Source network load storage optimization method, system and equipment based on photovoltaic power generation and medium

Abstract

The invention relates to the technical field of photovoltaic power generation, and provides a photovoltaic power generation-based source network load storage optimization method, system and device and a medium, and the method comprises the steps: constructing a photovoltaic power generation system model and a photovoltaic absorption system model based on an actual photoelectric use scene, the photovoltaic absorption system model is used for absorbing electricity generated by the photovoltaic power generation system model; dividing the total power generated by the photovoltaic power generation system model by the net power consumed by the photovoltaic consumption system model at the maximum t moment as a target function enabling the photovoltaic consumption capability to be the highest; solving the objective function to obtain a power value consumed by each device in the photovoltaic consumption system model at the moment t; and adjusting the power value consumed by each device in the photovoltaic consumption system model to keep the photovoltaic consumption capability to be the highest, thereby realizing source network load storage optimization. According to the invention, the photovoltaic absorption capability of the distributed photovoltaic system can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation, and in particular, relates to a source-grid-load-storage optimization method, system, equipment and medium based on photovoltaic power generation. Background Art

[0002] With the widespread application of technologies such as distributed photovoltaics, adjustable loads and distributed energy storage on the user side, the importance of source-grid-load-storage interactive operation strategies has become increasingly prominent.

[0003] However, distributed photovoltaics are easily affected by environmental factors such as weather and light, resulting in unstable output and limited photovoltaic absorption capacity, making it impossible to fully utilize the advantages of distributed energy. Summary of the invention

[0004] To solve the above problems, the present disclosure provides a source-grid-load-storage optimization method, system, equipment and medium based on photovoltaic power generation, which adopts a method of solving the objective function of maximizing the net power consumed by the photovoltaic absorption system model at time t divided by the total power generated by the photovoltaic power generation system model as the objective function to maximize the photovoltaic absorption capacity, thereby improving the photovoltaic absorption capacity of the distributed photovoltaic system.

[0005] The following is the scheme of the present invention:

[0006] A source-grid-load-storage optimization method based on photovoltaic power generation, characterized by comprising:

[0007] Based on the actual photovoltaic usage scenario, a photovoltaic power generation system model and a photovoltaic consumption system model are constructed, wherein the photovoltaic consumption system model is used to consume the electricity generated by the photovoltaic power generation system model;

[0008] The objective function to maximize the photovoltaic absorption capacity is to maximize the net power consumed by the photovoltaic absorption system model at time t divided by the total power generated by the photovoltaic power generation system model.

[0009] Solve the objective function to obtain the power consumed by each device in the photovoltaic consumption system model at time t;

[0010] Adjust the power consumed by each device in the photovoltaic absorption system model to maintain the highest photovoltaic absorption capacity and achieve source, grid, load and storage optimization.

[0011] Further,

[0012] When the load side in the actual photovoltaic use scenario includes electric vehicles and thermal storage devices, the photovoltaic consumption system model includes:

[0013] Electric vehicle charging model, energy storage system model, and thermal storage device model;

[0014] The photovoltaic power generation system model is:

[0015]

[0016] Where: P pv (t) is the actual output power of the photovoltaic cell; P STC , G STC is the light intensity and rated output power of the photovoltaic module under standard test conditions, G(t) is the actual solar radiation intensity; f pv is the loss coefficient; k is the power temperature coefficient; T c (t) is the current photovoltaic cell temperature, T r is the reference temperature;

[0017] The electric vehicle charging model is:

[0018]

[0019] Where: SOC ev,i (t) is the charge state of the ith electric vehicle at time t, E ev,i is the total battery capacity of the electric vehicle, ξ ev,i is the charging efficiency; the adjustable potential of electric vehicles and their initial state of charge SOC 0,i and estimated charging time h ev,i Related; p ev,i (t) is the power consumption of the i-th electric vehicle at time t;

