New energy partition layout optimization method considering space-time characteristics

Abstract

The present application relates to a kind of new energy partition layout optimization method considering space-time characteristics, comprising: establishing each partition space-time characteristic representation model, with the average value of typical month each time period photovoltaic output as whole point equivalent output;Establish new energy partition layout optimization model considering space-time characteristics, its constraint conditions include new energy scale constraint, curtailment rate constraint, power balance constraint, interzoning interconnection line power constraint and unit output constraint;Its objective function is the equivalent annual investment yield of new wind power and photovoltaic power generation installed capacity maximum;Newly added partition photovoltaic capacity, newly added partition wind power capacity and newly added wind-light space layout are output by using genetic algorithm to solve new energy partition layout optimization model considering space-time characteristics.The present application is more practical in engineering, easy to implement, at the same time, new energy power supply planning scheme is output by using genetic algorithm to solve new energy partition layout optimization model considering space-time characteristics, with fast calculation speed, good effect and the like.

Description

Technical Field

[0001] This invention relates to the field of power system new energy planning and optimized operation technology, and in particular to a new energy zoning layout optimization method that considers spatiotemporal characteristics. Background Technology

[0002] With continuous societal development, countries are increasingly focusing on climate change and shifts in energy demand. Currently, nations worldwide are undertaking a revolution in energy, encompassing large-scale development of new energy sources, decarbonization of fossil fuels, and intelligent energy management. They are actively building new power systems, and integrating new energy sources into the grid can reduce carbon emissions and promote energy structure transformation, thereby driving the steady expansion and development of the new energy industry.

[0003] Optimizing the zoning layout of renewable energy has always been a challenge in the field of power system planning and optimization. Although experts and scholars at home and abroad have constructed joint time-series scenarios for renewable energy using constant power models, joint probability distribution methods, and scenario reduction methods, and established renewable energy models that consider randomness and time-series correlation, and used intelligent optimization algorithms to solve for renewable energy zoning capacity and layout, this approach lacks consideration of the spatial characteristics of renewable energy. When integrating renewable energy into a new power system, it is necessary not only to consider the time-series correlation in the application process of renewable energy, but also to consider the impact of spatial correlation on the operation of the new power system. It is necessary to comprehensively analyze the specific dependence of different types of renewable energy on factors such as location, climate, topography, terrain, land area, and resource type, coordinate the temporal and spatial characteristics in the renewable energy zoning layout process, and select a renewable energy zoning layout scheme that conforms to the actual situation based on the final analysis results, so that its role in the new power system can be rationally realized.

[0004] Currently, domestic and international experts and scholars mainly employ methods such as constant power models, joint probability distribution methods, and scenario reduction methods to construct joint time-series scenarios for renewable energy, establish renewable energy models that consider randomness and temporal correlation, and use intelligent optimization algorithms to solve for renewable energy zoning capacity and layout. However, because time and space factors simultaneously affect renewable energy output, this approach leads to a relatively coarse zoning layout scheme that fails to fully realize the dispatch potential of renewable energy, exhibiting shortcomings such as a lack of spatiotemporal coupling characteristics and weak implementability. How to avoid the problems of the above methods, consider the temporal and spatial characteristics of renewable energy, and improve the engineering practicality of zoning layout optimization methods is an urgent technical problem to be solved in the field of power system planning and optimized operation. Summary of the Invention

[0005] To address the shortcomings of traditional capacity planning methods that neglect the spatiotemporal characteristics of new energy sources and have weak implementability, the present invention aims to provide a new energy zone layout optimization method that considers spatiotemporal characteristics, which has strong spatiotemporal coupling, strong engineering feasibility, is easy to implement, and has good results.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a new energy zone layout optimization method considering spatiotemporal characteristics, the method comprising the following sequential steps:

[0007] (1) Based on the wind and solar power output curves or historical measured data of the actual operation of the power grid in each region, establish a spatiotemporal characteristic characterization model for each region, and use the average wind and solar power output of each period in a typical month as the equivalent output at the whole hour.

