Distributed photovoltaic grid-connected point energy storage and reactive power equipment optimal configuration method and system

Abstract

The invention provides a distributed photovoltaic grid-connected point energy storage and reactive power equipment optimization configuration method and system, and the method comprises the steps: calculating the target reactive power of a distributed photovoltaic grid-connected point according to the obtained operation data of the distributed photovoltaic grid-connected point when the voltage of the distributed photovoltaic grid-connected point exceeds the limit; and based on the target reactive power and an adjustable capacity range of a photovoltaic inverter connected to the distributed photovoltaic grid-connected point, capacity distribution is carried out on energy storage equipment and reactive equipment connected to the distributed photovoltaic grid-connected point by using a pre-constructed coordinated optimization configuration model, obtaining a capacity configuration proportion between the energy storage equipment and the reactive equipment; taking the capacity configuration proportion between the energy storage equipment and the reactive equipment as an optimal configuration scheme of the distributed photovoltaic grid-connected point; according to the invention, by improving the coordination configuration efficiency of the energy storage device and the reactive power device which are connected to the distributed photovoltaic grid-connected point, the out-of-limit voltage can be stabilized, and the stability of the power grid can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of solar power generation, and in particular to a method and system for optimizing configuration of distributed photovoltaic grid-connected point energy storage and reactive power equipment. Background Art

[0002] At present, large-scale photovoltaic access will lead to increasingly serious voltage over-limit problems in the system, and the difficulty of regulation is increasing. At present, the voltage regulation methods for low-voltage distribution networks with large-scale photovoltaic access mainly include installing reactive compensation equipment, reducing photovoltaic output, reactive regulation of photovoltaic inverters, and active regulation of energy storage. Among them, the control effect and economy of reactive compensation equipment are poor for some low-voltage distribution networks with large impedance ratios; while reducing photovoltaic output reduces resource utilization and reduces user benefits. In recent years, photovoltaic inverters have the characteristics of frequent adjustment, flexible and fast response, and good economy. Energy storage systems (ESS) equipment has smooth voltage regulation, fast charging and discharging response speed, and gradually reducing costs. Therefore, coordinated control of photovoltaic inverters and energy storage is an important means to solve the problem of voltage over-limit in distribution networks. In current research, photovoltaic storage joint regulation or photovoltaic inverter control is more common. In this control method, although the photovoltaic inverter has a certain reactive power regulation capability, its reactive regulation capability and response speed may be limited, which increases the pressure and voltage fluctuation caused by the output fluctuation of the photovoltaic system on the power grid.

[0003] Therefore, in actual engineering, in terms of voltage control, reactive equipment is used for reactive compensation and energy storage equipment is used for coordinated regulation. However, this regulation method needs to consider both the constraints of voltage regulation and the cost of equipment. If the two cannot be balanced, the capacity configuration efficiency between energy storage equipment and reactive equipment will be low, thereby reducing the stability of the power grid. Summary of the invention

[0004] In order to solve the problem that the voltage regulation constraint and equipment cost cannot be balanced when energy storage equipment and reactive equipment are used for coordinated regulation in the prior art, resulting in low capacity configuration efficiency between energy storage equipment and reactive equipment, thereby reducing the stability of the power grid, the present invention proposes a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method, including:

[0005] When the voltage of the distributed photovoltaic grid-connected point exceeds the limit, the target reactive power of the distributed photovoltaic grid-connected point is calculated according to the acquired operation data of the distributed photovoltaic grid-connected point;

[0006] Based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point is allocated using a pre-built coordinated optimization configuration model to obtain a capacity configuration ratio between the energy storage device and the reactive device;

[0007] Taking the capacity configuration ratio between the energy storage device and the reactive device as the optimal configuration scheme of the distributed photovoltaic grid-connected point;

[0008] Among them, the coordinated optimization configuration model aims to minimize the total life cycle cost of the energy storage equipment and the reactive equipment, takes flow constraints, voltage constraints, output constraints and regulation capacity constraints as constraints, and takes the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary.

[0009] Optionally, the coordinated optimization configuration model includes the following construction process:

[0010] Determine the total life cycle cost of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid connection point based on the sum of the periodic cost of the reactive equipment, the investment cost of the energy storage equipment and the operation and maintenance cost of the energy storage equipment;

[0011] Taking the minimum total life cycle cost as the goal, constructing an objective function;

[0012] According to the active power and reactive power of the distributed photovoltaic grid-connected point, a power flow constraint is formulated using a Newton-Raphson polar coordinate algorithm;

[0013] Formulate voltage constraints according to the voltage allowable range and rated voltage of the distributed photovoltaic grid-connected point;

[0014] Formulate output constraints according to the reactive power of the photovoltaic inverter and / or the reactive device;

[0015] Formulate regulation capacity constraints based on active power, reactive power and apparent power of the energy storage device;

[0016] The power flow constraint, voltage constraint, output constraint and regulation capacity constraint are used as constraint conditions;

[0017] Taking the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary, a configuration boundary expression is formulated;

[0018] A coordinated optimization configuration model is constructed according to the objective function, constraint conditions and the configuration boundary expression.

[0019] Optionally, the expression corresponding to the objective function is as follows:

[0020] minF=C SVG +C IN +C OM ;

[0021] Wherein, F represents the total life cycle cost of the energy storage equipment and reactive equipment in the distributed photovoltaic grid-connected point; C SVG Represents the periodic cost of reactive equipment; C IN Represents the investment cost of energy storage equipment; C OM Represents the operation and maintenance cost of energy storage equipment;

[0022] The cycle cost S of the reactive equipment SVG The corresponding calculation formula is as follows:

[0023] C SVG =c 1 Q 1 +c 2 ×c 3 Q 1 ;

[0024] Among them, c 1 To adjust the cost coefficient; Q 1 Indicates the maximum adjustable capacity of reactive equipment; c 2 represents the conversion cost coefficient; c 3 represents the margin factor;

[0025] The investment cost of the energy storage equipment is C IN The corresponding calculation formula is as follows:

[0026]

[0027] Where r represents the base discount rate; n represents the operating period; C P Represents the unit investment of the power of energy storage equipment; P ESS Indicates the power of the energy storage device; C E Indicates the unit investment of the capacity of energy storage equipment; E ESS Indicates the installed capacity of the energy storage equipment;

[0028] The operation and maintenance cost of the energy storage equipment is C OM The corresponding calculation formula is as follows:

[0029] C OM =μC IN ;

[0030] Wherein, μ represents the operation and maintenance cost coefficient.

[0031] Optionally, the configuration boundary expression is as follows:

[0032] U T -U N=S PV ·P ESS +S QV ·(Q 1 +E ESS );

[0033] Among them, U T Indicates the over-limit voltage of the distributed photovoltaic grid-connected point; U N Indicates the rated voltage of the distributed photovoltaic grid-connected point; S PV P represents the partial derivative of the active power of the distributed photovoltaic grid-connected point with respect to the voltage; ESS Represents the active power of the energy storage device; S QV It represents the partial derivative of the reactive power of the distributed photovoltaic grid-connected point with respect to the voltage; Q 1 Indicates the maximum adjustable capacity of reactive equipment; E ESS Indicates the installed capacity of the energy storage equipment.

[0034] Optionally, based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, the pre-built coordinated optimization configuration model is used to perform capacity allocation on the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point to obtain the capacity configuration ratio between the energy storage device and the reactive device, including:

[0035] Based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, determining whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter;

[0036] If the target reactive power is not within the adjustable capacity range, the coordinated optimization configuration model is used to allocate the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point to obtain the capacity configuration ratio between the energy storage device and the reactive device.

[0037] Optionally, after determining whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter, the method further includes:

[0038] If the target reactive power is within the adjustable capacity range, the margin capacity of the photovoltaic inverter is obtained, and the over-limit voltage control is performed on the distributed photovoltaic grid-connected point according to the margin capacity.

[0039] Optionally, the calculating the target reactive power of the distributed photovoltaic grid-connected point according to the acquired operation data of the distributed photovoltaic grid-connected point includes:

[0040] Calculating the sensitivity of the distributed photovoltaic grid-connected point according to the acquired operating data of the distributed photovoltaic grid-connected point;

[0041] According to the sensitivity of the distributed photovoltaic grid-connected point, the target reactive power of the distributed photovoltaic grid-connected point is obtained.

[0042] Optionally, obtaining the target reactive power of the distributed photovoltaic grid-connected point according to the sensitivity of the distributed photovoltaic grid-connected point includes:

[0043] Calculating the corresponding reactive power change according to the sensitivity of the distributed photovoltaic grid-connected point;

[0044] The current reactive power of the distributed photovoltaic grid-connected point and the reactive power change are summed to obtain the target reactive power of the distributed photovoltaic grid-connected point.

