Photovoltaic power generation system load distribution optimization method and system

Abstract

The invention discloses a photovoltaic power generation system load distribution optimization method and system, and the method comprises the steps: taking the comprehensive operation safety risk of a photovoltaic power generation system as a target, and constructing a dynamic distribution model of loads in the photovoltaic power generation system; according to the operation requirements of the photovoltaic power generation system, establishing operation constraint conditions of the photovoltaic power generation system; and optimizing load distribution in the photovoltaic power generation system through an empire competition algorithm according to the operation constraint condition and the dynamic distribution model so as to improve the generating capacity of the power generation system.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power grids, and in particular to a method and system for optimizing load distribution in a photovoltaic power generation system. Background Art

[0002] Photovoltaic power generation is a common distributed power generation technology that uses renewable energy. Its basic principle is to use the photovoltaic effect of semiconductors to convert light energy into electrical energy, and transmit the generated electrical energy to the power grid through inverters, transformers and other equipment. Photovoltaic power generation has the characteristics of flexible installation, reliable performance and good economy. Its installed capacity and annual power generation account for a large proportion of all types of distributed power sources.

[0003] During the operation of distributed photovoltaic power stations, they are easily affected by factors such as the external environment, equipment and personnel, which in turn affect the quality and efficiency of power supply.

[0004] The operation of distributed photovoltaic power stations usually relies on solar radiation to obtain relevant energy, thereby ensuring that electricity can be produced and transmitted. Natural factors are often uncontrollable during the photovoltaic reception process. Once thunderstorms, snow and other weather conditions occur, coupled with dust coverage, it will have some adverse effects on the power generation efficiency of distributed photovoltaic power stations. The operation of distributed photovoltaic power stations involves multiple links, and each link has corresponding equipment configurations. Once the equipment fails, it will directly affect the generation and transmission of electricity. Therefore, it is necessary to perform performance testing on commonly used equipment, such as photovoltaic panels, and strengthen the construction of power conversion systems, so as to better improve the daily operation level of equipment and meet the needs of power supply.

[0005] However, distributed energy has the characteristics of randomness and intermittency in output. When connected to the grid on a large scale, it will have an impact on the power grid, reducing its stability, reliability and security. Summary of the invention

[0006] In order to solve the above technical problems, the present invention discloses a method and system for optimizing the distribution of loads in a photovoltaic power generation system, which are used to improve the stability of the power grid system when a distributed photovoltaic power generation system is connected to the grid.

[0007] In order to achieve the above object, the present invention discloses a method for optimizing the distribution of loads in a photovoltaic power generation system, comprising:

[0008] Taking the comprehensive operational safety risk of the photovoltaic power generation system as the goal, a dynamic allocation model of loads in the photovoltaic power generation system is constructed;

[0009] Establishing operation constraints of the photovoltaic power generation system according to the operation requirements of the photovoltaic power generation system;

[0010] According to the operation constraints and the dynamic allocation model, the load distribution in the photovoltaic power generation system is optimized by using an imperial competition algorithm.

[0011] The present invention discloses a method for optimizing the distribution of loads in a photovoltaic power generation system, wherein a dynamic distribution model of loads in the photovoltaic power generation system is constructed with the comprehensive operational safety risk of the photovoltaic power generation system as a target, so as to reduce the safety risk of the load distribution scheme ultimately output by the dynamic distribution model on the power grid system and improve the stability of the power grid system; then, corresponding constraints are established using the operational requirements of the photovoltaic power generation system itself to reduce the operational risk of load distribution on the photovoltaic power generation system; finally, based on the above-constructed dynamic distribution model and the constraints, the optimal solution is continuously solved through an imperial competition algorithm, so as to improve the energy utilization rate of the photovoltaic power generation system and reduce the energy waste of the photovoltaic power generation system when optimizing the load distribution in the photovoltaic power generation system.

[0012] As a preferred example, the step of constructing a dynamic load allocation model in the photovoltaic power generation system includes:

[0013] According to the reactive load demand corresponding to the photovoltaic power generation system, a dynamic allocation model of the load when the photovoltaic power generation system is running is constructed with the goal of achieving the highest voltage safety and stability margin index, the lowest active network loss index, or the smallest voltage offset index when the photovoltaic power generation system is running; wherein the expression of the dynamic allocation model is:

[0014]

[0015] Wherein, minF represents the voltage safety and stability margin index of the photovoltaic power generation system; f1 represents the network loss of the photovoltaic power generation system; f1 represents the voltage deviation of the photovoltaic power generation system; f1 represents the voltage stability margin of the photovoltaic power generation system; N L represents the number of load nodes of the photovoltaic power generation system; min represents the voltage stability margin index of the photovoltaic power generation system; the g k represents the conductance of branch k in the photovoltaic power generation system; i represents the voltage phase angle of load node i in the photovoltaic power generation system; j represents the voltage phase angle of load node j in the photovoltaic power generation system; i 、V j represents the voltage of the load node i and the load node j at both ends of the branch k; the Q gi , U i Respectively represent the reactive power of generator i and the voltage of load node i; the U imax , U imin, Q gimax , Q gimin are the upper and lower limits of the corresponding variables respectively; i1 represents the actual voltage value of the load node i; the Q gi1 Represents the actual reactive power value of generator i.

