Power distribution network load resource carbon reduction potential assessment method

Abstract

The invention relates to a power distribution network load resource carbon reduction potential assessment method. The method comprises the following steps: calculating the maximum adjustment capability of all load resources of a power distribution network; calculating the total variation of carbon emission; calculating and evaluating the total carbon reduction potential of the power distribution network to obtain a carbon reduction evaluation model; and solving the carbon reduction evaluation model, and optimizing by using a genetic algorithm to obtain an optimal load regulation scheme. According to the method, the instantaneous regulation rate and the nonlinear power response of each load resource are dynamically calculated, so that the carbon reduction potential of each type of load resources under different power regulation can be more accurately evaluated, and the maximization of the carbon reduction effect is realized; a higher carbon emission reduction effect can be obtained with lower economic cost, and the requirements of current low-carbon economic development are met; the load regulation strategy is optimized by adopting the genetic algorithm to maximize the carbon reduction potential, and the introduction of the intelligent algorithm not only improves the optimization accuracy, but also ensures the search of the global optimal solution, so that the regulation scheme can realize the carbon reduction target in the optimal load response mode.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and low-carbon operation, and in particular to a low-carbon operation method for a distribution network taking demand response into consideration. Background Art

[0002] The current power system and distribution network have many defects in load regulation and carbon emission management, mainly manifested in: lack of accurate load regulation control means, insufficient carbon emission assessment, and poor economy and efficiency of carbon reduction strategies. Traditional load regulation methods usually adopt static or simple rule control, fail to make full use of the flexibility of load resources, and do not consider the changes in carbon emissions.

[0003] For the regulation potential and carbon reduction effect of load resources, most of the existing methods are calculated through simple linear assumptions, ignoring the differences in the regulation response of load resources in different time periods, which leads to the lack of accuracy in the evaluation of regulation potential and carbon reduction effect. Secondly, due to the lack of full consideration of the dynamic regulation capacity and cost of load resources, the existing carbon reduction methods are difficult to optimize the balance between regulation cost and carbon reduction effect, and cannot meet the needs of low-carbon economic development. In addition, traditional carbon emission control methods are mainly based on global large-scale equipment transformation, which is difficult to directly reduce carbon through load regulation, and the implementation cost is high and the time period is long. Therefore, the existing technology lacks an effective method to dynamically evaluate and optimize the regulation potential and carbon emission impact of load resources, so that the potential of load regulation in carbon reduction has not been fully utilized. Summary of the invention

[0004] In order to solve the problems in the prior art that there is a lack of accurate assessment method for carbon reduction potential based on dynamic load regulation, no consideration of the optimal balance between carbon reduction and economy, and a lack of means to optimize the regulation strategy through intelligent algorithms, the purpose of the present invention is to provide a method for assessing the carbon reduction potential of distribution network load resources that can achieve more dynamic carbon reduction potential assessment, consider the balanced optimization of economy and carbon reduction effect, and achieve regulation strategy optimization through intelligent algorithms.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for evaluating the carbon reduction potential of distribution network load resources, the method comprising the following steps in order:

[0006] (1) Calculate the maximum regulation capacity P of all load resources in the distribution network max ;

[0007] (2) According to the maximum regulation capacity P of all load resources in the distribution network max , calculate the total carbon emissions E of the distribution network before regulation at time t total (t), the total carbon emissions of the distribution network after adjustment at time t E t ' otal(t), according to the total carbon emissions E of the distribution network before regulation at time t total (t), the total carbon emissions of the distribution network after adjustment at time t E t ' otal (t), calculate the total change in carbon emissions ΔE total ;

[0008] (3) According to the maximum regulation capacity P of all load resources in the distribution network max and the total change in carbon emissions ΔE total , calculate and evaluate the total carbon reduction potential E of the distribution network accum , and obtain the carbon reduction assessment model;

[0009] (4) Solve the carbon reduction assessment model and use genetic algorithm optimization to obtain the optimal load regulation plan.