[0045] Optionally, the calculation formula corresponding to the sensitivity is as follows:

[0046]

[0047] Among them, S en Represents the sensitivity of the distributed photovoltaic grid-connected point; S θP represents the partial derivative of the phase angle difference θ with respect to the active power of the distributed photovoltaic grid-connected point; S θQ represents the partial derivative of the phase angle difference θ with respect to the reactive power of the distributed photovoltaic grid-connected point; S VP represents the partial derivative of the grid connection point voltage with respect to the active power of the distributed photovoltaic grid connection point; S VQ Represents the partial derivative of the grid connection point voltage with respect to the reactive power of the distributed photovoltaic grid connection point;

[0048] The calculation formula corresponding to the reactive power change is as follows:

[0049] ΔQ=S VP ·Δθ+S VQ ΔV;

[0050] Among them, ΔQ represents the change in reactive power of the distributed photovoltaic grid-connected point; Δθ represents the phase angle difference; ΔV represents the change in voltage of the distributed photovoltaic grid-connected point.

[0051] Optionally, the expression corresponding to the power flow constraint is as follows:

[0052]

[0053] Among them, P i represents the active power of distributed photovoltaic grid-connected point i; Q i Represents the reactive power of distributed photovoltaic grid-connected point i; U i Indicates the voltage amplitude of distributed photovoltaic grid-connected point i; U j represents the voltage amplitude of the distributed photovoltaic grid-connected point j; j∈i means that the grid-connected point j is a node directly connected to the grid-connected point i; Gij represents the real part of the admittance matrix of distributed photovoltaic grid connection points i and j; B ij represents the imaginary part of the admittance matrix of distributed photovoltaic grid-connected points i and j; θ ij is the phase angle difference between grid connection points i and j;

[0054] The expression corresponding to the output constraint is as follows:

[0055]

[0056] Among them, Q PV Represents the reactive power of the photovoltaic inverter; Q PVmax Indicates the maximum reactive power output of the photovoltaic inverter; Q SVG.max Indicates the maximum reactive power of reactive equipment; P PV represents the active power of the photovoltaic inverter; S represents the apparent power of the photovoltaic inverter; A 3 Represents the apparent coefficient; Q SVG Indicates the reactive power of reactive equipment; Q SVG.min Indicates the minimum reactive power of the reactive device.

[0057] Based on the same inventive concept, the present invention also provides a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration system, including:

[0058] A target reactive power calculation module is used to calculate the target reactive power of the distributed photovoltaic grid-connected point according to the acquired operation data of the distributed photovoltaic grid-connected point when the voltage of the distributed photovoltaic grid-connected point exceeds the limit;

[0059] A capacity configuration module is used to allocate the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point using a pre-built coordinated optimization configuration model to obtain a capacity configuration ratio between the energy storage device and the reactive device;

[0060] A coordination optimization module, used to use the capacity configuration ratio between the energy storage device and the reactive device as an optimized configuration scheme for the distributed photovoltaic grid-connected point;

[0061] Among them, the coordinated optimization configuration model in the capacity configuration module is based on the goal of minimizing the total life cycle cost of the energy storage equipment and the reactive equipment, with flow constraints, voltage constraints, output constraints and regulation capacity constraints as constraints, and the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point is constructed as the control boundary.

[0062] Optionally, the optimization configuration system further includes: a model building module, which is used to:

[0063] Determine the total life cycle cost of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid connection point based on the sum of the periodic cost of the reactive equipment, the investment cost of the energy storage equipment and the operation and maintenance cost of the energy storage equipment;

[0064] Taking the minimum total life cycle cost as the goal, constructing an objective function;

[0065] According to the active power and reactive power of the distributed photovoltaic grid-connected point, a power flow constraint is formulated using a Newton-Raphson polar coordinate algorithm;

[0066] Formulate voltage constraints according to the voltage allowable range and rated voltage of the distributed photovoltaic grid-connected point;

[0067] Formulate output constraints according to the reactive power of the photovoltaic inverter and / or the reactive device;

[0068] Formulate regulation capacity constraints based on active power, reactive power and apparent power of the energy storage device;

[0069] The power flow constraint, voltage constraint, output constraint and regulation capacity constraint are used as constraint conditions;

[0070] Taking the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary, a configuration boundary expression is formulated;

[0071] A coordinated optimization configuration model is constructed according to the objective function, constraint conditions and the configuration boundary expression.

[0072] Optionally, the expression corresponding to the objective function is as follows:

[0073] minF=C SVG +C IN +C OM ;

[0074] Wherein, F represents the total life cycle cost of the energy storage equipment and reactive equipment in the distributed photovoltaic grid-connected point; C SVG Represents the periodic cost of reactive equipment; C IN Represents the investment cost of energy storage equipment; C OM Represents the operation and maintenance cost of energy storage equipment;

[0075] The cycle cost C of the reactive equipment SVG The corresponding calculation formula is as follows:

[0076] C SVG =c 1 Q 1 +c 2 ×c 3 Q 1 ;

[0077] Among them, c 1 To adjust the cost coefficient; Q 1 Indicates the maximum adjustable capacity of reactive equipment; c 2 represents the conversion cost coefficient; c 3 represents the margin factor;

[0078] The investment cost of the energy storage equipment is C IN The corresponding calculation formula is as follows:

[0079]

[0080] Where r represents the base discount rate; n represents the operating period; C P Represents the unit investment of the power of energy storage equipment; P ESS Indicates the power of the energy storage device; C E Indicates the unit investment of the capacity of energy storage equipment; E ESS Indicates the installed capacity of the energy storage equipment;

[0081] The operation and maintenance cost of the energy storage equipment is C OM The corresponding calculation formula is as follows:

[0082] C OM =μC IN ;

[0083] Wherein, μ represents the operation and maintenance cost coefficient.

[0084] Optionally, the configuration boundary expression is as follows:

[0085] U T -U N =S PV ·P ESS +S QV ·(Q 1 +E ESS );

[0086] Among them, U T Indicates the over-limit voltage of the distributed photovoltaic grid-connected point; U N Indicates the rated voltage of the distributed photovoltaic grid-connected point; S PV P represents the partial derivative of the active power of the distributed photovoltaic grid-connected point with respect to the voltage; ESS Represents the active power of the energy storage device; S QV It represents the partial derivative of the reactive power of the distributed photovoltaic grid-connected point with respect to the voltage; Q 1 Indicates the maximum adjustable capacity of reactive equipment; E ESS Indicates the installed capacity of the energy storage equipment.

[0087] Optionally, the capacity configuration module includes:

[0088] A power comparison submodule, configured to determine whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point;

[0089] The capacity allocation submodule is used to allocate the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point using the coordinated optimization configuration model when the target reactive power is not within the adjustable capacity range, so as to obtain the capacity configuration ratio between the energy storage device and the reactive device.

[0090] Optionally, the capacity configuration module further includes:

[0091] The inverter control submodule is used to obtain the margin capacity of the photovoltaic inverter when the target reactive power is within the adjustable capacity range, and perform over-limit voltage control on the distributed photovoltaic grid-connected point according to the margin capacity.

[0092] Optionally, the target reactive power calculation module includes:

[0093] A sensitivity calculation submodule, used to calculate the sensitivity of the distributed photovoltaic grid-connected point according to the acquired operation data of the distributed photovoltaic grid-connected point;

[0094] The target power determination submodule is used to obtain the target reactive power of the distributed photovoltaic grid-connected point according to the sensitivity of the distributed photovoltaic grid-connected point.

[0095] Optionally, the target power determination submodule includes:

[0096] A variation calculation unit, used to calculate the corresponding reactive power variation according to the sensitivity of the distributed photovoltaic grid-connected point;

[0097] The power determination unit is used to sum the current reactive power of the distributed photovoltaic grid-connected point with the reactive power change to obtain the target reactive power of the distributed photovoltaic grid-connected point.

[0098] Optionally, the calculation formula corresponding to the sensitivity is as follows:

[0099]

[0100] Among them, S en Represents the sensitivity of the distributed photovoltaic grid-connected point; S θP represents the partial derivative of the phase angle difference θ with respect to the active power of the distributed photovoltaic grid-connected point; S θQ represents the partial derivative of the phase angle difference θ with respect to the reactive power of the distributed photovoltaic grid-connected point; S VPrepresents the partial derivative of the grid connection point voltage with respect to the active power of the distributed photovoltaic grid connection point; S VQ Represents the partial derivative of the grid connection point voltage with respect to the reactive power of the distributed photovoltaic grid connection point;

[0101] The calculation formula corresponding to the reactive power change is as follows:

[0102] ΔQ=S VP ·Δθ+S VQ ΔV;

[0103] Among them, ΔQ represents the change in reactive power of the distributed photovoltaic grid-connected point; Δθ represents the phase angle difference; ΔV represents the change in voltage of the distributed photovoltaic grid-connected point.

