A wind-solar-storage station reactive power optimization control method and device

Abstract

The application relates to the technical field of new energy power generation, and discloses a wind-solar-storage station reactive power optimization control method and device, which comprises the following steps: calculating the network loss of a wind-solar-storage station based on transformer parameters and line parameters; calculating the comprehensive cost of wind-solar-storage reactive power control based on the output reactive power value of the wind-solar-storage station; establishing a wind-solar-storage station reactive power optimization model based on the comprehensive cost of wind-solar-storage reactive power control, the network loss of the wind-solar-storage station and the voltage offset of the grid-connected point of the wind-solar-storage station; and solving the wind-solar-storage station reactive power optimization model to obtain the optimal control result of the wind-solar-storage station reactive power. The comprehensive cost of wind-solar-storage reactive power control is fully considered on the basis of considering the reactive power regulation capacity of wind power, photovoltaic power and energy storage in the station, the reactive power regulation capacity of wind power, photovoltaic power and energy storage is fully utilized, and the operation benefit of the wind-solar-storage station is improved.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, specifically to an optimized control method and device for reactive power in wind, solar and energy storage power storage stations. Background Technology

[0002] Compared to traditional power plants, wind, solar, and energy storage power plants, in addition to being equipped with conventional reactive power compensation devices, can also participate in reactive power control due to the four-quadrant operation characteristics of their power electronic converters. However, wind and solar power output is highly random, volatile, and intermittent. Therefore, flexible control strategies need to be developed for the reactive power regulation equipment within the wind, solar, and energy storage power plant to improve the voltage stability of the plant.

[0003] Due to the uncertainty of wind and solar power output, the design capacity of reactive power regulation equipment in current wind, solar and energy storage power plants does not take into account the reactive power regulation capabilities of wind, solar and energy storage within the plant. Furthermore, research on reactive power optimization control strategies based on this does not consider the comprehensive cost of reactive power control in wind, solar and energy storage power plants, resulting in the incomplete utilization of the reactive power regulation capacity of wind, solar and energy storage, which does not contribute to improving the overall operational efficiency of the power plant. Summary of the Invention

[0004] In view of this, the present invention provides an optimized control method and apparatus for reactive power of wind, solar and energy storage stations, in order to solve the problem that the optimized control strategy for reactive power of wind, solar and energy storage stations does not take into account the comprehensive cost of reactive power control of wind, solar and energy storage stations, so that the reactive power regulation capacity of wind power, photovoltaic and energy storage cannot be fully utilized.

[0005] In a first aspect, the present invention provides an optimized control method for reactive power at wind-solar-storage power storage stations, the method comprising:

[0006] Obtain the transformer and line parameters of the wind, solar and energy storage station, and calculate the network loss of the wind, solar and energy storage station based on the transformer and line parameters;

[0007] Obtain the output reactive power value of the wind-solar-storage power storage station, and calculate the comprehensive cost of wind-solar-storage reactive power control based on the output reactive power value of the wind-solar-storage power storage station;

[0008] Obtain the grid connection point voltage deviation of the wind-solar-storage power storage station, and establish a reactive power optimization model for the wind-solar-storage power storage station based on the comprehensive cost of reactive power control of wind-solar-storage power storage, the grid loss of the wind-solar-storage power storage station, and the grid connection point voltage deviation of the wind-solar-storage power storage station.

[0009] The reactive power optimization model of the wind-solar-storage station is solved to obtain the optimal control result of the reactive power of the wind-solar-storage station.

[0010] This embodiment provides an optimized control method for reactive power in a wind-solar-storage power station. Based on the calculation of grid losses at the wind-solar-storage power station, the comprehensive cost of reactive power control is calculated using the output reactive power value of the station. Then, based on the comprehensive cost of reactive power control, grid losses, and grid connection voltage deviation, an optimization model for reactive power control at the wind-solar-storage power station is established. This method fully considers the comprehensive cost of reactive power control while taking into account the reactive power regulation capabilities of wind power, photovoltaics, and energy storage within the station, thus fully utilizing the reactive power regulation capacity of wind power, photovoltaics, and energy storage and improving the operational efficiency of the wind-solar-storage power station.

[0011] In one optional implementation, the comprehensive cost of reactive power control for wind, solar, and energy storage systems is calculated based on the output reactive power value of the wind-solar-energy storage station, including:

[0012] Based on the output reactive power value of the wind, solar and energy storage stations, the reactive power control costs of wind power, solar power and energy storage are determined by fitting functions of the pitch system operation and maintenance cost, photovoltaic reactive power control cost and energy storage reactive power control cost respectively.

[0013] The comprehensive cost of reactive power control for wind, solar and energy storage is calculated based on the reactive power control costs of wind power, photovoltaic power and energy storage.

