Energy-saving optimization control method and device and photovoltaic power distribution system

Abstract

The invention discloses an energy-saving optimization control method and device and a photovoltaic power distribution system. The method comprises the following steps: acquiring basic data of the photovoltaic power distribution system; obtaining line loss, transformer loss and voltage deviation based on the basic data; constructing a multi-target reactive power optimization model of the photovoltaic power distribution system according to the line loss, the transformer loss and the voltage deviation; based on a constraint condition, performing energy-saving optimization control on the multi-target reactive power optimization model according to an improved particle swarm algorithm to obtain an energy-saving optimization control strategy; and finally, optimizing the photovoltaic power distribution system according to the energy-saving optimization control strategy so as to realize energy-saving optimization control of the photovoltaic power distribution system. According to the energy-saving optimization control method, full-process energy-saving control of the photovoltaic power distribution system is realized, and overall optimization is achieved, so that the operation efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy conservation, and in particular relates to an energy conservation optimization control method, device and photovoltaic power distribution system. Background Art

[0002] Globally, energy demand continues to rise, while climate change becomes increasingly severe. This reality has led to a global consensus in the energy sector: the need to transform the energy structure toward a clean, low-carbon one. Among the many renewable energy sources, photovoltaic power generation has rapidly gained popularity worldwide due to its inexhaustible photovoltaic resources and significant environmental benefits.

[0003] However, with the continuous expansion of the scale of photovoltaic power station construction and the accumulation of operating time, the energy loss and safety and stability problems in their distribution systems have gradually surfaced and become increasingly prominent. These problems have become an important factor restricting the overall efficiency improvement and economic benefit guarantee of the power station.

[0004] Energy loss in photovoltaic power plants is a complex issue involving multiple factors, encompassing key areas such as power distribution systems, equipment energy efficiency, and operational management. Specifically, these factors include transformer losses, line losses, inverter losses, and reactive power losses. Transformer losses are particularly prominent and are a major source of losses in photovoltaic power plants. Typically, photovoltaic power plants are equipped with numerous transformers to ensure proper voltage conversion and efficient power transmission. However, photovoltaic power generation is significantly affected by sunlight conditions, resulting in significant fluctuations in output power. This often results in transformers operating in lightly loaded or no-load conditions. In these conditions, iron and copper losses compound, significantly reducing transformer efficiency.

[0005] Line losses are also a significant contributor to energy consumption in PV power plants. This is especially true in large-scale PV plants, where the large footprint and long collection lines mean that losses due to line resistance account for a significant portion of the overall loss system.

[0006] China has invested a lot of research efforts in the field of energy-saving control of photovoltaic power stations, and has also achieved certain results. Taking patent CN117937626A as an example, this patent innovatively determines the start and shutdown operation of the photovoltaic power generation substation by accurately judging whether the photovoltaic voltage meets the pre-set conditions, successfully realizing the automatic start-stop switching function, thereby effectively reducing the no-load loss. In addition, the patent fully considers multiple dimensional parameters such as switch status, light intensity, grid voltage, line current and photovoltaic voltage in its design concept, realizing accurate switching between no-load loss and normal power generation operation state, significantly improving the energy-saving effect of the photovoltaic power station. In addition, patent CN119134295A also discloses a photovoltaic power station energy-saving system. This system is mainly designed to improve the no-load operation problem of box-type transformers at night. By reasonably controlling the operation mode of the box-type transformer, the power loss caused by no-load operation at night is avoided. At the same time, the system also paid attention to the adverse effects that frequent operation of the circuit breaker may have on the main insulation of the transformer winding, inter-turn insulation and the arc extinguishing performance of the circuit breaker, and made corresponding optimizations, thereby effectively reducing the energy consumption level of the distribution station.

[0007] However, CN117937626A focuses on reducing no-load losses through automatic start-stop switching, but fails to develop a comprehensive energy-saving control solution from the perspective of the entire process of photovoltaic power station construction, operation, and maintenance. CN119134295A, on the other hand, focuses primarily on local energy-saving control of box-type transformers at night, lacks a comprehensive consideration of the energy consumption of the entire photovoltaic power station, and fails to form a comprehensive energy-saving strategy that covers the overall operation of the power station. Therefore, the existing technology, including these patents, has failed to optimize the overall energy-saving effect of photovoltaic power stations. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the existing technology and propose an energy-saving optimization control method, device, and photovoltaic power distribution system. This energy-saving optimization control method realizes full-process energy-saving control of the photovoltaic power distribution system, achieving overall optimization, thereby significantly improving operational efficiency.

[0009] In a first aspect, the present invention provides an energy-saving optimization control method for a photovoltaic power distribution system, the method comprising the following steps:

[0010] Step K1: Obtain a multi-objective reactive power optimization model for the photovoltaic distribution system;

[0011] Step K2: Based on the constraints, the multi-objective reactive power optimization model is subjected to energy-saving optimization control according to the improved particle swarm algorithm to obtain an energy-saving optimization control strategy;

[0012] Step K3: Optimizing the photovoltaic power distribution system according to the energy-saving optimization control strategy to achieve energy-saving optimization control of the photovoltaic power distribution system.

[0013] Furthermore, a multi-objective reactive power optimization model of the photovoltaic distribution system is obtained, which specifically includes:

[0014] Step S1: Obtain basic data of the photovoltaic power distribution system;

[0015] Step S2: Based on the basic data, obtain line loss, transformer loss and voltage deviation;

[0016] Step S3: constructing a multi-objective reactive power optimization model for a photovoltaic power distribution system according to the line loss, the transformer loss and the voltage deviation.

[0017] Furthermore, the step S1 specifically includes the following steps:

[0018] Arrange data acquisition devices at the inverter outlet, high-voltage side of the distribution transformer, low-voltage side of the distribution transformer, and each load node in the photovoltaic distribution system;

[0019] The data acquisition device collects node voltage, line current, active power, reactive power, and light intensity in real time;

[0020] Determine the operating status and load status of the photovoltaic distribution system based on node voltage, line current, active power, reactive power, and light intensity;

[0021] The node voltage, line current, active power, reactive power, light intensity, operating status and load status of the photovoltaic distribution system are summarized to obtain basic data of the photovoltaic distribution system.

[0022] Furthermore, before the step S1, the method further comprises a step S0: constructing a photovoltaic power station topology structure with three voltage levels, namely, a high-voltage grid-connected side, a main transformer transmission layer, and a low-voltage collector side;

[0023] The main transformer transmission layer is arranged between the high-voltage grid-connected side and the low-voltage collector side, and a dynamic topology switching mechanism is provided in the photovoltaic power station topology structure.

[0024] Furthermore, the dynamic topology switching mechanism specifically includes: automatic switching on and off of the main transformer high-voltage side switch at the main transformer transmission layer;

[0025] The automatic switching on and off of the main transformer high-voltage side switch of the main transformer transmission layer specifically includes the following steps:

[0026] Step A1: Check the status B of the switch on the high-voltage side of the main transformer;

[0027] If B = 0, the process is terminated and the current state is returned; if B = 1, step A2 is entered;

[0028] Step A2: Collect the current light intensity S; and compare the collected light intensity S with the preset minimum light intensity S of the photovoltaic module for power generation min , min as follows:

[0029] If S ≥ S min , after a delay time t w1 , data collection and comparison are performed again until it is detected that S < S min ; if S < S min , step A3 is entered;

[0030] Step A3: Compare the collected voltage U1 on the low-voltage side of the main transformer with the preset rated voltage U ref as follows:

[0031] If U1 < U ref , this process is terminated and the current state is returned; if U1 ≥ U ref , step A4 is entered;

[0032] Step A4: Compare the collected line current I with the preset first line current I0:

[0033] If I ≥ I0, after a delay time t w1 , the line current is obtained and compared again; if I < I0, after a delay time t w2 , I and I0 are compared again. If it is still I < I0, the switch on the high-voltage side of the transformer is switched on and off to realize the automatic switching on and off of the switch on the high-voltage side of the main transformer at the main transformer power transmission layer.