[0016] The present invention aims to achieve the highest voltage safety and stability margin index, the lowest active network loss index or the smallest voltage offset index when the photovoltaic power generation system is running, and constructs a dynamic distribution model corresponding to the load to ensure uniform distribution of the load while reducing the operating risk of the photovoltaic power generation system, thereby improving the energy utilization rate of the photovoltaic power generation system and increasing the power generation.

[0017] As a preferred example, the dynamic load allocation model of the photovoltaic power generation system during operation is constructed with the goal of achieving the highest voltage safety and stability margin index, the lowest active network loss index, or the smallest voltage offset index when the photovoltaic power generation system is in operation, including:

[0018] According to the power characteristics and network characteristics of the load nodes, the voltage stability margin index corresponding to each load node in the photovoltaic power generation system is determined; wherein the calculation expression of the voltage stability margin index is:

[0019]

[0020] Wherein, V0 represents the voltage of the load node in the current state; V cr represents the voltage of the load node in the critical state; the λ V Represents the voltage stability margin index of a single load node.

[0021] The present invention constructs a voltage stability margin index corresponding to each load node in the photovoltaic power generation system to ensure that each load node is more evenly distributed in time, thereby reducing energy waste in the photovoltaic power generation system and increasing power generation.

[0022] As a preferred example, the establishing of the operation constraint conditions of the photovoltaic power generation system includes:

[0023] Taking the reactive power optimization of the photovoltaic power generation system as an indicator, establishing a power balance constraint condition corresponding to the photovoltaic power generation system;

[0024] According to the specific transformation ratio constraint of the on-load transformer corresponding to the photovoltaic power generation system, the configuration capacity constraint of the power reactive compensation device, the terminal voltage constraint of the grid-connected generator, the generator reactive power constraint and the load node voltage constraint, the inequality constraint conditions corresponding to the photovoltaic power generation system are constructed.

[0025] The present invention establishes corresponding constraint conditions with reactive power optimization of photovoltaic power generation system as an indicator, so as to improve the energy utilization rate of photovoltaic power generation system while reducing the operation risk of photovoltaic power generation system in the load distribution process according to the constraint conditions, thereby increasing the power generation of photovoltaic power generation system.

[0026] As a preferred example, the power balance constraint condition corresponding to the photovoltaic power generation system is established, characterized in that:

[0027] According to the reactive power and active power of the load and the active power of the generator, the power balance constraint condition corresponding to the photovoltaic power generation system is constructed; wherein the expression of the power balance constraint condition is:

[0028]

[0029] Wherein, N is the number of load nodes in the photovoltaic power generation system; θ ij , G ij and B ij are the voltage phase angle difference, susceptance and conductance between load node i and load node j respectively; the Q ci is the reactive power compensation power; li , P li are the reactive power and active power of the load node respectively; gi is the active power of the generator; gi is the reactive power of the generator.

[0030] The present invention constructs power balance constraints corresponding to the photovoltaic power generation system according to the reactive power and active power of the load and the active power of the generator, so as to improve the energy utilization rate of the system, avoid energy waste, and further increase power generation.

[0031] As a preferred example, the inequality constraint condition is:

[0032]

[0033] Among them, the N d 、N k 、N C 、N g represents the number of load nodes, the number of on-load transformers, the number of reactive power compensation devices and the number of generator nodes in the photovoltaic power generation system in turn; the U gimin , U gimax , Q Cimin , Q Cimax , K imin , K imax In turn, they represent the upper and lower limits of the corresponding quantities; gi , U irepresents the reactive power of the generator and the voltage of the load node respectively; the K i , Q Ci , U gi It represents the given specific transformation ratio of the on-load transformer, the configuration capacity of the power reactive compensation device and the terminal voltage of the grid-connected generator in turn.

[0034] The present invention utilizes a given architecture in a photovoltaic power generation system to construct the inequality constraint condition, so as to help the dynamic allocation model better adapt to load changes in the photovoltaic power generation system and improve energy utilization and stability.

[0035] As a preferred example, the optimization of load distribution in the photovoltaic power generation system by using an imperial competition algorithm according to the operation constraint conditions and the dynamic allocation model includes:

[0036] Determine a load allocation scheme for the photovoltaic power generation system according to the dynamic allocation model, and determine a load factor for the photovoltaic power generation system according to the load allocation scheme; wherein the load factor is a ratio of daily average power generation to daily peak power generation within a preset time period;

[0037] With the goal of maximizing the load factor, the dynamic allocation model is solved by a preset imperial competition algorithm to obtain a load allocation scheme output by the dynamic allocation model, and the load distribution in the photovoltaic power generation system is optimized according to the load allocation scheme.