[0008] (2) Establish a new energy zoning layout optimization model that considers spatiotemporal characteristics. Its constraints include new energy scale constraints, curtailment rate constraints, power balance constraints, inter-regional tie line power constraints, and unit output constraints. Its objective function is to maximize the equivalent annual investment return rate of newly added wind power and photovoltaic power generation capacity.

[0009] (3) Use a genetic algorithm to solve the new energy zoning layout optimization model that takes into account the spatiotemporal characteristics, and output the new zoning photovoltaic capacity, new zoning wind power capacity and new wind and solar spatial layout.

[0010] Step (1) specifically includes the following steps:

[0011] (1a) The output characteristics of photovoltaic power generation are analyzed, and the photovoltaic output is expressed as:

[0012] (1)

[0013] in, Solar radiation intensity; The area of ​​the photovoltaic panel; Solar photovoltaic conversion efficiency;

[0014] (1b) Wind power output is expressed as:

[0015] (2)

[0016] in, , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; P w-rate This refers to the rated output power of the wind turbine generator set.

[0017] Since the characteristics of renewable energy output vary significantly with geographical location, the average renewable energy output for each period of a typical month is used as the equivalent output at the hour. Therefore, the renewable energy output power considering spatiotemporal characteristics is expressed as:

[0018] (3)

[0019] in, This represents the photovoltaic output at time t, taking into account spatiotemporal characteristics; Indicates the number of typical months; This represents the photovoltaic output at a typical time t under the moon. This represents the wind power output at time t, taking into account spatiotemporal characteristics; t represents the wind power output at a typical time under the moon, and formula (3) is a spatiotemporal characteristic model for each region.

[0020] In step (2), the constraints include:

[0021] The scale constraints for new energy sources are:

[0022] (4)

[0023] in, To increase the total capacity of photovoltaic and wind power; Add wind power capacity to zone i; Add photovoltaic capacity to zone i;

[0024] The constraint on the curtailment rate is:

[0025] (5)

[0026] in, The total wind power generation for zone i. The total photovoltaic power generation of zone i; Set the maximum curtailment rate for the zone; Add the amount of abandoned electricity caused by photovoltaic and wind power in the i-th partition;

[0027] The power balance constraint is:

[0028] (6)

[0029] in, , , , The output of wind power, photovoltaic power, thermal power, and hydropower in zone i at time t; Output of other types of power supplies besides those mentioned above; The actual power of the i-th tie line at time t; For partition i load;

[0030] There are two methods for power constraints on inter-regional tie lines. The first method involves pre-determining the power exchange of inter-provincial tie lines according to a trading agreement. In this case, the power exchange of inter-regional tie lines equals the planned value, expressed as:

[0031] (7)

[0032] The second method involves flexibly adjusting the power exchange of inter-regional power grid interconnections based on the power balance needs of each region. In this case, the power exchange of inter-regional interconnections is variable, but does not exceed the transmission capacity of the interconnection lines, as shown below:

[0033] (8)

[0034] in, This is the planned value for the regional link line dispatch; This is the maximum transmission capacity of the regional tie line, and this value is dynamically adjusted according to the grid structure.

[0035] The unit output constraint is:

[0036] (9)

[0037] in, and These represent the minimum output of all thermal and hydropower units in the region; and This is the maximum output of all thermal and hydropower units in the zone.

[0038] In step (2), the objective function is to maximize the equivalent annual rate of return on investment for the newly added wind power and photovoltaic power generation capacity, expressed as:

[0039]

[0040] in, , These represent the newly added wind power and photovoltaic power generation within each of the i partitions; For the i-th partition, the amount of power curtailed due to the addition of photovoltaic and wind power, , Grid connection price of wind power and solar power This is the penalty coefficient for power curtailment; , These are the annual converted values ​​of the newly added wind power and photovoltaic construction costs for the i-th partition, respectively. , These represent the annual operation and maintenance costs for newly added wind power and photovoltaic power in the i-th partition, respectively; i represents the i-th grid partition. This represents the number of power grid zones.