[0020] When the energy storage device of the energy storage system is a battery, the energy storage system model is:

[0021] During charging, P sb >0, the remaining energy storage capacity at time t is:

[0022] SOC(t)=SOC(t-1)(1-σ)+P sb (t)·δ c

[0023] During the discharge process, P sb <0, the remaining energy storage capacity at time t is:

[0024] SOC(t)=SOC(t-1)(1-σ)+P sb (t)·δ d

[0025] Where: SOC(t) is the remaining energy storage capacity at time t; δ c ,δ d are charging efficiency and discharging efficiency respectively; P sb (t) is the charge and discharge power at time t; σ is the self-discharge ratio of the battery per hour;

[0026] The operating power expression of the thermal storage device model is:

[0027]

[0028] Where: p ts,i (t) is the operating power of the i-th heat storage device at time t; P TS,i is the rated power of the i-th heat storage device; σ is a 0-1 switch variable that determines whether the heat storage device is working; α min To allow the water temperature inside the thermal storage device to float downward to a multiple of , α max T is the multiple of the water temperature inside the heat storage device that can float upward; ts,i (t) is the internal temperature of the i-th heat storage device at time t, T s,i represents the set temperature of the i-th thermal storage device;

[0029] The formula for the temperature change inside the thermal storage device is:

[0030]

[0031] Where: V ts,i is the volume of the i-th heat storage device; R f,i (t) is the flow rate of water outflow and water injection of the heat storage device; T inje,i The temperature of the water injection point of the heat storage device; η ts,i The water temperature increase per unit time of the heat storage device per unit power; ts,i It is the natural temperature loss rate of the heat storage device per unit time.

[0032] Further,

[0033] The objective function is:

[0034]

[0035] Among them, P pv (t) is the power generation of distributed photovoltaic at time t; P ev,i (t) is the power consumption of the i-th electric vehicle at time t; P ts,i (t) is the operating power of the i-th heat storage device at time t, P sb (t) is the charging and discharging power of the energy storage system at time t.

[0036] Further,

[0037] The power consumption value of each device in the photovoltaic absorption system model is adjusted so that the photovoltaic absorption capacity is maintained at the highest level, including:

[0038] Set the theoretical value x for the historical data of photovoltaic power generation in the actual photovoltaic scene, the historical charging load data of electric vehicles, the historical load data of thermal storage devices, and the historical charging and discharging state data of energy storage systems. OV ,;

[0039] The photovoltaic power generation data of the photovoltaic power generation system model and the power consumption data of the photovoltaic consumption system model are collected in real time as observation values ​​x AV ; Create a dynamic adjustment model to minimize the sum of the differences between theoretical values ​​and observed values;

[0040] The dynamic adjustment model is:

[0041] min[∑ x∈x |x AV -x OV |

[0042] Where X = [U PV，t , U EV，t , U AC，t , U ESdis，t , U ESch，t ],U PV，t is the photovoltaic power generation data at time t, U EV，t is the load data of the electric vehicle at time t, U AC，t is the operating power of the heat storage device at time t, U ESdis，t is the discharge state of the energy storage at time t, U ESch，t is the charging state of the energy storage at time t.

[0043] Further,

[0044] The particle swarm algorithm is used to solve the objective function.

[0045] Further,

[0046] The particle swarm algorithm is used to solve the objective function; comprising:

[0047] Initialization parameters, including: particle swarm size M, number of iterations N, self-learning factor C 1 , Population learning factor C 2 , speed limit V max , speed lower limit V min , annealing initial temperature T, annealing cycle number L, annealing fitness factor β;

[0048] Calculate individual fitness according to the fitness function, and save the optimal position of each particle, the individual best fitness value, and the group optimal position; wherein the fitness function is the objective function that maximizes the photovoltaic absorption capacity;

[0049] According to the iterative speed and position update formula, update the speed and position of the particle to find the current individual optimal solution and the current group optimal solution;

[0050] Apply the simulated annealing algorithm, starting from the current solution, generate new candidate solutions through random perturbations, and decide whether to accept the new solution based on the Metropolis criterion. If accepted, the new solution will be updated to the optimal solution and optimal fitness of the entire group. If not accepted, it will not be updated. When the number of annealing cycles is reached, the cooling operation is performed.