[0038] The present invention is based on the fact that the load factor reflects the distribution of the load over time. When the load factor is larger, the load is more evenly distributed over time, and the power generation rate of the system is larger. Therefore, the load factor is optimally solved by a preset imperial competition algorithm, thereby ensuring the maximization of the power generation of the photovoltaic power generation system.

[0039] As a preferred example, solving the dynamic allocation model by using a preset imperial competition algorithm includes:

[0040] Clustering several photovoltaic power generation unit empires of the photovoltaic power generation system, constructing several initial empires, and defining standardized rights of each of the initial empires;

[0041] Dividing a number of load nodes in the photovoltaic power generation system according to the standardized rights of each of the initial empires to obtain a number of colonies corresponding to each of the initial empires;

[0042] The right value of each of the initial empires is determined based on the colonies, and competition is carried out between the initial empires based on the right values ​​until only one empire remains and the maximum load factor is obtained.

[0043] The present invention utilizes the imperial competition algorithm to solve the optimization of the load factor of distributed photovoltaic power generation, ensuring that the photovoltaic power generation system can better adapt to load changes according to the optimized load factor, thereby improving energy utilization and stability.

[0044] As a preferred example, the method of solving the dynamic allocation model by using a preset imperial competition algorithm further includes:

[0045] According to the decision variables corresponding to each of the initial empires, the standardized rights of the initial empires are defined; wherein the calculation expression of the standardized rights is:

[0046]

[0047] Wherein, i represents the i-th country; n Indicates the standardization rights of the nth country; the f n represents the function value of the nth country; h represents the number of dimensions of the decision variable; f i represents the function value of the ith country;

[0048] According to the competition between the initial empires and the colonies corresponding to each of the initial empires, the right value of each of the empires after the competition is determined; wherein the calculation expression of the right value is:

[0049]

[0050] Among them, the is the average value of the objective function of empire k’s possession of colonies; σ is the weight coefficient; f k is the objective function value of Empire k;

[0051] The initial competition among empires is carried out according to the weight values; a colony is selected from the empire with the smallest weight value and allocated to several other empires according to a certain probability to complete the competition among empires; the probability of possession of each empire is:

[0052]

[0053] Among them, the P j is the probability of the jth empire occupying a colony; Ai is the total power value of the ith empire; Aj The total value of the jth empire; C-1 is the number of empires that can be allocated.

[0054] The present invention uses the imperial competition algorithm to find the optimal load distribution scheme in the load factor optimization process of distributed photovoltaic power generation, thereby improving the energy utilization rate and stability of the system.

[0055] On the other hand, the present invention also discloses a photovoltaic power generation system load distribution optimization system, including a load distribution module, an operation constraint module and a distribution optimization module;

[0056] The load distribution module is used to construct a dynamic distribution model of loads in the photovoltaic power generation system with the comprehensive operation safety risk of the photovoltaic power generation system as the target;

[0057] The operation constraint module is used to establish the operation constraint conditions of the photovoltaic power generation system according to the operation requirements of the photovoltaic power generation system;

[0058] The distribution optimization module is used to optimize the load distribution in the photovoltaic power generation system through an imperial competition algorithm according to the operating constraints and the dynamic distribution model.

[0059] The present invention discloses a load distribution optimization system for a photovoltaic power generation system, wherein a dynamic distribution model of the load in the photovoltaic power generation system is constructed with the comprehensive operational safety risk of the photovoltaic power generation system as a target, so as to reduce the safety risk of the load distribution scheme finally output by the dynamic distribution model on the power grid system and improve the stability of the power grid system. Then, corresponding constraints are established by utilizing the operational requirements of the photovoltaic power generation system itself to reduce the operational risk of the load distribution on the photovoltaic power generation system. Finally, according to the above-constructed dynamic distribution model and the constraints, the optimal solution is continuously solved through the imperial competition algorithm, so as to improve the energy utilization rate of the photovoltaic power generation system and reduce the energy waste of the photovoltaic power generation system when optimizing the load distribution in the photovoltaic power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 : A schematic diagram of a flow chart of a method for optimizing load distribution of a photovoltaic power generation system disclosed in an embodiment of the present invention;

[0061] Figure 2 : A structural schematic diagram of a photovoltaic power generation system load distribution optimization system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0063] With the widespread application of distributed photovoltaic power generation systems in power systems, increasing the power generation of photovoltaic power generation systems and reducing the impact of distributed photovoltaic power generation systems on power grids have become important research areas. In order to reduce the energy waste and instability of the power grid system during the grid connection process of distributed photovoltaic power generation systems, a method for optimizing the distribution of photovoltaic power generation system loads is disclosed in certain implementations of this embodiment, so as to improve the power generation of photovoltaic power generation systems and the stability of the power grid system during the grid connection process by optimizing the load distribution scheme.

