t;

[0072] Correspondingly, obtaining the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the real-time line loss of each controlled power station in the controlled power station group; if it is determined that the combined control instruction includes an energy storage power station, obtaining the real-time average battery state of charge, the maximum value of the average battery state of charge, and the minimum value of the average battery state of charge of each energy storage power station in the controlled power station group.

[0073] Optionally, for the method as described above, if the individual control instruction includes a thermal power plant, the optimization objective corresponding to the thermal power plant is to minimize the sum of the scheduling instruction tracking deviation and the operation and start-stop cost of the thermal power plant;

[0074] Among them, the formula for the optimization objective is:

[0075] minf g = E + Cg

[0076] In the formula, E is the scheduling instruction tracking deviation; Cg is the operation and start-up cost of the thermal power plant.

[0077] Optionally, for the method as described above, if the individual control instruction includes a wind farm, the optimization objective corresponding to the wind farm is to minimize the sum of the scheduling instruction tracking deviation and the wind power abandonment cost;

[0078] Among them, the formula of the optimization objective is:

[0079] min f w = E + Cw

[0080] In the formula, E is the tracking deviation of the dispatching instruction; Cw is the cost of wind power curtailment.

[0081] Optionally, for the method as described above, if the individual control instruction includes a photovoltaic power station, the optimization objective corresponding to the photovoltaic power station is to minimize the sum of the tracking deviation of the dispatching instruction and the cost of photovoltaic power curtailment;

[0082] Among them, the formula of the optimization objective is:

[0083] min f s = E + Cs

[0084] In the formula, E is the tracking deviation of the dispatching instruction; Cs is the cost of photovoltaic power curtailment.

[0085] Optionally, for the method as described above, if the individual control instruction includes an energy storage power station, the optimization objective corresponding to the energy storage power station is to minimize the sum of the tracking deviation of the dispatching instruction and the energy storage operation cost;

[0086] Among them, the formula of the optimization objective is:

[0087] min f e = E + Cess

[0088] In the formula, E is the tracking deviation of the dispatching instruction; Cess is the energy storage operation cost.

[0089] Optionally, for the method as described above, the dispatching instruction includes a grid dispatching instruction and a manual dispatching instruction.

[0090] In a second aspect, the present application provides a power control device for a power station, including:

[0091] An acquisition module, configured to acquire a dispatching instruction;

[0092] A sending module, configured to send the dispatching instruction to an administrator terminal, so that the administrator terminal generates an operation mode instruction in response to an input operation of the administrator according to the dispatching instruction; wherein the operation mode instruction includes an individual control instruction and a combined control instruction, wherein the individual control instruction includes a plurality of controlled power stations and a target power corresponding to each controlled power station, and wherein the combined control instruction includes a group of controlled power stations and a target power corresponding to the group of controlled power stations;

[0093] A receiving module, configured to receive the operation mode instruction sent by the administrator terminal;

[0094] A parsing module, configured to parse the operation mode instruction to obtain a combined control instruction, and obtain an optimization target and constraint conditions corresponding to the controlled power station group according to the combined control instruction;

[0095] An updating module, configured to update a preset optimization model according to the optimization target and constraint conditions;

[0096] The obtaining module is further configured to obtain real-time operation data of each controlled power station in the controlled power station group;

[0097] An input module, configured to input the real-time operation data and the target power corresponding to the controlled power station group into the updated optimization model, so as to obtain a to-be-processed power instruction for each controlled power station in the controlled power station group;

[0098] A data processing module, configured to perform data preprocessing on the to-be-processed power instructions of the controlled power stations, so as to obtain actual power instructions for the controlled power stations;

[0099] The sending module is further configured to separately send the actual power instructions of the controlled power stations to each controlled power station in the controlled power station group;

[0100] A separate control module, configured to parse the operation mode instruction to obtain the separate control instruction, and for each controlled power station in the separate control instruction, perform the following steps: obtain the optimization target and constraint conditions corresponding to the controlled power station; update a preset optimization model according to the optimization target and constraint conditions; obtain the real-time operation data of the controlled power station; input the real-time operation data of the controlled power station and the target power corresponding to the controlled power station into the updated optimization model, so as to obtain a to-be-processed power instruction for the controlled power station; perform data preprocessing on the to-be-processed power instruction of the controlled power station, so as to obtain an actual power instruction for the controlled power station; send the actual power instruction to the controlled power station.

[0101] In a third aspect, the present application provides a service device, including: a processor, and a memory communicatively connected to the processor;

[0102] The memory stores computer-executable instructions;

[0103] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the power control method for a power station yard as described in the first aspect and various possible designs of the first aspect above.

[0104] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method as described in the first aspect and various possible designs of the first aspect above.

[0105] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program which, when executed by a processor, implements the method described in the first aspect above and various possible designs of the first aspect.

[0106] The substation power control method, device, equipment and storage medium provided by the present application receive an operation mode instruction sent by an administrator terminal, obtain an optimization target and constraint conditions corresponding to a controlled power station or a group of controlled power stations according to the operation mode instruction; update a preset optimization model according to the optimization target and the constraint conditions; input real-time operation data and target power of the controlled power station or the group of controlled power stations into the updated optimization model to obtain actual power instructions for each controlled power station in the controlled power station or the group of controlled power stations, and send the actual power instructions to each controlled power station in the controlled power station or the group of controlled power stations. It realizes the selection of appropriate combinations of optimization targets and constraint conditions in different scenarios to form various optimization models, and the regulation mode is more flexible, and the scheduling mode can be flexibly switched according to the dispatching mode of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0108] Figure 1 It is a schematic diagram of the scenario of the substation power control method provided by an embodiment of the present application;

[0109] Figure 2 It is a schematic flowchart of the substation power control method provided by an embodiment of the present application;

[0110] Figure 3 It is a schematic structural diagram of the substation power control device provided by an embodiment of the present application;

[0111] Figure 4 It is a schematic hardware structure diagram of the service equipment provided by an embodiment of the present application.

[0112] Through the above accompanying drawings, the clear embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and the textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0113] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0114] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0115] Figure 1 It is a schematic diagram of the scenario of the station power control method provided by the embodiment of the present application. As Figure 1 shown, the scenario provided in this embodiment includes an administrator terminal 101 and a service device 102.

[0116] Among them, the administrator terminal 101 can be a mobile phone or a computer.

[0117] The service device 102 can be a server. Optionally, it can be a single server or a cluster composed of multiple servers.

[0118] Specifically, the service device 102 sends a scheduling instruction to the administrator terminal 101; the administrator terminal 101 generates an operation mode instruction in response to the input operation of the administrator according to the scheduling instruction; the administrator terminal 101 sends the operation mode instruction to the service device 102; the service device 102 performs station power control according to the operation mode instruction.

[0119] Figure 2 It is a schematic flowchart of the station power control method provided by an embodiment of the present application. The execution subject of this embodiment can be Figure 1 the service device 102 shown in Figure 2 or other computer devices, and no special limitation is made here for this embodiment. As

[0120] S201: Obtain a scheduling instruction.