[0104] Optionally, the expression corresponding to the power flow constraint is as follows:

[0105]

[0106] Among them, P i represents the active power of distributed photovoltaic grid-connected point i; Q i Represents the reactive power of distributed photovoltaic grid-connected point i; U i Indicates the voltage amplitude of distributed photovoltaic grid-connected point i; U j represents the voltage amplitude of the distributed photovoltaic grid-connected point j; j∈i means that the grid-connected point j is a node directly connected to the grid-connected point i; G ij represents the real part of the admittance matrix of distributed photovoltaic grid connection points i and j; B ij represents the imaginary part of the admittance matrix of distributed photovoltaic grid-connected points i and j; θ ij is the phase angle difference between grid connection points i and j;

[0107] The expression corresponding to the output constraint is as follows:

[0108]

[0109] Among them, Q PV Represents the reactive power of the photovoltaic inverter; Q PVmax Indicates the maximum reactive power output of the photovoltaic inverter; Q SVG.max Indicates the maximum reactive power of reactive equipment; P PV represents the active power of the photovoltaic inverter; S represents the apparent power of the photovoltaic inverter; A 3 Represents the apparent coefficient; Q SVG Indicates the reactive power of reactive equipment; Q SVG.min Indicates the minimum reactive power of the reactive device.

[0110] In another aspect, the present invention further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0111] The memory is used to store one or more programs;

[0112] When the one or more programs are executed by the at least one processor, a method for optimizing configuration of distributed photovoltaic grid-connected point energy storage and reactive equipment as described above is implemented.

[0113] On the other hand, the present invention also provides a computer-readable storage medium having an execution program stored thereon, which, when executed, implements a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method as described above.

[0114] Compared with the prior art, the present invention has the following beneficial effects:

[0115] The present invention provides a method and system for optimizing the configuration of energy storage and reactive equipment at distributed photovoltaic grid-connected points, including: when the voltage at the distributed photovoltaic grid-connected point exceeds the limit, the target reactive power of the distributed photovoltaic grid-connected point is calculated according to the acquired operating data of the distributed photovoltaic grid-connected point; based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, the capacity of the energy storage equipment and the reactive equipment connected to the distributed photovoltaic grid-connected point is allocated using a pre-constructed coordinated optimization configuration model to obtain the capacity configuration ratio between the energy storage equipment and the reactive equipment; the capacity configuration ratio between the energy storage equipment and the reactive equipment is used as the optimized configuration scheme of the distributed photovoltaic grid-connected point; wherein the coordinated optimization configuration model takes the lowest total life cycle cost of the energy storage equipment and the reactive equipment as the goal, and takes flow constraints, Voltage constraint, output constraint and regulation capacity constraint are used as constraint conditions, and the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point is used as the control boundary to construct; this application takes the lowest total life cycle cost of energy storage equipment and reactive equipment as the goal, takes flow constraint, voltage constraint, output constraint and regulation capacity constraint as constraint conditions, can balance the voltage regulation constraint and equipment cost of distributed photovoltaic grid-connected points, and achieves the suppression of voltage over-limit and ensures that active power is not overloaded. The voltage regulation capacity is reasonably allocated through low-cost reactive equipment to share the excessive cost of energy storage equipment, and by taking the difference between the over-limit voltage and the rated voltage as the control boundary, the coordinated configuration efficiency between the energy storage equipment and the reactive equipment connected to the distributed photovoltaic grid-connected point can be improved. Therefore, the method of the present invention is conducive to smoothing the over-limit voltage of distributed photovoltaic grid-connected points and increasing the stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1A schematic diagram of a flow chart of a distributed photovoltaic grid-connected point energy storage and reactive power equipment optimization configuration method provided by the present invention;

[0117] Figure 2 A schematic diagram of a framework for allocating the capacity of energy storage equipment and reactive equipment connected to a distributed photovoltaic grid-connected point using a coordinated optimization configuration model in a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method provided by the present invention;

[0118] Figure 3 A schematic diagram of the structure of a distributed photovoltaic grid-connected point energy storage and reactive power equipment optimization configuration system provided by the present invention;

[0119] Figure 4 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0120] The present invention proposes a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method and system, and the specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings.

[0121] Embodiment 1:

[0122] The present invention provides a distributed photovoltaic grid-connected point energy storage and reactive power equipment optimization configuration method, the flow chart is as follows Figure 1 As shown, including:

[0123] Step 1: When the voltage of the distributed photovoltaic grid-connected point exceeds the limit, the target reactive power of the distributed photovoltaic grid-connected point is calculated according to the obtained operation data of the distributed photovoltaic grid-connected point;

[0124] Step 2: Based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, the capacity of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid-connected point is allocated using the pre-built coordinated optimization configuration model to obtain the capacity configuration ratio between the energy storage equipment and the reactive equipment;

[0125] Step 3: Use the capacity configuration ratio between energy storage equipment and reactive equipment as the optimal configuration scheme for distributed photovoltaic grid connection points;

[0126] Among them, the coordinated optimization configuration model aims to minimize the total life cycle cost of energy storage equipment and reactive equipment, with flow constraints, voltage constraints, output constraints and regulation capacity constraints as constraints, and the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary.

[0127] In the embodiment of the present invention, when judging whether the voltage of the distributed photovoltaic grid-connected point is over-limited, the voltage over-limit level of the distributed photovoltaic grid-connected point collected in advance is compared with the real-time voltage data. If the voltage data exceeds the voltage over-limit level, it is determined that the voltage of the distributed photovoltaic grid-connected point is over-limited, and the target reactive power is calculated according to the acquired operation data of the distributed photovoltaic grid-connected point, and the reactive power output is adjusted to restore the voltage over-limit caused by the distributed photovoltaic access to the power grid; wherein, the voltage over-limit level and the real-time voltage data of the distributed photovoltaic grid-connected point can be pre-collected by a data acquisition device (such as a voltage transformer, a data acquisition device or a smart meter); the operation data of the distributed photovoltaic grid-connected point may include but is not limited to the time-varying characteristics of distributed photovoltaic power generation (for example, it can be obtained by a data acquisition terminal, and specifically may include information such as sunshine changes, geographical location, equipment characteristics and time series characteristics), local weather conditions (for example, it can be obtained by a meteorological station or an online meteorological service, and specifically may include information such as light intensity, temperature and humidity) and distributed photovoltaic operation mode (for example, it can be obtained by a remote monitoring platform, and specifically may include maximum power tracking mode, constant power output mode or microgrid mode).

[0128] In one implementation, the process of calculating the target reactive power of the distributed photovoltaic grid-connected point according to the acquired operation data of the distributed photovoltaic grid-connected point in the above step 1 may include:

[0129] According to the obtained operating data of the distributed photovoltaic grid-connected points, the sensitivity of the distributed photovoltaic grid-connected points is calculated through the power flow equation to establish the relationship between reactive power and voltage;

[0130] According to the sensitivity of the distributed photovoltaic grid-connected point, the target reactive power of the distributed photovoltaic grid-connected point is obtained.

[0131] In this implementation, sensitivity analysis is performed to quickly respond to voltage changes in the power grid. The target reactive power is calculated, which can accurately control the reactive output of distributed photovoltaics and reasonably distribute reactive power, which is beneficial to reducing reactive losses in the power grid, effectively suppressing voltage fluctuations, and thus maintaining the grid voltage within a reasonable range and improving the power quality. In addition, this implementation can also reduce the frequent start and stop of equipment due to voltage fluctuations by reasonably allocating reactive power, which is beneficial to extending the service life of equipment and reducing maintenance costs.

[0132] In one implementation, the process of obtaining the target reactive power of the distributed photovoltaic grid-connected point according to the sensitivity of the distributed photovoltaic grid-connected point may include:

[0133] According to the sensitivity of the distributed photovoltaic grid connection point, the corresponding reactive power change is calculated;

[0134] The current reactive power of the distributed photovoltaic grid-connected point and the reactive power change are summed to obtain the target reactive power of the distributed photovoltaic grid-connected point.

[0135] In this implementation, by calculating the target reactive power of the distributed photovoltaic grid-connected points, the reactive output of the distributed photovoltaic grid-connected points can be dynamically adjusted, which is beneficial to reducing voltage fluctuations and voltage flicker in the power grid and maintaining the grid voltage fluctuation within the allowable range. In addition, in the event of a fault, the reactive support can be quickly adjusted through the pre-calculated target reactive power, which can accelerate the system's recovery to normal operating conditions and reduce the duration and scope of power outages. In addition, for distribution networks including distributed photovoltaics, reasonable target reactive power calculations help support island operation mode and ensure stable power supply even when disconnected from the main grid.

