Light storage and charging integrated system regulation and control method and device, storage medium and equipment

Abstract

The invention discloses a light storage and charging integrated system regulation and control method and device, a storage medium and equipment, and belongs to the technical field of power system optimization regulation and control, and the method comprises the steps: obtaining an electric energy circulation parameter and a carbon emission parameter of a light storage and charging integrated system; based on the electric energy circulation parameters, the carbon emission parameters and an improved non-dominated genetic sorting algorithm, solving a pre-constructed optical storage and charging integrated system regulation and control model to obtain an optimal solution set which is a set of optimal values of all targets to be regulated and controlled in the optical storage and charging integrated system; the improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on a reference point adaptive updating strategy; and regulating and controlling a target to be regulated and controlled in the optical storage and charging integrated system according to the optimal solution set. According to the method, a reference point self-adaptive updating strategy is utilized, the convergence of a solution algorithm and the diversity of a solution are enhanced, the regulation and control efficiency is improved, and dynamic changes of a power grid are effectively coped with.

Description

Technical Field

[0001] The present invention relates to a control method, device, storage medium and equipment for a photovoltaic storage and charging integrated system, belonging to the technical field of power system optimization and control. Background Art

[0002] As the global demand for low-carbon economy and sustainable energy increases, new energy technologies such as photovoltaic power generation, energy storage equipment and electric vehicles are gradually being introduced into the power system. Although the access to these technologies provides great potential for diversifying energy supply and reducing carbon emissions, it also brings many challenges in grid operation and management. Traditional grid design is mainly based on centralized power supply, and the system operation is relatively stable. However, with the introduction of distributed power sources (such as photovoltaics), random access to electric vehicles and large-scale energy storage technology, the power volatility and uncertainty of the grid have increased significantly, and the complexity of grid dispatch and optimization has been greatly improved.

[0003] Existing grid control methods mostly rely on static optimization models, which cannot fully consider the real-time impact of the dynamic interaction between sources and loads on system operation, and are difficult to deal with the volatility and uncertainty problems caused by the increase in the proportion of new energy. In addition, traditional optimization algorithms, such as single-objective optimization methods, often only focus on reducing energy consumption or improving economic benefits, ignoring the balance between carbon emissions, energy costs and new energy consumption rates in multi-objective optimization. In this context, multi-objective optimization methods based on the non-dominated genetic sorting algorithm (NSGA-III) are gradually applied to the field of power system optimization. However, the traditional NSGA-III algorithm has the problem of insufficient adaptability to real-time changing source and load characteristics, and cannot dynamically adjust the reference point to track changes in system status, which may lead to insufficient diversity of solutions or poor convergence, resulting in the control scheme obtained. The control efficiency is low and the control scheme cannot achieve efficient and coordinated effects on the system. Summary of the invention

[0004] The purpose of the present invention is to provide a control method, device, storage medium and equipment for an integrated photovoltaic storage and charging system to solve the problem of low control efficiency in the prior art.

[0005] To achieve the above objectives, the present invention is implemented by adopting the following technical solutions: In a first aspect, the present invention provides a control method for an integrated photovoltaic storage and charging system, comprising: Obtain the power flow parameters and carbon emission parameters of the integrated photovoltaic storage and charging system; Based on the electric energy flow parameters, the carbon emission parameters and the improved non-dominated genetic sorting algorithm, a pre-constructed photovoltaic storage and charging integrated system control model is solved to obtain an optimal solution set, which is a set of optimal values ​​of all targets to be controlled in the photovoltaic storage and charging integrated system. The improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on a reference point adaptive update strategy; The target to be regulated in the photovoltaic storage and charging integrated system is regulated according to the optimal solution set.