[0064] Specifically, refer to Figure 1 , is a flow chart of the method for optimizing the distribution of loads of the photovoltaic power generation system disclosed in this embodiment, which mainly includes step 101 and step 103, and the steps are mainly:

[0065] Step 101: Taking the comprehensive operational safety risk of the photovoltaic power generation system as a target, a dynamic allocation model of loads in the photovoltaic power generation system is constructed.

[0066] Step 102: Establishing operation constraints of the photovoltaic power generation system according to the operation requirements of the photovoltaic power generation system.

[0067] Step 103: According to the operation constraints and the dynamic allocation model, the load allocation in the photovoltaic power generation system is optimized by using an imperial competition algorithm.

[0068] Furthermore, the power generation process based on the photovoltaic power generation system involves multiple links, and each link involves the relevant association of multiple equipment configurations. Therefore, in some implementations of this embodiment, in order to reduce the difficulty of load distribution analysis, it is set to perform load distribution analysis under the condition that the active load, power supply and photovoltaic power generation system grid conditions of the photovoltaic power generation system are given, and a corresponding dynamic distribution model is constructed to output a distribution plan corresponding to each load.

[0069] Specifically, referring to the above given situation, the capacitive or inductive reactive compensation capacity, transformer ratio and generator terminal voltage are used as control variables, and the state variables are selected as load node voltage amplitude and generator reactive output. Under the condition of meeting the reactive load demand of the power system, the network loss of the entire network and the over-limit situation of the state variables are comprehensively considered to construct the dynamic allocation model of the load in the photovoltaic power generation system. Then the specific step 101 also includes the following steps:

[0070] S1: According to the reactive load demand corresponding to the photovoltaic power generation system, a dynamic load allocation model for the operation of the photovoltaic power generation system is constructed with the goal of achieving the highest voltage safety and stability margin index, the lowest active network loss index, or the smallest voltage offset index when the photovoltaic power generation system is in operation; wherein the expression of the dynamic allocation model is:

[0071]

[0072] Wherein, minF represents the voltage safety and stability margin index of the photovoltaic power generation system; f1 represents the network loss of the photovoltaic power generation system; f1 represents the voltage deviation of the photovoltaic power generation system; f1 represents the voltage stability margin of the photovoltaic power generation system; N L represents the number of load nodes of the photovoltaic power generation system; min represents the voltage stability margin index of the photovoltaic power generation system; the g k represents the conductance of branch k in the photovoltaic power generation system; i represents the voltage phase angle of load node i in the photovoltaic power generation system; j represents the voltage phase angle of load node j in the photovoltaic power generation system; i 、V j represents the voltage of the load node i and the load node j at both ends of the branch k; the Q gi , U i Respectively represent the reactive power of generator i and the voltage of load node i; the U imax , U imin , Q gimax , Q gimin are the upper and lower limits of the corresponding variables respectively; i1 represents the actual voltage value of the load node i; the Q gi1 Represents the actual reactive power value of generator i.

[0073] S2: Determine the voltage stability margin index corresponding to each load node in the photovoltaic power generation system according to the power characteristics and network characteristics of the load node; wherein the calculation expression of the voltage stability margin index is:

[0074]

[0075] Wherein, V0 represents the voltage of the load node in the current state; V cr represents the voltage of the load node in the critical state; the λ V Represents the voltage stability margin index of a single load node.

[0076] In this embodiment, referring to step S1 to step S2, a dynamic distribution model corresponding to the load is constructed with the goal of achieving the highest voltage safety and stability margin index, the lowest active network loss index, or the smallest voltage offset index when the photovoltaic power generation system is in operation, so as to ensure uniform distribution of the load while reducing the operating risk of the photovoltaic power generation system, thereby improving the energy utilization rate of the photovoltaic power generation system and increasing power generation.

[0077] After the dynamic allocation model corresponding to the load in the photovoltaic power generation system is constructed with reference to step 101, the subsequent load allocation scheme needs to solve the dynamic allocation model. In the process of solving the dynamic allocation model, in order to reduce the operation risk of the photovoltaic power generation system, in some implementations of this embodiment, some constraints are constructed to limit the solution process of the dynamic allocation model according to the constraints.

[0078] Specifically, according to the goal of reactive power optimization of the photovoltaic power generation system, the process of constructing the constraint conditions in step 102 includes the following steps:

[0079] S3: Taking the reactive power optimization of the photovoltaic power generation system as an indicator, establishing power balance constraint conditions corresponding to the photovoltaic power generation system.

[0080] S4: Construct the inequality constraints corresponding to the photovoltaic power generation system according to the specific transformation ratio constraints of the on-load transformer corresponding to the photovoltaic power generation system, the configuration capacity constraints of the power reactive compensation device, the terminal voltage constraints of the grid-connected generator, the generator reactive power constraints and the load node voltage constraints.