[0041] Step (3) specifically includes the following steps:

[0042] (3a) When using the genetic algorithm to solve the problem, different encoding forms are adopted according to the different variables being solved. The main optimization is the installed capacity of photovoltaic and wind power in the region. Therefore, real number encoding is used to encode the chromosomes. In actual encoding, the chromosomes are still encoded in binary and converted into real number encoding through decoding. The chromosome is represented as X=(x1,x2,x3,x4,…,x…). 2N-1 ,x 2N x1, x2, x3, and x4 are arrays of binary codes, where x1 and x2 represent the wind power and solar power installed capacities of the first partition, respectively. 2N-1 ,x 2N The installed capacity of wind power and photovoltaic power in the Nth zone, respectively;

[0043] (3b) Generate the initial population of new photovoltaic capacity and its spatial layout in each region and new wind power capacity and its spatial layout in each region;

[0044] (3c) Adjust the newly added photovoltaic and wind power installed capacity and their spatial layout in each zone, and require that they meet the constraints of scale, curtailment rate, tie line power, power balance and generator output.

[0045] (3d) Decode the chromosome and calculate the individual fitness value, which is the equivalent annual investment return rate of the newly added wind power and photovoltaic power generation capacity. The chromosome fitness function value is the objective function value of the new energy zoning layout optimization model considering spatiotemporal characteristics.

[0046] (3e) Perform selection, crossover, and mutation operations: Selection refers to choosing superior individuals from the current population as parent individuals, giving them the opportunity to reproduce. The higher the fitness of an individual, the greater the chance of being selected. Individuals with lower fitness values ​​are gradually eliminated during the evolutionary process. The selection operation uses roulette wheel selection. Let the number of individuals in the population be n, where the fitness value of individual i is f. i Each individual is ranked according to the interval corresponding to its fitness. , , … In the interval A random number is generated in the process. The individual corresponding to the interval where the random number is located is selected as the parent individual. Individuals with larger fitness values ​​account for a larger proportion of the cumulative fitness and are more likely to be selected.

[0047] Crossover and mutation are operations that generate new individuals in a population. Crossover mimics the hybridization process in biological evolution theory, involving the exchange of parts of chromosomes, while mutation involves the mutation of certain genes on chromosomes to produce new individuals. For binary encoding, multi-point crossover and mutation operations are employed. An adaptive crossover and mutation algorithm is used, adjusting the crossover and mutation rates based on the fitness values ​​of individuals during calculation. The algorithm iterates to the g-th generation, with the adaptive crossover rate being:

[0048] (10)

[0049] in, and Given the upper and lower limits of the crossover rate;

[0050] The adaptive variability rate is expressed as:

[0051] (11)

[0052] in, and Given an upper and lower bound on the mutation rate, The fitness coefficient is calculated using the following formula:

[0053] (12)

[0054] in, The standard deviation of the chromosome fitness values ​​in the r-th generation; The standard deviation of chromosome fitness values ​​in generation g;

[0055] (3f) Determine whether the iteration termination condition is met, that is, the calculation error of two iterations meets the set parameter requirements or the number of calculation iterations reaches the set parameter requirements. If it is met, end the calculation and output the result; if it is not met, go to step (3c).

[0056] As can be seen from the above technical solution, the beneficial effects of the present invention are as follows: First, by taking the optimization method of new energy zoning layout as the research object, the present invention effectively solves the shortcomings of traditional capacity planning methods that ignore the spatiotemporal characteristics of new energy; Second, the spatiotemporal characteristic characterization model of each zone proposed in the present invention, based on the wind and solar power output curves or historical measured data of the actual operation of each zone's power grid, can combine the temporal and spatial correlations of new energy output power, which has the advantage of strong spatiotemporal coupling compared with traditional new energy zoning layout optimization methods; Third, based on the new energy resource conditions, construction costs, and power grid conditions of each zone, the present invention establishes a new energy zoning layout constraint formula that considers spatiotemporal characteristics, and obtains a new energy capacity planning and layout optimization model. Compared with traditional new energy zoning layout optimization methods, this model is more practical in engineering and easier to implement. At the same time, the genetic algorithm is used to solve the new energy zoning layout optimization model that considers spatiotemporal characteristics, and outputs a new energy power planning scheme, which has the characteristics of fast calculation speed and good effect. Attached Figure Description

[0057] Figure 1 This is a flowchart of the method of the present invention;