[0010] The step (1) specifically comprises the following steps in order:

[0011] (1a) Calculate the instantaneous regulation rate of the i-th load resource at time t

[0012]

[0013] Where ΔP i (t) is the regulation amount of the i-th load resource at time t, τ i is the response time of the i-th load resource; η i is a weighting factor used to measure the inertia or response delay of the load resource; is the second-order derivative, indicating the acceleration of load power change; P i (t) is the output power of the i-th load resource at time t before adjustment;

[0014] (1b) Calculate the cumulative regulation of the i-th load resource in the distribution network in the time period [t1, t2]

[0015]

[0016] In the formula, β i Indicates the power change ratio, which is used to measure the change trend of load resource power over time; t1 indicates the start time, and t2 indicates the end time;

[0017] (1c) Calculate the maximum regulation capacity P of all load resources in the distribution network max :

[0018]

[0019] Where N is the total number of load resource types in the distribution network; Pi,max and P i,min are the maximum power and minimum power of the i-th type of load resource respectively; is the average regulation rate of the i-th resource in the time period, which is used to balance the regulation capabilities of resources with different loads.

[0020] The step (2) specifically refers to: calculating the carbon emission E of the i-th load resource in the distribution network at time t i (t):

[0021]

[0022] Where, EF i is the carbon emission factor of the i-th load resource, indicating the carbon emission per unit power; P i (t) is the output power of the i-th load resource at time t before adjustment; δ i It represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emission with the adjustment rate; represents the power regulation rate of the i-th type of load resource before regulation; γ i Represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output; α i represents the carbon emission fluctuation amplitude coefficient, indicating the impact of load fluctuation on carbon emissions; ω represents the fluctuation frequency, simulating the periodic carbon emission characteristics; φ represents the phase angle, which is used to adjust the time position of the periodic impact;

[0023] Calculate the total carbon emissions E of the distribution network before regulation at time t total (t):

[0024]

[0025] In the formula, N represents the number of types of load resources;

[0026] Calculate the total carbon emissions E of the distribution network after regulation at time t t ' otal (t):

[0027]

[0028] Where P i ′(t) represents the output power of the i-th load resource at time t after adjustment; represents the power regulation rate of the i-th type of load resource after regulation;

[0029] Calculate the total change in carbon emissions ΔE total :

[0030]

[0031] The step (3) specifically refers to:

[0032] Calculation of instantaneous carbon reduction potential of a single load resource: Calculate the instantaneous carbon reduction potential ΔE of the i-th load resource at time t i (t):

[0033]

[0034] Where: P i (t) is the output power of the i-th load resource at time t before adjustment; P i ′(t) represents the output power of the i-th load resource at time t after adjustment; EF i represents the carbon emission factor of the i-th type of load resource; δ i represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emissions with the adjustment rate; γ i It represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output; and The term describes the dynamic response of carbon emissions during the regulation process, reflecting the impact of the regulation rate on the carbon reduction effect; γ i ·P i (t) 2 and γ i ·(P i ′(t)) 2 The term is used to represent the nonlinear carbon emission characteristics under high load conditions, capturing the additional carbon reduction potential brought by higher power regulation;

[0035] Calculation of the cumulative carbon reduction potential of load resources: In the entire time interval [t1, t2], the cumulative carbon reduction potential ΔE of the i-th type of load resources accum Calculated by integration:

[0036]

[0037] In the formula, (P i (t)-P i ′(t))·EF i Indicates the carbon reduction effect caused directly by power regulation; represents the additional carbon reduction caused by the dynamic adjustment rate; γ i (P i (t) 2 -(P i ′(t)) 2 ) represents the nonlinear carbon reduction effect under high power regulation;

[0038] Calculation of the total carbon reduction potential of the distribution network: The cumulative carbon reduction potential of all load resources is summed up to obtain the total carbon reduction potential E of the distribution network. accum Carbon reduction assessment model:

[0039]

[0040] Where N is the total number of types of load resources in the distribution network;

[0041] Total carbon reduction potential of distribution network E accum The larger it is, the better the carbon reduction effect is.