[0051] When the number of iterations is reached, the value of the particle is output as the optimal solution, and the power consumed by the photovoltaic consumption system model and the total power generated by the photovoltaic power generation system model at time t are obtained.

[0052] Further,

[0053] When using the particle swarm algorithm to solve the objective function, the constraints are used to screen the optimization results and eliminate invalid solutions. The constraints include:

[0054] Power balance constraints:

[0055]

[0056] Where: is the output power of the photovoltaic generator set; is the charging and discharging power of energy storage device i; Q LOSS is the power loss of transmission and distribution; D total is the total system load; Q d (i,t) is the discharge power of energy storage device i in time period t;

[0057] Distributed photovoltaic power generation constraints:

[0058]

[0059] Where: Q pv Reactive power output for distributed photovoltaic power stations. is the inverter capacity of the distributed photovoltaic power station; The minimum and maximum values ​​of the reactive power of the distributed photovoltaic power station; the distributed photovoltaic tracking maximum power point output active power is set, and the inverter operation power factor adjustment range is set to ±0.9;

[0060] Energy storage system constraints:

[0061]

[0062] Where: P ess,t is the active power of the energy storage system at time t, is the maximum charging power of the energy storage system, is the maximum discharge power; ηch is the charging efficiency of the energy storage system, η dis is the discharge efficiency, the charge and discharge efficiency is 0.9; E ess is the energy storage system capacity, SOC ess is the state of charge, Q is the minimum and maximum state of charge allowed by the energy storage system; ess is the reactive power output by the energy storage system, is the inverter capacity of the energy storage system, is the minimum and maximum reactive power of the energy storage system.

[0063] A source-grid-load-storage optimization system based on photovoltaic power generation, characterized by comprising:

[0064] A model building module, used to build a photovoltaic power generation system model and a photovoltaic consumption system model based on actual photovoltaic usage scenarios, wherein the photovoltaic consumption system model is used to consume the electricity generated by the photovoltaic power generation system model;

[0065] A function building module is used to maximize the net power consumed by the photovoltaic absorption system model at time t divided by the total power generated by the photovoltaic power generation system model as the objective function to maximize the photovoltaic absorption capacity;

[0066] The calculation module is used to solve the objective function and obtain the power consumed by each device in the photovoltaic consumption system model at time t;

[0067] The control module is used to adjust the power consumed by each device in the photovoltaic absorption system model so that the photovoltaic absorption capacity remains at the highest level and the source, grid, load and storage are optimized.

[0068] Compared with the prior art, the present invention has the following advantages:

[0069] The present invention constructs a photovoltaic power generation system model and a matching photovoltaic consumption system model based on actual photovoltaic usage scenarios, and sets the ratio of the maximum specific net power to the total power at time t as the objective function. By solving the objective function, the power value of each device at time t can be obtained, thereby providing a basis for adjusting device parameters.

[0070] In actual operation, by adjusting the power value of each device to ensure that the photovoltaic absorption capacity is always maintained at the highest level and optimizing the source, grid, load and storage, the problem of limited distributed photovoltaic absorption capacity can be effectively solved and the advantages of distributed energy can be fully utilized.