[0081] Furthermore, in the process of constructing the power balance constraint condition corresponding to the photovoltaic power generation system, the expression of the power balance constraint condition is:

[0082]

[0083] Wherein, N is the number of load nodes in the photovoltaic power generation system; θ ij , G ij and B ij are the voltage phase angle difference, susceptance and conductance between load node i and load node j respectively; the Q ci is the reactive power compensation power; li , P li are the reactive power and active power of the load node respectively; gi is the active power of the generator; gi is the reactive power of the generator.

[0084] The inequality constraints are:

[0085]

[0086] Among them, the N d 、N k 、N C 、N g represents the number of load nodes, the number of on-load transformers, the number of reactive power compensation devices and the number of generator nodes in the photovoltaic power generation system in turn; the U gimin, U gimax , Q Cimin , Q Cimax , K imin , K imax In turn, they represent the upper and lower limits of the corresponding quantities; gi , U i represents the reactive power of the generator and the voltage of the load node respectively; the K i , Q Ci , U gi It represents the given specific transformation ratio of the on-load transformer, the configuration capacity of the power reactive compensation device and the terminal voltage of the grid-connected generator in turn.

[0087] In this embodiment, referring to steps S3 to S4, corresponding constraints are established with reactive power optimization of the photovoltaic power generation system as an indicator, so as to reduce the operating risk of the photovoltaic power generation system during the load distribution process according to the constraints, while improving the energy utilization rate of the photovoltaic power generation system, thereby increasing the power generation of the photovoltaic power generation system. At the same time, the inequality constraints are constructed using the given architecture in the photovoltaic power generation system to help the dynamic allocation model better adapt to load changes in the photovoltaic power generation system and improve energy utilization and stability.

[0088] After constructing the dynamic allocation model corresponding to the load in the photovoltaic power generation system and the constraints in the model solution process with reference to step 101 and step 102, in order to ensure that the model outputs the optimal solution, some implementation methods of this embodiment set an optimization solution algorithm such as an imperial competition algorithm to solve the output structure of the model for the optimal solution.

[0089] In the process of controlling the optimal allocation scheme corresponding to the output load of the model, in distributed photovoltaic power generation, the load factor reflects the distribution of the load in time, that is, the larger the load factor, the more uniform the load distribution of the system in time, and vice versa. The relationship between the load factor and the load can be expressed by the following formula: load factor = average daily power generation / peak daily power generation; where the average daily power generation is the average value of the system's daily power generation over a period of time, and the peak daily power generation is the maximum value of the system's daily power generation over a period of time. When the load distribution is relatively uniform, the average daily power generation will be closer to the peak daily power generation, and the load factor will be relatively large; if the load distribution is uneven, such as the load is large in some time periods and small in other time periods, the average daily power generation will be smaller than the peak daily power generation, and the load factor will be relatively small. In practical applications, the load allocation scheme can be adjusted according to the value of the load factor to improve the energy utilization and stability of the system. Specifically, optimizing the load factor can make the photovoltaic power generation system in a high-load operation state for more time during the day, thereby giving full play to the power generation capacity and improving energy utilization. At the same time, through more reasonable load distribution, it is possible to avoid the load being too low or too high in certain time periods, thereby reducing energy waste and lowering the operating risks of the system.

[0090] In summary, in certain implementations of this embodiment, when the Imperial competition algorithm is used to optimize the load distribution in the photovoltaic power generation system, the optimal distribution scheme corresponding to the load is determined by obtaining the optimal solution of the load factor, and the process of outputting the optimal distribution scheme of the load in step 103 includes the following steps:

[0091] S5: Determine a load allocation scheme for the photovoltaic power generation system according to the dynamic allocation model, and determine a load factor for the photovoltaic power generation system according to the load allocation scheme; wherein the load factor is a ratio of daily average power generation to daily peak power generation within a preset time period.

[0092] S6: With the maximum load factor as the goal, the dynamic allocation model is solved by a preset imperial competition algorithm to obtain a load allocation scheme output by the dynamic allocation model, and the load distribution in the photovoltaic power generation system is optimized according to the load allocation scheme.

[0093] Specifically, the imperial competition algorithm used in step S6 is a new intelligent optimization algorithm inspired by imperial competition behavior. The algorithm process mainly includes four stages, namely, empire formation, colony absorption, imperial competition, and empire demise. Further, the stage process of the imperial competition algorithm is:

[0094] Phase 1: Empire formation. The country consists of h-dimensional decision variables country = [x1, x2, ..., xh], the function value of the country is represented by fcountry, and the standardized power of the nth country is defined as:

[0095]

[0096] Wherein, i represents the i-th country; n Indicates the standardization rights of the nth country; the f n represents the function value of the nth country; h represents the number of dimensions of the decision variable; f i represents the function value of the ith country;

[0097] Phase 2: Absorbing colonies. Assume that the distance l moved by each colony follows a uniform distribution l~U(0,δ×lD), and the colonies around the empire gradually move closer to the empire. Where δ>1, lD is the distance between the colony and the empire; the offset angle of the line between the empire and the direction of the colony's movement is θ, which follows a distribution of θ~U(-ψ,ψ), where ψ is the offset angle adjustment coefficient.