[0042] The step (4) specifically comprises the following steps in order:

[0043] (4a) Define the optimization objective function:

[0044] The optimization objective function is to maximize the total carbon reduction potential E of the distribution network. accum :

[0045]

[0046] Where: N is the total number of load resource types in the distribution network; t1 represents the start time, t2 represents the end time; P i (t) is the output power of the i-th load resource at time t before adjustment; P i ′(t) represents the output power of the i-th load resource at time t after adjustment; EF i represents the carbon emission factor of the i-th type of load resource; δ i represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emissions with the adjustment rate; γ i It represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output;

[0047] By maximizing the total carbon reduction potential E accum , find the optimal P for all load resources i (t);

[0048] (4b) Initialize the population:

[0049] Definition of chromosome: In the genetic algorithm, a chromosome represents a candidate solution, and each chromosome contains the power output of each load resource in different time periods:

[0050] chromosome=[P1(t1),P2(t1),...,P N (t1),P1(t2),...,P N (t n )]

[0051] Where: P i (t j ) represents the i-th type of load resource at time t j Power output; t n It is the final time period;

[0052] Initialize the population: Generate multiple random chromosomes, each chromosome represents a different resource scheduling scheme, and the population size is between 50 and 200;

[0053] (4c) Construct a fitness function, which is used to evaluate the quality of each chromosome. The fitness function is calculated based on the carbon reduction potential objective function, and the formula is:

[0054] fitness(chromosome)=E accum

[0055] The fitness value is equal to the total carbon reduction potential E accum , the individuals, i.e., chromosomes, with higher fitness values ​​indicate that the carbon reduction effect of the regulation scheme is better;

[0056] (4d) Use the roulette wheel selection method to select individuals with higher fitness from the population for mating. Individuals with higher fitness refer to the individuals with the top 30% fitness values:

[0057]

[0058] Where: P select (i) is the probability of the i-th individual being selected; fitness(i) is the fitness value of the i-th individual, fitness(j) is the fitness value of the j-th individual, and N′ represents the number of individuals in the population;

[0059] (4e) Perform a crossover operation, using the single-point crossover method to generate new offspring by exchanging some genes of the chromosome:

[0060] Child1=[Parent1 part1 ,Parent2 part2 ]

[0061] In the formula, Child1 represents the new offspring chromosome generated by the crossover operation; Parent1 represents the first parent chromosome; Parent2 represents the second parent chromosome;

[0062] (4f) The genetic algorithm iterates until the fitness value increase is less than the threshold Δfitness min After the iteration, the genetic algorithm outputs the chromosome with the highest fitness value. The regulation strategy corresponding to the chromosome is the optimal load regulation scheme. The optimal load regulation scheme includes the optimal power regulation scheme and the maximum cumulative carbon reduction potential. The optimal power regulation scheme refers to the power output of each load resource at different times; the maximum cumulative carbon reduction potential refers to the E corresponding to the optimal chromosome. accum , that is, the maximized total carbon reduction potential.