[0006] Furthermore, the photovoltaic storage and charging integrated system control model includes a carbon emission minimization objective function, an energy cost minimization objective function and a new energy utilization rate maximization objective function; The expressions of each objective function are: ; ; ; in, f 1 is the objective function of minimizing carbon emissions, α grid is the carbon emission coefficient per kilowatt-hour of electricity purchased from the grid, f 2 is the objective function of minimizing energy cost, f 3 is the objective function of maximizing the utilization rate of new energy, P output is the overall external power output of the integrated photovoltaic storage and charging system, || represents the absolute value, C pv is the cost per kWh of photovoltaic power generation, C buy is the cost per kilowatt-hour of electricity purchased from the power grid, C sell is the revenue per kilowatt-hour of electricity sold to the grid. P pv is the power output of photovoltaic power generation, with a positive sign, P ev is the power demand of the electric vehicle charging station, a positive sign indicates discharge, and a negative sign indicates charging; The electric energy circulation parameters include the cost per kilowatt-hour of photovoltaic power generation, the cost per kilowatt-hour of electricity purchased from the power grid, and the revenue per kilowatt-hour of electricity sold to the power grid. The carbon emission parameters include the carbon emission coefficient per kilowatt-hour of electricity purchased from the power grid.

[0007] Furthermore, there is the following power coupling relationship between the power output of the photovoltaic power generation, the power demand of the electric vehicle charging pile and the overall external power output of the photovoltaic storage and charging integrated system: P output = P pv+ P ev + P storage - P load ; in, P storage It is the power output of the energy storage device. A positive sign indicates discharge, and a negative sign indicates charge. P load is the load demand, with a positive sign.

[0008] Furthermore, the photovoltaic storage and charging integrated system control model also includes the following constraints: The power output of any device at any time is greater than or equal to the minimum power output limit of the device, and less than or equal to the maximum power output limit of the device; The voltage of any device at any time is greater than or equal to the minimum voltage limit of the device, and less than or equal to the maximum voltage limit of the device; The load demand of any device at any time is greater than or equal to the system minimum load limit, and less than or equal to the system maximum load limit; The equipment includes photovoltaic power generation equipment, energy storage equipment and electric vehicle charging piles.

[0009] Furthermore, the method of solving the pre-built photovoltaic storage and charging integrated system control model based on the power flow parameters, the carbon emission parameters and the improved non-dominated genetic sorting algorithm includes: A. Initialize a population of multiple individuals and a reference point. Each individual represents a solution set. The reference point is the optimal individual. Each individual is assigned the value of the corresponding objective function and constraint conditions in the photovoltaic storage and charging integrated system control model. B. For each individual, calculate the fitness according to the value of its objective function; C. Adaptively update the reference point based on the individual fitness and the value of the objective function in the photovoltaic storage and charging integrated system control model at the current moment; D. Sort the population by the non-dominated sorting method, and then select half of the individuals with the closest Euclidean distance to the reference point from the population; E. Select any two individuals as two parent solutions, perform crossover and mutation on the two parent solutions to generate two child solutions, and replace the two parent solutions with the two child solutions as new individuals until all individuals are polled; F. Check the constraints of the new individual. If it does not meet the preset constraints in the photovoltaic storage and charging integrated system control model, remove the new individual; G. Take the remaining new individuals as the new population and poll steps B to F until the number of iterations is greater than the first preset value or the fitness change ratio of the reference points of two adjacent iterations is less than the second preset value, terminate the iteration, and output the individual with the highest fitness at this time as the optimal solution set.

[0010] Furthermore, the first preset value is 100, and the second preset value is 1%.

[0011] Furthermore, the reference point is adaptively updated according to the individual fitness and the value of the objective function in the photovoltaic storage and charging integrated system control model at the current moment, which is performed through the following formula: ; in, r ( t )yes t The reference point of time, r ( t -1) Yes t -1 is the reference point at the moment, γ is the preset adaptive update coefficient, f k ( x ( t ))yes t The moment k The value of the objective function, k The value of is 1 / 2 / 3, z k It is k The historical optimal value of the objective function, w k It is k The sum of the weights of all objective functions is 1.

[0012] In a second aspect, the present invention provides a control device for an integrated photovoltaic storage and charging system, comprising: The parameter acquisition module is configured to: acquire the power flow parameters and carbon emission parameters of the photovoltaic storage and charging integrated system; The model solving module is configured to solve the pre-built photovoltaic storage and charging integrated system control model based on the electric energy flow parameter, the carbon emission parameter and the improved non-dominated genetic sorting algorithm to obtain an optimal solution set, wherein the optimal solution set is a set of optimal values ​​of all the targets to be controlled in the photovoltaic storage and charging integrated system, and the improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on the reference point adaptive update strategy; The control module is configured to: control the target to be controlled in the integrated photovoltaic storage and charging system according to the optimal solution set.