[0121] Among them, the scheduling instruction includes a grid scheduling instruction and a manual scheduling instruction.

[0122] S202: Send a scheduling instruction to the administrator terminal so that the administrator terminal generates an operation mode instruction in response to the administrator's input operation according to the scheduling instruction; the operation mode instruction includes a separate control instruction and a combined control instruction, where the separate control instruction includes multiple controlled power stations and a target power corresponding to each controlled power station, and the combined control instruction includes a group of controlled power stations and a target power corresponding to the group of controlled power stations.

[0123] Specifically, S202 specifically includes S2021 - S2022:

[0124] S2021: Verify and check the scheduling instruction to generate a verification result.

[0125] Specifically, obtain the scheduling instructions for two consecutive preset periods, and verify and check whether the contents of the two scheduling instructions are consistent.

[0126] S2022: If the verification result is that the verification is passed, send the scheduling instruction to the administrator terminal.

[0127] S203: Receive the operation mode instruction sent by the administrator terminal.

[0128] S204: Analyze the operation mode instruction to obtain the combined control instruction, and obtain the optimization target and constraint conditions corresponding to the group of controlled power stations according to the combined control instruction.

[0129] Specifically, analyze the operation mode instruction to obtain the combined control instruction, analyze the combined control instruction to obtain the low-altitude power station group, and obtain the corresponding optimization target and constraint conditions from the preset mapping table according to the group of controlled power stations.

[0130] S205: Update the preset optimization model according to the optimization target and constraint conditions.

[0131] S206: Obtain the real-time operation data of each controlled power station in the group of controlled power stations.

[0132] Among them, the real-time operation data of each controlled power station includes one or more of the following: the real-time output power of each controlled power station, the upper and lower limits of the power generation power, the maximum ramp rate of the thermal power plant, the real-time power and health status of the energy storage power station.

[0133] S207: Input the real-time operation data and the target power corresponding to the group of controlled power stations into the updated optimization model to obtain the power instructions to be processed for each controlled power station in the group of controlled power stations.

[0134] S208: Perform data preprocessing on the power instructions to be processed for each controlled power station to obtain the actual power instructions for each controlled power station.

[0135] Specifically, data preprocessing is performed on the to-be-processed power commands of each controlled power station according to preset standard power data to obtain the actual power commands of each controlled power station.

[0136] S209: Send the actual power commands of each controlled power station to each controlled power station in the controlled power station group.

[0137] S210: Analyze the operation mode command to obtain individual control commands, and for each controlled power station in the individual control commands, execute the following steps S2101 - S2106:

[0138] S2101: Obtain the optimization objective and constraint conditions corresponding to the controlled power station.

[0139] S2102: Update the preset optimization model according to the optimization objective and constraint conditions.

[0140] S2103: Obtain the real-time operation data of the controlled power station.

[0141] Among them, the real-time operation data of the controlled power station includes one or more of the following: the real-time output power of the controlled power station, the upper and lower limits of the power generation power, the maximum ramp rate, the real-time power, and the health status.

[0142] S2104: Input the real-time operation data of the controlled power station and the target power corresponding to the controlled power station into the updated optimization model to obtain the to-be-processed power command of the controlled power station.

[0143] S2105: Perform data preprocessing on the to-be-processed power command of the controlled power station to obtain the actual power command of the controlled power station.

[0144] Specifically, data preprocessing is performed on the to-be-processed power command of the controlled power station according to preset standard power data to obtain the actual power command of the controlled power station.

[0145] S2106: Send the actual power command to the controlled power station.

[0146] As can be seen from the above description, in this application, by receiving the operation mode command sent by the administrator terminal, the optimization objective and constraint conditions corresponding to the controlled power station or the controlled power station group are obtained according to the operation mode command; the preset optimization model is updated according to the optimization objective and constraint conditions; the real-time operation data and target power of the controlled power station or the controlled power station group are input into the updated optimization model to obtain the actual power commands of each controlled power station in the controlled power station or the controlled power station group, and the actual power commands are sent to each controlled power station in the controlled power station or the controlled power station group. It realizes the selection of appropriate optimization objective and constraint condition combinations to form various optimization models in different scenarios, the regulation mode is more flexible, and the dispatching mode can be flexibly switched according to the dispatching method of the power grid.

[0147] In an embodiment of the present application, the scheduling instruction is a scheduling instruction for an integrated wind-solar-thermal energy storage power plant. Correspondingly, in step S204, according to the combined control instruction, the optimization objectives and constraint conditions corresponding to the controlled power plant group are obtained, and in step S2101, another implementation manner is also provided, which is described in detail as follows:

[0148] S204: Analyze the operation mode instruction to obtain the combined control instruction, and obtain the optimization objectives and constraint conditions corresponding to the controlled power plant group according to the combined control instruction.

[0149] Among them, if the combined control instruction includes a thermal power plant, the constraint conditions include the upper and lower limits of the thermal power plant output and the ramp rate constraint of the thermal power plant.

[0150] Among them, the formula for the upper and lower limits of the thermal power plant output is:

[0151]

[0152] In the formula, Pg i,t represents the power generation power of the i-th thermal power plant at time t; represents the upper limit of the power generation power of the i-th thermal power plant at time t; represents the lower limit of the power generation power of the i-th thermal power plant at time t.

[0153] Among them, the formula for the ramp rate constraint of the thermal power plant is:

[0154] -P i down ≤Pg i,t -Pg i,t-1 ≤P i up

[0155] In the formula, P i down represents the maximum downhill rate of the i-th thermal power plant between two adjacent moments; P i up represents the maximum uphill rate of the i-th thermal power plant between two adjacent moments.

[0156] Among them, if the combined control instruction includes a wind farm, the constraint conditions include the upper and lower limits of the wind power.

[0157] Among them, the formula for the upper and lower limits of the wind power is:

[0158]

[0159] In the formula, Pw i,t is the power generation power of the i-th wind farm at time t; is the upper limit of the power generation power of the i-th wind farm at time t.

[0160] Among them, if the combined control instruction includes a photovoltaic power station, the constraint conditions include the upper and lower limits of photovoltaic power.

[0161] Among them, the formula for the upper and lower limits of photovoltaic power is:

[0162]

[0163] In the formula, Ps i,t is the power generation power of the i-th photovoltaic power station at time t; is the upper limit of the power generation power of the i-th photovoltaic power station at time t.

[0164] Among them, if the combined control instruction includes an energy storage power station, the constraint conditions include energy storage power constraint and the state of health constraint of the energy storage battery.

[0165] Among them, the formula for the energy storage power constraint is:

[0166]

[0167]

[0168] In the formula, Pessc i,t is the charging power of the i-th energy storage power station at time t; is the maximum value of the charging power of the i-th energy storage power station at time t; is the minimum value of the charging power of the i-th energy storage power station at time t; Pessd i,t is the discharging power of the i-th energy storage power station at time t; is the maximum value of the discharging power of the i-th energy storage power station at time t; is the minimum value of the discharging power of the i-th energy storage power station at time t.