[0136] In this implementation, according to the calculated sensitivity of the distributed photovoltaic grid-connected point, the relationship between the grid-connected point voltage and power can be established, and the relationship is used as the voltage parameter of the control target, and a direct association is established with the control variables reactive power and active power. The distribution network parameters are used to convert the over-limit voltage of the distributed photovoltaic grid-connected point (for example, using U T Indicated) and rated voltage (for example, using U N As a known parameter, the influence of the distributed photovoltaic grid connection point on the voltage and power angle is analyzed:

[0137] First, when the distributed photovoltaic power generation is not connected to the distribution network node, according to the power flow equation, the node voltage U of any node p is p It can be expressed as:

[0138]

[0139] Among them, U p represents the node voltage of node p; U 0 Represents the initial voltage; R a represents the resistance of node a; a=1…N; N represents the total number of distributed photovoltaic nodes; P b represents the active power of node b; b = a…N; X a represents the reactance of node a; Q a represents the reactive power of node a; U a-1 represents the node voltage of node a-1;

[0140] Secondly, the voltage boost of node voltage caused by distributed photovoltaic access to distribution network node m can be expressed as:

[0141]

[0142] Among them, ΔU mP represents the voltage increased after the distributed photovoltaic is connected to the distribution network node m; PV1 Indicates the active power of the distributed photovoltaic grid-connected point; Q PV1 Represents the reactive power of the distributed photovoltaic grid-connected point; R j represents the resistance of the grid connection point j; X j represents the reactance of the grid connection point j, j = 1…m, m represents the total number of distribution network nodes; U j-1 represents the node voltage of node j-1;

[0143] The distribution of load nodes determines that after the distributed photovoltaic is connected to a single node m, there must be a difference in node voltage before and after the connection point m. m From the calculation formula, we can know that the voltage level of distributed photovoltaic grid-connected points can be adjusted by adjusting both active power and reactive power. However, due to the limitation of power factor angle, it is impossible to completely use low-cost reactive equipment to ensure that the voltage is maintained at a normal level. When necessary, the active power of energy storage equipment must be supplemented to cooperate with the adjustment. While maintaining the priority, the photovoltaic output should be kept stable as much as possible, and the influence of photovoltaic operation status can be excluded. And when the sum of the active power of the node load is greater than the photovoltaic grid-connected power, the voltage of each node decreases according to the line distribution, and vice versa. Therefore, the fluctuation of photovoltaic output at distributed photovoltaic grid-connected points will cause the deviation of node voltage. Specifically, the deviation value can meet the provisions of GB / T 12325-2008 that the deviation of the three-phase power supply voltage of nodes of 20kV and below is ±7% of the nominal voltage.

[0144] For example, the power factor of the low-voltage side of the distribution transformer can be expressed as follows based on the power flow equation and the access of distributed photovoltaics:

[0145]

[0146] in, Indicates the power factor of the low-voltage side of the distribution transformer that can be obtained by taking into account the access of distributed photovoltaics; P L Indicates that the low-voltage side of the distribution transformer absorbs active power from the power grid; P PV1 Indicates the active power absorbed by the low-voltage side of the distribution transformer from the distributed photovoltaic, that is, the active power of the distributed photovoltaic grid-connected point; Q L Indicates that the low-voltage side of the distribution transformer absorbs reactive power from the grid; as the output of distributed photovoltaic power increases, the low-voltage side of the distribution transformer absorbs active power P from the grid L -P PV1 Gradually decreases, while the reactive power Q LRemains unchanged. At the same time, the reactive power compensation device in the substation, also referred to as reactive equipment in the present invention, such as a static var generator (SVG), is a reactive power compensation device that takes the total reactive power demand as the control object, and it will not increase the reactive power output as the distributed photovoltaic output changes. Therefore, the power factor at the distribution network transformer decreases as the photovoltaic output increases. When the photovoltaic output is greater than the active power demand of the substation load, P L -P PV1 If it is less than 0, there will be a reverse power flow problem and the power factor will change from positive to negative. The impact of the change in power of different nodes on the voltage at the grid connection point can be reflected by the voltage sensitivity factor (VSF), which describes the degree of influence of the active or reactive power injected by the node on the voltage change at a specific location in the network. The sensitivity obtained by inverting the Jacobian matrix can be used to describe the relationship between voltage and power.

[0147] For example, the calculation formula corresponding to the above sensitivity can be as follows:

[0148]

[0149] Among them, S en represents the sensitivity of the distributed photovoltaic grid-connected point; Ja represents the Jacobian matrix; S θP and Both represent the partial derivative of the phase angle θ with respect to the active power of the distributed photovoltaic grid-connected point; S θQ and Both represent the partial derivative of the phase angle θ with respect to the reactive power of the distributed photovoltaic grid connection point; S VP and Both represent the partial derivative of the grid connection point voltage with respect to the active power of the distributed photovoltaic grid connection point; S VQ and Both represent the partial derivative of the grid connection point voltage with respect to the reactive power of the distributed photovoltaic grid connection point;

[0150] By transforming the above sensitivity calculation formula, we can get:

[0151]

[0152]

[0153] The sensitivity of the grid connection point voltage can be obtained:

[0154] ΔV=S PV ΔP+S QV ΔQ

[0155] Among them, Δθ represents the phase angle difference; ΔV represents the voltage change; ΔP represents the active power change; ΔQ represents the reactive power change; S PV represents the partial derivative of the active power of the distributed photovoltaic grid-connected point with respect to the grid-connected point voltage; S QV Represents the partial derivative of the reactive power of the distributed photovoltaic grid-connected point with respect to the grid-connected point voltage;

[0156] Through the sensitivity of the grid connection point voltage, the numerical connection between the grid connection point voltage, network parameters and power flow can be established, mainly the sensitivity relationship between reactive power and voltage, which is conducive to the rational allocation of reactive power resources and the precise control of the voltage level of the grid connection point to avoid equipment overload problems caused by excessive voltage fluctuations. By optimizing the reactive power configuration, the grid's ability to absorb distributed photovoltaic power can be improved, reducing the phenomenon of abandoned light due to voltage problems.

[0157] For example, the calculation formula corresponding to the reactive power change can be as follows:

[0158] ΔQ=S VP ·Δθ+S VQ ΔV;

[0159] Among them, ΔQ represents the reactive power change of the distributed photovoltaic grid-connected point; Δθ represents the phase angle difference; ΔV represents the voltage change of the distributed photovoltaic grid-connected point, and based on this, the target reactive power of the grid-connected point can be obtained through the over-limit voltage amplitude and voltage deviation.

[0160] In one implementation, Figure 2 As shown, in the above step 2, based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point is allocated using the pre-built coordinated optimization configuration model, and the process of obtaining the capacity configuration ratio between the energy storage device and the reactive device may include:

[0161] Based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, it is determined whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter;

[0162] If the target reactive power is not within the adjustable capacity range, the coordinated optimization configuration model is used to allocate the capacity of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid-connected point, and the capacity configuration ratio between the energy storage equipment and the reactive equipment is obtained; when the present invention allocates the capacity of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid-connected point through the coordinated optimization configuration model, the influence of the distributed photovoltaic operation mode on the control method and algorithm can be ignored. Regardless of whether the distributed photovoltaic is in the maximum power tracking mode or the constant power operation mode, the control target can be achieved by the same algorithm. The optimal boundary of the energy storage equipment and the reactive equipment can be obtained, that is, the critical ratio configuration of the energy storage equipment and the reactive equipment when the total cycle cost is optimal, and the over-limit voltage of the distributed photovoltaic is smoothed in turn according to the priority order of reactive equipment compensating reactive power and energy storage equipment absorbing active power.

[0163] In this implementation, after determining whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter, the following steps may also be performed:

[0164] If the target reactive power is within the adjustable capacity range, it means that the available reactive capacity of the photovoltaic inverter can participate in reactive power regulation, that is, by obtaining the margin capacity of the photovoltaic inverter and performing over-limit voltage control on the distributed photovoltaic grid-connected point according to the margin capacity;

[0165] In this implementation, by reasonably allocating the capacity of energy storage equipment and reactive equipment, and giving priority to using reactive equipment to compensate for reactive power, unnecessary long-distance reactive power transmission can be reduced. By absorbing excess active power through energy storage equipment, the frequency fluctuation of the power system can be reduced and the dynamic stability of the system can be improved. The technical solution of the present invention makes reactive power management more flexible and efficient. By smoothing the over-limit voltage, equipment damage and other safety hazards caused by excessively high or low voltage can be avoided, thereby ensuring the safe operation of the power grid. In addition, the energy storage device in the present invention can also be used as a backup power supply to provide necessary power support in emergency situations.

[0166] In one implementation, the above-mentioned coordinated optimization configuration model may include the following construction process:

[0167] Determine the total life cycle cost of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid-connected point based on the sum of the cycle cost of reactive equipment, the investment cost of energy storage equipment, and the operation and maintenance cost of energy storage equipment;

[0168] Taking the minimum total life cycle cost as the goal, construct the objective function;

[0169] According to the active power and reactive power of the distributed photovoltaic grid-connected points, the Newton-Raphson polar coordinate algorithm is used to formulate power flow constraints;

[0170] Formulate voltage constraints based on the voltage allowable range and rated voltage of distributed photovoltaic grid-connected points;

[0171] Formulate output constraints based on the reactive power of PV inverters and / or reactive devices;

[0172] Formulate regulation capacity constraints based on the active power, reactive power and apparent power of the energy storage equipment;

[0173] The constraints are flow constraints, voltage constraints, output constraints and regulation capacity constraints;

[0174] Taking the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary, the configuration boundary expression is formulated;

[0175] According to the objective function, constraints and configuration boundary expressions, a coordinated optimization configuration model is constructed. During the model construction process, the model can be continuously adjusted and optimized according to the feedback and results in actual applications.