[0013] In a third aspect, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method for controlling an integrated photovoltaic storage and charging system described in any one of the first aspects are implemented.

[0014] In a fourth aspect, the present invention provides a computer device, comprising: Memory, for storing computer programs / instructions; A processor is used to execute the computer program / instructions to implement the steps of the photovoltaic storage and charging integrated system control method described in any one of the first aspects.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are: The present invention provides a control method, device, storage medium and equipment for an integrated photovoltaic storage and charging system, which improves a non-dominated genetic sorting algorithm through a reference point adaptive update strategy, and then uses the improved non-dominated genetic sorting algorithm and various acquired real-time parameters to solve the control model of the integrated photovoltaic storage and charging system, thereby enhancing the convergence of the algorithm and the diversity of solutions, improving the control efficiency of the integrated photovoltaic storage and charging system, and effectively responding to dynamic changes in the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a control method of a photovoltaic storage and charging integrated system provided by an embodiment of the present invention; Figure 2 is a graph showing actual photovoltaic power generation and load demand changes over time provided by an embodiment of the present invention; Figure 3 is a curve diagram of energy storage power changing over time provided by an embodiment of the present invention; Figure 4 is a graph showing power grid power changing over time provided by an embodiment of the present invention; Figure 5 This is a schematic diagram of real-time power balance of a photovoltaic storage and charging integrated system taking randomness into consideration provided by an embodiment of the present invention; Figure 6 is a graph showing carbon emissions changing over time provided by an embodiment of the present invention; Figure 7 is a graph showing changes in the new energy consumption rate over time provided by an embodiment of the present invention; Figure 8 It is a curve diagram of each power variation over time provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the protection scope of the present invention.

[0018] Example 1.

[0019] like Figure 1 As shown, the present invention provides a control method for an integrated photovoltaic storage and charging system, comprising the following steps: Obtain the power flow parameters and carbon emission parameters of the integrated photovoltaic storage and charging system; Based on the electric energy flow parameters, the carbon emission parameters and the improved non-dominated genetic sorting algorithm, a pre-constructed photovoltaic storage and charging integrated system control model is solved to obtain an optimal solution set, which is a set of optimal values ​​of all targets to be controlled in the photovoltaic storage and charging integrated system. The improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on a reference point adaptive update strategy; The target to be regulated in the photovoltaic storage and charging integrated system is regulated according to the optimal solution set.

[0020] The present invention improves the non-dominated genetic sorting algorithm through a reference point adaptive update strategy, and then uses the improved non-dominated genetic sorting algorithm and the obtained real-time parameters to solve the photovoltaic storage and charging integrated system control model, thereby enhancing the convergence of the algorithm and the diversity of solutions, improving the control efficiency of the photovoltaic storage and charging integrated system, and effectively responding to the dynamic changes of the power grid.

[0021] Example 2.

[0022] like Figure 1 As shown, the present invention provides a control method for an integrated photovoltaic storage and charging system, which is described in conjunction with a specific integrated photovoltaic storage and charging system.

[0023] An industrial park has adopted an integrated photovoltaic storage and charging system, which includes a 100MW photovoltaic power station, a 50MWh energy storage system and 20 electric vehicle charging piles. Figure 2 , Figure 3 , Figure 4 As shown in the figure, the changes of photovoltaic power generation and load demand over time in the integrated photovoltaic storage and charging system are shown, and the charging and discharging of the energy storage system and the input / output of the grid are included. By comparing photovoltaic power generation, energy storage and load demand, it is shown how the system adjusts power fluctuations through the energy storage system and the grid to maintain a stable power supply. The load demand of the park fluctuates greatly, especially during the noon period, when photovoltaic power generation reaches its peak (11:00~14:00), and the charging demand of electric vehicles also reaches its peak. Traditional control strategies are difficult to effectively respond to these fluctuations, resulting in low system utilization efficiency, insufficient new energy consumption rate, and high carbon emissions.