[0169] Among them, the formula for the state of health constraint of the energy storage battery is:

[0170]

[0171] In the formula, SOC i,t is the average state of charge of the battery of the i-th energy storage power station at time t; is the maximum value of the average state of charge of the battery of the i-th energy storage power station at time t; is the minimum value of the average state of charge of the battery of the i-th energy storage power station at time t.

[0172] Among them, the optimization objective corresponding to the combined control instruction is to minimize the sum of the scheduling instruction tracking deviation and the costs of each controlled power station in the combined control instruction.

[0173] Among them, the formula for the optimization objective is:

[0174] min fc = E + θ g Cg + θ w Cw + θ s Cs + θ ess Cess

[0175] Wherein, θ g , θ w , θ s , θ ess are binary variables, indicating whether a thermal power plant, a wind farm, a photovoltaic power station, and an energy storage power station participate in combined control. When it is 0, it means the corresponding power station does not participate in combined control. When it is 1, it means the corresponding power station participates in combined control; E is the dispatching instruction tracking deviation; Cg is the operation and startup cost of the thermal power plant; Cw is the wind power curtailment cost; Cs is the photovoltaic power curtailment cost; Cess is the energy storage operation cost.

[0176] Among them, the formula for the dispatching instruction tracking deviation is:

[0177]

[0178] Wherein, n is the total number of thermal power plants, wind farms, photovoltaic power stations, or energy storage power stations under individual control; Pcmd i,t represents the dispatching instruction value of the i-th power station at time t under individual control; Pact i,t is a decision variable, representing the actual output of the i-th power station at time t; Pl i,t represents the line loss of the i-th power station at time t.

[0179] Among them, the formula for the operation and startup cost of the thermal power plant is:

[0180]

[0181] Wherein, Tg represents the operation duration of the thermal power plant; ng represents the total number of controlled thermal power plants; a i Pg i,t 2 + b i Pg i,t + c i represents the operation cost of the i-th thermal power plant, a i , b i , c i are the operation cost function coefficients of the i-th thermal power plant, Pg i,t is a decision variable, representing the output of the i-th thermal power plant at time t; q i,t is a binary variable, indicating the startup state of the i-th unit at time t. When it is 0, it means the unit is shut down. When it is 1, it means the unit is operating; is the startup cost of the i-th thermal power plant.

[0182] Among them, the formula for the curtailment cost of wind power is:

[0183]

[0184] In the formula, Tw represents the operating duration of the wind farm; nw represents the total number of controlled wind farms; λw is the curtailment cost factor of the wind farm; is the maximum power generation of the i-th wind farm at time t; Pw i,t is a decision variable representing the power generation of the i-th wind farm at time t.

[0185] Among them, the formula for the curtailment cost of photovoltaic power is:

[0186]

[0187] In the formula, Ts represents the operating duration of the photovoltaic power station; ns represents the total number of controlled photovoltaic power stations; λs is the curtailment cost factor of the photovoltaic power station; is the maximum power generation of the i-th photovoltaic power station at time t; Ps i,t is a decision variable representing the power generation of the i-th photovoltaic power station at time t.

[0188] Among them, the formula for the operating cost of energy storage is:

[0189]

[0190] In the formula, Te represents the operating duration of the energy storage station; ne represents the total number of controlled energy storage stations; λe is the operating cost factor of the energy storage station; SOC i,t is the average battery state of charge of the i-th energy storage station at time t; is the maximum value of the average battery state of charge of the i-th energy storage station at time t; is the minimum value of the average battery state of charge of the i-th energy storage station at time t; Pess i,t is a decision variable representing the power of the i-th energy storage station at time t. When it is positive, it represents power generation, and at this time Pess i,t = Pessd i,t When it is negative, it represents charging, and at this time Pess i,t = -Pessc i,t Pessc i,t is the charging power of the i-th energy storage station at time t, and Pessd i,t is the discharging power of the i-th energy storage station at time t.

[0191] S2101: Obtain the optimization objective and constraint conditions corresponding to the controlled power station.

[0192] Among them, if the controlled power station is a thermal power plant, the constraint conditions include the upper and lower limits of the thermal power plant output and the ramp rate constraint of the thermal power plant.

[0193] Among them, the formula for the upper and lower limits of the thermal power plant output is:

[0194]

[0195] In the formula, Pg i,t represents the power generation power of the i-th thermal power plant at time t; represents the upper limit of the power generation power of the i-th thermal power plant at time t; represents the lower limit of the power generation power of the i-th thermal power plant at time t.

[0196] Among them, the formula for the ramp rate constraint of the thermal power plant is:

[0197] -P i down ≤Pg i,t -Pg i,t-1 ≤P i up

[0198] In the formula, P i down represents the maximum downhill rate of the i-th thermal power plant between two adjacent moments; P i up represents the maximum uphill rate of the i-th thermal power plant between two adjacent moments.

[0199] Among them, if the controlled power station is a wind farm, the constraint conditions include the upper and lower limits of the wind power.

[0200] Among them, the formula for the upper and lower limits of the wind power is:

[0201]

[0202] In the formula, Pw i,t is the power generation power of the i-th wind farm at time t; is the upper limit of the power generation power of the i-th wind farm at time t.

[0203] Among them, if the controlled power station is a photovoltaic power station, the constraint conditions include the upper and lower limits of the photovoltaic power.

[0204] Among them, the formula for the upper and lower limits of the photovoltaic power is:

[0205]

[0206] In the formula, Ps i,t is the power generation power of the i-th photovoltaic power station at time t; is the upper limit of the power generation power of the i-th photovoltaic power station at time t.

[0207] Among them, if the controlled power station is an energy storage power station, the constraint conditions include energy storage power constraint and state of health constraint of the energy storage battery.

[0208] Among them, the formula for the energy storage power constraint is:

[0209]

[0210]

[0211] In the formula, Pessc i,t is the charging power of the i-th energy storage power station at time t; is the maximum charging power of the i-th energy storage power station at time t; is the minimum charging power of the i-th energy storage power station at time t; Pessd i,t is the discharging power of the i-th energy storage power station at time t; is the maximum discharging power of the i-th energy storage power station at time t; is the minimum discharging power of the i-th energy storage power station at time t.

[0212] Among them, the formula for the state of health constraint of the energy storage battery is:

[0213]

[0214] In the formula, SOC i,t is the average state of charge of the battery of the i-th energy storage power station at time t; is the maximum average state of charge of the battery of the i-th energy storage power station at time t; is the minimum average state of charge of the battery of the i-th energy storage power station at time t.

[0215] Among them, if the individual control instruction includes a thermal power plant, the corresponding optimization objective of the thermal power plant is to minimize the sum of the dispatching instruction tracking deviation and the operation and start-up cost of the thermal power plant;

[0216] Among them, the formula for the optimization objective is:

[0217] minf g = E + Cg

[0218] In the formula, E is the dispatching instruction tracking deviation; Cg is the operation and start-up cost of the thermal power plant.