[0058] Figure 2 This is a typical daily power generation characteristic diagram of wind power in different seasons according to the present invention;

[0059] Figure 3 This is the chromosome coding diagram of the present invention;

[0060] Figure 4 This is a flowchart of the genetic algorithm of the present invention. Detailed Implementation

[0061] like Figure 1 As shown, a new energy zone layout optimization method considering spatiotemporal characteristics includes the following sequential steps:

[0062] (1) Based on the wind and solar power output curves or historical measured data of the actual operation of the power grid in each region, establish a spatiotemporal characteristic characterization model for each region, and use the average wind and solar power output of each period in a typical month as the equivalent output at the whole hour.

[0063] (2) Establish a new energy zoning layout optimization model that considers spatiotemporal characteristics. Its constraints include new energy scale constraints, curtailment rate constraints, power balance constraints, inter-regional tie line power constraints, and unit output constraints. Its objective function is to maximize the equivalent annual investment return rate of newly added wind power and photovoltaic power generation capacity.

[0064] (3) Use a genetic algorithm to solve the new energy zoning layout optimization model that takes into account the spatiotemporal characteristics, and output the new zoning photovoltaic capacity, new zoning wind power capacity and new wind and solar spatial layout.

[0065] Step (1) specifically includes the following steps:

[0066] (1a) The output characteristics of photovoltaic power generation are analyzed, and the photovoltaic output is expressed as:

[0067] (1)

[0068] in, Solar radiation intensity; The area of ​​the photovoltaic panel; Solar photovoltaic conversion efficiency;

[0069] (1b) Wind power output is expressed as:

[0070] (2)

[0071] in, , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; P w-rate This refers to the rated output power of the wind turbine generator set.

[0072] Since the characteristics of renewable energy output vary significantly with geographical location, the average renewable energy output for each period of a typical month is used as the equivalent output at the hour. Therefore, the renewable energy output power considering spatiotemporal characteristics is expressed as:

[0073] (3)

[0074] in, This represents the photovoltaic output at time t, taking into account spatiotemporal characteristics; Indicates the number of typical months; This represents the photovoltaic output at a typical time t under the moon. This represents the wind power output at time t, taking into account spatiotemporal characteristics; t represents the wind power output at a typical time under the moon, and formula (3) is a spatiotemporal characteristic model for each region.

[0075] In step (2), the constraints include:

[0076] The scale constraints for new energy sources are:

[0077] (4)

[0078] in, To increase the total capacity of photovoltaic and wind power; Add wind power capacity to zone i; Add photovoltaic capacity to zone i;

[0079] The constraint on the curtailment rate is:

[0080] (5)

[0081] in, The total wind power generation for zone i. The total photovoltaic power generation of zone i; Set the maximum curtailment rate for the zone; Add the amount of abandoned electricity caused by photovoltaic and wind power in the i-th partition;

[0082] The power balance constraint is:

[0083] (6)

[0084] in, , , , The output of wind power, photovoltaic power, thermal power, and hydropower in zone i at time t; Output of other types of power supplies besides those mentioned above; The actual power of the i-th tie line at time t; For partition i load;

[0085] There are two methods for power constraints on inter-regional tie lines. The first method involves pre-determining the power exchange of inter-provincial tie lines according to a trading agreement. In this case, the power exchange of inter-regional tie lines equals the planned value, expressed as:

[0086] (7)

[0087] The second method involves flexibly adjusting the power exchange of inter-regional power grid interconnections based on the power balance needs of each region. In this case, the power exchange of inter-regional interconnections is variable, but does not exceed the transmission capacity of the interconnection lines, as shown below:

[0088] (8)

[0089] in, This is the planned value for the regional link line dispatch; This is the maximum transmission capacity of the regional tie line, and this value is dynamically adjusted according to the grid structure.

[0090] The unit output constraint is:

[0091] (9)

[0092] in, and These represent the minimum output of all thermal and hydropower units in the region; and This is the maximum output of all thermal and hydropower units in the zone.