[0014] This embodiment provides an optimized control method for reactive power in wind, solar, and energy storage power plants. Due to the significant differences in power supply operation and control characteristics, the reactive power control costs for wind, solar, and energy storage power differ. Therefore, the method utilizes fitting functions for the operation and maintenance costs of the pitch system, photovoltaic reactive power control costs, and energy storage reactive power control costs to determine the reactive power control costs for wind, solar, and energy storage power, respectively. This fully considers the operational differences between wind, solar, and energy storage power, making the overall cost of reactive power control for wind, solar, and energy storage more accurate.

[0015] In one optional implementation, a reactive power optimization model for wind-solar-storage power storage stations is established based on the comprehensive cost of reactive power control, the grid losses of wind-solar-storage power storage stations, and the voltage deviation at the grid connection point of the wind-solar-storage power storage stations. This model includes:

[0016] Obtain the short-term power prediction value of wind and solar power, and determine the maximum adjustable reactive power value based on the output reactive power value of the wind and solar power storage station and the short-term power prediction value of wind and solar power.

[0017] Obtain the grid loss penalty coefficient and control cost penalty coefficient, and establish an optimized control objective function based on the comprehensive cost of reactive power control of wind, solar and energy storage, the grid connection point voltage deviation of wind, solar and energy storage power stations, the grid loss of wind, solar and energy storage power stations, the maximum adjustable reactive power value, the grid loss penalty coefficient and the control cost penalty coefficient;

[0018] Obtain the reactive power constraints and establish a reactive power optimization model for wind and solar power storage stations based on the optimization control objective function and reactive power constraints.

[0019] This embodiment provides an optimized control method for reactive power in wind, solar, and energy storage power plants. Based on the conventional reactive power optimization model for calculating network losses, it considers the costs brought about by wind, solar, and energy storage participating in reactive power control after optimizing the capacity configuration of reactive power compensation equipment, thereby improving the reactive power control efficiency of wind, solar, and energy storage power plants.

[0020] In one optional implementation, an optimized control objective function is established based on the comprehensive cost of reactive power control for wind, solar, and energy storage systems, the grid connection point voltage deviation of the wind, solar, and energy storage power station, the grid loss of the wind, solar, and energy storage power station, the maximum adjustable reactive power value, the grid loss penalty coefficient, and the control cost penalty coefficient; wherein, the expression of the optimized control objective function is as follows:

[0021]

[0022] In the above formula, f Q Let ΔU represent the objective function for optimizing control. PCC ΔU represents the voltage deviation at the grid connection point of the wind, solar, and energy storage station. PCC_max λ1 represents the maximum voltage deviation at the grid connection point, and P represents the network loss penalty coefficient. loss P represents the network loss of the wind-solar-storage station. loss_max λ represents the maximum value of network loss, λ2 represents the control cost penalty coefficient, and C represents the comprehensive cost of reactive power control for wind, solar, and energy storage. max This indicates the maximum adjustable reactive power value.

[0023] In one optional implementation, the reactive power constraint conditions include: equality constraints and inequality constraints, wherein the inequality constraints include wind, solar and energy storage power constraints, node voltage constraints and reactive power constraints of reactive power compensation devices.

[0024] In one optional implementation, the reactive power optimization model of the wind-solar-storage power storage station is solved to obtain the optimal control result of the reactive power of the wind-solar-storage power storage station, including:

[0025] Within the preset penalty coefficient range, the current penalty coefficient is selected sequentially as the network loss penalty coefficient and the control cost penalty coefficient, and the current penalty coefficient is input into the reactive power optimization model of the wind, solar and energy storage station to obtain multiple sets of control values ​​for wind, solar and energy storage reactive power.

[0026] By using the control value of reactive power corresponding to the minimum energy storage allocation value, the reactive power of the wind-solar-storage power storage station is optimized and controlled to obtain the optimal control result of reactive power of the wind-solar-storage power storage station.

[0027] This embodiment provides an optimized control method for reactive power of a wind-solar-storage power storage station. By inputting the current penalty coefficient into the reactive power optimization model of the wind-solar-storage power storage station, and using the control value of reactive power of wind-solar-storage power corresponding to the minimum energy storage allocation value, the reactive power of the wind-solar-storage power storage station is optimized and controlled. Based on the reactive power optimization model of the wind-solar-storage power storage station, the importance of reactive power control cost is comprehensively considered and the number of energy storage charge and discharge cycles is reduced, thereby realizing optimized reactive power control of the wind-solar-storage power storage station.

[0028] Secondly, the present invention provides an optimized control device for reactive power of a wind-solar-storage power storage station, the device comprising:

[0029] The first calculation module is used to obtain the transformer parameters and line parameters of the wind, solar and energy storage station, and calculate the network loss of the wind, solar and energy storage station based on the transformer parameters and line parameters.

[0030] The second calculation module is used to obtain the output reactive power value of the wind-solar-storage station and calculate the comprehensive cost of wind-solar-storage reactive power control based on the output reactive power value of the wind-solar-storage station.