[0034] Furthermore, the automatic switching on of the switch on the high-voltage side of the main transformer at the main transformer power transmission layer specifically includes the following steps:

[0035] Step B1: Check the state B of the switch on the high-voltage side of the main transformer;

[0036] If B = 1, the process is terminated and the current state is returned; if B = 0, step B2 is entered;

[0037] Step B2: Collect the current light intensity S; and compare the collected light intensity S with the preset minimum light intensity S of the photovoltaic module for power generation min as follows:

[0038] If S ≤ S min , after a delay time t w1 , the light intensity is collected again until it is detected that S > S min ; if S > S min , step B3 is entered;

[0039] Step B3: Compare the collected low-voltage side voltage U1 of the main transformer with the preset rated voltage U ref as follows:

[0040] If U1 < U ref , terminate this process and return to the current state; if U1 ≥ U ref , proceed to Step B4;

[0041] Step B4: Compare the collected line current I with the preset second line current I1:

[0042] If I is not equal to I1, re-collect the line current I; if I = I1, proceed to Step B5;

[0043] Step B5: Compare the collected photovoltaic voltage U2 with the set DC threshold voltage U3:

[0044] If U2 < U3, after a delay time t w2 , re-collect the photovoltaic voltage until it is detected that U2 ≥ U3; if U2 ≥ U3, switch on the high-voltage side switch of the transformer to automatically switch on the high-voltage side switch of the main transformer at the main transformer power transmission layer.

[0045] Furthermore, for the line loss in Step S2, the acquisition process specifically includes the following steps:

[0046] Step S211: Obtain the branch current and the resistance of the branch transmission line;

[0047] Step S212: Calculate the line loss based on the branch current and the resistance of the branch transmission line, thereby obtaining the line loss;

[0048] The formula for the line loss P loss is as follows:

[0049]

[0050] where N l is the number of branches, I l is the branch current, and R l is the resistance of the transmission line of the l-th branch.

[0051] Furthermore, for the transformer loss in Step S2, the acquisition process specifically includes the following steps:

[0052] Step S221: Calculate the no-load loss of the transformer; and calculate the load loss of the transformer;

[0053] The no-load loss P Fe of the transformer, its calculation formula is as follows:

[0054] P Fe =K p0 G Fe p t ;

[0055] Among them, K p0 is the iron loss coefficient, G Fe is the core mass, p t is the iron loss per unit mass;

[0056] The load loss P of the transformer cu , which is calculated as follows:

[0057]

[0058] Among them, I T is the high voltage side current of the transformer, R T It is the high voltage side winding of the transformer;

[0059] Step S222: Calculating the total loss of the transformer based on the no-load loss and load loss of the transformer to obtain the transformer loss;

[0060] The total loss of the transformer P T The calculation formula is as follows:

[0061]

[0062] Among them, N T is the number of transformers;

[0063] is the no-load loss of the i-th transformer;

[0064] is the load loss of the i-th transformer.

[0065] Furthermore, the voltage deviation in step S2 is obtained by the following steps:

[0066] Step S231: obtaining a reference voltage and a node operating voltage;

[0067] Step S232: Calculate the difference between the reference voltage and the node operating voltage to obtain a voltage deviation;

[0068] The voltage deviation is calculated as follows:

[0069]

[0070] Among them, U diff is the voltage deviation, U ref is the reference voltage, U iis the node voltage of the i-th node, N is the number of nodes, and N is a natural number greater than 1.

[0071] Furthermore, the step S3 specifically includes the following steps:

[0072] Normalizing the line loss, the transformer loss, and the voltage deviation to obtain a total objective function;

[0073] The expression of the total objective function minF is as follows:

[0074]

[0075] in,

[0076] f1=P loss ; f2=P T ; f3 = U diff ;

[0077] Among them, P loss is the line loss, P T is the total loss of the transformer, U diff is the voltage deviation, λ1 is The weight coefficient, λ2 is The weight coefficient, λ1+λ2=1, f1 0 、 These are the initial values corresponding to f1, f2, and f3 respectively.

[0078] Furthermore, before step K2, the method further includes step K0: setting constraint conditions;

[0079] The step K0 specifically includes:

[0080] Set supply and demand relationship constraints; set node voltage constraints; set transformer capacity constraints; set photovoltaic collection line power generation limits; and set energy storage line operation constraints.

[0081] Furthermore, the supply and demand relationship constraints are specifically set as follows:

[0082]

[0083] Among them, N ess is the number of energy storage lines, N inv is the number of photovoltaic collection lines, P PV , Q PV They are active dispatching instructions and reactive dispatching instructions received by the photovoltaic power station respectively. are the active output and reactive output of the photovoltaic inverter respectively, are the active power and reactive power of the energy storage converter, X lis the reactance of branch l transmission line, N l is the number of branches, I l is the branch current, R l is the resistance of the lth branch transmission line;

[0084] The node voltage constraint is specifically set as follows:

[0085]

[0086] Among them, U i is the voltage of the i-th node, is the reference voltage of the i-th node;

[0087] The transformer capacity constraint is set as follows:

[0088]

[0089] in, is the high voltage side current of the transformer, S TN is the rated capacity of the transformer, The rated voltage of the high-voltage side of the box-type transformer;

[0090] The setting of the photovoltaic collector line power generation limit is specifically as follows:

[0091]

[0092] in, is the active output of the photovoltaic inverter, is the reactive output of the photovoltaic inverter, The upper limit of active and reactive power output of the photovoltaic inverter;

[0093] The energy storage line operation constraints are specifically set as follows:

[0094]

[0095] in, is the active power of the energy storage converter;

[0096] The storage capacity of the i-th energy storage device at time t+1;

[0097] is the storage energy of the i-th energy storage device at time t;

[0098] is the active power of the i-th energy storage converter at time t;

[0099] P ch 、P dis They are the energy storage charging power limit and the energy storage discharging power limit respectively;

[0100] E ess_max 、E ess_min They are the upper limit and lower limit of energy storage capacity respectively.