[0063] It can be seen from the above technical scheme that the beneficial effects of the present invention are: First, a more dynamic carbon reduction potential assessment: the present invention can more accurately assess the carbon reduction potential of each type of load resource under different power adjustments by dynamically calculating the instantaneous regulation rate and nonlinear power response of each load resource. Unlike the traditional static assumption method, the present invention can capture the instantaneous response changes in the regulation process, thereby greatly improving the accuracy of the carbon reduction effect. Such a dynamic evaluation method is helpful to discover the efficient regulation opportunities of load resources, thereby maximizing the carbon reduction effect; second, the balance and optimization of economy and carbon reduction effect: the present invention introduces the calculation of regulation cost in the carbon reduction potential assessment, combines the carbon reduction effect with the regulation cost, and evaluates the cost performance of different schemes through carbon reduction efficiency. Cost-effectiveness ratio, so as to find the most economical regulation strategy; in this way, the present invention avoids the defect of existing methods that ignores cost in the process of carbon reduction, and can obtain higher carbon emission reduction effect at a lower economic cost, which meets the current needs of low-carbon economic development; thirdly, the regulation strategy is optimized through intelligent algorithms: the present invention adopts genetic algorithms to optimize the load regulation strategy to maximize the carbon reduction potential. The introduction of intelligent algorithms not only improves the accuracy of optimization, but also ensures the search for the global optimal solution, so that the regulation scheme can achieve the carbon reduction target with the best load response mode; in addition, the crossover and mutation operations of genetic algorithms can effectively avoid local optimal problems, so that the load regulation strategy can still maintain efficient carbon reduction effects under various complex conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0065] like Figure 1 As shown, a method for evaluating the carbon reduction potential of distribution network load resources comprises the following steps in order:

[0066] (1) Calculate the maximum regulation capacity P of all load resources in the distribution network max ;

[0067] (2) According to the maximum regulation capacity P of all load resources in the distribution network max , calculate the total carbon emissions E of the distribution network before regulation at time t total (t), the total carbon emissions of the distribution network after adjustment at time t E t ' otal (t), according to the total carbon emissions E of the distribution network before regulation at time t total (t), the total carbon emissions of the distribution network after adjustment at time t E t ' otal (t), calculate the total change in carbon emissions ΔE total ;

[0068] (3) According to the maximum regulation capacity P of all load resources in the distribution network max and the total change in carbon emissions ΔE total , calculate and evaluate the total carbon reduction potential E of the distribution network accum , and obtain the carbon reduction assessment model;

[0069] (4) Solve the carbon reduction assessment model and use genetic algorithm optimization to obtain the optimal load regulation plan.

[0070] The step (1) specifically comprises the following steps in order:

[0071] (1a) Calculate the instantaneous regulation rate of the i-th load resource at time t

[0072]

[0073] Where ΔP i (t) is the regulation amount of the i-th load resource at time t, τ i is the response time of the i-th load resource; η i is a weighting factor used to measure the inertia or response delay of the load resource; is the second-order derivative, indicating the acceleration of load power change; P i (t) is the output power of the i-th load resource at time t before adjustment;

[0074] (1b) Calculate the cumulative regulation of the i-th load resource in the distribution network in the time period [t1, t2]

[0075]

[0076] In the formula, β i Indicates the power change ratio, which is used to measure the change trend of load resource power over time; t1 indicates the start time, and t2 indicates the end time;

[0077] (1c) Calculate the maximum regulation capacity P of all load resources in the distribution network max :

[0078]

[0079] Where N is the total number of load resource types in the distribution network; P i,max and P i,min are the maximum power and minimum power of the i-th type of load resource respectively; is the average regulation rate of the i-th resource in the time period, which is used to balance the regulation capabilities of resources with different loads.

[0080] The step (2) specifically refers to: calculating the carbon emission E of the i-th load resource in the distribution network at time t i (t):

[0081]

[0082] Where, EF i is the carbon emission factor of the i-th load resource, indicating the carbon emission per unit power; P i (t) is the output power of the i-th load resource at time t before adjustment; δ i It represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emission with the adjustment rate; represents the power regulation rate of the i-th type of load resource before regulation; γ i Represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output; α i represents the carbon emission fluctuation amplitude coefficient, indicating the impact of load fluctuation on carbon emissions; ω represents the fluctuation frequency, simulating the periodic carbon emission characteristics; φ represents the phase angle, which is used to adjust the time position of the periodic impact;

[0083] Calculate the total carbon emissions E of the distribution network before regulation at time t total (t):

[0084]

[0085] In the formula, N represents the number of types of load resources;

[0086] Calculate the total carbon emissions E of the distribution network after regulation at time t t ' otal (t):