[0024] The scenario parameters of the integrated photovoltaic storage and charging system in this embodiment are shown in Table 1: Table 1- Scenario parameters of integrated photovoltaic storage and charging system

[0025] In order to meet the above challenges, the system adopts the photovoltaic storage and charging integrated system control method based on the reference point adaptive update strategy proposed in this invention. By real-time monitoring of photovoltaic power generation, energy storage equipment status and charging demand, the system can dynamically adjust and optimize the reference point and optimize the operation of photovoltaic power generation, energy storage system and charging piles.

[0026] like Figure 5 As shown in the figure, the power balance of the photovoltaic storage and charging system under the influence of random factors is demonstrated. Both photovoltaic power generation and load demand are uncertain, and real-time regulation is carried out through the energy storage system and the power grid. It shows how the photovoltaic storage and charging integrated system can dynamically respond to the fluctuations of photovoltaic power generation and load demand in the actual environment to maintain the balance of power supply and demand. The specific implementation strategy is shown below.

[0027] S1. Obtain the known quantities needed for the regulation process, including the cost per kilowatt-hour of photovoltaic power generation, the cost per kilowatt-hour of electricity purchased from the grid, the revenue per kilowatt-hour of electricity sold to the grid, and the carbon emission coefficient per kilowatt-hour of electricity purchased from the grid.

[0028] S2. Based on the known quantities obtained and the improved non-dominated genetic sorting algorithm, the pre-constructed photovoltaic storage and charging integrated system control model is solved to obtain the optimal solution set, which is the set of optimal values ​​of all the targets to be controlled in the photovoltaic storage and charging integrated system.

[0029] The pre-built integrated photovoltaic storage and charging system control model includes the objective function of minimizing carbon emissions, minimizing energy costs and maximizing new energy utilization. It is a multi-level coupled system model that includes photovoltaic power generation, energy storage equipment, charging pile loads and power grids. The model takes into account the power output, load demand and dynamic change characteristics of each device.

[0030] The expressions of each objective function are: ; ; ; in, f 1 is the objective function of minimizing carbon emissions, α grid is the carbon emission coefficient per kilowatt-hour of electricity purchased from the grid, f 2 is the objective function of minimizing energy cost, f 3 is the objective function of maximizing the utilization rate of new energy, P output is the overall external power output of the integrated photovoltaic storage and charging system, || represents the absolute value, C pv is the cost per kWh of photovoltaic power generation, Cbuy is the cost per kilowatt-hour of electricity purchased from the power grid, C sell is the revenue per kilowatt-hour of electricity sold to the grid. P pv is the power output of photovoltaic power generation, with a positive sign, P ev It is the power requirement of the electric vehicle charging station. A positive sign indicates discharge, and a negative sign indicates charging.

[0031] In the pre-built photovoltaic storage and charging integrated system control model, there is the following power coupling relationship between the power output of photovoltaic power generation, the power demand of electric vehicle charging piles, the overall external power output of the photovoltaic storage and charging integrated system, and the power of other loads: P output = P pv + P ev + P storage - P load ; in, P storage It is the power output of the energy storage device. A positive sign indicates discharge, and a negative sign indicates charge. P load is the load demand, with a positive sign.

[0032] The photovoltaic storage and charging integrated system control model also includes the following constraints: , that is, the power output of any device at any time is greater than or equal to the minimum power output limit of the device P min , ensure that the device does not fall below this value during normal operation and is less than or equal to the maximum power output limit of the device P max , to avoid equipment overload; among them, P i ( t ) indicates the i Devices in t Power output at the moment.

[0033] , that is, the voltage of any device at any time is greater than or equal to the minimum voltage limit of the device V min , ensure that the voltage is not lower than the threshold for safe operation of the equipment and is less than or equal to the maximum voltage limit of the equipment V max , to prevent system failure or equipment damage caused by excessive voltage; among them, Vi ( t ) indicates the i Devices in t Voltage at the moment.