[0101] Furthermore, in step K2, energy-saving optimization control is performed on the multi-objective reactive power optimization model according to the improved particle swarm algorithm, which specifically includes the following steps:

[0102] Step D1: Set the initial parameters of the particle swarm algorithm;

[0103] The initial parameters of the particle swarm algorithm include population size, maximum number of iterations, learning factor, initial value of inertia weight ω0, upper and lower limits of particle velocity, initial scale parameter γ0 and attenuation factor τ;

[0104] Step D2: according to the multi-objective reactive power optimization model and based on the initial parameters of the particle swarm algorithm, obtaining the fitness value of each particle;

[0105] Step D3: Initialize the optimal position p of the individual in the population according to the fitness value of each particle best and the global optimal position p gbest ;

[0106] Step D4: According to the optimal position p of the individual of the initialized population best and the global optimal position p gbest , calculate the average Euclidean distance ED avg ;

[0107] Step D5: Adaptive inertia weight based on population state, according to the average Euclidean distance ED avg Update the inertia weight to obtain an updated inertia weight;

[0108] Step D6: Calculate the new velocity and new position of the particle based on the updated inertia weight and learning factor;

[0109] Step D7: Process out-of-bounds particles according to the constraints of particle velocity and position;

[0110] Step D8: Update the fitness of each particle in the particle swarm according to the new position of the particle;

[0111] Step D9: Based on the updated fitness of each particle, update the individual optimal position p of the particle best and the global optimal position p gbest ;

[0112] Step D10: Randomly perturb the updated particles;

[0113] Step D11: Reprocess the particles that have crossed the boundary according to the constraints of the particle's velocity and position;

[0114] Step D12: Update the individual optimal position p of the particle after random perturbation best ;

[0115] Step D13: Determine whether the convergence condition is met, and when the maximum number of iterations is reached or the set convergence condition is met, end the iteration and output the optimal allocation solution; otherwise, repeat steps D4 to D13 until the convergence condition is met.

[0116] Furthermore, in step D6, the new velocity and new position of the particle are calculated as follows:

[0117]

[0118] Where N is the population size, k is the number of iterations, d is the dimension of the control variable, f is the fitness function; c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1], and ω is the inertia weight; is the velocity of the d-th dimension of the i-th particle at the k+1-th iteration, is the position of the d-th dimension of the i-th particle at the k+1-th iteration; is the velocity of the d-th dimension of the i-th particle at the k-th iteration, is the position of the d-th dimension of the i-th particle at the k-th iteration, is the individual optimal position of the d-th dimension of the i-th particle in the k-th iteration, is the global optimal position of the dth dimension of the entire population in k iterations;

[0119] Among them, the update formula of inertia weight is as follows:

[0120]

[0121] Among them, ω k is the inertia weight of the kth iteration;

[0122] ω k-1 is the inertia weight of the k-1th iteration;

[0123] is the average Euclidean distance of the kth iteration;

[0124] is the average Euclidean distance of the k-1th iteration;

[0125] k max The maximum number of iterations is set.

[0126] Furthermore, for the individual optimal position pbest and the global optimal position p gbest The random perturbation of particles in the system is based on the random trajectory correction strategy of Cauchy-Lorentz distribution;

[0127] The random trajectory correction formula based on the Cauchy-Lorentz distribution is as follows:

[0128]

[0129] Where, is the new position of the i-th particle at the k-th iteration after correction;

[0130] is the position of the i-th particle at the k-th iteration;

[0131] is the position of the global optimal particle, β∈[-1,1], indicating that the particle position is mutated in the positive or negative direction. represents the Euclidean distance between the particle and the optimal particle;

[0132] The position of the global optimal particle The position parameter of the probability density function of the Cauchy-Lorentz distribution is used to correct the trajectory of particles in the population. The calculation formula is as follows:

[0133]

[0134] Among them, γ k is the scale parameter, γ0 is the initial scale parameter, and τ is the attenuation factor;

[0135] is the position of the i-th particle after the k-th iteration; is the optimal position after the kth iteration;

[0136] The probability density function of the Cauchy-Lorentz distribution is as follows:

[0137]

[0138] Where x is a random variable; x0 is the location parameter of the Cauchy distribution peak; and γ is the scale parameter at half the height of the Cauchy distribution peak.

[0139] In a second aspect, the present invention provides an energy-saving optimization control device for a photovoltaic power distribution system, the device comprising:

[0140] An acquisition unit, used for acquiring a multi-objective reactive power optimization model of a photovoltaic power distribution system;

[0141] A control unit, connected to the acquisition unit, is used to perform energy-saving optimization control on the multi-objective reactive power optimization model based on constraint conditions and an improved particle swarm algorithm to obtain an energy-saving optimization control strategy;

[0142] An optimization unit is connected to the control unit and is used to optimize the photovoltaic power distribution system according to the energy-saving optimization control strategy to achieve energy-saving optimization control of the photovoltaic power distribution system.

[0143] Furthermore, the acquisition unit specifically includes:

[0144] Acquisition module, used to obtain basic data of photovoltaic power distribution system;

[0145] a calculation module, connected to the acquisition module, for obtaining line loss, transformer loss and voltage deviation based on the basic data;

[0146] A construction module is connected to the calculation module and is used to construct a multi-objective reactive power optimization model of the photovoltaic power distribution system according to the line loss, the transformer loss and the voltage deviation.

[0147] In a third aspect, the present invention provides a photovoltaic distribution system, which includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the energy-saving optimization control method of the photovoltaic distribution system according to the first aspect.

[0148] This paper uses an improved particle swarm optimization algorithm to provide a comprehensive energy-saving control solution for photovoltaic power distribution systems, achieving global optimal performance and significantly improving their operating efficiency. The benefits of this method are specifically reflected in the following aspects:

[0149] 1. Full-Process Optimization: This invention provides a complete energy-saving optimization process, from establishing the PV power plant topology to solving reactive power optimization problems. This process includes multiple key steps, including system modeling, data collection, status detection, loss calculation, and command optimization and allocation, ensuring full-process energy-saving control of the PV power plant.

[0150] 2. Intelligent Control: Based on the operational characteristics of the photovoltaic power station, this invention dynamically adjusts the topology of the photovoltaic power station through real-time data collection and status detection. This helps reduce energy loss in the system under no-load and light-load conditions, thereby achieving more efficient energy utilization.

[0151] 3. Optimized Command Allocation: Incorporating an improved optimization algorithm, this invention achieves intelligent control of the photovoltaic power distribution system and rationalized command allocation. This approach reduces reliance on manual intervention and improves the operational efficiency of the photovoltaic power station.

[0152] 4. Improve operational efficiency: Through the above measures, the present invention not only optimizes the energy consumption of the photovoltaic power station, but also improves the overall operational efficiency, making the photovoltaic power distribution system more intelligent and automated. BRIEF DESCRIPTION OF THE DRAWINGS

[0153] Figure 1 Schematic diagram of an energy-saving optimization control method for a photovoltaic power distribution system in an embodiment of the present invention;

[0154] Figure 2 This is a flow chart of energy-saving optimization control of a photovoltaic power distribution system in an embodiment of the present invention;

[0155] Figure 3 This is an integrated topology diagram of a photovoltaic power station in an embodiment of the present invention;

[0156] Figure 4 This is a flowchart of reactive power optimization solution based on improved particle swarm optimization algorithm in an embodiment of the present invention;

[0157] Figure 5 Schematic diagram of an energy-saving optimization control device for a photovoltaic power distribution system in an embodiment of the present invention.

[0158] Reference numerals: 10, acquisition unit, 20, control unit, 30, optimization unit. DETAILED DESCRIPTION

[0159] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0160] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0161] It is understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments may be combined with each other.

[0162] It can be understood that, for the convenience of description, the drawings of the present invention only show parts related to the present invention, while parts unrelated to the present invention are not shown in the drawings.

[0163] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0164] It will be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0165] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented using a hardware-based system that implements the specified functions, or may be implemented using a combination of hardware and computer instructions.