[0071] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0073] Figure 1 A schematic diagram of the method of the present invention is shown;

[0074] Figure 2 A schematic diagram of the particle swarm optimization algorithm is shown. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0076] Figure 1 A schematic diagram of a method according to the present invention is shown, and the method according to the present invention specifically comprises:

[0077] 1. Based on the actual photovoltaic usage scenario, a photovoltaic power generation system model and a photovoltaic consumption system model are constructed. The photovoltaic consumption system model is used to consume the electricity generated by the photovoltaic power generation system model;

[0078] 2. The objective function to maximize the photovoltaic absorption capacity is to divide the net power consumed by the photovoltaic absorption system model at time t by the total power generated by the photovoltaic power generation system model;

[0079] 3. Solve the objective function to obtain the power consumed by each device in the photovoltaic consumption system model at time t;

[0080] 4. Adjust the power consumed by each device in the photovoltaic absorption system model to keep the photovoltaic absorption capacity at the highest level and achieve source, grid, load and storage optimization.

[0081] The following is an example of applying the method of the present invention to the photovoltaic use scenario of an electric vehicle:

[0082] 1. Step 1: Construct simulation models of distributed photovoltaics, adjustable loads, distributed energy storage and other elements, and establish a user-side source-grid-load-storage optimization scheduling model with the highest photovoltaic consumption rate as the objective function:

[0083] (1) Photovoltaic power generation system:

[0084]

[0085] Where: P pv (t) is the actual output power of the photovoltaic cell; P STC , G STC is the light intensity and rated output power of the photovoltaic module under standard test conditions, G(t) is the actual solar radiation intensity; f pv is the loss coefficient; k is the power temperature coefficient; T c (t) is the current photovoltaic cell temperature, T r is the reference temperature;

[0086] (2) Electric vehicles:

[0087] Electric vehicles can only be charged at constant power, not variable power, and do not have the ability to discharge externally. The charging power of electric vehicles is determined by the rated power of the charging pile, whether the electric vehicle is connected to the charging pile, and the control strategy of the load aggregator.

[0088] p ev，i (t) = P EV，i ·L EV，i (t)·S EV，i (t)

[0089] Where: P EV,i is the rated power of the i-th electric vehicle charging pile. EV,i (t) is a 0-1 variable, indicating whether the electric vehicle is connected to a charging station. EV,i (t) is a 0-1 variable, indicating the control strategy of the load aggregator on the electric vehicles.

[0090] The electric vehicle charging model is:

[0091]

[0092] Where: SOC ev,i (t) is the state of charge of the ith electric vehicle t, E ev,i is the total battery capacity of the electric vehicle, ξ ev,i is the charging efficiency; the adjustable potential of electric vehicles and their initial state of charge SOC 0,i and estimated charging time h ev,i Related.

[0093] (3) Heat storage device:

[0094] The adjustable potential of industrial and commercial electric boilers and distributed household heat storage devices depends on the temperature setting value, the current water temperature inside the device, the water temperature at the water inlet, the water outlet flow rate and flow rate, etc.

[0095] The internal water temperature range of the heat storage device is allowed to be within the set value T s Float up to α max T ss ,α max >1, allow floating down to α min T s ,α min <1. The water temperature of the heat storage device drops to α min T s , the heat storage device is in the heating state, and the temperature rises to α max T s , the heat storage device stops heating. The temperature is between the minimum and maximum temperatures allowed, and the load aggregator has control over the device and decides whether to heat at the current moment. The operating power expression is:

[0096]

[0097] Where: p ts,i (t) is the operating power of the i-th heat storage device at time t, P TS,i is the rated power of the i-th heat storage device, and σ is a 0-1 switch variable that determines whether the heat storage device is working. min To allow the water temperature inside the thermal storage device to float downward to a multiple of , α max T is the multiple of the water temperature inside the heat storage device that can be allowed to float upward; ts,i (t) is the internal temperature of the i-th heat storage device at time t, T s,i represents the set temperature of the i-th thermal storage device;

[0098] The formula for the temperature change inside the thermal storage device is:

[0099]

[0100] Where: V ts,i is the capacity of the ith thermal storage device (gallons), R f,i (t) is the flow rate of water outflow and water injection of the thermal storage device (gallons / minute), T inje,i is the temperature of the water injection point of the thermal storage device (Fahrenheit), η ts,i is the heating coefficient of the heat storage device, that is, the increase in water temperature per unit time when the heat storage device operates at unit power, υ ts,i It is the natural temperature loss rate of the heat storage device per unit time, which is related to the heat preservation capacity of the heat storage device and the external temperature.