[0219] Among them, if the individual control instruction includes a wind farm, the corresponding optimization objective of the wind farm is to minimize the sum of the dispatching instruction tracking deviation and the wind power curtailment cost;

[0220] Among them, the formula for the optimization objective is:

[0221] minfw = E + Cw

[0222] In the formula, E is the tracking deviation of the dispatching instruction; Cw is the cost of curtailed wind power.

[0223] Among them, if the individual control instruction includes a photovoltaic power station, the optimization objective corresponding to the photovoltaic power station is to minimize the sum of the tracking deviation of the dispatching instruction and the cost of curtailed photovoltaic power.

[0224] Among them, the formula for the optimization objective is:

[0225] min f s = E + Cs

[0226] In the formula, E is the tracking deviation of the dispatching instruction; Cs is the cost of curtailed photovoltaic power.

[0227] Among them, if the individual control instruction includes an energy storage power station, the optimization objective corresponding to the energy storage power station is to minimize the sum of the tracking deviation of the dispatching instruction and the operation cost of the energy storage.

[0228] Among them, the formula for the optimization objective is:

[0229] min f e = E + Cess

[0230] In the formula, E is the tracking deviation of the dispatching instruction; Cess is the operation cost of the energy storage.

[0231] Correspondingly, the specific process of step S206 is described in detail as follows:

[0232] S206: Obtain the real-time operation data of each controlled power station in the controlled power station group.

[0233] Specifically, if the combined control instruction includes a thermal power plant, obtain the upper limit of the power generation power, the lower limit of the power generation power, the maximum downhill rate between two adjacent moments, and the maximum uphill rate between two adjacent moments of each thermal power plant.

[0234] Specifically, if the combined control instruction includes a wind farm, obtain the upper and lower limits of the wind power of each wind farm.

[0235] Specifically, if the combined control instruction includes a photovoltaic power station, obtain the upper and lower limits of the photovoltaic power of each photovoltaic power station.

[0236] Specifically, if the combined control instruction includes an energy storage power station, obtain the maximum charging power, the minimum charging power, the maximum discharging power, the minimum discharging power, the real-time average state of charge of the battery, the maximum state of charge of the battery, and the minimum state of charge of the battery of each energy storage power station.

[0237] Specifically, obtain the real-time line loss of each controlled power station in the controlled power station group.

[0238] Correspondingly, the specific process of step S2103 is described in detail as follows:

[0239] S2103: Obtain the real-time operation data of the controlled power station.

[0240] Specifically, if the individual control instruction includes a thermal power plant, obtain the upper limit of the power generation, the lower limit of the power generation, the maximum downhill rate between two adjacent moments, and the maximum uphill rate between two adjacent moments of each thermal power plant.

[0241] Specifically, if the individual control instruction includes a wind farm, obtain the upper and lower limits of the wind power of each wind farm.

[0242] Specifically, if the individual control instruction includes a photovoltaic power station, obtain the upper and lower limits of the photovoltaic power of each photovoltaic power station.

[0243] Specifically, if the individual control instruction includes an energy storage power station, obtain the maximum charging power, the minimum charging power, the maximum discharging power, the minimum discharging power, the real-time average state of charge of the battery, the maximum state of charge of the battery, and the minimum state of charge of the battery of each energy storage power station.

[0244] Specifically, obtain the real-time line loss of each controlled power station.

[0245] As can be seen from the above description, in this application, different characteristics of thermal power, wind power, photovoltaic power generation, and energy storage power stations are comprehensively considered, different optimization objectives and constraint conditions are set, and appropriate optimization objective and constraint condition combinations are selected in different scenarios to form a variety of optimization models, ultimately achieving the coordinated control of wind, light, fire, and storage, thereby improving the economic benefits and control performance of the power station.

[0246] In an embodiment of this application, steps S208 and S2105 also provide another implementation method, which is described in detail as follows:

[0247] S208: Perform data preprocessing on the to-be-processed power instructions of each controlled power station to obtain the actual power instructions of each controlled power station.

[0248] Specifically, S208 specifically includes S2081 to S2082:

[0249] S2081: Perform data verification on the to-be-processed power instructions of each controlled power station to generate depolarized power instructions for each controlled power station.

[0250] Specifically, compare the to-be-processed power instructions of each controlled power station in adjacent preset periods, remove the maximum values among them, and obtain the depolarized power instructions for each controlled power station.

[0251] S2082: Limit and speed - limit the depolarization power commands of each controlled power station to obtain the actual power commands of each controlled power station.

[0252] Specifically, statistically calculate the maximum value, minimum value of the available power generation of each power station, and the upper limit requirement for the change of the power value per minute or per second. According to the statistical results, limit the upper and lower limits of the power of each controlled power station in each cycle; limit and speed - limit the depolarization power commands of each controlled power station according to the upper and lower limits of the power of each controlled power station to obtain the actual power commands of each controlled power station.

[0253] S2105: Perform data pre - processing on the to - be - processed power commands of the controlled power station to obtain the actual power commands of the controlled power station.

[0254] Specifically, S2105 specifically includes Sa - Sb:

[0255] Sa: Perform data verification on the to - be - processed power commands of the controlled power station to obtain the depolarization power commands of the controlled power station.

[0256] Sb: Limit and speed - limit the depolarization power commands of the controlled power station to obtain the actual power commands of the controlled power station.

[0257] From the above description, it can be seen that this application can avoid the adverse effects caused by assigning abnormal values as actual calculation results to power stations. It can also avoid the adverse effects caused by the issued commands not conforming to the actual operating conditions of the power stations.

[0258] Figure 3 It is a schematic structural diagram of the sub - station power control device provided by the embodiment of this application. As Figure 3 shown, the sub - station power control device 30 includes: an acquisition module 301, a sending module 302, a receiving module 303, an analysis module 304, an update module 305, an input module 306, a data processing module 307, and a separate control module 308.