[0166] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0167] Example 1:

[0168] This embodiment provides an energy-saving optimization control method for a photovoltaic power distribution system. This method, applicable to large-scale photovoltaic power plants, aims to effectively allocate photovoltaic power generation and energy storage to meet grid dispatch instructions through real-time monitoring and intelligent scheduling. This method dynamically adjusts system operation strategies to minimize energy loss, improve overall operational efficiency, and ensure safe and stable plant operation in the face of changing sunlight conditions and fluctuating grid demand.

[0169] like Figure 1 and Figure 2 As shown, the energy-saving optimization control method for a photovoltaic power distribution system provided in this embodiment includes the following steps:

[0170] Step K1: Obtain a multi-objective reactive power optimization model for the photovoltaic distribution system.

[0171] As a specific implementation method, obtaining a multi-objective reactive power optimization model for a photovoltaic power distribution system specifically includes:

[0172] Step S1: Obtain basic data of the photovoltaic power distribution system.

[0173] This step S1 specifically includes the following steps:

[0174] Arrange data acquisition devices at the inverter outlet, high-voltage side of the distribution transformer, low-voltage side of the distribution transformer, and each load node in the photovoltaic distribution system;

[0175] The data acquisition device collects node voltage, line current, active power, reactive power, and light intensity in real time;

[0176] Determine the operating status and load status of the photovoltaic distribution system based on node voltage, line current, active power, reactive power, and light intensity;

[0177] The node voltage, line current, active power, reactive power, light intensity, operating status and load status of the photovoltaic distribution system are summarized to obtain basic data of the photovoltaic distribution system.

[0178] As a specific implementation, before step S1, the method further includes step S0: constructing a photovoltaic power station topology structure with three voltage levels: a high-voltage grid-connected side, a main transformer transmission layer, and a low-voltage collector side, to obtain basic data of the photovoltaic power distribution system;

[0179] The main transformer transmission layer is arranged between the high-voltage grid-connected side and the low-voltage collector side, and a dynamic topology switching mechanism is provided in the photovoltaic power station topology structure.

[0180] The operating status of a photovoltaic distribution system can be determined by monitoring and analyzing data such as node voltage, line current, active power, reactive power, and light intensity. The specific process involves first acquiring real-time information such as node voltage and current, and branch active and reactive power, to assess the system's energy transmission. Next, combined with light intensity data, the photovoltaic power generation capacity is evaluated to determine whether the system is operating optimally. By comparing and analyzing this data, it is possible to determine whether the system is operating normally, enabling intelligent scheduling and optimized control to ensure stable and secure power supply.

[0181] The topology diagram is as follows Figure 3 The figure shows a 750kV-220kV-35kV three-level PV power station. PV panels, connected to inverters and box-type transformers, form a photovoltaic collection circuit. This circuit, in conjunction with the energy storage circuit, is then collected and transmitted to the 35kV busbar. The energy storage circuit stores energy during periods of high PV generation and surplus capacity, and releases it when PV power generation is insufficient to meet dispatch instructions. This ensures precise response to dispatch instructions, improves power supply and demand balance, and enhances grid stability. The collected power is then boosted from 35kV to 220kV via a 220kV main transformer and then connected to the main grid via a 750kV upstream substation, enabling efficient power transmission and consumption.

[0182] As a specific implementation, the dynamic topology switching mechanism specifically includes: automatic switching on and off of the main transformer high-voltage side switch at the main transformer transmission layer;

[0183] The automatic switching on and off of the main transformer high-voltage side switch of the main transformer transmission layer specifically includes the following steps:

[0184] Step A1: Check the status B of the switch on the high-voltage side of the main transformer;

[0185] If B=0, terminate the process and return to the current state; if B=1, proceed to step A2;

[0186] Step A2: Collect the current light intensity S; and compare the collected light intensity S with the preset minimum light intensity S for photovoltaic module power generation min for comparison:

[0187] If S≥S min , after a delay time t w1 , re - collect and compare data until it is detected that S < S min ; if S < S min , then enter Step A3;

[0188] Step A3: Compare the collected voltage U1 on the low - voltage side of the main transformer with the preset rated voltage U ref for comparison:

[0189] If U1 < U ref , terminate this process and return to the current state; if U1≥U ref , then enter Step A4;

[0190] Step A4: Compare the collected line current I with the preset first line current I0:

[0191] If I≥I0, after a delay time t w1 , re - obtain the line current and compare; if I < I0, after a delay time t w2 , compare I and I0 again. If still I < I0, then switch the high - voltage side switch of the transformer to achieve automatic switching of the high - voltage side switch of the main transformer at the main transformer power transmission layer.

[0192] As a specific implementation method, the automatic switching - on of the high - voltage side switch of the main transformer at the main transformer power transmission layer specifically includes the following steps:

[0193] Step B1: Check the state B of the high - voltage side switch of the main transformer;

[0194] If B = 1, terminate the process and return to the current state; if B = 0, then enter Step B2;

[0195] Step B2: Collect the current light intensity S; and compare the collected light intensity S with the preset minimum light intensity S for photovoltaic module power generation min for comparison:

[0196] If S≤S min , after a delay time t w1 , re - collect the light intensity until it is detected that S > S min ; if S > S min , then enter Step B3;

[0197] Step B3: Compare the collected voltage U1 on the low-voltage side of the main transformer with the preset rated voltage U ref as follows:

[0198] If U1 < U ref , terminate this process and return to the current state; if U1 ≥ U ref , proceed to Step B4;

[0199] Step B4: Compare the collected line current I with the preset second line current I1:

[0200] If I is not equal to I1, re-collect the line current I; if I = I1, proceed to Step B5;

[0201] Step B5: Compare the collected photovoltaic voltage U2 with the set DC threshold voltage U3:

[0202] If U2 < U3, after a delay time t w2 , re-collect the photovoltaic voltage until it is detected that U2 ≥ U3; if U2 ≥ U3, switch on the high-voltage side switch of the transformer to achieve automatic switching on of the high-voltage side switch of the main transformer at the main transformer power transmission layer. [[ID=2*]]

[0203] Step S2: Based on the said basic data, obtain line losses, transformer losses and voltage deviations.

[0204] As a specific implementation manner, the process of obtaining the line losses in Step S2 specifically includes the following steps:

[0205] Step S211: Obtain the branch current and the resistance of the branch transmission line;

[0206] Step S212: Calculate the line losses according to the branch current and the resistance of the branch transmission line, so as to obtain the line losses;

[0207] The calculation formula of the line losses P loss is as follows:

[0208]

[0209] where N l is the number of branches, I l is the branch current, and R l is the resistance of the transmission line of the l-th branch.

[0210] As a specific implementation manner, the process of obtaining the transformer losses in Step S2 specifically includes the following steps:

[0211] Step S221: Calculate the no-load losses of the transformer; and calculate the load losses of the transformer;

[0212] The no-load loss P of the transformer Fe , which is calculated as follows:

[0213] P Fe =K p0 G Fe p t ;

[0214] Among them, K p0 is the iron loss coefficient, G Fe is the core mass, p t is the iron loss per unit mass;

[0215] The load loss P of the transformer cu , which is calculated as follows:

[0216]

[0217] Among them, I T is the high voltage side current of the transformer, R T It is the high voltage side winding of the transformer;

[0218] Step S222: Calculating the total loss of the transformer based on the no-load loss and load loss of the transformer to obtain the transformer loss;

[0219] The total loss of the transformer P T The calculation formula is as follows:

[0220]

[0221] Among them, N T is the number of transformers;

[0222] is the no-load loss of the i-th transformer;

[0223] is the load loss of the i-th transformer.

