Distributed photovoltaic flexible regulation output optimization method and system

Abstract

The invention discloses a distributed photovoltaic flexible regulation output optimization method and system, and the method comprises the steps: enabling an automatic power generation control system to receive an adjustment instruction regularly issued by a dispatching master station, calculating the active power demand of a distributed photovoltaic power generation system with an adjustment instruction value as a target, and carrying out the active power distribution through the selection of control equipment, and the final control instruction is automatically issued to the controlled equipment to realize active power control of the distributed photovoltaic power generation system. According to the method, photovoltaic power generation historical data and load power consumption typical day historical data are combined, an adaptive robustness two-stage optimization scheduling model is established, and optimization scheduling of two stages including a pre-scheduling stage and a re-scheduling stage is included; and solving the adaptive robustness two-stage optimization scheduling model through an improved sparrow search algorithm to obtain a photovoltaic output optimization result. According to the method, the robustness optimization capability of distributed photovoltaic power generation output is enhanced, and the overall economic benefit is maximized while the operation of the system does not exceed the limit.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distributed photovoltaic flexible control output optimization method and system. Background Art

[0002] New distributed photovoltaic power generation projects should have the functions of "observable, measurable, controllable and adjustable" when connected to the grid, further standardize the construction and operation management of distributed photovoltaic "four-capable" master stations, promote the healthy and orderly development of distributed photovoltaics, and ensure the safe and stable operation of the power grid.

[0003] The uncertainty of traditional 10 kV household photovoltaic output and the volatility of load demand have an important impact on the dispatch of microgrids. The traditional microgrid dispatch mode directly adopts the results of the prediction stage, and the accuracy of the prediction is directly related to the reliability of the optimized dispatch results. Studies have shown that by using multi-level coordination of prediction data of different time scales and accuracies, the dispatch plan can be gradually improved. In addition, the prediction stage is added to the traditional day-ahead-real-time dispatch framework, and the prediction data generated by the long short-term memory network is used to reduce the cost of microgrid participation in the electricity market. However, most of them simply apply the prediction data without fully considering the characteristic information in the prediction data sequence. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed photovoltaic flexible control output optimization method and system to enhance the robustness optimization capability of distributed photovoltaic power generation output, while keeping the system operation within the limit and maximizing the overall economic benefit.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a distributed photovoltaic flexible control output optimization method, the optimization method comprising the following steps: The automatic power generation control system receives the regulation instructions regularly issued by the dispatching master station, calculates the active power demand of the distributed photovoltaic power generation system based on the regulation instruction value, selects the control device and distributes the active power, and automatically issues the final control instruction to the controlled device, so as to realize the active power control of the distributed photovoltaic power generation system; Combining the historical data of photovoltaic power generation and the historical data of typical days of load power consumption, an adaptive robust two-stage optimization scheduling model is established, which includes two stages of optimization scheduling: pre-scheduling stage and re-scheduling stage. The target of the pre-scheduling stage is the expected pre-scheduling cost, and the target of the re-scheduling stage is the subsequent re-scheduling cost. The adaptive robust two-stage optimization scheduling model is solved by using an improved sparrow search algorithm, and the result of photovoltaic output optimization is obtained.

[0006] In a preferred embodiment, active power control of a distributed photovoltaic power generation system is achieved by selecting a control device and performing active power distribution, and automatically issuing a final control instruction to the controlled device, including the following steps: According to the rising margin and falling margin of each inverter, the active power distribution strategy is formulated. Under the premise of meeting the response target instruction, the adjustment frequency of the inverter group participating in the adjustment is minimized. The active power distribution setting expression is: ; In the formula, Plan total active power for distributed photovoltaic power generation systems; is the sum of the active powers of the inverters that do not participate in the regulation, is the total active power that needs to be adjusted; The automatic power generation control system distributes the target value required for adjustment to each or each group of inverters participating in the adjustment according to the margin of the inverters participating in the adjustment, so as to achieve the target active power distribution of the distributed photovoltaic power generation system. The active load distributed to each inverter is as follows: ; In the formula, N j For the j The number of inverters or groups; X is the power loss of transformer equipment; P j裕度 For the current j The capacity of one or more inverters can be adjusted.