[0101] (4) Energy storage system:

[0102] The energy storage device uses batteries. Under the battery charging and discharging constraints, the mathematical model of charging and discharging is:

[0103] During charging, P sb >0, the remaining energy storage capacity at time t is:

[0104] SOC(t)=SOC(t-1)(1-σ)+P sb (t)·δ c

[0105] During the discharge process, P sb <0, the remaining energy storage capacity at time t is:

[0106] SOC(t)=SOC(t-1)(1-σ)+P sb (t)·δ d

[0107] Where: SOC(t) is the remaining energy storage capacity at time t; δ c ,δ d are charging efficiency and discharging efficiency respectively; P sb (t) is the charge and discharge power at time t; σ is the self-discharge ratio of the battery per hour.

[0108] (5) Distribution network source, grid, load and storage optimization scheduling model

[0109] Taking the highest photovoltaic consumption rate as the goal, the objective function is:

[0110]

[0111] The constraints are:

[0112] 1) Power balance constraints

[0113]

[0114] Where: is the output power of the photovoltaic generator set; is the charging and discharging power of energy storage device i; Q LOSS is the power loss of transmission and distribution; D total is the total system load; Q d (i, t) is the discharge power of energy storage device i in time period t.

[0115] 2) Distributed Photovoltaic (PV) Generation Constraints:

[0116]

[0117] Where: Q pv Reactive power output for distributed photovoltaic power stations. is the inverter capacity of distributed photovoltaic power station. The minimum and maximum values ​​of the reactive power of distributed photovoltaic power stations. This project sets the distributed photovoltaic tracking maximum power point to output active power, and the inverter operating power factor adjustment range is set at ±0.9.

[0118] 3) Energy storage system (ESS) constraints:

[0119]

[0120] Where: P ess,t is the active power of the energy storage system at time t, is the maximum charging power of the energy storage system, is the maximum discharge power. ch is the charging efficiency of the energy storage system, η dis E is the discharge efficiency, and the charge and discharge efficiency is 0.9. ess is the energy storage system capacity, SOC ess is the state of charge, It is the minimum and maximum state of charge allowed by the energy storage system. ess is the reactive power output by the energy storage system, is the inverter capacity of the energy storage system, is the minimum and maximum reactive power of the energy storage system.

[0121] 2. Step 2: Introduce a dynamic adjustment mechanism into the simulation model to monitor the operating status and environmental parameters of each device in real time, and dynamically adjust the model parameters according to the monitoring results to improve the adaptability of the simulation model;

[0122] The historical photovoltaic power generation data, the historical electric vehicle charging load data, the historical thermal storage device load data, and the historical charging and discharging state data of the energy storage system are obtained as the theoretical value of the adjustable variable, that is, x OV ;

[0123] Collect the real-time operation data of each device, including real-time photovoltaic power generation data, real-time electric vehicle charging load data, real-time load parameters of thermal storage devices, and real-time charging and discharging data of energy storage systems, as the actual value of the adjustable variable, i.e. x AV .

[0124] A dynamic adjustment model is constructed with the goal of minimizing the value of dynamic adjustable variables, and each adjustable variable in the model is adjusted in real time.

[0125] Dynamically adjustable variable value A 1 =min[∑ x∈X |x AV -x OV |, where X = [U PV，t , U EV，t , U AC，t , U ESdis，t , U ESch，t],U PV，t is the photovoltaic power generation data at time t, U EV，t is the load data of the electric vehicle at time t, U AC，t is the operating power of the heat storage device at time t, U ESdis，t is the discharge state of the energy storage at time t, U ESch，t is the charging state of the energy storage at time t.