[0176] In this implementation, the present invention constructs an objective function of minimizing the total cost by comprehensively considering the cycle cost of reactive equipment, the investment cost of energy storage equipment and the operation and maintenance cost, thereby maximizing the cost-effectiveness of equipment configuration, and then optimizing the cost structure to make the investment more reasonable; and by formulating strict voltage constraints, ensuring that the voltage level of distributed photovoltaic grid-connected points is within the allowable range, which helps to maintain the stability of the system: through output constraints, it is ensured that the photovoltaic inverter and reactive equipment work within a safe range to prevent overload or underload; by considering the regulation capacity of energy storage equipment, the active power and reactive power are reasonably allocated, so that the system can better cope with load changes and fluctuations in intermittent energy; by formulating control boundaries, the working status of energy storage equipment and reactive equipment can be adjusted in time when the voltage exceeds the normal range to prevent the negative impact caused by abnormal voltage; therefore, the method of the present invention can achieve the optimization of the configuration of energy storage equipment and reactive equipment at distributed photovoltaic grid-connected points, thereby improving the economic benefits, operating efficiency and reliability of the power system.

[0177] In this implementation, the above-mentioned periodic cost of reactive equipment, investment cost of energy storage equipment and operation and maintenance cost of energy storage equipment are cost data converted and normalized according to the daily duration; when setting the objective function with the total life cycle cost determined by the sum of the periodic cost of reactive equipment, the investment cost of energy storage equipment and the operation and maintenance cost of energy storage equipment, the problem of time scale conversion must also be considered. For example, the investment cost is generally based on years, and the operation and maintenance cost is generally based on days, so the investment cost needs to be converted, and the corresponding conversion coefficient can be added according to the actual physical properties; in addition, when constructing the objective function, as long as the ultimate goal is achieved, there is no need to rigidly set which specific items the total life cycle cost includes, and one or more combinations can be selected from a large number of cost combinations as the objective function, which is not limited to the cost combination method in the objective function of the present invention, and because the output calculation result of the coordinated optimization configuration model of the present invention is the allocation and installation boundary between reactive equipment and energy storage equipment, it is reasonable for the investment capacity and the maximum call capacity to have a set specific margin relationship.

[0178] For example, the expression corresponding to the above objective function can be as follows:

[0179] minF=C SVG +C IN +C OM ;

[0180] Where F represents the total life cycle cost of energy storage equipment and reactive equipment in distributed photovoltaic grid-connected points; C SVG Represents the periodic cost of reactive equipment; C IN Represents the investment cost of energy storage equipment; C OM represents the operation and maintenance cost of the energy storage equipment; in this example, the network loss cost can also be considered in the objective function. The network loss cost can be composed of the parallel network loss and the series network loss. Its expression can be as follows:

[0181]

[0182] Among them, C GC Represents network loss cost; V I Represents the voltage of the Ith branch; V J represents the voltage of the Jth branch; I,J∈B; B represents the total set of branches; k l Represents the impedance coefficient of line l; l = 1…n; l∈B L ; B L Represents the total set of lines, consisting of n lines; G l represents the admittance of line l; θ I Represents the phase angle difference of the Ith branch; θ J represents the phase angle difference of the Jth branch; θ S Represents the phase angle difference of the reference node; Vl represents the voltage of the lth branch; represents the zero-sequence admittance of the lth branch.

[0183] For example, the cycle cost C of the above-mentioned reactive equipment (for example, a static reactive generator) is SVG The corresponding calculation formula can be as follows:

[0184] C SVG =c 1 Q 1 +c 2 ×c 3 Q 1 ;

[0185] Among them, c 1 To adjust the cost coefficient; Q 1 Indicates the maximum adjustable capacity of reactive equipment; c 2 represents the cost factor taking into account the annual value; c 3 Indicates the margin factor, which can be set to 1.1, i.e. 1.1Q 1 Indicates 0.1Q 1 The preset margin of

[0186] For example, the investment cost C of the above energy storage equipment is IN The corresponding calculation formula can be as follows:

[0187]

[0188] Where r is the base discount rate; n is the operating period, in years; C P Represents the unit investment of the power of energy storage equipment; P ESS Indicates the power of the energy storage device; C E Indicates the unit investment of the capacity of energy storage equipment; E ESS represents the installed capacity of the energy storage device; in this example, (C P P ESS +CE ESS ) represents the original investment cost of the single-node energy storage device. However, considering the time value of money, the original investment cost of the single-node energy storage device needs to be revised, that is, by introducing the annual value coefficient The investment cost of the original single-node energy storage device (C P P ESS +C E E ESS ) is corrected to obtain the final converted investment cost C of the energy storage equipment IN ;

[0189] For example, the operation and maintenance cost C of the above energy storage equipment OM The corresponding calculation formula can be as follows:

[0190] C OM =μC IN ;

[0191] Where μ represents the operation and maintenance cost coefficient; and the annual cost of the energy storage equipment (for example, C ESS It can be expressed as: C ESS =C IN +C OM =(1+μ)C IN ; That is, the above objective function can also be expressed as:

[0192] minF=C SVG +(1+μ)G IN ;

[0193] In theory, the optimal time scale for calculating the cost objective function is daily, and the control boundary is based on the maximum voltage over-limit day in a year, so the cost functions of reactive equipment and energy storage equipment can be averaged to a single day, that is, the final objective function can also be expressed as follows:

[0194]

[0195] Among them, f represents the objective function considering the time scale after the day; G SVG represents the cycle cost of reactive equipment (such as static VAR generator); μ represents the operation and maintenance cost coefficient; C IN represents the investment cost of energy storage equipment;

[0196] In this example, the operation and maintenance cost of the energy storage equipment is C OM It can also be calculated using the following formula:

[0197] C OM =K O P ESS +K M Q ESS ;

[0198] Among them, K O Represents the annual operation and maintenance cost coefficient per unit power of energy storage equipment; K M Represents the annual operation and maintenance cost coefficient per unit capacity of energy storage equipment; Q ESS Represents the annual power generation of the energy storage equipment. When the annual operation cost coefficient and annual operation and maintenance cost coefficient of the energy storage equipment are easy to determine, the operation and maintenance cost of the energy storage equipment can be calculated according to this calculation formula;

[0199] For example, the above configuration boundary expression can be as follows:

[0200] U T -U N =SPV ·P ESS +S QV ·(Q 1 +E ESS );

[0201] Among them, U T Indicates the over-limit voltage of the distributed photovoltaic grid-connected point; U N Indicates the rated voltage of the distributed photovoltaic grid-connected point; S PV P represents the partial derivative of the active power of the distributed photovoltaic grid-connected point with respect to the voltage; ESS Represents the active power of the energy storage device; S QV It represents the partial derivative of the reactive power of the distributed photovoltaic grid-connected point with respect to the voltage; Q 1 Indicates the maximum adjustable capacity of reactive equipment; E ESS Indicates the installed capacity of the energy storage equipment.

[0202] In this implementation, the objective function can be used to represent a minimum cost optimization problem, and U T -U N =S PV ·P ESS +S QV (Q 1 +E ESS ) is a sensitivity analysis based on actual working conditions. The key is that the establishment of the control boundary enables the installed capacity and the used capacity to achieve a fixed numerical relationship, thereby reducing the control variables and greatly simplifying the calculation. The setting of the control boundary only directly depends on the U within a year. T -U N Therefore, the final optimization result can be expressed as Q 1 The installed capacity of reactive equipment under boundary conditions can be expressed by E ESS It represents the installed capacity of the energy storage equipment under boundary conditions, that is, the calculated capacity configuration ratio between the reactive equipment and the energy storage equipment can be used as the optimal configuration scheme for the distributed photovoltaic grid-connected point. Through the separate operation of the reactive equipment and the energy storage equipment, the cost-optimal over-limit voltage control under actual working conditions can be achieved, solving the problem of the distribution and installation of energy storage equipment and reactive compensation equipment for smoothing the over-limit voltage.