[0007] In a preferred embodiment, the expression of the adaptive robust two-stage optimization scheduling model is: ; In the formula, is the expected lead-time scheduling cost, is the subsequent redispatch cost, is the sum of the startup and shutdown costs of the micro turbine MT and the distributed photovoltaic power generation system DG, are all other costs in the pre-scheduling phase, u , U are the uncertain variables and uncertain variable sets including the photovoltaic power output power and load power consumption in the microgrid, respectively. is the decision variable in the pre-scheduling stage, is the decision variable in the rescheduling phase, In the pre-scheduling stage, the decision variables With uncertain variables u The feasible domain formed after joint decision, for Variables in the feasible domain; The expression of the pre-scheduling stage is: ; In the formula, and denote the startup cost and shutdown cost of the microturbine MT, respectively, and They represent the startup cost and shutdown cost of the distributed photovoltaic power generation system DG respectively; The expression for the mulligan phase is: ; In the formula, represents the microturbine MT operating cost, represents the operating cost of distributed photovoltaic power generation system DG, represents the charging and discharging cost of the energy storage device, Represents the electricity transaction cost between the microgrid and the large grid.

[0008] In a preferred embodiment, the constraints of the adaptive robust two-stage optimization scheduling model include: The output power constraints of the new energy power generation device are: ; In the formula, represents the expected maximum power generation capacity of the distributed photovoltaic power generation system, It indicates the unused solar power generation of distributed photovoltaic power generation system; It represents the adjustable light energy generation of distributed photovoltaics; The constraints of the equilibrium state of power supply and demand are: ; In the formula, for the The devices participating in demand response are The situation at the moment, , , They represent the base load power, the power contributed by valley-filling demand response, and the power reduced by peak-shaving demand response respectively; Indicates Thermal power units in The actual power generation at the moment; and Respectively represent renewable energy generation and distributed photovoltaic power generation system Predicted actual output power at the moment; and Respectively represent Energy storage devices in Discharging power and charging power at the moment; express The amount of electricity demand that cannot be met at any given moment is called the load gap; is the total number of devices participating in demand response, , , They are the number of equipment, energy storage devices and thermal power units that fail to meet the power demand; The constraints on the capacity of the energy storage device are: ; In the formula, Indicates the total capacity of the energy storage device; A numerical value representing the demand response; Indicates the power provided by the energy storage device; Indicates the electrical energy required by the load; The constraints for power storage control are: ; In the formula, and They represent the minimum and maximum limits of the energy storage device’s power, f Indicates the power of the energy storage device; Indicates the natural charge decay rate of the energy storage device per hour; For The amount of power stored at any time, For Adjustable control amount of power storage at all times; The constraints of the flexible load are: ; In the formula, and They represent the upper and lower power limits of the flexible load respectively. is the power of the flexible load.

[0009] In a preferred embodiment, the improved sparrow search algorithm comprises the following steps: The sparrows were divided into three roles: discoverers were responsible for exploring and sharing information about food locations, followers followed the clues of discoverers to forage, and sentinels were randomly distributed in the group at a ratio of 10% to 20% and were responsible for monitoring the safety of the foraging area. The location update strategy of the discoverer is expressed as follows: ; In the formula, E max is the maximum number of iterations, is the number of iterations of the current population, and Respectively Generation and the first +1st in the generation The position of a sparrow, is the warning value, is a random factor, E is a safe value, and the range of E is in the interval [0.5,1]; is a random number that follows a normal distribution, is a matrix where every element is 1; The follower's position update strategy is expressed as follows: ; In the formula, is a row vector whose elements are either 1 or -1; Indicates the current location of the finder. is the current worst position, is the number of follower sparrows, is the total number of sparrows, The best position for the finder; The position update strategy of the sentinel is expressed as follows: ; In the formula, and They are all randomly generated numbers, ranging from -1 to 1, and follow the normal distribution law; is a constant; is the current global worst position, is the fitness value of the current sparrow individual, is the worst fitness value of the sparrow; A mutation strategy based on sine and cosine functions is introduced to give new properties to candidate solutions through the characteristics of sine and cosine functions. The expression is as follows: ; In the formula, For the current solution, is the updated solution, is a uniformly distributed random number, To control the magnitude of variation, is the scale parameter of the Cauchy distribution; Using the characteristics of Cauchy distribution, random perturbations are added to the current coordinates. The expression is as follows: ; In the formula, the random value It comes from the Cauchy distribution, is an integer parameter used to measure the strength of the mutation. Indicates the current coordinates, Indicates the updated coordinates.