[0031] A module is established to obtain the grid connection point voltage deviation of the wind, solar and energy storage station. Based on the comprehensive cost of reactive power control of wind, solar and energy storage, the grid loss of the wind, solar and energy storage station and the grid connection point voltage deviation of the wind, solar and energy storage station, a reactive power optimization model of the wind, solar and energy storage station is established.

[0032] The solution module is used to solve the reactive power optimization model of the wind, solar and energy storage station to obtain the optimal control result of the reactive power of the wind, solar and energy storage station.

[0033] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the optimized control method for reactive power of wind and solar power storage stations as described in the first aspect or any corresponding embodiment.

[0034] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the optimized control method for reactive power of a wind and solar power storage station according to the first aspect or any corresponding embodiment described above.

[0035] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the optimized control method for reactive power of wind and solar power storage stations according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0036] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating an optimized control method for reactive power at a wind-solar-storage power storage station according to an embodiment of the present invention.

[0038] Figure 2 This is a flowchart illustrating another method for optimizing the control of reactive power in a wind and solar power storage station according to an embodiment of the present invention.

[0039] Figure 3 This is a flowchart illustrating another method for optimizing the control of reactive power in a wind-solar-storage power storage station according to an embodiment of the present invention.

[0040] Figure 4 This is a structural block diagram of an optimized control device for reactive power at a wind and solar power storage station according to an embodiment of the present invention.

[0041] Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] There are many types of reactive power sources in wind, solar and energy storage power plants, including reactive power compensation devices such as SVG (Static Var Generator) and STATCOM (Static Synchronous Compensator), as well as wind power, photovoltaic and energy storage units (lithium-ion battery energy storage). Their reactive power regulation principles and regulation costs are somewhat different. Since a lot of research has been carried out on the capacity optimization configuration of various power sources in the power plant, the optimized configuration capacity often cannot meet the actual needs, and the cooperation of various reactive power sources is required.

[0044] This invention provides an optimized control method for reactive power in wind, solar, and energy storage power plants. Applied to server-type equipment, the method considers the optimized configuration of various reactive power sources within the power plant, taking into account the operating conditions for reactive power control. It calculates the maximum reactive power that wind, solar, and energy storage can generate for reactive power control by combining the current output power of wind and solar power with ultra-short-term power prediction results. Based on this, the cost of wind, solar, and energy storage participating in reactive power optimization control is calculated, and a reactive power optimization model for the wind, solar, and energy storage power plant is established to obtain the optimal control scheme, thus achieving optimized reactive power control for the wind, solar, and energy storage power plant.

[0045] According to an embodiment of the present invention, an embodiment of an optimized control method for reactive power of a wind and solar power storage station is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0046] This embodiment provides an optimized control method for reactive power in wind and solar power storage stations, which can be used for the aforementioned server-type equipment. Figure 1 This is a flowchart of an optimized control method for reactive power at a wind-solar-storage power storage station according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0047] Step S101: Obtain the transformer parameters and line parameters of the wind-solar-storage station, and calculate the network loss of the wind-solar-storage station based on the transformer parameters and line parameters.

[0048] Specifically, the network loss P of the wind-solar-storage station loss The calculation formula is as follows:

[0049]

[0050] In the above formula, N T This indicates the number of transformer nodes within the wind, solar, and energy storage facility, where m represents the number of transformer nodes and G represents the number of transformer nodes. Tm U represents the conductance of the m-th transformer node. i N represents the voltage at the m-th transformer node. L R represents the number of lines within the wind and solar power storage station, where n represents the node where the line is located. n U represents the resistance of the nth line. n P represents the voltage at the nth node. n Q n These represent the active power and reactive power injected at the nth node, respectively.

[0051] Step S102: Obtain the output reactive power value of the wind-solar-storage station, and calculate the comprehensive cost of wind-solar-storage reactive power control based on the output reactive power value of the wind-solar-storage station.

[0052] Step S103: Obtain the grid connection point voltage offset of the wind-solar-storage power storage station, and establish a reactive power optimization model for the wind-solar-storage power storage station based on the comprehensive cost of reactive power control of wind-solar-storage power storage, the grid loss of the wind-solar-storage power storage station, and the grid connection point voltage offset of the wind-solar-storage power storage station.

[0053] Step S104: Solve the reactive power optimization model of the wind-solar-storage station to obtain the optimal control result of the reactive power of the wind-solar-storage station.

[0054] Specifically, due to the obvious anti-peak-shaving characteristics of wind and solar power, the power grid will frequently issue power curtailment dispatch orders, resulting in some wind and solar curtailment. Under this operating condition, wind and solar power can reduce their output active power and increase their output reactive power to provide reactive power support capabilities, in coordination with reactive power compensation devices. When wind and solar power generation units operate in MPPT (Maximum Power Point Tracking) control mode, energy storage power generation units can discharge and output reactive power under the control of the grid-connected converter, in coordination with reactive power compensation devices.