[0034] , that is, the load demand of any device at any time is greater than or equal to the system minimum load limit L min , ensuring that at least the minimum load requirement of the system is met and is less than or equal to the maximum load limit of the system L max , ensuring that the load demand does not exceed the system's carrying capacity; among them, L i ( t ) indicates the i Devices in t The load demand (or power consumption) at a given moment.

[0035] The equipment includes photovoltaic power generation equipment, energy storage equipment and electric vehicle charging piles. N Indicates the total number of devices.

[0036] In step S2, the solving steps specifically include: (1) Initialize the population: Initialize a population consisting of M individuals and a reference point. Each individual represents a solution set. The reference point is the optimal individual. Each individual is assigned the value of the corresponding objective function and constraint conditions in the photovoltaic storage and charging integrated system control model.

[0037] (2) Calculate fitness: For each individual, according to the value of its objective function ( f 1. f 2 and f 3) Calculate the fitness through the formula Calculation shows that the higher the fitness, the better the solution set corresponding to the individual. w k It is k The sum of the weights of all objective functions is 1.

[0038] (3) Adaptive update of reference point: The reference point is adaptively updated according to the individual fitness and the value of the objective function in the photovoltaic storage and charging integrated system control model at the current moment.

[0039] The reference point adaptive update is performed by the following formula: ; in, r ( t )yes t The reference point of time, r ( t -1) Yes t-1 is the reference point at the moment, γ is the preset adaptive update coefficient, f k ( x ( t ))yes t The moment k The value of the objective function, k The value of is 1 / 2 / 3, z k It is k The historical optimal value of the objective function, w k It is k The sum of the weights of all objective functions is 1.

[0040] (4) Selection operation: Sort the population using the non-dominated sorting method, and then select half of the individuals with the closest Euclidean distance to the reference point from the population.

[0041] (5) Crossover and mutation: After the selection operation, select any two individuals as the two parent solutions. x 1 and x 2. Perform crossover and mutation on the two parent solutions to generate two child solutions y 1 and y 2. Replace the two parent solutions with the two child solutions as new individuals until all individuals are polled. The child solutions are generated using the following formula: ; ; in, β It is a parameter that controls the crossover amplitude, and its value is usually between 0.5 and 2. In this embodiment, it is 1.1.

[0042] (6) Constraint processing: A constraint check is performed on the new individual. If it does not meet the preset constraint conditions in the integrated photovoltaic storage and charging system control model, the new individual is removed.

[0043] (7) Update the population and use the remaining new individuals as the new population and repeat steps (2) to (6) until the number of iterations is greater than the first preset value (100) or the fitness change ratio of the reference points of two adjacent iterations is less than the second preset value (1%). Then terminate the iteration and output the individual with the highest fitness at this time as the optimal solution set.

[0044] S3. According to the optimal solution set obtained in step S2, the target to be regulated in the integrated photovoltaic storage and charging system is regulated.

[0045] In this embodiment, after implementing the control method, the performance of the park in different scenarios is significantly improved. Figure 6 , Figure 7 and Figure 8 As shown, the interaction between photovoltaic power generation, industrial load, residential load, energy storage charging and discharging, and grid input / output power is demonstrated, and the optimized operation process of the entire photovoltaic storage and charging system is carefully demonstrated. Through the strategy of this embodiment, the coordinated work of photovoltaic power generation, energy storage, and the grid is comprehensively demonstrated, showing how the system balances the demands of different types of loads through real-time regulation, maximizes the utilization of new energy, and reduces carbon emissions. The specific effects are shown in Table 2.

[0046] Table 2-Implementation Effect Table

[0047] By adopting the control method of the present invention, the park significantly reduces its dependence on the power grid during peak load periods, and maximizes the use of photovoltaic power generation and energy storage equipment. The system runs smoothly, the utilization rate of new energy is significantly improved, carbon emissions are reduced, and the energy cost of the park is also significantly reduced.

[0048] Example 3.