[0010] In a preferred embodiment, the adaptive robustness two-stage optimization scheduling model is solved by an improved sparrow search algorithm to obtain the result of photovoltaic output optimization, including the following steps: Use the improved sparrow search algorithm to solve the problem independently and , to determine the minimum scheduling operation cost; then use the improved sparrow search algorithm to ensure the minimum Based on this, we further solve for another variable , get the corresponding minimum operating cost, and set it as the boundary value in the Pareto optimal solution set; solve the optimal solution of the inner objective function based on different outer objective function constraints; through the outer constraints, obtain multiple sets of candidate optimal solutions, and find the best system operation plan that meets the optimization goal, that is, to achieve the minimization of operating costs and load balance Optimization.

[0011] A distributed photovoltaic flexible control output optimization system, including an active power control module, a model building module, and an output optimization module; Active power control module: Receives adjustment instructions regularly issued by the dispatching master station, calculates the active power demand of the distributed photovoltaic power generation system based on the adjustment instruction value, selects the control device and distributes the active power, and automatically issues the final control instruction to the controlled device, so as to realize the active power control of the distributed photovoltaic power generation system; Model building module: Combine the historical data of photovoltaic power generation and the historical data of typical days of load power consumption to establish an adaptive robust two-stage optimization scheduling model, which includes two stages of optimization scheduling: pre-scheduling stage and re-scheduling stage. The target of the pre-scheduling stage is the expected pre-scheduling cost, and the target of the re-scheduling stage is the subsequent re-scheduling cost. Output optimization module: The adaptive robust two-stage optimization scheduling model is solved through the improved sparrow search algorithm to obtain the result of photovoltaic output optimization.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The flexible control output optimization method of the present invention combines the historical data of photovoltaic power generation and the historical data of typical daily load power consumption to establish an adaptive robust two-stage optimization scheduling model, and uses the improved sparrow search algorithm model for prediction and calculation. It can adjust the system state more quickly to adapt to changes in the external environment, thereby improving the flexibility and response speed of the system.

[0013] 2. The present invention can realize intelligent dispatching of energy storage equipment by accurately predicting the output and power demand of distributed photovoltaic power generation systems. When the predicted power generation is higher than the actual demand, the excess power is stored in the energy storage device; when the predicted power generation is insufficient, the power is supplemented by adjusting the discharge strategy of the energy storage device. This intelligent dispatching method helps to optimize the allocation of energy resources, reduce energy waste, and improve energy utilization efficiency.

[0014] 3. The present invention can more accurately control the output of distributed photovoltaic power generation systems and reduce their impact on the main power grid through a flexible control output optimization method based on an improved sparrow search algorithm. At the same time, the method can also be dynamically adjusted according to the actual needs of the power system, which helps to enhance the stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic diagram of the flexible control scheme; Figure 2 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 As shown in the figure, in the flexible control scheme, the inverter is connected to the data collector via RS485, and the data from the data collector is collected by the switch and interacts with the multi-functional integrated gateway via Ethernet. The multi-functional integrated gateway performs edge computing and communication protocol conversion on the data, and collects the data to the IoT management platform through the 4G / 5G wireless virtual private network. The IoT management platform communicates and interacts with the "four-capable" dispatching master station of the management information area.

[0019] The multifunctional integrated gateway is an automated device used in distributed photovoltaic power generation systems. It integrates data collection, processing, remote transmission, power control, vertical encryption, wireless communication and other functions to achieve direct data interaction with the dispatching master station.

[0020] The flexible control scheme includes the following steps: Step 1: Use the DN1000 software to configure the inverter communication parameters and point table information, generate the collection information, and download it to the gateway device; Step 2: Collect inverter data through configured communication parameters (various methods, such as modbus, IEC104, 61850, etc.); Step 3: The dispatching master station sends the adjustment instruction through the MQTT communication method of the IoT management platform; after receiving the adjustment instruction, the gateway device analyzes the adjustment instruction parameters and sends it to the automatic generation control system (AGC) for operation and processing; Step 4: Analyze the adjustment instructions and adjust the inverter according to the configured AGC control mode, operation mode, installed capacity, allocation mode and other parameters; Step 5: After the inverter is adjusted, it is fed back to the dispatching master station through the IoT management platform. The dispatching master station receives the instructions and the actual adjustment results and completes the adjustment.