[0224] As a specific implementation, the voltage deviation in step S2 is obtained by the following steps:

[0225] Step S231: obtaining a reference voltage and a node operating voltage;

[0226] Step S232: Calculate the difference between the reference voltage and the node operating voltage to obtain a voltage deviation;

[0227] The voltage deviation is calculated as follows:

[0228]

[0229] Among them, Udiff is the voltage deviation, U ref is the reference voltage, U i is the node voltage of the i-th node, N is the number of nodes, and N is a natural number greater than 1.

[0230] Step S3: constructing a multi-objective reactive power optimization model for a photovoltaic power distribution system according to the line loss, the transformer loss and the voltage deviation.

[0231] Normalizing the line loss, the transformer loss, and the voltage deviation to obtain a total objective function;

[0232] The expression of the total objective function minF is as follows:

[0233]

[0234] in,

[0235] f1=P loss ; f2=P T ; f3 = U diff ;

[0236] Among them, P loss is the line loss, P T is the total loss of the transformer, U diff is the voltage deviation, λ1 is The weight coefficient, λ2 is The weight coefficient, λ1+λ2=1, f1 0 、 These are the initial values corresponding to f1, f2, and f3 respectively.

[0237] Step K2: Based on the constraints, the multi-objective reactive power optimization model is subjected to energy-saving optimization control according to the improved particle swarm algorithm to obtain an energy-saving optimization control strategy.

[0238] As a specific implementation, before step K2, the method further includes step K: setting constraint conditions;

[0239] The step K specifically includes:

[0240] Set supply and demand relationship constraints; set node voltage constraints; set transformer capacity constraints; set photovoltaic collection line power generation limits; and set energy storage line operation constraints.

[0241] As a specific implementation method, the supply-demand relationship constraint is set as follows:

[0242]

[0243] Among them, Ness is the number of energy storage lines, N inv is the number of photovoltaic collection lines, P PV , Q PV Active and reactive dispatching instructions received by the photovoltaic power station, is the active and reactive output of the photovoltaic inverter, is the active and reactive power of the energy storage converter, X l is the reactance of branch l transmission line, N l is the number of branches, I l is the branch current, R l is the resistance of the lth branch transmission line;

[0244] The setting node voltage constraint is specifically:

[0245]

[0246] Among them, U i is the voltage of the i-th node, is the reference voltage of the i-th node;

[0247] The transformer capacity constraint is set as follows:

[0248]

[0249] in, is the high voltage side current of the transformer, S TN is the rated capacity of the transformer, The rated voltage of the high-voltage side of the box-type transformer;

[0250] The setting of the photovoltaic collector line power generation limit is specifically as follows:

[0251]

[0252] in, is the active output of the photovoltaic inverter, is the reactive output of the photovoltaic inverter, The upper limit of active and reactive power output of the photovoltaic inverter;

[0253] The energy storage line operation constraints are specifically set as follows:

[0254]

[0255] in, is the active power of the energy storage converter;

[0256] The storage capacity of the i-th energy storage device at time t+1;

[0257] is the storage energy of the i-th energy storage device at time t;

[0258] is the active power of the i-th energy storage converter at time t;

[0259] P ch 、P dis is the energy storage charging and discharging power limit, E ess_max 、E ess_min The upper and lower limits of energy storage capacity.

[0260] As a specific implementation, in step K2, energy-saving optimization control is performed on the multi-objective reactive power optimization model according to the improved particle swarm algorithm, which specifically includes the following steps:

[0261] Step D1: Set the initial parameters of the particle swarm algorithm;

[0262] The initial parameters of the particle swarm algorithm include population size, maximum number of iterations, learning factor, initial value of inertia weight ω0, upper and lower limits of particle velocity, initial scale parameter γ0 and attenuation factor τ;

[0263] Step D2: according to the multi-objective reactive power optimization model and based on the initial parameters of the particle swarm algorithm, obtaining the fitness value of each particle;

[0264] Step D3: Initialize the optimal position p of the individual in the population according to the fitness value of each particle best and the global optimal position p gbest ;

[0265] Step D4: According to the optimal position p of the individual of the initialized population best and the global optimal position p gbest , calculate the average Euclidean distance ED avg ;

[0266] Step D5: Adaptive inertia weight based on population state, according to the average Euclidean distance ED avg Update the inertia weight to obtain an updated inertia weight;

[0267] Step D6: Calculate the new velocity and new position of the particle based on the updated inertia weight and learning factor;

[0268] Step D7: Process out-of-bounds particles according to the constraints of particle velocity and position;

[0269] Step D8: Update the fitness of each particle in the particle swarm according to the new position of the particle;

[0270] Step D9: Based on the updated fitness of each particle, update the individual optimal position p of the particle bestand the global optimal position p gbest ;

[0271] Step D10: Randomly perturb the updated particles;

[0272] Step D11: Reprocess the particles that have crossed the boundary according to the constraints of the particle's velocity and position;

[0273] Step D12: Update the individual optimal position p of the particle after random perturbation best ;

[0274] Step D13: Determine whether the convergence condition is met, and when the maximum number of iterations is reached or the set convergence condition is met, end the iteration and output the optimal allocation solution; otherwise, repeat steps D4 to D13 until the convergence condition is met.

[0275] As a specific implementation, in step D6, the new velocity and new position of the particle are calculated using the following formula:

[0276]

[0277] Where N is the population size, k is the number of iterations, d is the dimension of the control variable, f is the fitness function; c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1], and ω is the inertia weight; is the velocity of the d-th dimension of the i-th particle at the k+1-th iteration, is the position of the d-th dimension of the i-th particle at the k+1-th iteration; is the velocity of the d-th dimension of the i-th particle at the k-th iteration, is the position of the d-th dimension of the i-th particle at the k-th iteration, is the individual optimal position of the d-th dimension of the i-th particle in the k-th iteration, is the global optimal position of the dth dimension of the entire population in k iterations;

[0278] Among them, the update formula of inertia weight is as follows:

[0279]

[0280] Among them, ω k is the inertia weight of the kth iteration;

[0281] ω k-1 is the inertia weight of the k-1th iteration;

[0282] is the average Euclidean distance of the kth iteration;

[0283] is the average Euclidean distance of the k-1th iteration;

[0284] k max The maximum number of iterations is set.

[0285] As a specific implementation method, for the individual optimal position p best and the global optimal position p gbest The particles in the random perturbation are randomly perturbed, and the random trajectory correction strategy of the particles is based on the Cauchy-Lorentz distribution; in order to avoid falling into the local optimum of the objective function during the energy-saving optimization process of this patent and improve the global search capability, an improved particle swarm algorithm is provided, which introduces an adaptive inertia weight adjustment strategy and a random trajectory correction strategy of the Cauchy-Lorentz distribution.