[0049] The present invention provides a control device for an integrated photovoltaic storage and charging system, comprising: The parameter acquisition module is configured to: acquire the power flow parameters and carbon emission parameters of the photovoltaic storage and charging integrated system; The model solving module is configured to solve the pre-built photovoltaic storage and charging integrated system control model based on the power flow parameters, the carbon emission parameters and the improved non-dominated genetic sorting algorithm to obtain an optimal solution set, wherein the optimal solution set is a set of optimal values ​​of all the targets to be controlled in the photovoltaic storage and charging integrated system, and the improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on the reference point adaptive update strategy; The control module is configured to: control the target to be controlled in the integrated photovoltaic storage and charging system according to the optimal solution set.

[0050] Example 4.

[0051] The present invention provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the photovoltaic storage and charging integrated system control method provided in Example 1 are implemented: Obtain the power flow parameters and carbon emission parameters of the integrated photovoltaic storage and charging system; Solve the pre-built photovoltaic storage and charging integrated system control model based on the power flow parameters, carbon emission parameters and the improved non-dominated genetic sorting algorithm to obtain the optimal solution set, which is the set of optimal values ​​of all the targets to be controlled in the photovoltaic storage and charging integrated system. The improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on the reference point adaptive update strategy; The targets to be regulated in the integrated photovoltaic storage and charging system are regulated according to the optimal solution set.

[0052] Example 5.

[0053] The present invention provides a computer device, comprising: Memory, for storing computer programs / instructions; A processor is used to execute the computer program / instructions to implement the steps of the photovoltaic storage and charging integrated system control method provided in Example 1: Obtain the power flow parameters and carbon emission parameters of the integrated photovoltaic storage and charging system; Solve the pre-built photovoltaic storage and charging integrated system control model based on the power flow parameters, carbon emission parameters and the improved non-dominated genetic sorting algorithm to obtain the optimal solution set, which is the set of optimal values ​​of all the targets to be controlled in the photovoltaic storage and charging integrated system. The improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on the reference point adaptive update strategy; The targets to be regulated in the integrated photovoltaic storage and charging system are regulated according to the optimal solution set.

[0054] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0055] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0058] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A control method for an integrated photovoltaic storage and charging system, characterized in that: include: Obtain the power flow parameters and carbon emission parameters of the integrated photovoltaic storage and charging system; Based on the electric energy flow parameters, the carbon emission parameters and the improved non-dominated genetic sorting algorithm, a pre-constructed photovoltaic storage and charging integrated system control model is solved to obtain an optimal solution set, which is a set of optimal values ​​of all targets to be controlled in the photovoltaic storage and charging integrated system. The improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on a reference point adaptive update strategy; The target to be regulated in the photovoltaic storage and charging integrated system is regulated according to the optimal solution set.

2. The control method of the integrated photovoltaic storage and charging system according to claim 1, characterized in that: The photovoltaic storage and charging integrated system control model includes a carbon emission minimization objective function, an energy cost minimization objective function and a new energy utilization rate maximization objective function; The expressions of each objective function are: ； ； ； in, f 1 is the objective function of minimizing carbon emissions, α grid is the carbon emission coefficient per kilowatt-hour of electricity purchased from the grid, f 2 is the objective function of minimizing energy cost, f 3 is the objective function of maximizing the utilization rate of new energy, P output is the overall external power output of the integrated photovoltaic storage and charging system, || represents the absolute value, C pv is the cost per kWh of photovoltaic power generation, C buy is the cost per kilowatt-hour of electricity purchased from the power grid, C sell is the revenue per kilowatt-hour of electricity sold to the grid. P pv is the power output of photovoltaic power generation, with a positive sign, P ev is the power demand of the electric vehicle charging station, a positive sign indicates discharge, and a negative sign indicates charging; The electric energy circulation parameters include the cost per kilowatt-hour of photovoltaic power generation, the cost per kilowatt-hour of electricity purchased from the power grid, and the revenue per kilowatt-hour of electricity sold to the power grid. The carbon emission parameters include the carbon emission coefficient per kilowatt-hour of electricity purchased from the power grid.