[0098] Stage 3: Empire Competition. The total power value of the empire is defined as:

[0099]

[0100] Among them, the is the average value of the objective function of empire k’s possession of colonies; σ is the weight coefficient; f k is the objective function value of Empire k;

[0101] According to the total power value, a weak colony is selected from the empire with the weakest total power, and distributed to the other k empires according to a certain probability. C-1 empires, the probability of occupying the jth empire is as follows:

[0102]

[0103] Among them, the P j is the probability of the jth empire occupying a colony; Ai is the total power value of the ith empire; Aj The total value of the jth empire; C-1 is the number of empires that can be allocated.

[0104] Stage 4: Empire demise. After the competition between empires, the colonies owned by the less powerful empire will be occupied by the more powerful empire. When all colonies are occupied, the empire will automatically perish and its position will be eliminated in the algorithm. When the competition between empires ends, there is only one empire that has been established, and all the remaining colonies have been occupied by the empire. At this time, the algorithm iteration stops and the optimal solution is obtained; otherwise, it returns to stage 2.

[0105] In some implementations of this embodiment, referring to each stage of the above-mentioned imperial competition algorithm, the process of solving the dynamic allocation model by the imperial competition algorithm in step S6 includes the following steps:

[0106] S601: clustering several photovoltaic power generation unit empires of the photovoltaic power generation system, constructing several initial empires, and defining standardized rights of each of the initial empires;

[0107] S602: dividing a number of load nodes in the photovoltaic power generation system according to the standardized rights of each of the initial empires, and obtaining a number of colonies corresponding to each of the initial empires;

[0108] S603: Determine the weight value of each of the initial empires based on the colonies, and conduct competition among the initial empires based on the weight values ​​until only one empire remains, and obtain the maximum load factor.

[0109] Specifically, when performing the initial construction of the empire and the division of colonies in step S601 and step S602, a group of empires (solutions) are set, and each empire has its own load distribution plan. At the same time, the competitiveness coefficient and productivity coefficient of each empire are set. Then, according to the load distribution plan of each empire, its competitiveness coefficient and productivity evaluation are calculated. The calculation of the competitiveness coefficient can be based on factors such as the power generation power of the photovoltaic panel and the storage capacity of the battery. The calculation of the productivity coefficient can be based on factors such as the nature and size of the electrical load and the intensity of sunlight.

[0110] Then, in step S603, when the empires compete, the empires compete according to the competitiveness coefficient and the productivity coefficient. The empire with stronger competitiveness may occupy the territory of other empires, while the empire with lower productivity may be eliminated. According to the results of the empire competition, the load distribution plan of each empire is updated to seek a better solution. It is judged whether the algorithm reaches the termination condition, such as reaching the maximum number of iterations or finding a solution that meets the requirements and outputting the optimal load distribution plan, that is, the load distribution plan of the empire with the strongest competitiveness and the highest productivity.

[0111] In a specific application scenario, there is a distributed photovoltaic power generation system including four photovoltaic power generation units (A, B, C, D) and three load points (1, 2, 3). Referring to step S601 to step S603, the four photovoltaic power generation units are clustered into two empires: Empire 1 includes A and B, and Empire 2 includes C and D. Among them, the center of Empire 1 is A (because of its large capacity and good performance), and its boundaries cover load points 1 and 2. The center of Empire 2 is C (moderate capacity and stable performance), and its boundaries cover load point 3. Among them, the capacity of the empire obtains the real capacity and performance of each photovoltaic power generation unit according to the actual photovoltaic power generation situation.

[0112] After the division of the empire and the colonies, the load demand corresponding to each colony is analyzed: load point 1 is set to high priority, with a demand of 5kW; load point 2 is set to medium priority, with a demand of 3kW; load point 3 is set to low priority, with a demand of 2kW.

[0113] According to the above load demand analysis, the allocation plan within each empire is as follows: In Empire 1, an allocation plan is formulated based on the capacity and performance of A and B, as well as the demand and priority of load points 1 and 2. For example, A supplies power to load point 1 (5kW), and B supplies power to load point 2 (3kW); while in Empire 2, C is responsible for supplying power to load point 3 (2kW), and D is used as a backup or supplies power to other temporary loads.

[0114] Furthermore, if a photovoltaic power generation unit in a certain empire cannot meet its load demand, it can coordinate with other empires to solve the problem by exchanging information or adjusting operation strategies. For example, if A in Empire 1 fails and B cannot meet the demand of load point 1, it can coordinate with Empire 2 to have C or D provide additional power support.