[0087]

[0088] Where P i ′(t) represents the output power of the i-th load resource at time t after adjustment; represents the power regulation rate of the i-th type of load resource after regulation;

[0089] Calculate the total change in carbon emissions ΔE total :

[0090]

[0091] The step (3) specifically refers to:

[0092] Calculation of instantaneous carbon reduction potential of a single load resource: Calculate the instantaneous carbon reduction potential ΔE of the i-th load resource at time t i (t):

[0093]

[0094] Where: P i (t) is the output power of the i-th load resource at time t before adjustment; P i ′(t) represents the output power of the i-th load resource at time t after adjustment; EF i represents the carbon emission factor of the i-th type of load resource; δ i represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emissions with the adjustment rate; γ i It represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output; and The term describes the dynamic response of carbon emissions during the regulation process, reflecting the impact of the regulation rate on the carbon reduction effect; γ i ·P i (t) 2 and γ i ·(P i ′(t)) 2 The term is used to represent the nonlinear carbon emission characteristics under high load conditions, capturing the additional carbon reduction potential brought by higher power regulation;

[0095] Calculation of the cumulative carbon reduction potential of load resources: In the entire time interval [t1, t2], the cumulative carbon reduction potential ΔE of the i-th type of load resources accum Calculated by integration:

[0096]

[0097] In the formula, (P i (t)-P i ′(t))·EF i Indicates the carbon reduction effect caused directly by power regulation; represents the additional carbon reduction caused by the dynamic adjustment rate; γ i (P i (t) 2 -(P i ′(t)) 2 ) represents the nonlinear carbon reduction effect under high power regulation;

[0098] Calculation of the total carbon reduction potential of the distribution network: The cumulative carbon reduction potential of all load resources is summed up to obtain the total carbon reduction potential E of the distribution network. accum Carbon reduction assessment model:

[0099]

[0100] Where N is the total number of types of load resources in the distribution network;

[0101] Total carbon reduction potential of distribution network E accum The larger it is, the better the carbon reduction effect is.

[0102] The step (4) specifically comprises the following steps in order:

[0103] (4a) Define the optimization objective function:

[0104] The optimization objective function is to maximize the total carbon reduction potential E of the distribution network. accum :

[0105]

[0106] Where: N is the total number of load resource types in the distribution network; t1 represents the start time, t2 represents the end time; P i (t) is the output power of the i-th load resource at time t before adjustment; P i ′(t) represents the output power of the i-th load resource at time t after adjustment; EF i represents the carbon emission factor of the i-th type of load resource; δ i represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emissions with the adjustment rate; γ i It represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output;

[0107] By maximizing the total carbon reduction potential E accum , find the optimal P for all load resources i (t);

[0108] (4b) Initialize the population:

[0109] Definition of chromosome: In the genetic algorithm, a chromosome represents a candidate solution, and each chromosome contains the power output of each load resource in different time periods:

[0110] chromosome=[P1(t1),P2(t1),...,P N (t1),P1(t2),...,P N (t n )]

[0111] Where: P i (t j ) represents the i-th type of load resource at time t j Power output; t n It is the final time period;

[0112] Initialize the population: Generate multiple random chromosomes, each chromosome represents a different resource scheduling scheme, and the population size is between 50 and 200;

[0113] (4c) Construct a fitness function, which is used to evaluate the quality of each chromosome. The fitness function is calculated based on the carbon reduction potential objective function, and the formula is:

[0114] fitness(chromosome)=E accum

[0115] The fitness value is equal to the total carbon reduction potential E accum , the individuals, i.e., chromosomes, with higher fitness values ​​indicate that the carbon reduction effect of the regulation scheme is better;

[0116] (4d) Use the roulette wheel selection method to select individuals with higher fitness from the population for mating. Individuals with higher fitness refer to the individuals with the top 30% fitness values:

[0117]