[0055] Furthermore, due to the uncertainty of wind and solar power output, the optimal control scheme can be obtained by solving the reactive power optimization model of the wind-solar-storage station, thereby realizing the reactive power optimization control of the wind-solar-storage station.

[0056] This embodiment provides an optimized control method for reactive power in a wind-solar-storage power station. Based on the calculation of grid losses at the wind-solar-storage power station, the comprehensive cost of reactive power control is calculated using the output reactive power value of the station. Then, based on the comprehensive cost of reactive power control, grid losses, and grid connection voltage deviation, an optimization model for reactive power control at the wind-solar-storage power station is established. This method fully considers the comprehensive cost of reactive power control while taking into account the reactive power regulation capabilities of wind power, photovoltaics, and energy storage within the station, thus fully utilizing the reactive power regulation capacity of wind power, photovoltaics, and energy storage and improving the operational efficiency of the wind-solar-storage power station.

[0057] This embodiment provides an optimized control method for reactive power in wind and solar power storage stations, which can be used for the aforementioned server-type equipment. Figure 2 This is a flowchart of an optimized control method for reactive power at a wind-solar-storage power storage station according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0058] Step S201: Obtain the transformer and line parameters of the wind-solar-storage power station, and calculate the network loss of the wind-solar-storage power station based on the transformer and line parameters. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0059] Step S202: Obtain the output reactive power value of the wind-solar-storage station, and calculate the comprehensive cost of wind-solar-storage reactive power control based on the output reactive power value of the wind-solar-storage station.

[0060] Specifically, although the reactive power control devices for wind power, photovoltaic power, and energy storage are all power electronic converters, their active power control costs differ due to significant differences in power supply operation and control characteristics. Therefore, it is necessary to evaluate the reactive power control costs of wind, photovoltaic, and energy storage separately.

[0061] Specifically, step S202 includes:

[0062] Step S2021: Based on the output reactive power value of the wind, solar and energy storage stations, determine the wind power reactive power control cost, photovoltaic reactive power control cost and energy storage reactive power control cost respectively using the fitting function of the pitch system operation and maintenance cost, the fitting function of the photovoltaic reactive power control cost and the fitting function of the energy storage reactive power control cost.

[0063] Specifically, wind turbines normally operate under MPPT control, meaning they do not generate reactive power. For wind power to perform reactive power control, it must meet active power dispatching requirements. This means that during power curtailment, the turbine's output power is increased by adjusting the pitch angle. The turbine's grid-connected converter adjusts the output power factor to provide the necessary reactive power control while meeting active power control commands. The larger the capacity required for reactive power control, the greater the fluctuation in turbine output power and pitch angle, thus increasing the turbine's operation and maintenance costs. Simultaneously, the turbine's active power losses also increase accordingly. Therefore, the cost of wind power reactive power control mainly stems from the operation and maintenance of the pitch system and power losses, both of which are positively correlated with the magnitude of the turbine's reactive power output. Therefore, the cost of wind power reactive power control, C... Wind It can be represented as:

[0064] C Wind =f W (Q Wind (2)

[0065] In the above formula, Q Wind f represents the reactive power output of the wind power plant. W The function for fitting the maintenance cost of the pitch system can be obtained from data provided by the manufacturer.

[0066] Furthermore, photovoltaic (PV) power generation units primarily control their output power through power electronic converters, normally operating in MPPT control mode. For PV to perform reactive power control, it must meet the requirements of active power dispatch instructions, i.e., by controlling the power factor of a portion of the PV inverters to ensure that the magnitude of both active and reactive power output from the PV meets power control requirements. Since the cost of PV reactive power control mainly stems from power losses and is positively correlated with the magnitude of reactive power output from the wind turbine, the cost C of PV reactive power control is... PV It can be represented as:

[0067] C PV =f PV (Q PV (3)

[0068] In the above formula, Q PV f represents the reactive power output of the photovoltaic system. PV This is the fitting function for the photovoltaic reactive power control cost, which can be obtained from data provided by the manufacturer.

[0069] Furthermore, the control principle of energy storage is similar to that of photovoltaics. Both processes incur costs due to power losses during charging and discharging. Therefore, the reactive power control cost of energy storage can be expressed as:

[0070] C S =f S (Q S (4)

[0071] In the above formula, Q S f represents the reactive power output of the energy storage system. S This is the fitting function for the reactive power control cost of energy storage, which can be obtained from data provided by the manufacturer.

[0072] Step S2022: Calculate the comprehensive cost of reactive power control for wind, solar and energy storage based on the reactive power control costs of wind power, photovoltaic power and energy storage.