[0203] In one implementation, the process of formulating power flow constraints based on the active power and reactive power of the distributed photovoltaic grid-connected point using the Newton-Raphson polar coordinate algorithm may include:

[0204] Obtain real-time active power and reactive power data of distributed photovoltaic grid-connected points; these data can be obtained through Supervisory Control and Data Acquisition System (SCADA), smart meters or remote terminal units (RTU);

[0205] Set the initial voltage phase angle and voltage amplitude, and construct the Jacobian matrix Ja to describe the relationship between the grid connection point voltage and the active and reactive power;

[0206] The Newton-Raphson polar coordinate algorithm is used to iteratively solve the power flow equation until convergence and obtain the power flow constraint;

[0207] For example, the expression corresponding to the above power flow constraint can be as follows:

[0208]

[0209] Among them, P i represents the active power of distributed photovoltaic grid-connected point i; Q i Represents the reactive power of distributed photovoltaic grid-connected point i; U i Indicates the voltage amplitude of distributed photovoltaic grid-connected point i; U j represents the voltage amplitude of the distributed photovoltaic grid-connected point j; j∈i means that the grid-connected point j is a node directly connected to the grid-connected point i; G ij represents the real part of the admittance matrix of distributed photovoltaic grid connection points i and j; B ij represents the imaginary part of the admittance matrix of distributed photovoltaic grid-connected points i and j; θ ij is the phase angle difference between grid connection points i and j;

[0210] For example, the expression corresponding to the above voltage constraint can be as follows:

[0211] A 1 ·U N ≤U L ≤A 2 ·U N ;

[0212] Among them, A 1 Indicates the grid connection point voltage lower limit coefficient, which can be set to 0.9; A 2 Indicates the grid connection point voltage upper limit coefficient, which can be set to 1.1; U N Indicates the rated voltage of the distributed photovoltaic grid-connected point; U L Indicates the voltage of the distributed photovoltaic grid-connected point;

[0213] For example, the expression corresponding to the above output constraint can be as follows:

[0214]

[0215] Among them, Q PV Represents the reactive power of the photovoltaic inverter; Q PVmax Indicates the maximum reactive power output of the photovoltaic inverter; Q SVG.max Indicates the maximum reactive power of reactive equipment; P PV represents the active power of the photovoltaic inverter; S represents the apparent power of the photovoltaic inverter; A 3 Represents the apparent coefficient; Q SVG Indicates the reactive power of reactive equipment; Q SVG.min Indicates the minimum reactive power of the reactive device.

[0216] For example, the expression corresponding to the above adjustment capability constraint can be as follows:

[0217]

[0218] Among them, P E Indicates the active power of the energy storage device; Q E Represents the reactive power of the energy storage device; S E Indicates the apparent power of the energy storage device.

[0219] In view of the problem that in the prior art, when energy storage equipment and reactive equipment are used for coordinated regulation, it is impossible to balance the voltage regulation constraints and equipment costs, resulting in low capacity configuration efficiency between energy storage equipment and reactive equipment, thereby reducing the stability of the power grid, the present invention proposes a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method, which combines the actual operating conditions of distributed photovoltaic grid-connected points and artificial intelligence algorithms, takes economy as the goal, and reasonably allocates voltage regulation capabilities through low-cost reactive compensation equipment while suppressing voltage over-limit and ensuring that active power is not overloaded, so as to share the excessive cost of energy storage equipment, and finally achieves, under the same regulation results, the best economy, and under the same economic benefits, the best regulation effect, effectively improving the coordinated configuration efficiency of distributed photovoltaic grid-connected point energy storage equipment and reactive compensation. And this method can ignore the influence of the distributed photovoltaic operation mode on the control mode and algorithm. Whether the distributed photovoltaic is in the maximum power point tracking (MPPT) operation mode or the constant power operation mode, it can ensure that the control target is achieved with the same algorithm, effectively reducing the pressure and voltage fluctuation caused by the output fluctuation of the distributed photovoltaic system on the power grid, thereby significantly improving the stability of the power grid. At the same time, this metering and allocation strategy of the present invention makes the use of energy storage equipment more efficient, which is conducive to saving economic costs and optimizing the operation of the entire energy system. In addition, the method of the present invention can provide more accurate data and predictions for power grid operators and energy managers, so that more informed decisions can be made in terms of energy allocation, storage and utilization; and in terms of environmental protection, the present invention can help reduce dependence on fossil fuels by improving the efficiency of distributed photovoltaic systems, thereby reducing carbon emissions and having a positive impact on the environment. In summary, the present invention can not only improve the accuracy of photovoltaic power generation prediction, but also provide important support for the sustainable development of power grids and the effective use of renewable energy.

[0220] Embodiment 2:

[0221] The present invention based on the same inventive concept also provides a distributed photovoltaic grid-connected point energy storage and reactive power equipment optimization configuration system, the structural composition diagram is as follows Figure 3 As shown, including:

[0222] The target reactive power calculation module is used to calculate the target reactive power of the distributed photovoltaic grid-connected point according to the acquired operation data of the distributed photovoltaic grid-connected point when the voltage of the distributed photovoltaic grid-connected point exceeds the limit;

[0223] The capacity configuration module is used to allocate the capacity of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid-connected point based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, and obtain the capacity configuration ratio between the energy storage equipment and the reactive equipment by using the pre-built coordinated optimization configuration model;

[0224] A coordination optimization module is used to use the capacity configuration ratio between energy storage equipment and reactive equipment as an optimized configuration scheme for distributed photovoltaic grid-connected points;

[0225] Among them, the coordinated optimization configuration model in the capacity configuration module aims to minimize the total life cycle cost of energy storage equipment and reactive equipment, with flow constraints, voltage constraints, output constraints and regulation capacity constraints as constraints, and the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary.

[0226] In one implementation, the target reactive power calculation module may include:

[0227] A sensitivity calculation submodule, used to calculate the sensitivity of the distributed photovoltaic grid-connected points according to the acquired operation data of the distributed photovoltaic grid-connected points;

[0228] The target power determination submodule is used to obtain the target reactive power of the distributed photovoltaic grid-connected point according to the sensitivity of the distributed photovoltaic grid-connected point.

[0229] In this implementation, the above-mentioned target power determination submodule may include:

[0230] A change calculation unit is used to calculate the corresponding reactive power change according to the sensitivity of the distributed photovoltaic grid connection point;

[0231] The power determination unit is used to sum the current reactive power of the distributed photovoltaic grid-connected point and the reactive power change to obtain the target reactive power of the distributed photovoltaic grid-connected point.

[0232] For example, the calculation formula corresponding to the above sensitivity can be as follows:

[0233]

[0234] Among them, S en Represents the sensitivity of the distributed photovoltaic grid-connected point; S θP represents the partial derivative of the phase angle difference θ with respect to the active power of the distributed photovoltaic grid-connected point; S θQ represents the partial derivative of the phase angle difference θ with respect to the reactive power of the distributed photovoltaic grid-connected point; S VP represents the partial derivative of the grid connection point voltage with respect to the active power of the distributed photovoltaic grid connection point; S VQ Represents the partial derivative of the grid connection point voltage with respect to the reactive power of the distributed photovoltaic grid connection point;

[0235] For example, the calculation formula corresponding to the reactive power change can be as follows:

[0236] ΔQ=S VP ·Δθ+SVQ ΔV;

[0237] Among them, ΔQ represents the change in reactive power of the distributed photovoltaic grid-connected point; Δθ represents the phase angle difference; ΔV represents the change in voltage of the distributed photovoltaic grid-connected point.

[0238] In one implementation, the capacity configuration module may include:

[0239] A power comparison submodule is used to determine whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point;

[0240] The capacity allocation submodule is used to allocate the capacity of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid-connected point using the coordinated optimization configuration model when the target reactive power is not within the adjustable capacity range, and obtain the capacity configuration ratio between the energy storage equipment and the reactive equipment.

[0241] In this implementation, the capacity configuration module may further include:

[0242] The inverter control submodule is used to obtain the margin capacity of the photovoltaic inverter when the target reactive power is within the adjustable capacity range, and to perform over-limit voltage control on the distributed photovoltaic grid-connected point according to the margin capacity.

[0243] In one implementation, the optimization configuration system may further include: a model building module, which may be used to:

[0244] Determine the total life cycle cost of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid-connected point based on the sum of the cycle cost of reactive equipment, the investment cost of energy storage equipment, and the operation and maintenance cost of energy storage equipment;

[0245] Taking the minimum total life cycle cost as the goal, construct the objective function;

[0246] According to the active power and reactive power of the distributed photovoltaic grid-connected points, the Newton-Raphson polar coordinate algorithm is used to formulate power flow constraints;

[0247] Formulate voltage constraints based on the voltage allowable range and rated voltage of distributed photovoltaic grid-connected points;

[0248] Formulate output constraints based on the reactive power of PV inverters and / or reactive devices;

[0249] Formulate regulation capacity constraints based on the active power, reactive power and apparent power of the energy storage equipment;

[0250] The constraints are flow constraints, voltage constraints, output constraints and regulation capacity constraints;

[0251] Taking the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary, the configuration boundary expression is formulated;

[0252] A coordinated optimization configuration model is constructed based on the objective function, constraints and configuration boundary expressions.

[0253] For example, the expression corresponding to the above objective function can be as follows:

[0254] minF=C SVG +C IN +C OM ;

[0255] Where F represents the total life cycle cost of energy storage equipment and reactive equipment in distributed photovoltaic grid-connected points; S SVG Represents the periodic cost of reactive equipment; C IN Represents the investment cost of energy storage equipment; C OM Represents the operation and maintenance cost of energy storage equipment;

[0256] For example, the cycle cost C of the above reactive equipment is SVG The corresponding calculation formula can be as follows:

[0257] C SVG =c 1 Q 1 +c 2 ×c 3 Q 1 ;

[0258] Among them, c 1 To adjust the cost coefficient; Q 1 Indicates the maximum adjustable capacity of reactive equipment; c 2 represents the conversion cost coefficient; c 3 represents the margin factor;

[0259] For example, the investment cost C of the above energy storage equipment is IN The corresponding calculation formula can be as follows:

[0260]

[0261] Where r represents the base discount rate; n represents the operating period; C P Represents the unit investment of the power of energy storage equipment; P ESS Indicates the power of the energy storage device; C E Indicates the unit investment of the capacity of energy storage equipment; E ESS Indicates the installed capacity of the energy storage equipment;

[0262] For example, the operation and maintenance cost C of the above energy storage equipment OM The corresponding calculation formula can be as follows:

[0263] C OM =μC IN ;

[0264] Wherein, μ represents the operation and maintenance cost coefficient.