[0286] The random trajectory correction formula based on the Cauchy-Lorentz distribution is as follows:

[0287]

[0288] Where, is the new position of the i-th particle at the k-th iteration after correction;

[0289] is the position of the i-th particle at the k-th iteration; is the position of the global optimal particle, β∈[-1,1], indicating that the particle position is mutated in the positive or negative direction. represents the Euclidean distance between the particle and the optimal particle;

[0290] The position of the global optimal particle The position parameter of the probability density function of the Cauchy-Lorentz distribution is used to correct the trajectory of particles in the population. The calculation formula is as follows:

[0291]

[0292] Among them, γ k is the scale parameter, γ0 is the initial scale parameter, and τ is the attenuation factor;

[0293] is the position of the i-th particle after the k-th iteration; is the optimal position after the kth iteration;

[0294] The probability density function of the Cauchy-Lorentz distribution is as follows:

[0295]

[0296] Where x is a random variable; x0 is the location parameter of the Cauchy distribution peak; and γ is the scale parameter at half the height of the Cauchy distribution peak.

[0297] Step K3: Optimizing the photovoltaic power distribution system according to the energy-saving optimization control strategy to achieve energy-saving optimization control of the photovoltaic power distribution system.

[0298] This embodiment provides a comprehensive energy-saving optimization process, from establishing the photovoltaic power station topology to solving reactive power optimization problems. This process includes multiple steps, including system modeling, data collection, state detection, loss calculation, and command optimization allocation, to achieve full-process energy-saving control of the photovoltaic power station and improve operational efficiency. Furthermore, to address the high losses and stability and safety issues of the photovoltaic distribution system, the active and reactive output of the photovoltaic inverter and the switching state of the transformer high-voltage side are selected as decision variables to establish a reactive power optimization model with clear optimization objectives, decision variables, and constraints. To solve this model, an improved particle swarm optimization algorithm is provided. This algorithm uses an adaptive inertia weight update strategy based on population state and a random trajectory correction strategy based on the Cauchy-Lorentz distribution for update and solution. This effectively avoids the algorithm from falling into local optimality, improves convergence speed, and thus better achieves energy-saving effects in the photovoltaic distribution system. Furthermore, through data collection and state detection, the photovoltaic power station topology is dynamically adjusted to reduce system losses in no-load and light-load states. Combined with the improved optimization algorithm, this achieves intelligent control and rational command allocation for the photovoltaic distribution system, reducing manual intervention and further improving operational efficiency.

[0299] Improved particle swarm algorithm, its specific process is as follows Figure 4 As shown, the solution steps are as follows:

[0300] ① Initialization parameters: Set the initial parameters of the algorithm, including population size, maximum number of iterations, learning factor, initial inertia weight value ω0, upper and lower limits of particle velocity, γ0, and τ. Input PV power plant data, including PV installed capacity, transformer capacity, energy storage installed capacity, and line impedance.

[0301] ② Calculate the fitness of each particle in the particle swarm: Calculate the fitness value of each particle according to the objective function, which includes the weighted sum of line loss, transformer loss and voltage deviation.

[0302] ③ Initialize the optimal position p of the individual in the population best and the global optimal position p gbest .

[0303] ④Calculate the average Euclidean distance ED avg .

[0304] ⑤ Update the inertia weight based on the adaptive inertia weight update strategy of the population state.

[0305] ⑥ Update the speed and position of the particle: according to the individual optimal position p of the current particle best and the global optimal position p of the entire particle population gbest , combining parameters such as inertia weight and learning factor to calculate the new velocity and new position of the particle.

[0306] ⑦ Constraint processing: According to the constraints of particle position and velocity, out-of-bounds particles are processed according to the constraint processing method.

[0307] ⑧Calculate the fitness of each particle in the particle swarm: Calculate the fitness based on the position of the updated particle.

[0308] ⑨Update and record the individual optimal position p of the particle best and the global optimal position p g b es t.

[0309] ⑩ Randomly perturb the particles based on the random trajectory correction strategy of Cauchy-Lorentz distribution.

[0310] Constraint processing: According to the constraints of particle position and velocity, out-of-bounds particles are processed according to the constraint processing method.

[0311] Update the optimal position of the individual: After adding random perturbations, update the particle's p best . Determine whether the convergence conditions are met: When the maximum number of iterations is reached or the set convergence conditions are met, the iteration ends and the optimal allocation solution is output. Otherwise, return to step ④ to continue the iterative calculation.

[0312] like Figure 2 As shown, the energy-saving optimization control method of the photovoltaic power distribution system of this embodiment can be summarized into the following steps:

[0313] 1. Establish an integrated photovoltaic power station topology. Construct a topology model for three voltage levels, from 750kV to 35kV, encompassing photovoltaic power collection lines (PV panels, inverters, and box transformers), energy storage lines, and main transformers. A dynamic topology switching mechanism is introduced to adjust the topology in real time based on sunlight intensity and scheduling requirements.

[0314] 2. Establish a multi-objective reactive power optimization model for the photovoltaic distribution system. Based on the transformer and line loss mechanism, establish a reactive power optimization model and introduce a multi-objective optimization algorithm.

[0315] 3. Determine the data collection scope. Deploy data collection devices at key nodes in the PV power station, including the inverter outlet, the high-voltage and low-voltage sides of the distribution transformer, and major load nodes. A centralized computing controller receives switch status signals from the main transformer's high-voltage side, along with online data from light collection sensors, voltage collectors, line current collectors, and photovoltaic voltage collectors. This allows for real-time data collection on voltage, current, active power, reactive power, and light intensity.

[0316] 4. Status detection and identification: The system determines the no-load status by real-time monitoring of parameters such as the light intensity, active and reactive power of the photovoltaic inverter, the active and reactive power of the energy storage converter, node voltage, and line current. Intelligent identification algorithms are introduced to improve the accuracy and response speed of status identification, and accurately identify the standby and power generation operating states of the photovoltaic power station.

[0317] 5. Real-time calculation of photovoltaic distribution system losses and energy-saving optimization. When the upper-level substation issues a power demand command, the losses of each collector line in the photovoltaic distribution system are calculated in real time based on loss mechanisms, reactive power optimization models, and collected data. With the optimization goal of minimizing system active power losses and voltage deviation, and considering the economic and safety of photovoltaic power station operation, an improved particle swarm optimization algorithm is proposed to optimize the allocation of active and reactive power commands to each photovoltaic collector line.

[0318] This embodiment provides an energy-saving control method for a photovoltaic power distribution system. This method uses an improved particle swarm optimization algorithm to implement full-process energy-saving control of a photovoltaic power station, from topology establishment to reactive power optimization. The specific steps include obtaining basic data for the photovoltaic power distribution system, such as node voltage, line current, active power, reactive power, and light intensity. Line losses, transformer losses, and voltage deviation are then calculated based on this data. Next, a multi-objective reactive power optimization model is constructed, and corresponding constraints, such as supply and demand relationships, node voltage constraints, and transformer capacity constraints, are set. The model is then optimized and controlled using the improved particle swarm optimization algorithm to obtain an energy-saving optimization strategy. Finally, the photovoltaic power distribution system is optimized based on this strategy to achieve energy-saving goals. The entire process reduces manual intervention, improves operational efficiency, and reduces losses under no-load and light-load conditions through dynamic topology adjustment and intelligent control.