[0073] Specifically, the formula for calculating the comprehensive cost C of reactive power control for wind, solar, and energy storage is as follows:

[0074] C = f W (Q Wind )+f PV (Q PV )+f S (Q S (5)

[0075] Step S203: Obtain the grid connection point voltage deviation of the wind-solar-storage power storage station. Based on the comprehensive cost of reactive power control for wind-solar-storage power storage, the grid loss of the wind-solar-storage power storage station, and the grid connection point voltage deviation, establish a reactive power optimization model for the wind-solar-storage power storage station. For details, please refer to [link to details]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0076] Step S204 involves solving the reactive power optimization model for the wind-solar-storage power storage station to obtain the optimal control result for the reactive power of the station. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0077] This embodiment provides an optimized control method for reactive power in wind, solar, and energy storage power plants. Due to the significant differences in power supply operation and control characteristics, the reactive power control costs for wind, solar, and energy storage power differ. Therefore, the method utilizes fitting functions for the operation and maintenance costs of the pitch system, photovoltaic reactive power control costs, and energy storage reactive power control costs to determine the reactive power control costs for wind, solar, and energy storage power, respectively. This fully considers the operational differences between wind, solar, and energy storage power, making the overall cost of reactive power control for wind, solar, and energy storage more accurate.

[0078] This embodiment provides an optimized control method for reactive power in wind and solar power storage stations, which can be used for the aforementioned server-type equipment. Figure 3 This is a flowchart of an optimized control method for reactive power at a wind-solar-storage power storage station according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0079] Step S301: Obtain the transformer and line parameters of the wind-solar-storage power station, and calculate the network loss of the wind-solar-storage power station based on the transformer and line parameters. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0080] Step S302: Obtain the output reactive power value of the wind-solar-storage power storage station, and calculate the comprehensive cost of reactive power control based on the output reactive power value of the wind-solar-storage power storage station. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0081] Step S303: Obtain the grid connection point voltage deviation of the wind-solar-storage power storage station, and establish a reactive power optimization model for the wind-solar-storage power storage station based on the comprehensive cost of reactive power control of wind-solar-storage power storage, the grid loss of the wind-solar-storage power storage station, and the grid connection point voltage deviation of the wind-solar-storage power storage station.

[0082] Specifically, step S303 includes:

[0083] Step S3031: Obtain the wind and solar ultra-short-term power prediction value, and determine the maximum adjustable reactive power value based on the output reactive power value of the wind, solar and energy storage station and the wind and solar ultra-short-term power prediction value.

[0084] Specifically, the adjustable reactive power value is determined based on the difference between the output reactive power value of the wind-solar-storage station and the ultra-short-term power prediction value of wind and solar power, and then the maximum adjustable reactive power value is selected.

[0085] Step S3032: Obtain the network loss penalty coefficient and the control cost penalty coefficient. Based on the comprehensive cost of reactive power control of wind, solar and energy storage, the voltage deviation at the grid connection point of the wind, solar and energy storage power station, the network loss of the wind, solar and energy storage power station, the maximum adjustable reactive power value, the network loss penalty coefficient and the control cost penalty coefficient, establish an optimized control objective function.

[0086] Specifically, the expression for the optimization control objective function is as follows:

[0087]

[0088] In the above formula, f Q Let ΔU represent the objective function for optimizing control. PCC ΔU represents the voltage deviation at the grid connection point of the wind, solar, and energy storage station. PCC_max λ1 represents the maximum voltage deviation at the grid connection point, and P represents the network loss penalty coefficient. loss P represents the network loss of the wind-solar-storage station. loss_max λ represents the maximum value of network loss, λ2 represents the control cost penalty coefficient, and C represents the comprehensive cost of reactive power control for wind, solar, and energy storage. max This indicates the maximum adjustable reactive power value.

[0089] Step S3033: Obtain reactive power constraints and establish a reactive power optimization model for the wind-solar-storage station based on the optimization control objective function and reactive power constraints.

[0090] Specifically, reactive power constraints include equality constraints and inequality constraints. Inequality constraints include wind, solar and energy storage power constraints, node voltage constraints, and reactive power constraints of reactive power compensation devices (SVG).

[0091] Furthermore, the equality constraints satisfy the power flow equations of the wind-solar-storage station, and the equality constraints are expressed as follows:

[0092]

[0093] In the above formula, P i Q i U represents the active power and reactive power injected into node i, respectively; i U j G represents the voltage magnitudes at nodes i and j, respectively; ij Bij δ represents the conductance and susceptance between nodes i and j, respectively; ij N represents the phase angle difference between nodes i and j; N represents the number of branches of the station (group), i.e., the number of nodes.