[0093] In step (2), the objective function is to maximize the equivalent annual rate of return on investment for the newly added wind power and photovoltaic power generation capacity, expressed as:

[0094]

[0095] in, , These represent the newly added wind power and photovoltaic power generation within each of the i partitions; For the i-th partition, the amount of power curtailed due to the addition of photovoltaic and wind power, , Grid connection price of wind power and solar power This is the penalty coefficient for power curtailment; , These are the annual converted values ​​of the newly added wind power and photovoltaic construction costs for the i-th partition, respectively. , These represent the annual operation and maintenance costs for newly added wind power and photovoltaic power in the i-th partition, respectively; i represents the i-th grid partition. This represents the number of power grid zones.

[0096] like Figure 4 As shown, step (3) specifically includes the following steps:

[0097] (3a) When using the genetic algorithm to solve the problem, different encoding forms are adopted according to the different variables being solved. The main optimization is the installed capacity of photovoltaic and wind power in the region. Therefore, real number encoding is used to encode the chromosomes. In actual encoding, the chromosomes are still encoded in binary and converted into real number encoding through decoding. The chromosome is represented as X=(x1,x2,x3,x4,…,x…). 2N-1 ,x 2N x1, x2, x3, and x4 are arrays of binary codes, where x1 and x2 represent the wind power and solar power installed capacities of the first partition, respectively. 2N-1 ,x 2N The installed capacity of wind power and photovoltaic power in the Nth zone, respectively;

[0098] (3b) Generate the initial population of new photovoltaic capacity and its spatial layout in each region and new wind power capacity and its spatial layout in each region;

[0099] (3c) Adjust the newly added photovoltaic and wind power installed capacity and their spatial layout in each zone, and require that they meet the constraints of scale, curtailment rate, tie line power, power balance and generator output.

[0100] (3d) Decode the chromosome and calculate the individual fitness value, which is the equivalent annual investment return rate of the newly added wind power and photovoltaic power generation capacity. The chromosome fitness function value is the objective function value of the new energy zoning layout optimization model considering spatiotemporal characteristics.

[0101] (3e) Perform selection, crossover, and mutation operations: Selection refers to choosing superior individuals from the current population as parent individuals, giving them the opportunity to reproduce. The higher the fitness of an individual, the greater the chance of being selected. Individuals with lower fitness values ​​are gradually eliminated during the evolutionary process. The selection operation uses roulette wheel selection. Let the number of individuals in the population be n, where the fitness value of individual i is f. i Each individual is ranked according to the interval corresponding to its fitness. , , … In the interval A random number is generated in the process. The individual corresponding to the interval where the random number is located is selected as the parent individual. Individuals with larger fitness values ​​account for a larger proportion of the cumulative fitness and are more likely to be selected.

[0102] Crossover and mutation are operations that generate new individuals in a population. Crossover mimics the hybridization process in biological evolution theory, involving the exchange of parts of chromosomes, while mutation involves the mutation of certain genes on chromosomes to produce new individuals. For binary encoding, multi-point crossover and mutation operations are employed. An adaptive crossover and mutation algorithm is used, adjusting the crossover and mutation rates based on the fitness values ​​of individuals during calculation. The algorithm iterates to the g-th generation, with the adaptive crossover rate being:

[0103] (10)

[0104] in, and Given the upper and lower limits of the crossover rate;

[0105] The adaptive variability rate is expressed as:

[0106] (11)

[0107] in, and Given an upper and lower bound on the mutation rate, The fitness coefficient is calculated using the following formula:

[0108] (12)

[0109] in, The standard deviation of the chromosome fitness values ​​in the r-th generation; The standard deviation of chromosome fitness values ​​in generation g;

[0110] (3f) Determine whether the iteration termination condition is met, that is, the calculation error of two iterations meets the set parameter requirements or the number of calculation iterations reaches the set parameter requirements. If it is met, end the calculation and output the result; if it is not met, go to step (3c).

[0111] like Figure 2 As shown, statistical analysis of historical data of wind farms already in operation in our province shows that wind power output is highest in spring, and the system faces the greatest peak-shaving pressure. The maximum output rate is about 73%, and the peak power generation period occurs from 1 to 4 am.

[0112] like Figure 3 As shown, the actual encoding is in binary format, and then converted into real number encoding through decoding. Specifically, x1 and x2 represent the wind power and photovoltaic installed capacity of the first zone, respectively. 2N-1 ,x 2N The installed capacity of wind power and photovoltaic power in the Nth zone, respectively.