[0115] In some implementations of this embodiment, referring to step S5 to step S6, the optimal load distribution scheme can be found in the load factor optimization problem of distributed photovoltaic power generation, thereby improving the energy utilization and stability of the system. However, the imperial competition algorithm is prone to fall into local optimality. In order to enhance the global search capability, chaotic mapping is introduced into the ICA (Imperial Competition) algorithm. One-dimensional irreversible mapping is the simplest system that can produce chaotic motion.

[0116] In this embodiment, referring to step S1 to step S6, the load distribution scheme corresponding to the photovoltaic power generation system is obtained. The load distribution scheme includes matching photovoltaic power generation units with load points: determining which load points each photovoltaic power generation unit should supply power to; power distribution: determining the power to be allocated to each photovoltaic power generation unit according to the demand of the load point and the power generation capacity of the photovoltaic power generation unit; time scheduling: formulating the operation strategy of the photovoltaic power generation unit in different time periods to adapt to changes in load demand and changes in light conditions.

[0117] On the other hand, this embodiment also discloses a photovoltaic power generation system load distribution optimization system. The specific structure of the optimization system is shown in FIG. Figure 2 , including a load distribution module 201, an operation constraint module 202 and a distribution optimization module 203.

[0118] The load distribution module 201 is used to construct a dynamic distribution model of the load in the photovoltaic power generation system with the comprehensive operation safety risk of the photovoltaic power generation system as the target.

[0119] The operation constraint module 202 is used to establish the operation constraint conditions of the photovoltaic power generation system according to the operation requirements of the photovoltaic power generation system.

[0120] The distribution optimization module 203 is used to optimize the load distribution in the photovoltaic power generation system through the imperial competition algorithm according to the operating constraints and the dynamic distribution model.

[0121] The present embodiment discloses a method and system for optimizing the distribution of loads in a photovoltaic power generation system, which can improve the energy utilization and stability of the system by optimizing the load factor. In the process of optimizing the load factor, the best load distribution scheme is found through the imperial competition algorithm, so that the total load of the system is effectively distributed among different empires (i.e., different optimization solutions). By simulating the imperial competition behavior, the algorithm can compare and compete between different solutions to find the optimal load distribution scheme.

[0122] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the distribution of loads in a photovoltaic power generation system, characterized in that: include: Taking the comprehensive operational safety risk of the photovoltaic power generation system as the goal, a dynamic allocation model of loads in the photovoltaic power generation system is constructed; Establishing operation constraints of the photovoltaic power generation system according to the operation requirements of the photovoltaic power generation system; According to the operation constraints and the dynamic allocation model, the load distribution in the photovoltaic power generation system is optimized by using an imperial competition algorithm.

2. A method for optimizing load distribution of a photovoltaic power generation system according to claim 1, characterized in that: The step of constructing a dynamic load allocation model in the photovoltaic power generation system includes: According to the reactive load demand corresponding to the photovoltaic power generation system, a dynamic allocation model of the load when the photovoltaic power generation system is running is constructed with the goal of achieving the highest voltage safety and stability margin index, the lowest active network loss index, or the smallest voltage offset index when the photovoltaic power generation system is running; wherein the expression of the dynamic allocation model is: Wherein, minF represents the voltage safety and stability margin index of the photovoltaic power generation system; f1 represents the network loss of the photovoltaic power generation system; f1 represents the voltage deviation of the photovoltaic power generation system; f1 represents the voltage stability margin of the photovoltaic power generation system; N L represents the number of load nodes of the photovoltaic power generation system; min represents the voltage stability margin index of the photovoltaic power generation system; the g k represents the conductance of branch k in the photovoltaic power generation system; i represents the voltage phase angle of load node i in the photovoltaic power generation system; j represents the voltage phase angle of load node j in the photovoltaic power generation system; i 、V j represents the voltage of the load node i and the load node j at both ends of the branch k; the Q gi , U i Respectively represent the reactive power of generator i and the voltage of load node i; the U imax , U imin , Q gimax , Q gimin are the upper and lower limits of the corresponding variables respectively; i1 represents the actual voltage value of the load node i; the Q gi1 Represents the actual reactive power value of generator i.

3. A method for optimizing load distribution of a photovoltaic power generation system according to claim 2, characterized in that: The dynamic load allocation model of the photovoltaic power generation system during operation is constructed with the goal of achieving the highest voltage safety and stability margin index, the lowest active network loss index, or the smallest voltage offset index when the photovoltaic power generation system is in operation, including: According to the power characteristics and network characteristics of the load nodes, the voltage stability margin index corresponding to each load node in the photovoltaic power generation system is determined; wherein the calculation expression of the voltage stability margin index is: Wherein, V0 represents the voltage of the load node in the current state; V cr represents the voltage of the load node in the critical state; the λ V Represents the voltage stability margin index of a single load node.