[0021] The uncertainty of traditional 10 kV household photovoltaic output and the volatility of load demand have an important impact on the dispatch of microgrids. In view of this, this application designs a distributed photovoltaic flexible control output optimization method. This method combines the adaptive generation technology of photovoltaic power generation historical data and load power consumption typical day historical data, as well as the uncertainty set construction method in robust optimization, to establish an adaptive robust two-stage optimization scheduling model, and uses the improved sparrow search algorithm (SSA) to solve the parameter optimization in the adjustment decision process to define the variable part of the uncertainty set, thereby clarifying the uncertainty set boundaries under different dispatch conservative levels. On this basis, the prediction quality evaluation index is used to compare the optimization degree of distributed photovoltaic flexible control output.

[0022] See also Figure 2 As shown, the distributed photovoltaic flexible control output optimization method described in this embodiment includes the following steps: The automatic generation control system (AGC) receives the regulation instructions regularly issued by the dispatching master station, calculates the active power demand of the distributed photovoltaic power generation system based on the regulation instruction value, selects the control device and distributes the active power, and automatically issues the final control instruction to the controlled device, thus realizing the active power control of the distributed photovoltaic power generation system; Combining the historical data of photovoltaic power generation and the historical data of typical days of load power consumption, an adaptive robust two-stage optimization scheduling model is established, which includes two stages of optimization scheduling: pre-scheduling stage and re-scheduling stage. The target of the pre-scheduling stage is the expected pre-scheduling cost, and the target of the re-scheduling stage is the subsequent re-scheduling cost. The adaptive robust two-stage optimization scheduling model is solved by using an improved sparrow search algorithm, and the result of photovoltaic output optimization is obtained.

[0023] By selecting the control device and distributing the active power, and automatically issuing the final control instruction to the controlled device, the active power control of the distributed photovoltaic power generation system is finally realized, including the following steps: According to the rising margin and falling margin of each inverter, the active power distribution strategy is formulated. Under the premise of meeting the fast response target instruction, the adjustment frequency of the inverter group participating in the regulation is minimized. The active power distribution setting expression is: ; In the formula, Plan total active power for distributed photovoltaic power generation systems; is the sum of the active powers of the inverters that do not participate in the regulation, is the total active power that needs to be adjusted; The automatic power generation control system distributes the target value required for adjustment to each or each group of inverters participating in the adjustment according to the margin of the inverters participating in the adjustment, so as to achieve the target active power distribution of the distributed photovoltaic power generation system. The active load distributed to each inverter is as follows: ; In the formula, N j For the j The number of inverters or groups; X is the power loss of transformer equipment; P j裕度 For the current j The adjustable capacity of the inverter or group is j The current output of the inverter or group of inverters can be adjusted upward by using the sample inverter (that is, the user specifies several inverters in free power generation state and does not participate in regulation) calculation method to calculate the first j The margin can be adjusted upward for each inverter or group of inverters.

[0024] In order to solve the uncertainty problem caused by the fluctuation of photovoltaic power generation and load demand and enhance the robustness of distributed photovoltaic power generation output, the present invention constructs an adaptive robust two-stage optimization scheduling model, which is expressed as follows: ; In the formula, is the expected lead-time scheduling cost, is the subsequent redispatch cost, is the sum of the startup and shutdown costs of the micro turbine MT and the distributed photovoltaic power generation system DG, are all other costs in the pre-scheduling phase, u , Uare the uncertain variables and uncertain variable sets including the photovoltaic power output power and load power consumption in the microgrid, respectively. is the decision variable in the pre-scheduling stage, is the decision variable in the rescheduling phase, In the pre-scheduling stage, the decision variables With uncertain variables u The feasible domain formed after joint decision, for Variables in the feasible domain; In the pre-dispatch stage, by determining the operating status of the micro turbine (MT) and the distributed photovoltaic power generation system (DG), the microgrid can flexibly respond to various changes in photovoltaic power generation output and load demand. The core of this stage is to optimize the operating status of the entire system. The expression of the pre-dispatch stage is: ; In the formula, and denote the startup cost and shutdown cost of the microturbine MT, respectively, and They represent the startup cost and shutdown cost of the distributed photovoltaic power generation system DG respectively; According to the results of the pre-dispatch stage, in the re-dispatch stage, based on the known operating status of the distributed photovoltaic power generation system (DG), a deterministic optimization method is used to calculate the equipment power output and the power exchange between the microgrid and the large grid to ensure the stability and reliability of the microgrid operation. The expression of the re-dispatch stage is: ; In the formula, represents the microturbine MT operating cost, represents the operating cost of distributed photovoltaic power generation system DG, represents the charging and discharging cost of the energy storage device, Represents the electricity transaction cost between the microgrid and the large grid.