3. The control method of the integrated photovoltaic storage and charging system according to claim 2, characterized in that: The following power coupling relationship exists between the power output of the photovoltaic power generation, the power demand of the electric vehicle charging pile and the overall external power output of the photovoltaic storage and charging integrated system: P output = P pv + P ev + P storage - P load ； in, P storage It is the power output of the energy storage device. A positive sign indicates discharge, and a negative sign indicates charge. P load is the load demand, with a positive sign.

4. The control method of the integrated photovoltaic storage and charging system according to claim 1, characterized in that: The photovoltaic storage and charging integrated system control model also includes the following constraints: The power output of any device at any time is greater than or equal to the minimum power output limit of the device, and less than or equal to the maximum power output limit of the device; The voltage of any device at any time is greater than or equal to the minimum voltage limit of the device, and less than or equal to the maximum voltage limit of the device; The load demand of any device at any time is greater than or equal to the system minimum load limit, and less than or equal to the system maximum load limit; The equipment includes photovoltaic power generation equipment, energy storage equipment and electric vehicle charging piles.

5. The control method of the integrated photovoltaic storage and charging system according to claim 1, characterized in that: The method of solving the pre-built photovoltaic storage and charging integrated system control model based on the electric energy flow parameters, the carbon emission parameters and the improved non-dominated genetic sorting algorithm includes: A. Initialize a population of multiple individuals and a reference point. Each individual represents a solution set. The reference point is the optimal individual. Each individual is assigned the value of the corresponding objective function and constraint conditions in the photovoltaic storage and charging integrated system control model. B. For each individual, calculate the fitness according to the value of its objective function; C. Adaptively update the reference point based on the individual fitness and the value of the objective function in the photovoltaic storage and charging integrated system control model at the current moment; D. Sort the population by the non-dominated sorting method, and then select half of the individuals with the closest Euclidean distance to the reference point from the population; E. Select any two individuals as two parent solutions, perform crossover and mutation on the two parent solutions to generate two child solutions, and replace the two parent solutions with the two child solutions as new individuals until all individuals are polled; F. Check the constraints of the new individual. If it does not meet the preset constraints in the photovoltaic storage and charging integrated system control model, remove the new individual; G. Take the remaining new individuals as the new population and poll steps B to F until the number of iterations is greater than the first preset value or the fitness change ratio of the reference points of two adjacent iterations is less than the second preset value, terminate the iteration, and output the individual with the highest fitness at this time as the optimal solution set.

6. The control method of the integrated photovoltaic storage and charging system according to claim 5, characterized in that: The first preset value is 100, and the second preset value is 1%.

7. The control method of the integrated photovoltaic storage and charging system according to claim 5, characterized in that: According to the individual fitness and the value of the objective function in the photovoltaic storage and charging integrated system control model at the current moment, the reference point is adaptively updated through the following formula: ； in, r ( t )yes t The reference point of time, r ( t -1) Yes t -1 is the reference point at the moment, γ is the preset adaptive update coefficient, f k ( x ( t ))yes t The moment k The value of the objective function, k The value of is 1 / 2 / 3, z k It is k The historical optimal value of the objective function, w k It is k The sum of the weights of all objective functions is 1.

8. A control device for an integrated photovoltaic storage and charging system, characterized in that: include: The parameter acquisition module is configured to: acquire the power flow parameters and carbon emission parameters of the photovoltaic storage and charging integrated system; The model solving module is configured to solve the pre-built photovoltaic storage and charging integrated system control model based on the electric energy flow parameter, the carbon emission parameter and the improved non-dominated genetic sorting algorithm to obtain an optimal solution set, wherein the optimal solution set is a set of optimal values ​​of all the targets to be controlled in the photovoltaic storage and charging integrated system, and the improved non-dominated genetic sorting algorithm is obtained by improving the non-dominated genetic sorting algorithm based on the reference point adaptive update strategy; The control module is configured to: control the target to be controlled in the integrated photovoltaic storage and charging system according to the optimal solution set.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the photovoltaic storage and charging integrated system control method described in any one of claims 1 to 7 are implemented.

10. A computer device, characterized in that: include: Memory, for storing computer programs / instructions; A processor is used to execute the computer program / instructions to implement the steps of the photovoltaic storage and charging integrated system control method according to any one of claims 1 to 7.

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