[0094] Furthermore, the inequality constraints are expressed as follows:

[0095]

[0096] In the above formula, P Wind_min P represents the active power output of the wind turbine group. Wind_min Q Wind_min P represents the minimum values ​​of active power and reactive power output from the wind turbine group, respectively. Wind_max Q Wind_max P represents the maximum values ​​of the active power and reactive power output of the wind turbine group, respectively. PV P represents the active power output of the photovoltaic (PV) cluster. PV_min Q PV_min P represents the minimum active power and reactive power output of the photovoltaic power unit, respectively. PV_max Q PV_max P represents the maximum value of the active power and reactive power output of the photovoltaic power group, respectively. S P represents the active power output of the energy storage group. S_min Q S_min P represents the minimum active and reactive power output of the energy storage group, respectively; S_max Q S_max P represents the maximum values ​​of the active power and reactive power output of the energy storage group, respectively; S_min Q S_min Q represents the minimum active and reactive power output of the energy storage group, respectively; SVG Q represents the output reactive power value of the SVG device; SVG_min Q SVG_max These represent the minimum and maximum values ​​of the reactive power output by the SVG device, respectively.

[0097] Step S304: Solve the reactive power optimization model of the wind-solar-storage station to obtain the optimal control result of the reactive power of the wind-solar-storage station.

[0098] Specifically, step S304 includes:

[0099] Step S3041: Select the current penalty coefficient as the network loss penalty coefficient and the control cost penalty coefficient in sequence within the preset penalty coefficient range, and input the current penalty coefficient into the reactive power optimization model of the wind-solar-storage power station to obtain multiple sets of control values ​​for wind-solar-storage reactive power.

[0100] For example, the preset penalty coefficient range is [0.3, 0.7]. Within the range of [0.3, 0.7], λ1 and λ2 are selected and input sequentially at intervals of 0.05. The reactive power optimization model of the wind-solar-storage power station is used to calculate the reactive power control values ​​of wind power, photovoltaic power, and energy storage, i.e., Q. Wind Q PV and Q S .

[0101] Step S3042: Optimize the reactive power control of the wind-solar-storage power storage station using the control value of the reactive power corresponding to the minimum energy storage allocation value, and obtain the optimal control result of the reactive power of the wind-solar-storage power storage station.

[0102] Specifically, select a set of Q values ​​when the energy storage allocation value is minimized (i.e., the minimum energy storage reactive power control value). Wind Q PV and Q S The final reactive power optimization allocation result of the wind, solar, and energy storage turbine cluster is used to allocate reactive power to the wind, solar, and energy storage power storage station, thereby obtaining the optimal control result of reactive power of the wind, solar, and energy storage power storage station.

[0103] This embodiment provides an optimized control method for reactive power in wind-solar-storage power storage stations. Based on the conventional reactive power optimization model calculating network losses, it considers the costs associated with wind, solar, and storage participation in reactive power control after optimizing the capacity configuration of reactive power compensation equipment, thereby improving the reactive power control efficiency of wind-solar-storage power storage stations. Secondly, by inputting the current penalty coefficient into the reactive power optimization model of the wind-solar-storage power storage station and using the control value of the reactive power corresponding to the minimum energy storage allocation value, the reactive power of the wind-solar-storage power storage station is optimized. Based on the reactive power optimization model of the wind-solar-storage power storage station, the method comprehensively considers the importance of reactive power control costs and reduces the number of energy storage charge-discharge cycles, thus achieving optimized reactive power control of the wind-solar-storage power storage station.

[0104] This embodiment also provides an optimized control device for reactive power of a wind-solar-storage power storage station. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0105] This embodiment provides an optimized control device for reactive power at a wind-solar-storage power storage station, such as... Figure 4 As shown, it includes:

[0106] The first calculation module 401 is used to obtain the transformer parameters and line parameters of the wind, solar and energy storage station, and calculate the network loss of the wind, solar and energy storage station based on the transformer parameters and line parameters.

[0107] The second calculation module 402 is used to obtain the output reactive power value of the wind-solar-storage station and calculate the comprehensive cost of wind-solar-storage reactive power control based on the output reactive power value of the wind-solar-storage station.

[0108] Module 403 is established to obtain the grid connection point voltage deviation of the wind-solar-storage station. Based on the comprehensive cost of reactive power control of wind-solar-storage station, the grid loss of wind-solar-storage station, and the grid connection point voltage deviation of wind-solar-storage station, a reactive power optimization model of wind-solar-storage station is established.

[0109] The solver module 404 is used to solve the reactive power optimization model of the wind-solar-storage station to obtain the optimal control result of the reactive power of the wind-solar-storage station.

[0110] In some alternative implementations, the second computing module 402 includes:

[0111] The first determining unit is used to determine the wind power reactive power control cost, photovoltaic reactive power control cost and energy storage reactive power control cost based on the output reactive power value of the wind, solar and energy storage stations, using the fitting function of the pitch system operation and maintenance cost, the fitting function of the photovoltaic reactive power control cost and the fitting function of the energy storage reactive power control cost respectively.

[0112] The calculation unit is used to calculate the comprehensive cost of reactive power control for wind, solar and energy storage based on the reactive power control costs of wind power, photovoltaic power and energy storage.