[0025] The constraints of the adaptive robust two-stage optimization scheduling model include: The output power constraints of the new energy power generation device are: ; In the formula, represents the expected maximum power generation capacity of the distributed photovoltaic power generation system, It indicates the unused solar power generation of distributed photovoltaic power generation system; It represents the adjustable light energy generation of distributed photovoltaics; The constraints of the equilibrium state of power supply and demand are: ; In the formula, for the The devices participating in demand response are The situation at the moment, , , They represent the base load power, the power contributed by valley-filling demand response, and the power reduced by peak-shaving demand response respectively; Indicates Thermal power units in The actual power generation at the moment; and Respectively represent renewable energy generation and distributed photovoltaic power generation system Predicted actual output power at the moment; and Respectively represent Energy storage devices in Discharging power and charging power at the moment; express The amount of electricity demand that cannot be met at any given moment is called the load gap; is the total number of devices participating in demand response, , , They are the number of equipment, energy storage devices and thermal power units that fail to meet the power demand; The constraints on the capacity of the energy storage device are: ; In the formula, Indicates the total capacity of the energy storage device; A numerical value representing the demand response; Indicates the power provided by the energy storage device; Indicates the electrical energy required by the load; The constraints for power storage control are: ; In the formula, and They represent the minimum and maximum limits of the energy storage device’s power, f Indicates the power of the energy storage device; Indicates the natural charge decay rate of the energy storage device per hour; For The amount of power stored at any time, For Adjustable control amount of power storage at all times; The constraints of the flexible load are: ; In the formula, and They represent the upper and lower power limits of the flexible load respectively. is the power of the flexible load.

[0026] The improved sparrow search algorithm includes the following steps: The sparrows were divided into three roles: discoverers were responsible for exploring and sharing information about food locations, followers followed the clues of discoverers to forage, and sentinels were randomly distributed in the group at a ratio of 10% to 20% and were responsible for monitoring the safety of the foraging area. The location update strategy of the discoverer is expressed as follows: ; In the formula, E max is the maximum number of iterations, is the number of iterations of the current population, and Respectively Generation and the first +1st in the generation The position of a sparrow, is the warning value, is a random factor, E is a safe value, and the range of E is in the interval [0.5,1]; is a random number that follows a normal distribution, is a matrix where every element is 1; The follower's position update strategy is expressed as follows: ; In the formula, is a row vector whose elements are either 1 or -1; Indicates the current location of the finder. is the current worst position, is the number of follower sparrows, is the total number of sparrows, The best position for the finder; The position update strategy of the sentinel is expressed as follows: ; In the formula, and They are all randomly generated numbers, ranging from -1 to 1, and follow the normal distribution law; is a constant; is the current global worst position, is the fitness value of the current sparrow individual, is the worst fitness value of the sparrow.

[0027] In view of the fact that the traditional SSA algorithm is strongly dependent on the initial value and has insufficient global search capabilities, this paper introduces a mutation strategy based on sine and cosine functions, which aims to give new properties to candidate solutions through the characteristics of sine and cosine functions, thereby reducing the dependence on the initial value. This strategy focuses on performing mutation operations on the neighboring areas of the current solution, while effectively balancing the exploration of new areas and the use of existing information. The expression is as follows: ; In the formula, For the current solution, is the updated solution, is a uniformly distributed random number, To control the magnitude of variation, is the scale parameter of the Cauchy distribution; The characteristics of the Cauchy distribution are used to enhance the global search performance of the SSA algorithm. This is achieved by adding random perturbations to the current coordinates. The specific process expression is as follows: ; In the formula, the random value It comes from the Cauchy distribution, is an integer parameter used to measure the strength of the mutation. Indicates the current coordinates, Represents the updated coordinates. By introducing the large variation caused by the Cauchy distribution, the diversity of the search path is increased, thereby improving the efficiency of the global search. Adjust The value of can achieve a balance between exploring new areas and utilizing known information, which helps to avoid falling into the dilemma of local optimal solutions in complex environments.