[0113] In summary, this invention, by focusing on the optimization method for renewable energy zoning layout, effectively addresses the shortcomings of traditional capacity planning methods that neglect the spatiotemporal characteristics of renewable energy. The proposed spatiotemporal characteristic characterization model for each zone, based on the actual wind and solar power output curves or historical measured data of the grid's operation, combines the temporal and spatial correlations of renewable energy output power, exhibiting stronger spatiotemporal coupling compared to traditional renewable energy zoning layout optimization methods. Furthermore, this invention establishes renewable energy zoning layout constraint formulas considering spatiotemporal characteristics based on the renewable energy resource conditions, construction costs, and grid conditions of each zone, resulting in a renewable energy zoning layout optimization model that is more practical in engineering and easier to implement than traditional methods. Simultaneously, a genetic algorithm is used to solve the renewable energy zoning layout optimization model considering spatiotemporal characteristics, outputting a renewable energy power planning scheme, which features fast computation speed and good performance.

Claims

1. A method for optimizing the zoning layout of new energy sources considering spatiotemporal characteristics, characterized in that: The method includes the following steps in sequence: (1) Based on the wind and solar power output curves or historical measured data of the actual operation of the power grid in each region, establish a spatiotemporal characteristic characterization model for each region, and use the average wind and solar power output of each period in a typical month as the equivalent output at the whole hour. (2) Establish a new energy zoning layout optimization model that considers spatiotemporal characteristics. Its constraints include new energy scale constraints, curtailment rate constraints, power balance constraints, inter-regional tie line power constraints, and unit output constraints. Its objective function is to maximize the equivalent annual investment return rate of newly added wind power and photovoltaic power generation capacity. (3) Use a genetic algorithm to solve the new energy zoning layout optimization model that considers spatiotemporal characteristics, and output the new zoning photovoltaic capacity, new zoning wind power capacity and new wind and solar spatial layout; In step (2), the constraints include: The scale constraints for new energy sources are: (4) in, To increase the total capacity of photovoltaic and wind power; Add wind power capacity to zone i; Add photovoltaic capacity to zone i; The constraint on the curtailment rate is: (5) in, The total wind power generation for zone i. The total photovoltaic power generation of zone i; Set the maximum curtailment rate for the zone; Add the amount of abandoned electricity caused by photovoltaic and wind power in the i-th partition; The power balance constraint is: (6) in, , , , The output of wind power, photovoltaic power, thermal power, and hydropower in zone i at time t; Output of other types of power supplies besides those mentioned above; The actual power of the i-th tie line at time t; For partition i load; There are two methods for power constraints on inter-regional tie lines. The first method involves pre-determining the power exchange of inter-provincial tie lines according to a trading agreement. In this case, the power exchange of inter-regional tie lines equals the planned value, expressed as: (7) The second method involves flexibly adjusting the power exchange of inter-regional power grid interconnections based on the power balance needs of each region. In this case, the power exchange of inter-regional interconnections is variable, but does not exceed the transmission capacity of the interconnection lines, as shown below: (8) in, This is the planned value for the regional link line dispatch; This is the maximum transmission capacity of the regional tie line, and this value is dynamically adjusted according to the grid structure. The unit output constraint is: (9) in, and These represent the minimum output of all thermal and hydropower units in the region; and To achieve maximum output for all thermal and hydropower units in the zone; In step (2), the objective function is to maximize the equivalent annual rate of return on investment for the newly added wind power and photovoltaic power generation capacity, expressed as: ， in, , These represent the newly added wind power and photovoltaic power generation within each of the i partitions; For the i-th partition, the amount of power curtailed due to the addition of photovoltaic and wind power, , Grid connection price of wind power and solar power This is the penalty coefficient for power curtailment; , These are the annual converted values ​​of the newly added wind power and photovoltaic construction costs for the i-th partition, respectively. , These represent the annual operation and maintenance costs for newly added wind power and photovoltaic power in the i-th partition, respectively; i represents the i-th grid partition. This represents the number of power grid zones.