[0259] The acquisition module 301 is used to acquire a dispatching instruction;

[0260] The sending module 302 is used to send the dispatching instruction to the administrator terminal, so that the administrator terminal generates an operation mode instruction in response to the input operation of the administrator according to the dispatching instruction; wherein the operation mode instruction includes a separate control instruction and a combined control instruction, wherein the separate control instruction includes multiple controlled power stations and a target power corresponding to each controlled power station, and the combined control instruction includes a controlled power station group and a target power corresponding to the controlled power station group;

[0261] The receiving module 303 is used to receive the operation mode instruction sent by the administrator terminal;

[0262] A parsing module 304, configured to parse the operation mode instruction to obtain a combined control instruction, and obtain an optimization target and constraint conditions corresponding to the controlled power station group according to the combined control instruction;

[0263] An updating module 305, configured to update a preset optimization model according to the optimization target and constraint conditions;

[0264] The obtaining module 301 is further configured to obtain real-time operation data of each controlled power station in the controlled power station group;

[0265] An input module 306, configured to input the real-time operation data and the target power corresponding to the controlled power station group into the updated optimization model to obtain a to-be-processed power instruction for each controlled power station in the controlled power station group;

[0266] A data processing module 307, configured to perform data preprocessing on the to-be-processed power instructions of the controlled power stations to obtain actual power instructions for the controlled power stations;

[0267] The sending module 302 is further configured to separately send the actual power instructions of the controlled power stations to each controlled power station in the controlled power station group;

[0268] A separate control module 308, configured to parse the operation mode instruction to obtain the separate control instruction, and for each controlled power station in the separate control instruction, perform the following steps: obtain the optimization target and constraint conditions corresponding to the controlled power station; update a preset optimization model according to the optimization target and constraint conditions; obtain the real-time operation data of the controlled power station; input the real-time operation data of the controlled power station and the target power corresponding to the controlled power station into the updated optimization model to obtain a to-be-processed power instruction for the controlled power station; perform data preprocessing on the to-be-processed power of the controlled power station to obtain an actual power instruction for the controlled power station; send the actual power instruction to the controlled power station.

[0269] In a possible design, the sending module 302 is specifically configured to: perform approval verification on the scheduling instruction to generate a verification result; if the verification result is verification passed, send the scheduling instruction to the administrator terminal.

[0270] In a possible design, the data processing module 307 is specifically configured to: perform data verification on the to-be-processed power instructions of the controlled power stations to obtain depolarization power instructions for the controlled power stations; perform amplitude limiting and speed limiting processing on the depolarization power instructions of the controlled power stations to obtain actual power instructions for the controlled power stations.

[0271] In a possible design, the scheduling instruction is a scheduling instruction for a wind-solar-thermal-storage integrated power plant.

[0272] In a possible design, if the combined control instruction or the individual control instruction includes a thermal power plant, the constraint conditions include the upper and lower limits of the thermal power plant output and the ramp rate constraint of the thermal power plant:

[0273] Among them, the formula for the upper and lower limits of the thermal power plant output is:

[0274]

[0275] In the formula, Pg i,t represents the power generation power of the i-th thermal power plant at time t; represents the upper limit of the power generation power of the i-th thermal power plant at time t; represents the lower limit of the power generation power of the i-th thermal power plant at time t;

[0276] Among them, the formula for the ramp rate constraint of the thermal power plant is:

[0277] -P i down ≤Pg i,t -Pg i,t-1 ≤P i up

[0278] In the formula, P i down represents the maximum downhill rate of the i-th thermal power plant between two adjacent moments; P i up represents the maximum uphill rate of the i-th thermal power plant between two adjacent moments;

[0279] Correspondingly, obtaining the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the upper limit of the power generation power, the lower limit of the power generation power, the maximum downhill rate between two adjacent moments, and the maximum uphill rate between two adjacent moments of each thermal power plant; Correspondingly, obtaining the real-time operation data of the controlled power station includes: obtaining the upper limit of the power generation power, the lower limit of the power generation power, the maximum downhill rate between two adjacent moments, and the maximum uphill rate between two adjacent moments of each thermal power plant.

[0280] In a possible design, if the combined control instruction or the individual control instruction includes a wind farm, the constraint conditions include the upper and lower limits of the wind power.

[0281] Among them, the formula for the upper and lower limits of the wind power is:

[0282]

[0283] In the formula, Pw i,t is the power generation power of the i-th wind farm at time t; is the upper limit of the power generation of the i-th wind farm at time t;

[0284] Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the upper and lower limits of the wind power of each wind farm; Correspondingly, the obtaining of the real-time operation data of the controlled power station includes: obtaining the upper and lower limits of the wind power of each wind farm.

[0285] In a possible design, if the combined control instruction or the individual control instruction includes a photovoltaic power station, the constraint condition includes a photovoltaic power upper and lower limit constraint;

[0286] Among them, the formula for the photovoltaic power upper and lower limit constraint is:

[0287]

[0288] In the formula, Ps i,t is the power generation of the i-th photovoltaic power station at time t; is the upper limit of the power generation of the i-th photovoltaic power station at time t;

[0289] Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the upper and lower limits of the photovoltaic power of each photovoltaic power station; Correspondingly, the obtaining of the real-time operation data of the controlled power station includes: obtaining the upper and lower limits of the photovoltaic power of each photovoltaic power station.

[0290] In a possible design, if the combined control instruction or the individual control instruction includes an energy storage power station, the constraint condition includes an energy storage power constraint and an energy storage battery state of health constraint;

[0291] Among them, the formula for the energy storage power constraint is:

[0292]

[0293]

[0294] In the formula, Pessc i,t is the charging power of the i-th energy storage power station at time t; is the maximum value of the charging power of the i-th energy storage power station at time t; is the minimum value of the charging power of the i-th energy storage power station at time t; Pessd i,t is the discharging power of the i-th energy storage power station at time t; is the maximum value of the discharging power of the i-th energy storage power station at time t; is the minimum value of the discharging power of the i-th energy storage power station at time t;

[0295] Among them, the formula for the energy storage battery state of health constraint is:

[0296]

[0297] In the formula, SOC i,t is the average state of charge of the battery of the i-th energy storage power station at time t; is the maximum value of the average state of charge of the battery of the i-th energy storage power station at time t; is the minimum value of the average state of charge of the battery of the i-th energy storage power station at time t;

[0298] Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the maximum charging power, minimum charging power, maximum discharging power, minimum discharging power, maximum average state of charge of the battery, and minimum average state of charge of the battery of each energy storage power station; Correspondingly, the obtaining of the real-time operation data of the controlled power station includes: obtaining the maximum charging power, minimum charging power, maximum discharging power, minimum discharging power, maximum average state of charge of the battery, and minimum average state of charge of the battery of each energy storage power station.

[0299] In a possible design, the optimization target corresponding to the combined control instruction is to minimize the sum of the scheduling instruction tracking deviation and the costs of each controlled power station in the combined control instruction;

[0300] Among them, the formula of the optimization target is:

[0301] minf c = E + θ g Cg + θ w Cw + θ s Cs + θ ess Cess

[0302] In the formula, θ g , θ w , θ s , θ ess are binary variables, indicating whether the thermal power plant, wind farm, photovoltaic power station, and energy storage power station participate in the combined control. When it is 0, it means that the corresponding power station does not participate in the combined control. When it is 1, it means that the corresponding power station participates in the combined control; E is the scheduling instruction tracking deviation; Cg is the operation and startup cost of the thermal power plant; Cw is the cost of wind power curtailment; Cs is the cost of photovoltaic power curtailment; Cess is the operation cost of the energy storage;

[0303] Among them, the formula of the scheduling instruction tracking deviation is:

[0304]

[0305] In the formula, n is the total number of thermal power plants, wind farms, photovoltaic power stations, or energy storage power stations under individual control; Pcmd i,tDenote the scheduling instruction value of the \(i\)-th power station controlled individually at time \(t\); \(P_{act}\) i,t Is a decision variable, representing the actual output of the \(i\)-th power station at time \(t\); \(P_{l}\) i,t Denote the line loss of the \(i\)-th power station at time \(t\);