4. A method for optimizing load distribution of a photovoltaic power generation system according to claim 1, characterized in that: The establishing of the operation constraint conditions of the photovoltaic power generation system comprises: Taking the reactive power optimization of the photovoltaic power generation system as an indicator, establishing a power balance constraint condition corresponding to the photovoltaic power generation system; According to the specific transformation ratio constraint of the on-load transformer corresponding to the photovoltaic power generation system, the configuration capacity constraint of the power reactive compensation device, the terminal voltage constraint of the grid-connected generator, the generator reactive power constraint and the load node voltage constraint, the inequality constraint conditions corresponding to the photovoltaic power generation system are constructed.

5. A method for optimizing load distribution of a photovoltaic power generation system according to claim 4, characterized in that: The power balance constraint condition corresponding to the photovoltaic power generation system is established, characterized in that: According to the reactive power and active power of the load and the active power of the generator, a power balance constraint condition corresponding to the photovoltaic power generation system is constructed; wherein the expression of the power balance constraint condition is: Wherein, N is the number of load nodes in the photovoltaic power generation system; θ ij , G ij and B ij are the voltage phase angle difference, susceptance and conductance between load node i and load node j respectively; the Q ci is the reactive power compensation power; li , P li are the reactive power and active power of the load node respectively; gi is the active power of the generator; gi is the reactive power of the generator.

6. A method for optimizing load distribution of a photovoltaic power generation system according to claim 4, characterized in that: The inequality constraints are: Among them, the N d 、N k 、N C 、N g represents the number of load nodes, the number of on-load transformers, the number of reactive power compensation devices and the number of generator nodes in the photovoltaic power generation system in turn; the U gimin , U gimax , Q Cimin , Q Cimax , K imin , K imax In turn, they represent the upper and lower limits of the corresponding quantities; gi , U i represents the reactive power of the generator and the voltage of the load node respectively; the K i , Q Ci , U gi It represents the given specific transformation ratio of the on-load transformer, the configuration capacity of the power reactive compensation device and the terminal voltage of the grid-connected generator in turn.

7. A method for optimizing load distribution of a photovoltaic power generation system according to claim 1, characterized in that: The method of optimizing the load distribution in the photovoltaic power generation system by using an imperial competition algorithm according to the operation constraint conditions and the dynamic allocation model includes: Determine a load allocation scheme for the photovoltaic power generation system according to the dynamic allocation model, and determine a load factor for the photovoltaic power generation system according to the load allocation scheme; wherein the load factor is a ratio of daily average power generation to daily peak power generation within a preset time period; With the goal of maximizing the load factor, the dynamic allocation model is solved by a preset imperial competition algorithm to obtain a load allocation scheme output by the dynamic allocation model, and the load distribution in the photovoltaic power generation system is optimized according to the load allocation scheme.

8. A method for optimizing load distribution of a photovoltaic power generation system according to claim 7, characterized in that: The solving of the dynamic allocation model by using a preset imperial competition algorithm includes: Clustering several photovoltaic power generation unit empires of the photovoltaic power generation system, constructing several initial empires, and defining standardized rights of each of the initial empires; Dividing a number of load nodes in the photovoltaic power generation system according to the standardized rights of each of the initial empires to obtain a number of colonies corresponding to each of the initial empires; The right value of each of the initial empires is determined based on the colonies, and competition is carried out between the initial empires based on the right values ​​until only one empire remains and the maximum load factor is obtained.

9. A method for optimizing load distribution of a photovoltaic power generation system according to claim 8, characterized in that: The method of solving the dynamic allocation model by using a preset imperial competition algorithm further includes: According to the decision variables corresponding to each of the initial empires, the standardized rights of the initial empires are defined; wherein the calculation expression of the standardized rights is: Wherein, i represents the i-th country; n Indicates the standardization rights of the nth country; the f n represents the function value of the nth country; h represents the number of dimensions of the decision variable; f i represents the function value of the ith country; According to the competition between the initial empires and the colonies corresponding to each of the initial empires, the right value of each of the empires after the competition is determined; wherein the calculation expression of the right value is: Among them, the is the average value of the objective function of empire k’s possession of colonies; σ is the weight coefficient; f k is the objective function value of Empire k; The initial competition among empires is carried out according to the rights; a colony is selected from the empire with the smallest rights and distributed to several other empires according to a certain probability to complete the competition among empires; the probability of possession of each empire is: Among them, the P j is the probability of the jth empire occupying a colony; Ai is the total power value of the ith empire; Aj The total value of the jth empire; C-1 is the number of empires that can be allocated.

10. A photovoltaic power generation system load distribution optimization system, characterized in that: It includes load distribution module, operation constraint module and distribution optimization module; The load distribution module is used to construct a dynamic distribution model of loads in the photovoltaic power generation system with the comprehensive operation safety risk of the photovoltaic power generation system as the target; The operation constraint module is used to establish the operation constraint conditions of the photovoltaic power generation system according to the operation requirements of the photovoltaic power generation system; The distribution optimization module is used to optimize the load distribution in the photovoltaic power generation system through an imperial competition algorithm according to the operating constraints and the dynamic distribution model.

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