[0319] Example 2:

[0320] like Figure 5 As shown, this embodiment provides an energy-saving optimization control device for a photovoltaic power distribution system, the device comprising:

[0321] An acquisition unit 10 is used to acquire a multi-objective reactive power optimization model of a photovoltaic power distribution system;

[0322] The control unit 20 is connected to the acquisition unit 10 and is used to perform energy-saving optimization control on the multi-objective reactive power optimization model based on the constraint conditions and the improved particle swarm algorithm to obtain an energy-saving optimization control strategy;

[0323] The optimization unit 30 is connected to the control unit 20 and is used to optimize the photovoltaic power distribution system according to the energy-saving optimization control strategy to achieve energy-saving optimization control of the photovoltaic power distribution system.

[0324] As a specific implementation, the acquiring unit includes:

[0325] Acquisition module, used to obtain basic data of photovoltaic power distribution system;

[0326] a calculation module, connected to the acquisition module, for obtaining line loss, transformer loss and voltage deviation based on the basic data;

[0327] A construction module is connected to the calculation module and is used to construct a multi-objective reactive power optimization model of the photovoltaic power distribution system according to the line loss, the transformer loss and the voltage deviation.

[0328] The device in this embodiment can execute the method in embodiment 1.

[0329] Example 3:

[0330] This embodiment provides a photovoltaic power distribution system, which includes a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the energy-saving optimization control method for the photovoltaic power distribution system according to Example 1.

[0331] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Those skilled in the art will appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for energy-saving optimization control of a photovoltaic power distribution system, characterized in that: The method comprises the following steps: Step K1: Obtain a multi-objective reactive power optimization model for the photovoltaic distribution system; Step K2: Based on the constraints, the multi-objective reactive power optimization model is subjected to energy-saving optimization control according to the improved particle swarm algorithm to obtain an energy-saving optimization control strategy; Step K3: Optimizing the photovoltaic power distribution system according to the energy-saving optimization control strategy to achieve energy-saving optimization control of the photovoltaic power distribution system.

2. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 1, characterized in that: Obtain a multi-objective reactive power optimization model for the photovoltaic distribution system, including: Step S1: Obtain basic data of the photovoltaic power distribution system; Step S2: Based on the basic data, obtain line loss, transformer loss and voltage deviation; Step S3: constructing a multi-objective reactive power optimization model for a photovoltaic power distribution system according to the line loss, the transformer loss and the voltage deviation.

3. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 2, characterized in that: The step S1 specifically includes the following steps: Arrange data acquisition devices at the inverter outlet, high-voltage side of the distribution transformer, low-voltage side of the distribution transformer, and each load node in the photovoltaic distribution system; The data acquisition device collects node voltage, line current, active power, reactive power, and light intensity in real time; Determine the operating status and load status of the photovoltaic distribution system based on node voltage, line current, active power, reactive power, and light intensity; The node voltage, line current, active power, reactive power, light intensity, operating status and load status of the photovoltaic distribution system are summarized to obtain basic data of the photovoltaic distribution system.

4. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 2, characterized in that: Before step S1, the method further includes step S0: constructing a photovoltaic power station topology structure with three voltage levels: a high-voltage grid-connected side, a main transformer transmission layer, and a low-voltage collector side; The main transformer transmission layer is arranged between the high-voltage grid-connected side and the low-voltage collector side, and a dynamic topology switching mechanism is provided in the photovoltaic power station topology structure.

5. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 4, characterized in that: The dynamic topology switching mechanism specifically includes: automatic switching on and off of the main transformer high-voltage side switch at the main transformer transmission layer; The automatic switching on and off of the main transformer high-voltage side switch of the main transformer transmission layer specifically includes the following steps: Step A1: Check the status B of the switch on the high-voltage side of the main transformer; If B=0, terminate the process and return to the current state; if B=1, proceed to step A2; Step A2: Collect the current light intensity S; compare the collected light intensity S with the preset minimum light intensity S for photovoltaic module power generation. min For comparison: If S≥S min , then after the delay time t w1 Then re-collect and compare data until S is detected. min If S min , then proceed to step A3;​​ Step A3: Compare the collected main transformer low-voltage side voltage U1 with the preset rated voltage U ref For comparison: If U1<U ref , terminate this process and return to the current state; if U1≥U ref , then proceed to step A4; Step A4: Compare the collected line current I with the preset first line current I0: If I ≥ I0, then after a delay time t w1 obtain the line current and make a comparison again; if I < I0, then after a delay time t w2 make a comparison between I and I0 again. If it is still I < I0, then trip or close the high-voltage side switch of the transformer to achieve automatic tripping or closing of the high-voltage side switch of the main transformer at the main transformer power transmission layer.

6. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 5, characterized in that: The main transformer high-voltage side switch of the main transformer transmission layer is automatically put into operation, specifically comprising the following steps: Step B1: Check the status B of the switch on the high-voltage side of the main transformer; If B=1, the process is terminated and returned to the current state; if B=0, it goes to step B2; Step B2: Collect the current light intensity S; The collected light intensity S is compared with the preset minimum light intensity S for photovoltaic module power generation. min For comparison: If S≤S min , then after the delay time t w1 Then re-collect the light intensity until S>S min ; If S>S min , then proceed to step B3; Step B3: Compare the collected main transformer low-voltage side voltage U1 with the preset rated voltage U ref For comparison: If U1<U ref , then terminate this process and return to the current state; if U1≥U ref , then proceed to step B4; Step B4: Compare the collected line current I with the preset second line current I1: If I is not equal to I1, then re-collect the line circuit I; if I=I1, then go to step B5; Step B5: Compare the collected photovoltaic voltage U2 with the set DC threshold voltage U3: If U2 < U3, then after a delay time t w2 re - perform the photovoltaic voltage acquisition until it is detected that U2 ≥ U3; if U2 ≥ U3, then put into the high - voltage side switch of the transformer to achieve the automatic input of the high - voltage side switch of the main transformer in the main transformer transmission layer.

7. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 2, characterized in that: The line loss in step S2 is obtained by: Step S211: obtaining branch current and branch transmission line resistance; Step S212: Calculating line loss according to the branch current and the resistance of the branch transmission line, thereby obtaining the line loss; The line loss P loss The calculation formula is as follows: Among them, N l is the number of branches, I l is the branch current, R l is the resistance of the lth branch transmission line.

8. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 2, characterized in that: The transformer loss in step S2 is obtained by: Step S221: Calculating the no-load loss of the transformer; and calculating the load loss of the transformer; The no-load loss P of the transformer Fe , which is calculated as follows: P Fe =K p0 G Fe p t ; Among them, K p0 is the iron loss coefficient, G Fe is the core mass, p t is the iron loss per unit mass; The load loss P of the transformer cu , which is calculated as follows: Among them, I T is the high voltage side current of the transformer, R T It is the high voltage side winding of the transformer; Step S222: Calculating the total loss of the transformer based on the no-load loss and load loss of the transformer to obtain the transformer loss; The total loss of the transformer P T The calculation formula is as follows: Among them, N T is the number of transformers; is the no-load loss of the i-th transformer; is the load loss of the i-th transformer.

9. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 2, characterized in that: The voltage deviation in step S2 is obtained by: Step S231: obtaining a reference voltage and a node operating voltage; Step S232: Calculate the difference between the reference voltage and the node operating voltage to obtain a voltage deviation; The voltage deviation is calculated as follows: Among them, U diff is the voltage deviation, U ref is the reference voltage, U i is the node voltage of the i-th node, N is the number of nodes, and N is a natural number greater than 1.

10. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 2, characterized in that: The step S3 specifically includes the following steps: Normalizing the line loss, the transformer loss, and the voltage deviation to obtain a total objective function; The expression of the total objective function minF is as follows: in, f1=P loss ;f2=P T ;f3=U diff ; Among them, P loss is the line loss, P T is the total loss of the transformer, U diff is the voltage deviation, λ1 is The weight coefficient, λ2 is The weight coefficient, λ1+λ2=1, f1 0 、 These are the initial values corresponding to f1, f2, and f3 respectively.

11. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 1, characterized in that: Before step K2, the method further includes step K0: setting constraint conditions; The step K0 specifically includes: Set supply and demand relationship constraints; set node voltage constraints; set transformer capacity constraints; set photovoltaic collection line power generation limits; and set energy storage line operation constraints.

12. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 11, characterized in that: The supply and demand relationship constraints are specifically set as follows: Among them, N ess is the number of energy storage lines, N inv is the number of photovoltaic collection lines, P PV , Q PV They are active dispatching instructions and reactive dispatching instructions received by the photovoltaic power station respectively. are the active output and reactive output of the photovoltaic inverter respectively, are the active power and reactive power of the energy storage converter, X l is the reactance of branch l transmission line, N l is the number of branches, I l is the branch current, R l is the resistance of the lth branch transmission line; The setting node voltage constraint is specifically: Among them, U i is the voltage of the i-th node, is the reference voltage of the i-th node; The transformer capacity constraint is set as follows: in, is the high voltage side current of the transformer, S TN is the rated capacity of the transformer, The rated voltage of the high-voltage side of the box-type transformer; The setting of the photovoltaic collector line power generation limit is specifically as follows: in, is the active output of the photovoltaic inverter, is the reactive output of the photovoltaic inverter, The upper limit of active and reactive power output of the photovoltaic inverter; The energy storage line operation constraints are specifically set as follows: in, is the active power of the energy storage converter; The storage capacity of the i-th energy storage device at time t+1; is the storage energy of the i-th energy storage device at time t; is the active power of the i-th energy storage converter at time t; P ch 、P dis They are the energy storage charging power limit and the energy storage discharging power limit respectively; E ess_max 、E ess_min They are the upper limit and lower limit of energy storage capacity respectively.

13. The energy-saving optimization control method for a photovoltaic power distribution system according to any one of claims 1 to 12, characterized in that: In the step K2, energy-saving optimization control is performed on the multi-objective reactive power optimization model according to the improved particle swarm algorithm, which specifically includes the following steps: Step D1: Set the initial parameters of the particle swarm algorithm; The initial parameters of the particle swarm algorithm include population size, maximum number of iterations, learning factor, initial value of inertia weight ω0, upper and lower limits of particle velocity, initial scale parameter γ0 and attenuation factor τ; Step D2: according to the multi-objective reactive power optimization model and based on the initial parameters of the particle swarm algorithm, obtaining the fitness value of each particle; Step D3: Initialize the optimal position p of the individual in the population according to the fitness value of each particle best and the global optimal position p gbest ; Step D4: According to the optimal position p of the individual of the initialized population best and the global optimal position p gbest , calculate the average Euclidean distance ED avg ; Step D5: Adaptive inertia weight based on population state, according to the average Euclidean distance ED avg Update the inertia weight to obtain an updated inertia weight; Step D6: Calculate the new velocity and new position of the particle based on the updated inertia weight and learning factor; Step D7: Process out-of-bounds particles according to the constraints of particle velocity and position; Step D8: Update the fitness of each particle in the particle swarm according to the new position of the particle; Step D9: Based on the updated fitness of each particle, update the individual optimal position p of the particle best and the global optimal position p gbest ; Step D10: Randomly perturb the updated particles; Step D11: Reprocess the particles that have crossed the boundary according to the constraints of the particle's velocity and position; Step D12: Update the individual optimal position p of the particle after random perturbation best ; Step D13: Determine whether the convergence condition is met, and when the maximum number of iterations is reached or the set convergence condition is met, end the iteration and output the optimal allocation solution; otherwise, repeat steps D4 to D13 until the convergence condition is met.

14. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 13, characterized in that: In step D6, the new velocity and new position of the particle are calculated as follows: Where N is the population size, k is the number of iterations, d is the dimension of the control variable, f is the fitness function; c1 and c2 are learning factors, r1 and r2 are random numbers between [0,1], and ω is the inertia weight; is the velocity of the d-th dimension of the i-th particle at the k+1-th iteration, is the position of the d-th dimension of the i-th particle at the k+1-th iteration; is the velocity of the d-th dimension of the i-th particle at the k-th iteration, is the position of the d-th dimension of the i-th particle at the k-th iteration, is the individual optimal position of the d-th dimension of the i-th particle in the k-th iteration, is the global optimal position of the dth dimension of the entire population in k iterations; Among them, the update formula of inertia weight is as follows: Among them, ω k is the inertia weight of the kth iteration; ω k-1 is the inertia weight of the k-1th iteration; is the average Euclidean distance of the kth iteration; is the average Euclidean distance of the k-1th iteration; k max The maximum number of iterations is set.

15. The energy-saving optimization control method for a photovoltaic power distribution system according to claim 13, characterized in that: For the individual optimal position p best and the global optimal position p gbest The random perturbations of particles in the system are performed based on the random trajectory correction strategy of the Cauchy-Lorentz distribution. The random trajectory correction formula based on the Cauchy-Lorentz distribution is as follows: Where, is the new position of the i-th particle at the k-th iteration after correction; is the position of the i-th particle at the k-th iteration; is the position of the global optimal particle, β∈[-1,1], indicating that the particle position is mutated in the positive or negative direction. represents the Euclidean distance between the particle and the optimal particle; The position of the global optimal particle The position parameter of the probability density function of the Cauchy-Lorentz distribution is used to correct the trajectory of particles in the population. The calculation formula is as follows: Among them, γ k is the scale parameter, γ0 is the initial scale parameter, and τ is the attenuation factor; is the position of the i-th particle after the k-th iteration; is the optimal position after the kth iteration; The probability density function of the Cauchy-Lorentz distribution is as follows: Where x is a random variable; x0 is the location parameter of the Cauchy distribution peak; and γ is the scale parameter at half the height of the Cauchy distribution peak.

16. An energy-saving optimization control device for a photovoltaic power distribution system, characterized in that: include: An acquisition unit, used for acquiring a multi-objective reactive power optimization model of a photovoltaic power distribution system; A control unit, connected to the acquisition unit, is used to perform energy-saving optimization control on the multi-objective reactive power optimization model based on constraint conditions and an improved particle swarm algorithm to obtain an energy-saving optimization control strategy; An optimization unit is connected to the control unit and is used to optimize the photovoltaic power distribution system according to the energy-saving optimization control strategy to achieve energy-saving optimization control of the photovoltaic power distribution system.

17. The energy-saving optimization control device for a photovoltaic power distribution system according to claim 16, characterized in that: The acquisition unit specifically includes: Acquisition module, used to obtain basic data of photovoltaic power distribution system; a calculation module, connected to the acquisition module, for obtaining line loss, transformer loss and voltage deviation based on the basic data; A construction module is connected to the calculation module and is used to construct a multi-objective reactive power optimization model of the photovoltaic power distribution system according to the line loss, the transformer loss and the voltage deviation.

18. A photovoltaic power distribution system, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the energy-saving optimization control method for the photovoltaic power distribution system according to any one of claims 1 to 15.

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