[0113] In some alternative implementations, the establishment module 403 includes:

[0114] The second determining unit is used to obtain the wind and solar ultra-short-term power prediction value and determine the maximum adjustable reactive power value based on the output reactive power value of the wind and solar storage station and the wind and solar ultra-short-term power prediction value.

[0115] The first unit is used to obtain the network loss penalty coefficient and the control cost penalty coefficient. Based on the comprehensive cost of reactive power control of wind, solar and energy storage, the voltage deviation of the grid connection point of the wind, solar and energy storage station, the network loss of the wind, solar and energy storage station, the maximum adjustable reactive power value, the network loss penalty coefficient and the control cost penalty coefficient, the optimized control objective function is established.

[0116] The second unit is used to obtain reactive power constraints and establish a reactive power optimization model for wind and solar power storage stations based on the optimization control objective function and reactive power constraints.

[0117] In some optional implementations, the expression for the optimization control objective function in the first establishment unit is as follows:

[0118]

[0119] In the above formula, fQ Let ΔU represent the objective function for optimizing control. PCC ΔU represents the voltage deviation at the grid connection point of the wind, solar, and energy storage station. PCC_max λ1 represents the maximum voltage deviation at the grid connection point, and P represents the network loss penalty coefficient. loss P represents the network loss of the wind-solar-storage station. loss_max λ represents the maximum value of network loss, λ2 represents the control cost penalty coefficient, and C represents the comprehensive cost of reactive power control for wind, solar, and energy storage. max This indicates the maximum adjustable reactive power value.

[0120] In some optional implementations, the reactive power constraints in the second establishment unit include: equality constraints and inequality constraints, wherein the inequality constraints include wind, solar and energy storage power constraints, node voltage constraints and reactive power constraints of the reactive power compensation device.

[0121] In some alternative implementations, the solver module 404 includes:

[0122] The selection unit is used to sequentially select the current penalty coefficient as the network loss penalty coefficient and the control cost penalty coefficient within the preset penalty coefficient range, and input the current penalty coefficient into the reactive power optimization model of the wind, solar and energy storage station to obtain multiple sets of control values ​​for wind, solar and energy storage reactive power.

[0123] The optimization control unit is used to optimize the reactive power control of the wind, solar and energy storage power storage station by using the control value of the reactive power corresponding to the minimum energy storage allocation value, so as to obtain the optimal control result of the reactive power of the wind, solar and energy storage power storage station.

[0124] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0125] In this embodiment, the reactive power optimization control device for a wind and solar power storage station is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0126] This invention also provides a computer device having the above-described features. Figure 4 The diagram shows an optimized control device for reactive power at a wind and solar power storage station.

[0127] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 5As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0128] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0129] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0130] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0131] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0132] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0133] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0134] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0135] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An optimized control method for reactive power in a wind-solar-storage power storage station, characterized in that, The method includes: Obtain the transformer parameters and line parameters of the wind, solar and energy storage station, and calculate the network loss of the wind, solar and energy storage station based on the transformer parameters and line parameters; Obtain the output reactive power value of the wind-solar-storage station, and calculate the comprehensive cost of wind-solar-storage reactive power control based on the output reactive power value of the wind-solar-storage station; Obtain the grid connection point voltage deviation of the wind-solar-storage power storage station, and establish a reactive power optimization model for the wind-solar-storage power storage station based on the comprehensive cost of the reactive power control of the wind-solar-storage power storage station, the grid loss of the wind-solar-storage power storage station, and the grid connection point voltage deviation of the wind-solar-storage power storage station. The reactive power optimization model of the wind-solar-storage station is solved to obtain the optimal control result of the reactive power of the wind-solar-storage station; The establishment of a reactive power optimization model for wind-solar-storage power storage stations based on the comprehensive cost of reactive power control, the grid loss of the wind-solar-storage power storage stations, and the voltage deviation at the grid connection point of the wind-solar-storage power storage stations includes: Obtain the wind and solar ultra-short-term power prediction value, and determine the maximum adjustable reactive power value based on the output reactive power value of the wind and solar energy storage station and the wind and solar ultra-short-term power prediction value. Obtain the network loss penalty coefficient and the control cost penalty coefficient, and establish an optimized control objective function based on the comprehensive cost of reactive power control of wind, solar and energy storage, the grid connection point voltage deviation of the wind, solar and energy storage power station, the network loss of the wind, solar and energy storage power station, the maximum adjustable reactive power value, the network loss penalty coefficient and the control cost penalty coefficient; Obtain the reactive power constraints, and establish the reactive power optimization model of the wind and solar power storage station based on the optimization control objective function and the reactive power constraints; Solving the reactive power optimization model of the wind-solar-storage station to obtain the optimal control result of the reactive power of the wind-solar-storage station includes: Within a preset penalty coefficient range, the current penalty coefficient is selected sequentially as the network loss penalty coefficient and the control cost penalty coefficient, and the current penalty coefficient is input into the reactive power optimization model of the wind, solar and energy storage station to obtain multiple sets of control values ​​for wind, solar and energy storage reactive power. The reactive power of the wind-solar-storage power storage station is optimized by using the control value of the reactive power corresponding to the minimum energy storage allocation value, so as to obtain the optimal control result of the reactive power of the wind-solar-storage power storage station.