[0028] The adaptive robust two-stage optimization scheduling model is solved by the improved sparrow search algorithm to obtain the photovoltaic output optimization result, which includes the following steps: Use the improved sparrow search algorithm to solve the problem independently and , to determine the minimum scheduling operation cost; then use the improved sparrow search algorithm to ensure the minimum Based on this, we further solve for another variable , get the corresponding minimum operating cost, and set it as the boundary value in the Pareto optimal solution set; solve the optimal solution of the inner objective function based on different outer objective function constraints; through the outer constraints, obtain multiple sets of candidate optimal solutions, and find the best system operation plan that meets the optimization goal, that is, to achieve the minimization of operating costs and load balance Optimization.

[0029] In order to evaluate the model performance more effectively, MAE (mean absolute error) and MAPE (mean absolute percentage error) are used as indicators to measure the accuracy of the model: ; In the formula represent The actual observed value at time, express The model prediction value at the moment. C is the total number of data points in the time series. The smaller the values ​​of MAE and MAPE, the closer the results of photovoltaic output optimization are to the actual observed values, that is, the higher the optimization accuracy.

[0030] The present invention proposes an adaptive robust two-stage optimization scheduling model for distributed photovoltaic flexible output. The model closely links the day-ahead prediction stage with the day-ahead scheduling stage, thereby reducing the conservatism of the scheduling results. First, the mean and variance of the day-ahead output prediction value are adaptively generated, and the key data features of the prediction stage are introduced to reduce the error of the robust optimization uncertainty set. In the pre-scheduling stage, the model uses the main grid power supply as the decision variable, and uses the improved SSA algorithm to optimize the scheduling parameters to participate in the optimization between the two stages of adaptive robustness, which is more conducive to exploring the intelligent control, safe operation and business model of virtual power plants, strongly supporting the reform of the power market, and facilitating the high-quality construction of new power systems.

[0031] The present invention proposes a distributed photovoltaic flexible control output optimization method. Through the in-depth analysis of historical photovoltaic power generation data by this method, power dispatchers can more accurately predict future photovoltaic power generation, thereby formulating a more reasonable dispatch plan, which helps to reduce the problem of insufficient or excessive power supply caused by inaccurate predictions and improve the overall operating efficiency of the power system.

[0032] Based on the historical data of photovoltaic power generation and the historical data of typical days of load power consumption, this method can predict the power consumption and photovoltaic power generation in different time periods, thereby achieving the optimal allocation of energy resources. It helps to reduce energy waste, improve energy utilization efficiency, and reduce the operating cost of the power system. It can analyze and predict photovoltaic power generation output in real time. Once an abnormal situation is detected, the system can respond quickly and take corresponding measures to ensure the stable operation of the photovoltaic power generation system. This helps to reduce the power outage time caused by system failures and improve the user's power experience. By optimizing the photovoltaic power generation output, the volatility of photovoltaic power generation can be reduced and the stability of the system can be improved.

[0033] According to the real-time electricity consumption data and forecast results, a reasonable energy storage device charging and discharging plan can be formulated. When the photovoltaic power generation is sufficient, the energy storage device can charge and store excess electricity; when the photovoltaic power generation is insufficient, the energy storage device can discharge to supplement the electricity, thereby ensuring the stable operation of the power system. This helps to extend the service life of the energy storage equipment and reduce the operating cost of the energy storage equipment. By responding to weather changes and electricity demand in real time, the output of the distributed photovoltaic power generation system can be adjusted, thereby improving the grid-connected absorption capacity of new energy. This helps to reduce the waste of new energy and promote the sustainable development of the new energy industry. Through the application of this method, the power system can be transformed to a cleaner, low-carbon and efficient energy structure.

[0034] Embodiment 2: A distributed photovoltaic flexible control output optimization system described in this embodiment includes an active power control module, a model building module, and an output optimization module; Active power control module: Receives adjustment instructions regularly issued by the dispatching master station, calculates the active power demand of the distributed photovoltaic power generation system based on the adjustment instruction value, selects the control device and distributes the active power, and automatically issues the final control instruction to the controlled device, so as to realize the active power control of the distributed photovoltaic power generation system; Model building module: Combine the historical data of photovoltaic power generation and the historical data of typical days of load power consumption to establish an adaptive robust two-stage optimization scheduling model, which includes two stages of optimization scheduling: pre-scheduling stage and re-scheduling stage. The target of the pre-scheduling stage is the expected pre-scheduling cost, and the target of the re-scheduling stage is the subsequent re-scheduling cost. Output optimization module: The adaptive robust two-stage optimization scheduling model is solved through the improved sparrow search algorithm to obtain the result of photovoltaic output optimization.