[0306] Among them, the formula for the operation and start-up cost of the thermal power plant is:

[0307]

[0308] In the formula, \(T_{g}\) represents the operation duration of the thermal power plant; \(n_{g}\) represents the total number of controlled thermal power plants; \(a\) i \(P_{g}\) i,t 2 \(+b\) i \(P_{g}\) i,t \(+c\) i Represents the operation cost of the \(i\)-th thermal power plant, \(a\) i 、\(b\) i 、\(c\) i Are the operation cost function coefficients of the \(i\)-th thermal power plant, \(P_{g}\) i,t Is a decision variable, representing the output of the \(i\)-th thermal power plant at time \(t\); \(q\) i,t Is a binary variable, representing the start-up state of the \(i\)-th unit at time \(t\), 0 means the unit is shut down, and 1 means the unit is operating; Is the start-up cost of the \(i\)-th thermal power plant;

[0309] Among them, the formula for the wind power curtailment cost is:

[0310]

[0311] In the formula, \(T_{w}\) represents the operation duration of the wind farm; \(n_{w}\) represents the total number of controlled wind farms; \(\lambda_{w}\) is the wind power curtailment cost factor; Is the maximum power generation of the \(i\)-th wind farm at time \(t\); \(P_{w}\) i,t Is a decision variable, representing the power generation of the \(i\)-th wind farm at time \(t\);

[0312] Among them, the formula for the photovoltaic power curtailment cost is:

[0313]

[0314] In the formula, \(T_{s}\) represents the operation duration of the photovoltaic power station; \(n_{s}\) represents the total number of controlled photovoltaic power stations; \(\lambda_{s}\) is the photovoltaic power curtailment cost factor; Is the maximum power generation of the \(i\)-th photovoltaic power station at time \(t\); \(P_{s}\) i,t Is a decision variable, representing the power generation of the \(i\)-th photovoltaic power station at time \(t\);

[0315] Among them, the formula for the energy storage operation cost is:

[0316]

[0317] Wherein, Te represents the operation duration of the energy storage power station; ne represents the total number of controlled energy storage power stations; λe is the operation cost factor of the energy storage power station; SOC i,t is the average battery state of charge of the i-th energy storage power station at time t; is the maximum value of the average battery state of charge of the i-th energy storage power station at time t; is the minimum value of the average battery state of charge of the i-th energy storage power station at time t; Pess i,t is a decision variable, representing the power of the i-th energy storage power station at time t. When it is a positive number, it represents power generation. At this time, Pess i,t = Pessd i,t , when it is a negative number, it represents charging. At this time, Pess i,t = -Pessc i,t , Pessc i,t is the charging power of the i-th energy storage power station at time t, and Pessd i,t is the discharging power of the i-th energy storage power station at time t;

[0318] Correspondingly, obtaining the real-time operation data of each controlled power station in the controlled power station group includes: obtaining the real-time line loss of each controlled power station in the controlled power station group; if it is determined that the combined control instruction includes an energy storage power station, obtaining the real-time average battery state of charge, the maximum value of the average battery state of charge, and the minimum value of the average battery state of charge of each energy storage power station in the controlled power station group.

[0319] In a possible design, if the individual control instruction includes a thermal power plant, the optimization objective corresponding to the thermal power plant is to minimize the sum of the dispatching instruction tracking deviation and the operation and start-stop cost of the thermal power plant;

[0320] Among them, the formula of the optimization objective is:

[0321] min f g = E + Cg

[0322] Wherein, E is the dispatching instruction tracking deviation; Cg is the operation and start-up cost of the thermal power plant.

[0323] In a possible design, if the individual control instruction includes a wind farm, the optimization objective corresponding to the wind farm is to minimize the sum of the dispatching instruction tracking deviation and the wind power curtailment cost;

[0324] Among them, the formula of the optimization objective is:

[0325] min f w = E + Cw

[0326] In the formula, E is the scheduling instruction tracking deviation; Cw is the cost of curtailed wind power.

[0327] In a possible design, if the individual control instruction includes a photovoltaic power station, the optimization objective corresponding to the photovoltaic power station is to minimize the sum of the scheduling instruction tracking deviation and the cost of curtailed photovoltaic power;

[0328] Among them, the formula for the optimization objective is:

[0329] min f s = E + Cs

[0330] In the formula, E is the scheduling instruction tracking deviation; Cs is the cost of curtailed photovoltaic power.

[0331] In a possible design, if the individual control instruction includes an energy storage power station, the optimization objective corresponding to the energy storage power station is to minimize the sum of the scheduling instruction tracking deviation and the energy storage operation cost;

[0332] Among them, the formula for the optimization objective is:

[0333] min f e = E + Cess

[0334] In the formula, E is the scheduling instruction tracking deviation; Cess is the energy storage operation cost.

[0335] In a possible design, the scheduling instruction includes a grid scheduling instruction and a manual scheduling instruction.

[0336] The device provided in this embodiment can be used to execute the technical solution of the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0337] Figure 4 It is a schematic hardware structure diagram of the service device provided in the embodiment of the present application. As Figure 4 shown, the service device 40 in this embodiment includes: at least one processor 401 and a memory 402; the memory stores computer execution instructions; at least one processor executes the computer execution instructions stored in the memory, so that at least one processor executes the above-mentioned power control method for the power station;

[0338] Optionally, the memory 402 can be either independent or integrated with the processor 401.

[0339] When the memory 402 is independently provided, the service device further includes a bus 403 for connecting the memory 402 and the processor 401.

[0340] The embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored. When the processor executes the computer-executable instructions, the above-mentioned power control method for a station is implemented.

[0341] The embodiments of the present application further provide a computer program product, including a computer program. When the computer program is executed by a processor, the above-mentioned power control method for a station is implemented.

[0342] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0343] Furthermore, it should be noted that although the steps in the flowchart are sequentially shown according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0344] It should be understood that the above-mentioned device embodiments are illustrative, and the devices of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0345] In addition, without special description, in each embodiment of the present application, each functional unit / module can be integrated in one unit / module, or each unit / module can exist physically alone, or two or more units / modules can be integrated together. The above-mentioned integrated unit / module can be implemented in the form of hardware or in the form of a software program module.

[0346] When the integrated unit / module is implemented in the form of hardware, the hardware can be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0347] When the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. And the aforementioned memory includes: USB flash drives, read-only memory (ROM), random access memory (RAM), external hard drives, magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0348] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combinations of these technical features do not conflict, they should all be considered as within the scope described in this specification.