2. The method according to claim 1, characterized in that, The comprehensive cost of wind-solar-storage reactive power control calculated based on the output reactive power value of the wind-solar-storage power station includes: Based on the output reactive power value of the wind, solar and energy storage station, the reactive power control cost of wind power, reactive power control cost of photovoltaic power and reactive power control cost of energy storage are determined by fitting functions of pitch system operation and maintenance cost, photovoltaic reactive power control cost and energy storage reactive power control cost respectively. The comprehensive cost of wind-solar-storage reactive power control is calculated based on the wind power reactive power control cost, the photovoltaic reactive power control cost, and the energy storage reactive power control cost.

3. The method according to claim 1, characterized in that, An optimized control objective function is established based on the comprehensive cost of reactive power control of the wind-solar-storage power station, the grid connection point voltage deviation of the wind-solar-storage power station, the grid loss of the wind-solar-storage power station, the maximum adjustable reactive power value, the grid loss penalty coefficient, and the control cost penalty coefficient; wherein, the expression of the optimized control objective function is as follows: In the above formula, This represents the optimization control objective function. This indicates the voltage deviation at the grid connection point of the wind, solar, and energy storage station. This indicates the maximum value of the voltage offset at the grid connection point. This represents the network loss penalty coefficient. This indicates the network loss of the wind, solar and energy storage station. This represents the maximum value of network loss. This represents the penalty coefficient for controlling costs. This represents the comprehensive cost of reactive power control in wind, solar, and energy storage systems. This indicates the maximum adjustable reactive power value.

4. The method according to claim 1, characterized in that, The reactive power constraints include equality constraints and inequality constraints. The inequality constraints include wind, solar and energy storage power constraints, node voltage constraints, and reactive power constraints of reactive power compensation devices.

5. An optimized control device for reactive power in a wind-solar-storage power storage station, characterized in that, The device includes: The first calculation module is used to obtain the transformer parameters and line parameters of the wind, solar and energy storage station, and calculate the network loss of the wind, solar and energy storage station based on the transformer parameters and line parameters. The second calculation module is used to obtain the output reactive power value of the wind-solar-storage station and calculate the comprehensive cost of wind-solar-storage reactive power control based on the output reactive power value of the wind-solar-storage station. A module is established to obtain the grid connection point voltage deviation of the wind, solar and energy storage station. Based on the comprehensive cost of the reactive power control of the wind, solar and energy storage station, the grid loss of the wind, solar and energy storage station and the grid connection point voltage deviation of the wind, solar and energy storage station, a reactive power optimization model of the wind, solar and energy storage station is established. The solution module is used to solve the reactive power optimization model of the wind-solar-storage station to obtain the optimal control result of the reactive power of the wind-solar-storage station. The module to be built includes: The second determining unit is used to obtain the wind and solar ultra-short-term power prediction value and determine the maximum adjustable reactive power value based on the output reactive power value of the wind and solar storage station and the wind and solar ultra-short-term power prediction value. The first unit is used to obtain the network loss penalty coefficient and the control cost penalty coefficient. Based on the comprehensive cost of reactive power control of wind, solar and energy storage, the voltage deviation of the grid connection point of the wind, solar and energy storage station, the network loss of the wind, solar and energy storage station, the maximum adjustable reactive power value, the network loss penalty coefficient and the control cost penalty coefficient, the optimized control objective function is established. The second establishment unit is used to obtain reactive power constraints and establish a reactive power optimization model for wind and solar power storage stations based on the optimization control objective function and reactive power constraints. The solution module includes: The selection unit is used to sequentially select the current penalty coefficient as the network loss penalty coefficient and the control cost penalty coefficient within the preset penalty coefficient range, and input the current penalty coefficient into the reactive power optimization model of the wind, solar and energy storage station to obtain multiple sets of control values ​​for wind, solar and energy storage reactive power. The optimization control unit is used to optimize the reactive power control of the wind, solar and energy storage power storage station by using the control value of the reactive power corresponding to the minimum energy storage allocation value, so as to obtain the optimal control result of the reactive power of the wind, solar and energy storage power storage station.

6. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the optimized control method for reactive power of the wind and solar power storage station as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the optimized control method for reactive power of the wind and solar power storage station as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The method includes computer instructions for causing a computer to execute the optimized control method for reactive power of a wind and solar power storage station as described in any one of claims 1 to 4.

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