[0126] Technical effect of step 2: By introducing dynamic detection methods, the accuracy and adaptability of the model can be improved.

[0127] 3. Step three: Use the particle swarm algorithm improved by simulated annealing algorithm to solve the user-side source-grid-load-storage optimization scheduling model.

[0128] Steps to combine the simulated degradation algorithm and particle swarm algorithm:

[0129] A. Initialization parameters: particle swarm size M, number of iterations N, self-learning factor C 1 , Population learning factor C 2 , speed limit V max , speed lower limit V min , annealing initial temperature T, annealing cycle number L, annealing fitness factor β;

[0130] B. Calculate the individual fitness according to the fitness function, save the optimal position of each particle, the individual best fitness value, and the group optimal position;

[0131] C. Update the particle's speed and position according to the iterative speed and position update formula to find the current individual optimal solution P beSt and the current group optimal solution G best .

[0132] D. Apply the simulated annealing algorithm, starting from the current solution, generate new candidate solutions through random perturbations, and decide whether to accept the new solution based on the Metropolis criterion. If accepted, update the new solution to the optimal solution and optimal fitness of the entire group. If not accepted, do not update;

[0133] E. Determine whether the annealing cycle number L has been reached, if so, execute step F, otherwise return to step D;

[0134] F. Perform cooling operation T = β × T;

[0135] G. Determine whether the number of iterations N has been reached. If so, output the solution as the optimal solution; otherwise, return to step C.

[0136] Step 3 Technical Effect: Conventional particle swarm algorithm is prone to fall into local extreme values ​​during the solution process, resulting in failure to find the optimal solution, and the solution obtained has a certain error. The particle swarm algorithm improved by the simulated annealing algorithm adopted in the present invention can solve this problem.

[0137] Based on the method of the present invention, the embodiment of the present disclosure also provides a system corresponding to the above method, which includes:

[0138] A model building module, used to build a photovoltaic power generation system model and a photovoltaic consumption system model based on actual photovoltaic usage scenarios, wherein the photovoltaic consumption system model is used to consume the electricity generated by the photovoltaic power generation system model;

[0139] A function building module is used to maximize the net power consumed by the photovoltaic absorption system model at time t divided by the total power generated by the photovoltaic power generation system model as the objective function to maximize the photovoltaic absorption capacity;

[0140] The calculation module is used to solve the objective function and obtain the power consumed by each device in the photovoltaic consumption system model at time t;

[0141] The control module is used to adjust the power consumed by each device in the photovoltaic absorption system model so that the photovoltaic absorption capacity remains at the highest level and the source, grid, load and storage are optimized.

[0142] Based on the same inventive concept as the above disclosed content, an embodiment of the present disclosure also provides a prediction device corresponding to the above method, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above method.

[0143] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. An indirect connection method can be applied to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0144] Based on the same inventive concept, the present disclosure also provides a computer storage medium on which executable instructions are stored. When the instructions are executed by a processor, the processor performs the above method.

[0145] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A source-grid-load-storage optimization method based on photovoltaic power generation, characterized in that: include: Based on the actual photovoltaic usage scenario, a photovoltaic power generation system model and a photovoltaic consumption system model are constructed, wherein the photovoltaic consumption system model is used to consume the electricity generated by the photovoltaic power generation system model; The objective function to maximize the photovoltaic absorption capacity is to maximize the net power consumed by the photovoltaic absorption system model at time t divided by the total power generated by the photovoltaic power generation system model. Solve the objective function to obtain the power consumed by each device in the photovoltaic consumption system model at time t; Adjust the power consumed by each device in the photovoltaic absorption system model to maintain the highest photovoltaic absorption capacity and achieve source, grid, load and storage optimization.