[0349] Other embodiments of the present application will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0350] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for controlling the power of a station, characterized in that, Applied to a service device, including: Obtain a scheduling instruction; Send the scheduling instruction to an administrator terminal so that the administrator terminal generates an operation mode instruction in response to an input operation of an administrator according to the scheduling instruction; wherein the operation mode instruction includes a separate control instruction and a combined control instruction, wherein the separate control instruction includes a plurality of controlled power stations and a target power corresponding to each controlled power station, and wherein the combined control instruction includes a controlled power station group and a target power corresponding to the controlled power station group; Receive the operation mode instruction sent by the administrator terminal; Analyze the operation mode instruction to obtain a combined control instruction, and obtain an optimization target and constraint conditions corresponding to the controlled power station group according to the combined control instruction; Update a preset optimization model according to the optimization target and constraint conditions; Obtain the real-time operation data of each controlled power station in the controlled power station group; Input the real-time operation data and the target power corresponding to the controlled power station group into the updated optimization model to obtain a to-be-processed power instruction for each controlled power station in the controlled power station group; Perform data preprocessing on the to-be-processed power instructions of each controlled power station to obtain actual power instructions for each controlled power station; Send the actual power instructions of each controlled power station to each controlled power station in the controlled power station group respectively; Analyze the operation mode instruction to obtain the separate control instruction, and for each controlled power station in the separate control instruction, perform the following steps: Obtain the optimization target and constraint conditions corresponding to the controlled power station; Update a preset optimization model according to the optimization target and constraint conditions; Obtain the real-time operation data of the controlled power station; Input the real-time operation data of the controlled power station and the target power corresponding to the controlled power station into the updated optimization model to obtain a to-be-processed power instruction for the controlled power station; Perform data preprocessing on the to-be-processed power instruction of the controlled power station to obtain the actual power instruction of the controlled power station; Send the actual power instruction to the controlled power station.

2. The method according to claim 1, wherein The sending the scheduling instruction to the administrator terminal includes: Perform approval verification on the scheduling instruction to generate a verification result; If the verification result is verification passed, send the scheduling instruction to the administrator terminal.

3. The method according to claim 1, wherein The performing data preprocessing on the to-be-processed power instructions of each controlled power station to obtain actual power instructions for each controlled power station includes: Perform data verification on the to-be-processed power instructions of each controlled power station to obtain a depolarized power instruction for each controlled power station; Perform amplitude limiting and speed limiting processing on the depolarized power instructions of each controlled power station to obtain actual power instructions for each controlled power station.

4. The method according to claim 1, wherein The scheduling instruction is a scheduling instruction for an integrated wind-solar-thermal-energy-storage power plant.

5. The method according to claim 4, characterized in that If the combined control instruction or the separate control instruction includes a thermal power plant, the constraint conditions include upper and lower limits constraints on the output of the thermal power plant and a ramp rate constraint on the thermal power plant: Wherein, the formula for the upper and lower limits constraints on the output of the thermal power plant is: where, Pg i,t represents the power generation of the i-th thermal power plant at time t; represents the upper limit of the power generation of the i-th thermal power plant at time t; represents the lower limit of the power generation of the i-th thermal power plant at time t; Wherein, the formula for the ramp rate constraint on the thermal power plant is: -P i down ≤Pg i,t -Pg i,t-1 ≤P i up In the formula, P i down represents the maximum downhill rate of the i-th thermal power plant at two adjacent moments; P i up represents the maximum uphill rate of the i-th thermal power plant at two adjacent moments; Correspondingly, the obtaining the real-time operation data of each controlled power station in the controlled power station group includes: Obtain the upper limit of the power generation capacity, the lower limit of the power generation capacity, the maximum downhill rate between two adjacent moments, and the maximum uphill rate between two adjacent moments of each thermal power plant; Correspondingly, the obtaining of the real-time operation data of the controlled power station includes: Obtain the upper limit of the power generation capacity, the lower limit of the power generation capacity, the maximum downhill rate between two adjacent moments, and the maximum uphill rate between two adjacent moments of each thermal power plant.

6. The method according to claim 4, characterized in that If the combined control instruction or the individual control instruction includes a wind farm, the constraint conditions include the upper and lower limits of the wind power constraint; Among them, the formula for the upper and lower limits of the wind power constraint is: where, Pw i,t is the power generation of the i-th wind farm at time t; is the upper limit of the power generation of the i-th wind farm at time t; Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: Obtain the upper and lower limits of the wind power of each wind farm; Correspondingly, the obtaining of the real-time operation data of the controlled power station includes: Obtain the upper and lower limits of the wind power of each wind farm.

7. The method according to claim 4, wherein If the combined control instruction or the individual control instruction includes a photovoltaic power station, the constraint conditions include the upper and lower limits of the photovoltaic power constraint; Among them, the formula for the upper and lower limits of the photovoltaic power constraint is: where, Ps i,t is the power generation of the i-th PV power station at time t; is the upper limit of the power generation of the i-th PV power station at time t; Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: Obtain the upper and lower limits of the photovoltaic power of each photovoltaic power station; Correspondingly, the obtaining of the real-time operation data of the controlled power station includes: Obtain the upper and lower limits of the photovoltaic power of each photovoltaic power station.

8. The method according to claim 4, characterized in that, If the combined control instruction or the individual control instruction includes an energy storage power station, the constraint conditions include the energy storage power constraint and the energy storage battery state of health constraint; Among them, the formula for the energy storage power constraint is: Where, Pessc i,t is the charging power of the i-th energy storage power station at time t; is the maximum charging power of the i-th energy storage power station at time t; is the minimum charging power of the i-th energy storage power station at time t; Pessd i,t is the discharging power of the i-th energy storage power station at time t; is the maximum discharging power of the i-th energy storage power station at time t; is the minimum discharging power of the i-th energy storage power station at time t; Among them, the formula for the energy storage battery state of health constraint is: where SOC i,t is the average battery state of charge of the i-th energy storage power station at time t; is the maximum value of the average battery state of charge of the i-th energy storage power station at time t; is the minimum value of the average battery state of charge of the i-th energy storage power station at time t; Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: Obtain the maximum charging power, the minimum charging power, the maximum discharging power, the minimum discharging power, the maximum average state of charge of the battery, and the minimum average state of charge of the battery of each energy storage power station; Correspondingly, the obtaining of the real-time operation data of the controlled power station includes: Obtain the maximum charging power, the minimum charging power, the maximum discharging power, the minimum discharging power, the maximum average state of charge of the battery, and the minimum average state of charge of the battery of each energy storage power station.