[0265] For example, the above configuration boundary expression can be as follows:

[0266] U T -U N =S PV ·P ESS +S QV ·(Q 1 +E ESS );

[0267] Among them, U T Indicates the over-limit voltage of the distributed photovoltaic grid-connected point; U N Indicates the rated voltage of the distributed photovoltaic grid-connected point; S PV P represents the partial derivative of the active power of the distributed photovoltaic grid-connected point with respect to the voltage; ESS Represents the active power of the energy storage device; S QV It represents the partial derivative of the reactive power of the distributed photovoltaic grid-connected point with respect to the voltage; Q 1 Indicates the maximum adjustable capacity of reactive equipment; E ESS Indicates the installed capacity of the energy storage equipment.

[0268] For example, the expression corresponding to the above power flow constraint can be as follows:

[0269]

[0270] Among them, P i represents the active power of distributed photovoltaic grid-connected point i; Q i Represents the reactive power of distributed photovoltaic grid-connected point i; U i Indicates the voltage amplitude of distributed photovoltaic grid-connected point i; U j represents the voltage amplitude of the distributed photovoltaic grid-connected point j; j∈i means that the grid-connected point j is a node directly connected to the grid-connected point i; G ij represents the real part of the admittance matrix of distributed photovoltaic grid connection points i and j; B ij represents the imaginary part of the admittance matrix of distributed photovoltaic grid-connected points i and j; θ ij is the phase angle difference between grid connection points i and j;

[0271] For example, the expression corresponding to the above output constraint can be as follows:

[0272]

[0273] Among them, Q PVRepresents the reactive power of the photovoltaic inverter; Q PVmax Indicates the maximum reactive power output of the photovoltaic inverter; Q SVG.max Indicates the maximum reactive power of reactive equipment; P PV represents the active power of the photovoltaic inverter; S represents the apparent power of the photovoltaic inverter; A 3 Represents the apparent coefficient; Q SVG Indicates the reactive power of reactive equipment; Q SVG.min Indicates the minimum reactive power of the reactive device.

[0274] Embodiment 3:

[0275] like Figure 4 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0276] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method in the above embodiment.

[0277] Embodiment 4:

[0278] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method in the above embodiment.

[0279] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0280] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0281] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0282] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0283] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method, characterized in that: include: When the voltage of the distributed photovoltaic grid-connected point exceeds the limit, the target reactive power of the distributed photovoltaic grid-connected point is calculated according to the acquired operation data of the distributed photovoltaic grid-connected point; Based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point is allocated using a pre-built coordinated optimization configuration model to obtain a capacity configuration ratio between the energy storage device and the reactive device; Taking the capacity configuration ratio between the energy storage device and the reactive device as the optimal configuration scheme of the distributed photovoltaic grid-connected point; Among them, the coordinated optimization configuration model aims to minimize the total life cycle cost of the energy storage equipment and the reactive equipment, takes flow constraints, voltage constraints, output constraints and regulation capacity constraints as constraints, and takes the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary.

2. The method according to claim 1, characterized in that The coordinated optimization configuration model includes the following construction process: Determine the total life cycle cost of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid connection point based on the sum of the periodic cost of the reactive equipment, the investment cost of the energy storage equipment and the operation and maintenance cost of the energy storage equipment; Taking the minimum total life cycle cost as the goal, constructing an objective function; According to the active power and reactive power of the distributed photovoltaic grid-connected point, a power flow constraint is formulated using a Newton-Raphson polar coordinate algorithm; Formulate voltage constraints according to the voltage allowable range and rated voltage of the distributed photovoltaic grid-connected point; Formulate output constraints according to the reactive power of the photovoltaic inverter and / or the reactive device; Formulate regulation capacity constraints based on active power, reactive power and apparent power of the energy storage device; The power flow constraint, voltage constraint, output constraint and regulation capacity constraint are used as constraint conditions; Taking the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary, a configuration boundary expression is formulated; A coordinated optimization configuration model is constructed according to the objective function, constraint conditions and the configuration boundary expression.

3. The method according to claim 2, characterized in that The expression corresponding to the objective function is as follows: minF=C SVG +C IN +C OM ; Wherein, F represents the total life cycle cost of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid point; C SVG Represents the periodic cost of reactive equipment; C IN Represents the investment cost of energy storage equipment; C OM Represents the operation and maintenance cost of energy storage equipment; The cycle cost C of the reactive equipment SVG The corresponding calculation formula is as follows: C SVG =c1Q1+c2×c3Q1; Among them, c1 is the adjustment cost coefficient; Q1 represents the maximum adjustable capacity of reactive equipment; c2 represents the conversion cost coefficient; c3 represents the margin coefficient; The investment cost of the energy storage equipment is C IN The corresponding calculation formula is as follows: Where r represents the base discount rate; n represents the operating period; C P Represents the unit investment of the power of energy storage equipment; P ESS Indicates the power of the energy storage device; C E Indicates the unit investment of the capacity of energy storage equipment; E ESS Indicates the installed capacity of the energy storage equipment; The operation and maintenance cost of the energy storage equipment is C OM The corresponding calculation formula is as follows: C OM =μC IN ; Wherein, μ represents the operation and maintenance cost coefficient.

4. The method according to claim 2 or 3, characterized in that The configuration boundary expression is as follows: U T -U N =S PV ·P ESS +S QV ·(Q1+E ESS ); Among them, U T Indicates the over-limit voltage of the distributed photovoltaic grid-connected point; U N Indicates the rated voltage of the distributed photovoltaic grid-connected point; S PV P represents the partial derivative of the active power of the distributed photovoltaic grid-connected point with respect to the voltage; ESS Represents the active power of the energy storage device; S QV represents the partial derivative of the reactive power of the distributed photovoltaic grid-connected point with respect to the voltage; Q1 represents the maximum adjustable capacity of the reactive equipment; E ESS Indicates the installed capacity of the energy storage equipment.

5. The method according to claim 1 or 2, characterized in that: The method of allocating the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point using a pre-built coordinated optimization configuration model to obtain a capacity configuration ratio between the energy storage device and the reactive device includes: Based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point, determining whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter; If the target reactive power is not within the adjustable capacity range, the coordinated optimization configuration model is used to allocate the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point to obtain the capacity configuration ratio between the energy storage device and the reactive device.

6. The method according to claim 5, characterized in that After determining whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter, the method further includes: If the target reactive power is within the adjustable capacity range, the margin capacity of the photovoltaic inverter is obtained, and the over-limit voltage control is performed on the distributed photovoltaic grid-connected point according to the margin capacity.

7. The method according to claim 1, characterized in that The step of calculating the target reactive power of the distributed photovoltaic grid-connected point according to the acquired operation data of the distributed photovoltaic grid-connected point comprises: Calculating the sensitivity of the distributed photovoltaic grid-connected point according to the acquired operating data of the distributed photovoltaic grid-connected point; According to the sensitivity of the distributed photovoltaic grid-connected point, the target reactive power of the distributed photovoltaic grid-connected point is obtained.

8. The method according to claim 7, characterized in that The step of obtaining the target reactive power of the distributed photovoltaic grid-connected point according to the sensitivity of the distributed photovoltaic grid-connected point comprises: Calculating the corresponding reactive power change according to the sensitivity of the distributed photovoltaic grid-connected point; The current reactive power of the distributed photovoltaic grid-connected point and the reactive power change are summed to obtain the target reactive power of the distributed photovoltaic grid-connected point.

9. The method according to claim 8, characterized in that The calculation formula corresponding to the sensitivity is as follows: Among them, S en Represents the sensitivity of the distributed photovoltaic grid-connected point; S θP represents the partial derivative of the phase angle difference θ with respect to the active power of the distributed photovoltaic grid-connected point; S θQ represents the partial derivative of the phase angle difference θ with respect to the reactive power of the distributed photovoltaic grid-connected point; S VP represents the partial derivative of the grid connection point voltage with respect to the active power of the distributed photovoltaic grid connection point; S VQ Represents the partial derivative of the grid connection point voltage with respect to the reactive power of the distributed photovoltaic grid connection point; The calculation formula corresponding to the reactive power change is as follows: ΔQ=S VP ·Δθ+S VQ ·ΔV; Among them, ΔQ represents the change in reactive power of the distributed photovoltaic grid-connected point; Δθ represents the phase angle difference; ΔV represents the change in voltage of the distributed photovoltaic grid-connected point.