2. The new energy zoning layout optimization method considering spatiotemporal characteristics according to claim 1, characterized in that: Step (1) specifically includes the following steps: (1a) The output characteristics of photovoltaic power generation are analyzed, and the photovoltaic output is expressed as: (1) in, Solar radiation intensity; The area of ​​the photovoltaic panel; Solar photovoltaic conversion efficiency; (1b) Wind power output is expressed as: (2) in, , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; P w-rate This refers to the rated output power of the wind turbine generator set. Since the characteristics of renewable energy output vary significantly with geographical location, the average renewable energy output for each period of a typical month is used as the equivalent output at the hour. Therefore, the renewable energy output power considering spatiotemporal characteristics is expressed as: (3) in, This represents the photovoltaic output at time t, taking into account spatiotemporal characteristics; Indicates the number of typical months; This represents the photovoltaic output at a typical time t under the moon. This represents the wind power output at time t, taking into account spatiotemporal characteristics; t represents the wind power output at a typical time under the moon, and formula (3) is a spatiotemporal characteristic model for each region.

3. The new energy zoning layout optimization method considering spatiotemporal characteristics according to claim 1, characterized in that: Step (3) specifically includes the following steps: (3a) When using a genetic algorithm to solve the problem, different encoding forms are adopted according to different solution variables to optimize the installed capacity of photovoltaic and wind power in different areas. Therefore, real number encoding is used to encode the chromosomes; in actual encoding, binary encoding is still used, which is converted into real number encoding through decoding. The chromosome is represented as X=(x1,x2,x3,x4,…,x…). 2N-1 ,x 2N x1, x2, x3, and x4 are arrays of binary codes, where x1 and x2 represent the wind power and solar power installed capacities of the first partition, respectively. 2N-1 ,x 2N The installed capacity of wind power and photovoltaic power in the Nth zone, respectively; (3b) Generate the initial population of new photovoltaic capacity and its spatial layout in each region and new wind power capacity and its spatial layout in each region; (3c) Adjust the newly added photovoltaic and wind power installed capacity and their spatial layout in each zone, and require that they meet the constraints of scale, curtailment rate, tie line power, power balance and generator output. (3d) Decode the chromosome and calculate the individual fitness value, which is the equivalent annual investment return rate of the newly added wind power and photovoltaic power generation capacity. The chromosome fitness function value is the objective function value of the new energy zoning layout optimization model considering spatiotemporal characteristics. (3e) Perform selection, crossover, and mutation operations: Selection refers to choosing superior individuals from the current population as parent individuals, giving them the opportunity to reproduce. The higher the fitness of an individual, the greater the chance of being selected. Individuals with lower fitness values ​​are gradually eliminated during the evolutionary process. The selection operation uses roulette wheel selection. Let the number of individuals in the population be n, where the fitness value of individual i is f. i Each individual is ranked according to the interval corresponding to its fitness. , , … In the interval A random number is generated in the process. The individual corresponding to the interval where the random number is located is selected as the parent individual. Individuals with larger fitness values ​​account for a larger proportion of the cumulative fitness and are more likely to be selected. Crossover and mutation are operations that generate new individuals in a population. Crossover mimics the hybridization process in biological evolution theory, involving the exchange of parts of chromosomes, while mutation involves the mutation of certain genes on chromosomes to produce new individuals. For binary encoding, multi-point crossover and mutation operations are employed. An adaptive crossover and mutation algorithm is used, adjusting the crossover and mutation rates based on the fitness values ​​of individuals during calculation. The algorithm iterates to the g-th generation, with the adaptive crossover rate being: (10) in, and Given the upper and lower limits of the crossover rate; The adaptive variability rate is expressed as: (11) in, and Given an upper and lower bound on the mutation rate, The fitness coefficient is calculated using the following formula: (12) in, The standard deviation of the chromosome fitness values ​​in the r-th generation; The standard deviation of chromosome fitness values ​​in generation g; (3f) Determine whether the iteration termination condition is met, that is, the calculation error of two iterations meets the set parameter requirements or the number of calculation iterations reaches the set parameter requirements. If it is met, end the calculation and output the result; if it is not met, go to step (3c).

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