9. The method according to claim 4, characterized in that, The optimization objective corresponding to the combined control instruction is to minimize the sum of the scheduling instruction tracking deviation and the costs of each controlled power station in the combined control instruction; Among them, the formula for the optimization objective is: minf c = E + θ g Cg + θ w Cw + θ s Cs + θ ess Cess where θ g , θ w , θ s , θ ess are binary variables, indicating whether a thermal power plant, a wind farm, a PV power station, and an energy storage power station participate in combined control. When it is 0, it means the corresponding power station does not participate in combined control; when it is 1, it means the corresponding power station participates in combined control; E is the tracking deviation of the dispatching instruction; Cg is the operation and start-up cost of the thermal power plant; Cw is the cost of wind power curtailment; Cs is the cost of PV power curtailment; Cess is the operation cost of the energy storage. Among them, the formula for the scheduling instruction tracking deviation is: Where n is the total number of thermal power plants, wind farms, photovoltaic power stations or energy storage power stations that are independently controlled; Pcmd i,t represents the dispatching instruction value of the i-th power station that is independently controlled at time t; Pact i,t is a decision variable, representing the actual output of the i-th power station at time t; Pl i,t represents the line loss of the i-th power station at time t; Among them, the formula for the operation and start-up cost of the thermal power plant is: Where, Tg represents the operating duration of the thermal power plant; ng represents the total number of controlled thermal power plants; a i Pg i,t 2 +b i Pg i,t +c i represents the operating cost of the i-th thermal power plant, a i 、b i 、c i are the operating cost function coefficients of the i-th thermal power plant, Pg i,t is the decision variable, representing the output of the i-th thermal power plant at time t; q i,t is a binary variable, representing the starting state of the i-th unit at time t. When it is 0, it means the unit is shut down, and when it is 1, it means the unit is operating; is the starting cost of the i-th thermal power plant; Among them, the formula for the wind power curtailment cost is: Where Tw represents the operating duration of the wind farm; nw represents the total number of controlled wind farms; λw is the curtailment cost factor of the wind farm; is the maximum power generation of the i-th wind farm at time t; Pw i,t is a decision variable representing the power generation of the i-th wind farm at time t; Among them, the formula for the photovoltaic power curtailment cost is: Wherein, Ts represents the operation duration of the PV power station; ns represents the total number of controlled PV power stations; λs is the curtailment cost factor of the PV power station; is the maximum power generation of the i-th PV power station at time t; Ps i,t is a decision variable, representing the power generation of the i-th PV power station at time t; Among them, the formula for the energy storage operation cost is: where Te represents the operating duration of the energy storage power station; ne represents the total number of controlled energy storage power stations; λe is the operating cost factor of the energy storage power station; SOC i,t is the average battery state of charge of the i-th energy storage power station at time t; is the maximum value of the average battery state of charge of the i-th energy storage power station at time t; is the minimum value of the average battery state of charge of the i-th energy storage power station at time t; Pess i,t is a decision variable representing the power of the i-th energy storage power station at time t. When it is positive, it represents power generation, and at this time Pess i,t = Pessd i,t When it is negative, it represents charging, and at this time Pess i,t = -Pessc i,t Pessc i,t is the charging power of the i-th energy storage power station at time t, and Pessd i,t is the discharging power of the i-th energy storage power station at time t; Correspondingly, the obtaining of the real-time operation data of each controlled power station in the controlled power station group includes: Obtain the real-time line loss of each controlled power station in the controlled power station group; If it is determined that the combined control instruction includes an energy storage power station, obtain the real-time average state of charge of the battery, the maximum average state of charge of the battery, and the minimum average state of charge of the battery of each energy storage power station in the controlled power station group.

10. The method according to claim 4, characterized in that If the individual control instruction includes a thermal power plant, the optimization objective corresponding to the thermal power plant is to minimize the sum of the scheduling instruction tracking deviation and the operation and start-stop costs of the thermal power plant; Among them, the formula for the optimization objective is: minf g = E + Cg In the formula, E is the scheduling instruction tracking deviation; Cg is the operation and start-up cost of the thermal power plant.

11. The method according to claim 4, wherein If the individual control instruction includes a wind farm, the optimization objective corresponding to the wind farm is to minimize the sum of the scheduling instruction tracking deviation and the wind power curtailment cost; Among them, the formula for the optimization objective is: minf w = E + Cw In the formula, E is the scheduling instruction tracking deviation; Cw is the wind power curtailment cost.

12. The method according to claim 4, wherein If the individual control instruction includes a photovoltaic power station, the optimization objective corresponding to the photovoltaic power station is to minimize the sum of the scheduling instruction tracking deviation and the photovoltaic power curtailment cost; Among them, the formula for the optimization objective is: minf s = E + Cs In the formula, E is the scheduling instruction tracking deviation; Cs is the photovoltaic power curtailment cost.

13. The method according to claim 4, wherein If the individual control instruction includes an energy storage power station, the optimization objective corresponding to the energy storage power station is to minimize the sum of the scheduling instruction tracking deviation and the energy storage operation cost; Among them, the formula for the optimization objective is: minf e = E + Cess In the formula, E is the scheduling instruction tracking deviation; Cess is the energy storage operation cost.

14. The method according to any one of claims 1 to 13, characterized in that The scheduling instruction includes a power grid scheduling instruction and a manual scheduling instruction.

15. A station power control device, characterized in that, Applied to a service device, it includes: An acquisition module, configured to acquire a scheduling instruction; A sending module, configured to send the scheduling instruction to an administrator terminal, so that the administrator terminal generates an operation mode instruction in response to an input operation of the administrator according to the scheduling instruction; wherein the operation mode instruction includes an individual control instruction and a combined control instruction, wherein the individual control instruction includes a plurality of controlled power stations and a target power corresponding to each controlled power station, and wherein the combined control instruction includes a group of controlled power stations and a target power corresponding to the group of controlled power stations; A receiving module, configured to receive the operation mode instruction sent by the administrator terminal; An analysis module, configured to analyze the operation mode instruction to obtain a combined control instruction, and obtain an optimization objective and constraint conditions corresponding to the group of controlled power stations according to the combined control instruction; An update module, configured to update a preset optimization model according to the optimization objective and the constraint conditions; The acquisition module is further configured to acquire real-time operation data of each controlled power station in the group of controlled power stations; An input module, configured to input the real-time operation data and the target power corresponding to the group of controlled power stations into the updated optimization model to obtain a to-be-processed power instruction for each controlled power station in the group of controlled power stations; A data processing module, configured to perform data preprocessing on the to-be-processed power instructions of the controlled power stations to obtain actual power instructions for the controlled power stations; The sending module is further configured to send the actual power instructions of the controlled power stations to each controlled power station in the group of controlled power stations respectively; A separate control module, configured to parse the operation mode instruction to obtain the separate control instruction, and for each controlled power station in the separate control instruction, perform the following steps: obtain the optimization objective and constraint conditions corresponding to the controlled power station; update a preset optimization model according to the optimization objective and constraint conditions; obtain the real-time operation data of the controlled power station; input the real-time operation data of the controlled power station and the target power corresponding to the controlled power station into the updated optimization model to obtain the to-be-processed power instruction of the controlled power station; perform data preprocessing on the to-be-processed power instruction of the controlled power station to obtain the actual power instruction of the controlled power station; and send the actual power instruction to the controlled power station.

16. A service device, characterized in that, Comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the substation power control method according to any one of claims 1 to 14.

17. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when executed by the processor, the computer-executable instructions are used to implement the substation power control method according to any one of claims 1 to 14.

18. A computer program product, comprising a computer program, characterized in that, When executed by the processor, the computer program implements the method according to any one of claims 1 to 14.

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