[0035] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0036] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0037] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0038] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed photovoltaic flexible control output optimization method, characterized by: The optimization method comprises the following steps: The automatic power generation control system receives the regulation instructions regularly issued by the dispatching master station, calculates the active power demand of the distributed photovoltaic power generation system based on the regulation instruction value, selects the control device and distributes the active power, and automatically issues the final control instruction to the controlled device, so as to realize the active power control of the distributed photovoltaic power generation system; Combining the historical data of photovoltaic power generation and the historical data of typical days of load power consumption, an adaptive robust two-stage optimization scheduling model is established, which includes two stages of optimization scheduling: pre-scheduling stage and re-scheduling stage. The target of the pre-scheduling stage is the expected pre-scheduling cost, and the target of the re-scheduling stage is the subsequent re-scheduling cost. The adaptive robust two-stage optimization scheduling model is solved by using an improved sparrow search algorithm, and the result of photovoltaic output optimization is obtained.

2. A distributed photovoltaic flexible control output optimization method according to claim 1, characterized in that: Active power control of distributed photovoltaic power generation system is realized by selecting control equipment and distributing active power, and automatically issuing final control instructions to controlled equipment, including the following steps: According to the rising margin and falling margin of each inverter, the active power distribution strategy is formulated. Under the premise of meeting the response target instruction, the adjustment frequency of the inverter group participating in the adjustment is minimized. The active power distribution setting expression is: ； In the formula, Plan total active power for distributed photovoltaic power generation systems; is the sum of the active powers of the inverters that do not participate in the regulation, is the total active power that needs to be adjusted; The automatic power generation control system distributes the target value required for adjustment to each or each group of inverters participating in the adjustment according to the margin of the inverters participating in the adjustment, so as to achieve the target active power distribution of the distributed photovoltaic power generation system. The active load distributed to each inverter is as follows: ； In the formula, N j For the j The number of inverters or groups; X is the power loss of transformer equipment; P j裕度 For the current j The capacity of one or more inverters can be adjusted.

3. A distributed photovoltaic flexible control output optimization method according to claim 1, characterized in that: The expression of the adaptive robust two-stage optimization scheduling model is: ； In the formula, is the expected lead-time scheduling cost, is the subsequent redispatch cost, is the sum of the startup and shutdown costs of the micro turbine MT and the distributed photovoltaic power generation system DG, are all other costs in the pre-scheduling phase, u , U are the uncertain variables and uncertain variable sets including the photovoltaic power output power and load power consumption in the microgrid, respectively. is the decision variable in the pre-scheduling stage, is the decision variable in the rescheduling phase, In the pre-scheduling stage, the decision variables With uncertain variables u The feasible domain formed after joint decision, for Variables in the feasible domain; The expression of the pre-scheduling stage is: ； In the formula, and denote the startup cost and shutdown cost of the microturbine MT, respectively, and They represent the startup cost and shutdown cost of the distributed photovoltaic power generation system DG respectively; The expression for the mulligan phase is: ； In the formula, represents the microturbine MT operating cost, represents the operating cost of distributed photovoltaic power generation system DG, represents the charging and discharging cost of the energy storage device, Represents the electricity transaction cost between the microgrid and the large grid.

4. A distributed photovoltaic flexible control output optimization method according to claim 3, characterized in that: The constraints of the adaptive robust two-stage optimization scheduling model include: The output power constraints of the new energy power generation device are: ； In the formula, represents the expected maximum power generation capacity of the distributed photovoltaic power generation system, It indicates the unused solar power generation of distributed photovoltaic power generation system; It represents the adjustable light energy generation of distributed photovoltaics; The constraints of the equilibrium state of power supply and demand are: ； In the formula, for the The devices participating in demand response are The situation at the moment, , , They represent the base load power, the power contributed by valley-filling demand response, and the power reduced by peak-shaving demand response respectively; Indicates Thermal power units in The actual power generation at the moment; and Respectively represent renewable energy generation and distributed photovoltaic power generation system Predicted actual output power at the moment; and Respectively represent Energy storage devices in Discharging power and charging power at the moment; express The amount of electricity demand that cannot be met at any given moment is called the load gap; is the total number of devices participating in demand response, , , They are the number of equipment, energy storage devices and thermal power units that fail to meet the power demand; The constraints on the capacity of the energy storage device are: ； In the formula, Indicates the total capacity of the energy storage device; A numerical value representing the demand response; Indicates the power provided by the energy storage device; Indicates the electrical energy required by the load; The constraints for power storage control are: ； In the formula, and They represent the minimum and maximum limits of the energy storage device’s power, f Indicates the power of the energy storage device; Indicates the natural charge decay rate of the energy storage device per hour; For The amount of power stored at any time, For Adjustable control amount of power storage at all times; The constraints of the flexible load are: ； In the formula, and They represent the upper and lower power limits of the flexible load respectively. is the power of the flexible load.