2. A source-grid-load-storage optimization method based on photovoltaic power generation according to claim 1, characterized in that: When the load side in the actual photovoltaic use scenario includes electric vehicles and thermal storage devices, the photovoltaic consumption system model includes: Electric vehicle charging model, energy storage system model, and thermal storage device model; The photovoltaic power generation system model is: Where: P pv (t) is the actual output power of the photovoltaic cell; P STC , G sTC is the light intensity and rated output power of the photovoltaic module under standard test conditions, G(t) is the actual solar radiation intensity; f pv is the loss coefficient; k is the power temperature coefficient; T c (t) is the current photovoltaic cell temperature, T r is the reference temperature; The electric vehicle charging model is: Where: SOC ev,i (t) is the charge state of the ith electric vehicle at time t, E ev,i is the total battery capacity of the electric vehicle, ξ ev,i is the charging efficiency; the adjustable potential of electric vehicles and their initial state of charge SOC 0,i and estimated charging time h ev,i Related; p ev,i (t) is the power consumption of the i-th electric vehicle at time t; When the energy storage device of the energy storage system is a battery, the energy storage system model is: During charging, P sb >0, the remaining energy storage capacity at time t is: SOC(t)=SOC(t-1)(1-σ)+P sb (t)·δ c During the discharge process, P sb <0, the remaining energy storage capacity at time t is: SOC(t)=SOC(t-1)(1-σ)+P sb (t)·δ d Where: SOC(t) is the remaining energy storage capacity at time t; δ c , δ d are charging efficiency and discharging efficiency respectively; P sb (t) is the charge and discharge power at time t; σ is the self-discharge ratio of the battery per hour; The operating power expression of the thermal storage device model is: Where: p ts,i (t) is the operating power of the i-th heat storage device at time t; P TS,i is the rated power of the i-th heat storage device; σ is a 0-1 switch variable that determines whether the heat storage device is working; α min To allow the water temperature inside the thermal storage device to float downward to a multiple of , α max T is the multiple of the water temperature inside the heat storage device that can float upward; ts,i (t) is the internal temperature of the i-th heat storage device at time t, T s,i represents the set temperature of the i-th thermal storage device; The temperature change formula inside the thermal storage device is: Where: V ts,i is the volume of the i-th heat storage device; R f,i (t) is the flow rate of water outflow and water injection of the heat storage device; T inje,i is the temperature of the water injection point of the heat storage device; η ts,i The water temperature increase per unit time of the heat storage device per unit power; ts,i It is the natural temperature loss rate of the heat storage device per unit time.

3. The source-grid-load-storage optimization method based on photovoltaic power generation according to claim 2, characterized in that: The objective function is: Among them, P pv (t) is the power generation of distributed photovoltaic at time t; P ev,i (t) is the power consumption of the i-th electric vehicle at time t; P ts,i (t) is the operating power of the i-th heat storage device at time t, P sb (t) is the charging and discharging power of the energy storage system at time t.

4. The source-grid-load-storage optimization method based on photovoltaic power generation according to claim 2, characterized in that: The power consumption value of each device in the photovoltaic absorption system model is adjusted so that the photovoltaic absorption capacity is maintained at the highest level, including: Set the theoretical value x for the historical data of photovoltaic power generation in the actual photovoltaic scene, the historical charging load data of electric vehicles, the historical load data of thermal storage devices, and the historical charging and discharging state data of energy storage systems. OV ,; The photovoltaic power generation data of the photovoltaic power generation system model and the power consumption data of the photovoltaic consumption system model are collected in real time as observation values ​​x AV ; Create a dynamic adjustment model to minimize the sum of the differences between theoretical values ​​and observed values; The dynamic adjustment model is: Where X = [U PV，t , U EV，t , U AC，t , U ESdis，t , U ESch，t ],U PV，t is the photovoltaic power generation data at time t, U EV，t is the load data of the electric vehicle at time t, U AC，t is the operating power of the heat storage device at time t, U ESdis，t is the discharge state of the energy storage at time t, U ESch，t is the charging state of the energy storage at time t.