10. The method according to claim 1 or 2, characterized in that: The expression corresponding to the power flow constraint is as follows: Among them, P i represents the active power of distributed photovoltaic grid-connected point i; Q i Represents the reactive power of distributed photovoltaic grid-connected point i; U i Indicates the voltage amplitude of distributed photovoltaic grid-connected point i; U j represents the voltage amplitude of the distributed photovoltaic grid-connected point j; j∈i means that the grid-connected point j is a node directly connected to the grid-connected point i; G ij represents the real part of the admittance matrix of distributed photovoltaic grid connection points i and j; B ij represents the imaginary part of the admittance matrix of distributed photovoltaic grid-connected points i and j; θ ij is the phase angle difference between grid connection points i and j; The expression corresponding to the output constraint is as follows: Among them, Q PV Represents the reactive power of the photovoltaic inverter; Q PVmax Indicates the maximum reactive power output of the photovoltaic inverter; Q SVG.max Indicates the maximum reactive power of reactive equipment; P PV represents the active power of the photovoltaic inverter; S represents the apparent power of the photovoltaic inverter; A3 represents the apparent coefficient; Q SVG Indicates the reactive power of reactive equipment; Q SVG.min Indicates the minimum reactive power of the reactive device.

11. A distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration system, characterized in that: include: A target reactive power calculation module is used to calculate the target reactive power of the distributed photovoltaic grid-connected point according to the acquired operation data of the distributed photovoltaic grid-connected point when the voltage of the distributed photovoltaic grid-connected point exceeds the limit; A capacity configuration module is used to allocate the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point using a pre-built coordinated optimization configuration model to obtain a capacity configuration ratio between the energy storage device and the reactive device; A coordination optimization module, used to use the capacity configuration ratio between the energy storage device and the reactive device as an optimized configuration scheme for the distributed photovoltaic grid-connected point; Among them, the coordinated optimization configuration model in the capacity configuration module is based on the goal of minimizing the total life cycle cost of the energy storage equipment and the reactive equipment, with flow constraints, voltage constraints, output constraints and regulation capacity constraints as constraints, and the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point is constructed as the control boundary.

12. The system according to claim 11, characterized in that Also includes: Model building modules for: Determine the total life cycle cost of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid connection point based on the sum of the periodic cost of the reactive equipment, the investment cost of the energy storage equipment and the operation and maintenance cost of the energy storage equipment; Taking the minimum total life cycle cost as the goal, constructing an objective function; According to the active power and reactive power of the distributed photovoltaic grid-connected point, a power flow constraint is formulated using a Newton-Raphson polar coordinate algorithm; Formulate voltage constraints according to the voltage allowable range and rated voltage of the distributed photovoltaic grid-connected point; Formulate output constraints according to the reactive power of the photovoltaic inverter and / or the reactive device; Formulate regulation capacity constraints based on active power, reactive power and apparent power of the energy storage device; The power flow constraint, voltage constraint, output constraint and regulation capacity constraint are used as constraint conditions; Taking the difference between the over-limit voltage and the rated voltage of the distributed photovoltaic grid-connected point as the control boundary, a configuration boundary expression is formulated; A coordinated optimization configuration model is constructed according to the objective function, constraint conditions and the configuration boundary expression.

13. The system of claim 12, wherein: The expression corresponding to the objective function is as follows: minF=C SVG +G IN +C OM ; Wherein, F represents the total life cycle cost of the energy storage equipment and reactive equipment connected to the distributed photovoltaic grid point; C SVG Represents the periodic cost of reactive equipment; C IN Represents the investment cost of energy storage equipment; C OM Represents the operation and maintenance cost of energy storage equipment; The cycle cost C of the reactive equipment SVG The corresponding calculation formula is as follows: C SVG =c1Q1+c2×c3Q1; Among them, c1 is the adjustment cost coefficient; Q1 represents the maximum adjustable capacity of reactive equipment; c2 represents the conversion cost coefficient; c3 represents the margin coefficient; The investment cost of the energy storage equipment is C IN The corresponding calculation formula is as follows: Where r represents the base discount rate; n represents the operating period; C P Represents the unit investment of the power of energy storage equipment; P ESS Indicates the power of the energy storage device; C E Indicates the unit investment of the capacity of energy storage equipment; E ESS Indicates the installed capacity of the energy storage equipment; The operation and maintenance cost of the energy storage equipment is C OM The corresponding calculation formula is as follows: C OM =μC IN ; Wherein, μ represents the operation and maintenance cost coefficient.

14. The system according to claim 12 or 13, characterized in that The configuration boundary expression is as follows: U T -U N =S PV ·P ESS +S QV ·(Q1+E ESS ); Among them, U T Indicates the over-limit voltage of the distributed photovoltaic grid-connected point; U N Indicates the rated voltage of the distributed photovoltaic grid-connected point; S PV P represents the partial derivative of the active power of the distributed photovoltaic grid-connected point with respect to the voltage; ESS Represents the active power of the energy storage device; S QV represents the partial derivative of the reactive power of the distributed photovoltaic grid-connected point with respect to the voltage; Q1 represents the maximum adjustable capacity of the reactive equipment; E ESS Indicates the installed capacity of the energy storage equipment.

15. The system according to claim 11 or 12, characterized in that The capacity configuration module includes: A power comparison submodule, configured to determine whether the target reactive power is within the adjustable capacity range of the photovoltaic inverter based on the target reactive power and the adjustable capacity range of the photovoltaic inverter connected to the distributed photovoltaic grid-connected point; The capacity allocation submodule is used to allocate the capacity of the energy storage device and the reactive device connected to the distributed photovoltaic grid-connected point using the coordinated optimization configuration model when the target reactive power is not within the adjustable capacity range, so as to obtain the capacity configuration ratio between the energy storage device and the reactive device.

16. The system of claim 15, wherein: The capacity configuration module further includes: The inverter control submodule is used to obtain the margin capacity of the photovoltaic inverter when the target reactive power is within the adjustable capacity range, and perform over-limit voltage control on the distributed photovoltaic grid-connected point according to the margin capacity.

17. The system of claim 11, wherein: The target reactive power calculation module comprises: A sensitivity calculation submodule, used to calculate the sensitivity of the distributed photovoltaic grid-connected point according to the acquired operation data of the distributed photovoltaic grid-connected point; The target power determination submodule is used to obtain the target reactive power of the distributed photovoltaic grid-connected point according to the sensitivity of the distributed photovoltaic grid-connected point.

18. The system of claim 17, wherein: The target power determination submodule includes: A variation calculation unit, used to calculate the corresponding reactive power variation according to the sensitivity of the distributed photovoltaic grid-connected point; The power determination unit is used to sum the current reactive power of the distributed photovoltaic grid-connected point with the reactive power change to obtain the target reactive power of the distributed photovoltaic grid-connected point.

19. The system of claim 18, wherein: The calculation formula corresponding to the sensitivity is as follows: Among them, S en Represents the sensitivity of the distributed photovoltaic grid-connected point; S θP represents the partial derivative of the phase angle difference θ with respect to the active power of the distributed photovoltaic grid-connected point; S θQ represents the partial derivative of the phase angle difference θ with respect to the reactive power of the distributed photovoltaic grid-connected point; S VP represents the partial derivative of the grid connection point voltage with respect to the active power of the distributed photovoltaic grid connection point; S VQ Represents the partial derivative of the grid connection point voltage with respect to the reactive power of the distributed photovoltaic grid connection point; The calculation formula corresponding to the reactive power change is as follows: ΔQ=S VP ·Δθ+S VQ ·ΔV; Among them, ΔQ represents the change in reactive power of the distributed photovoltaic grid-connected point; Δθ represents the phase angle difference; ΔV represents the change in voltage of the distributed photovoltaic grid-connected point.

20. The system according to claim 11 or 12, characterized in that The expression corresponding to the power flow constraint is as follows: Among them, P i represents the active power of distributed photovoltaic grid-connected point i; Q i Represents the reactive power of distributed photovoltaic grid-connected point i; U i Indicates the voltage amplitude of distributed photovoltaic grid-connected point i; U j represents the voltage amplitude of the distributed photovoltaic grid-connected point j; j∈i means that the grid-connected point j is a node directly connected to the grid-connected point i; G ij represents the real part of the admittance matrix of distributed photovoltaic grid connection points i and j; B ij represents the imaginary part of the admittance matrix of distributed photovoltaic grid-connected points i and j; θ ij is the phase angle difference between grid connection points i and j; The expression corresponding to the output constraint is as follows: Among them, Q PV Represents the reactive power of the photovoltaic inverter; Q PVmax Indicates the maximum reactive power output of the photovoltaic inverter; Q SVG.max Indicates the maximum reactive power of reactive equipment; P PV represents the active power of the photovoltaic inverter; S represents the apparent power of the photovoltaic inverter; A3 represents the apparent coefficient; Q SVG Indicates the reactive power of reactive equipment; Q SVG.min Indicates the minimum reactive power of the reactive device.

21. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method as described in any one of claims 1 to 10 is implemented.

22. A computing device readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a distributed photovoltaic grid-connected point energy storage and reactive equipment optimization configuration method as described in any one of claims 1 to 10 is implemented.

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