5. A distributed photovoltaic flexible control output optimization method according to claim 4, characterized in that: The improved sparrow search algorithm includes the following steps: The sparrows were divided into three roles: discoverers were responsible for exploring and sharing information about food locations, followers followed the clues of discoverers to forage, and sentinels were randomly distributed in the group at a ratio of 10% to 20% and were responsible for monitoring the safety of the foraging area. The location update strategy of the discoverer is expressed as follows: ； In the formula, E max is the maximum number of iterations, is the number of iterations of the current population, and Respectively Generation and the first +1st in the generation The position of a sparrow, is the warning value, is a random factor, E is a safe value, and the range of E is in the interval [0.5,1]; is a random number that follows a normal distribution, is a matrix where every element is 1; The follower's position update strategy is expressed as follows: ； In the formula, is a row vector whose elements are either 1 or -1; Indicates the current location of the finder. is the current worst position, is the number of follower sparrows, is the total number of sparrows, The best position for the finder; The position update strategy of the sentinel is expressed as follows: ； In the formula, and They are all randomly generated numbers, ranging from -1 to 1, and follow the normal distribution law; is a constant; is the current global worst position, is the fitness value of the current sparrow individual, is the worst fitness value of the sparrow; A mutation strategy based on sine and cosine functions is introduced to give new properties to candidate solutions through the characteristics of sine and cosine functions. The expression is as follows: ； In the formula, For the current solution, is the updated solution, is a uniformly distributed random number, To control the magnitude of variation, is the scale parameter of the Cauchy distribution; Using the characteristics of Cauchy distribution, random perturbations are added to the current coordinates. The expression is as follows: ； In the formula, the random value It comes from the Cauchy distribution, is an integer parameter used to measure the strength of the mutation. Indicates the current coordinates, Indicates the updated coordinates.

6. A distributed photovoltaic flexible control output optimization method according to claim 5, characterized in that: The adaptive robust two-stage optimization scheduling model is solved by the improved sparrow search algorithm to obtain the photovoltaic output optimization result, which includes the following steps: Use the improved sparrow search algorithm to solve the problem independently and , to determine the minimum scheduling operation cost; then use the improved sparrow search algorithm to ensure the minimum Based on this, we further solve for another variable , get the corresponding minimum operating cost, and set it as the boundary value in the Pareto optimal solution set; solve the optimal solution of the inner objective function based on different outer objective function constraints; through the outer constraints, obtain multiple sets of candidate optimal solutions, and find the best system operation plan that meets the optimization goal, that is, to achieve the minimization of operating costs and load balance Optimization.

7. A distributed photovoltaic flexible control output optimization system, used to implement the optimization method according to any one of claims 1 to 6, characterized in that: Including active power control module, model building module, output optimization module; Active power control module: Receives adjustment instructions regularly issued by the dispatching master station, calculates the active power demand of the distributed photovoltaic power generation system based on the adjustment instruction value, selects the control device and distributes the active power, and automatically issues the final control instruction to the controlled device, so as to realize the active power control of the distributed photovoltaic power generation system; Model building module: Combine the historical data of photovoltaic power generation and the historical data of typical days of load power consumption to establish an adaptive robust two-stage optimization scheduling model, which includes two stages of optimization scheduling: pre-scheduling stage and re-scheduling stage. The target of the pre-scheduling stage is the expected pre-scheduling cost, and the target of the re-scheduling stage is the subsequent re-scheduling cost. Output optimization module: The adaptive robust two-stage optimization scheduling model is solved through the improved sparrow search algorithm to obtain the result of photovoltaic output optimization.

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