5. The source-grid-load-storage optimization method based on photovoltaic power generation according to claim 2, characterized in that: The particle swarm algorithm is used to solve the objective function.

6. A source-grid-load-storage optimization method based on photovoltaic power generation according to claim 5, characterized in that: The particle swarm algorithm is used to solve the objective function; comprising: Initialization parameters, including: particle swarm size M, number of iterations N, self-learning factor C1, population learning factor C2, speed limit V max , speed lower limit V min , annealing initial temperature T, annealing cycle number L, annealing fitness factor β; Calculate individual fitness according to the fitness function, and save the optimal position of each particle, the individual best fitness value, and the group optimal position; wherein the fitness function is the objective function that maximizes the photovoltaic absorption capacity; According to the iterative speed and position update formula, update the speed and position of the particle to find the current individual optimal solution and the current group optimal solution; Apply the simulated annealing algorithm, starting from the current solution, generate new candidate solutions through random perturbations, and decide whether to accept the new solution based on the Metropolis criterion. If accepted, the new solution will be updated to the optimal solution and optimal fitness of the entire group. If not accepted, it will not be updated. When the number of annealing cycles is reached, the cooling operation is performed. When the number of iterations is reached, the value of the particle is output as the optimal solution, and the power consumed by the photovoltaic consumption system model and the total power generated by the photovoltaic power generation system model at time t are obtained.

7. The source-grid-load-storage optimization method based on photovoltaic power generation according to claim 6, characterized in that: When using the particle swarm algorithm to solve the objective function, the constraints are used to screen the optimization results and eliminate invalid solutions. The constraints include: Power balance constraints: Where: is the output power of the photovoltaic generator set; is the charging and discharging power of energy storage device i; Q LOSS is the power loss of transmission and distribution; D total is the total system load; Q d (i,t) is the discharge power of energy storage device i in time period t; Distributed photovoltaic power generation constraints: Where: Q pv Reactive power output for distributed photovoltaic power stations. is the inverter capacity of the distributed photovoltaic power station; The minimum and maximum values ​​of the reactive power of the distributed photovoltaic power station; the distributed photovoltaic tracking maximum power point output active power is set, and the inverter operation power factor adjustment range is set to ±0.9; Energy storage system constraints: Where: P ess,t is the active power of the energy storage system at time t, is the maximum charging power of the energy storage system, is the maximum discharge power; η ch is the charging efficiency of the energy storage system, η dis is the discharge efficiency, the charge and discharge efficiency is 0.9; E ess is the energy storage system capacity, SOC ess is the state of charge, Q is the minimum and maximum state of charge allowed by the energy storage system; ess is the reactive power output by the energy storage system, is the inverter capacity of the energy storage system, is the minimum and maximum reactive power of the energy storage system.

8. A source-grid-load-storage optimization system based on photovoltaic power generation, characterized in that: include: A model building module, used to build a photovoltaic power generation system model and a photovoltaic consumption system model based on actual photovoltaic usage scenarios, wherein the photovoltaic consumption system model is used to consume the electricity generated by the photovoltaic power generation system model; A function building module is used to maximize the net power consumed by the photovoltaic absorption system model at time t divided by the total power generated by the photovoltaic power generation system model as the objective function to maximize the photovoltaic absorption capacity; The calculation module is used to solve the objective function and obtain the power consumed by each device in the photovoltaic consumption system model at time t; The control module is used to adjust the power consumed by each device in the photovoltaic absorption system model so that the photovoltaic absorption capacity remains at the highest level and the source, grid, load and storage are optimized.

9. A source-grid-load-storage optimization device based on photovoltaic power generation, comprising: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the source-grid-load-storage optimization method based on photovoltaic power generation as described in any one of claims 1 to 7.

10. A computer storage medium having executable instructions stored thereon, wherein when the instructions are executed by a processor, the processor implements the method according to any one of claims 1 to 7.

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