[0118] Where: P select (i) is the probability of the i-th individual being selected; fitness(i) is the fitness value of the i-th individual, fitness(j) is the fitness value of the j-th individual, and N′ represents the number of individuals in the population;

[0119] (4e) Perform a crossover operation, using the single-point crossover method to generate new offspring by exchanging some genes of the chromosome:

[0120] Child1=[Parent1 part1 ,Parent2 part2 ]

[0121] In the formula, Child1 represents the new offspring chromosome generated by the crossover operation; Parent1 represents the first parent chromosome; Parent2 represents the second parent chromosome;

[0122] (4f) The genetic algorithm iterates until the fitness value increase is less than the threshold Δfitness min After the iteration, the genetic algorithm outputs the chromosome with the highest fitness value. The regulation strategy corresponding to the chromosome is the optimal load regulation scheme. The optimal load regulation scheme includes the optimal power regulation scheme and the maximum cumulative carbon reduction potential. The optimal power regulation scheme refers to the power output of each load resource at different times; the maximum cumulative carbon reduction potential refers to the E corresponding to the optimal chromosome. accum , that is, the maximized total carbon reduction potential.

[0123] In summary, the present invention can more accurately evaluate the carbon reduction potential of each type of load resource under different power regulation by dynamically calculating the instantaneous regulation rate and nonlinear power response of each load resource, and can capture the instantaneous response changes in the regulation process, thereby greatly improving the accuracy of the carbon reduction effect. Such a dynamic evaluation method is helpful to find the efficient regulation time of load resources, thereby maximizing the carbon reduction effect; the present invention introduces the regulation cost calculation in the carbon reduction potential assessment, combines the carbon reduction effect with the regulation cost, and evaluates the cost-effectiveness ratio of different schemes through the carbon reduction efficiency, so as to find the most economical regulation strategy; it can obtain higher carbon emission reduction effects at a lower economic cost, which meets the current needs of low-carbon economic development; the present invention adopts genetic algorithms to optimize load regulation strategies to maximize carbon reduction potential. The introduction of intelligent algorithms not only improves the accuracy of optimization, but also ensures the search for the global optimal solution, so that the regulation scheme can achieve the carbon reduction target with the best load response mode; in addition, the crossover and mutation operations of genetic algorithms can effectively avoid local optimal problems, so that the load regulation strategy can still maintain efficient carbon reduction effects under various complex conditions.

Claims

1. A method for evaluating the carbon reduction potential of distribution network load resources, characterized by: The method comprises the following steps in order: (1) Calculate the maximum regulation capacity P of all load resources in the distribution network max ; (2) According to the maximum regulation capacity P of all load resources in the distribution network max , calculate the total carbon emissions E of the distribution network before regulation at time t total (t), the total carbon emissions of the distribution network after adjustment at time t E t ' otal (t), according to the total carbon emissions E of the distribution network before regulation at time t total (t), the total carbon emissions of the distribution network after adjustment at time t E t ' otal (t), calculate the total change in carbon emissions ΔE total ; (3) According to the maximum regulation capacity P of all load resources in the distribution network max and the total change in carbon emissions ΔE total , calculate and evaluate the total carbon reduction potential E of the distribution network accum , and obtain the carbon reduction assessment model; (4) Solve the carbon reduction assessment model and use genetic algorithm optimization to obtain the optimal load regulation plan.

2. The method for evaluating the carbon reduction potential of distribution network load resources according to claim 1 is characterized in that: The step (1) specifically comprises the following steps in order: (1a) Calculate the instantaneous regulation rate of the i-th load resource at time t Where ΔP i (t) is the regulation amount of the i-th load resource at time t, τ i is the response time of the i-th load resource; η i is a weighting factor used to measure the inertia or response delay of the load resource; is the second-order derivative, indicating the acceleration of load power change; P i (t) is the output power of the i-th load resource at time t before adjustment; (1b) Calculate the cumulative regulation amount ΔP of the i-th load resource in the distribution network in the time period [t1, t2] i total : In the formula, β i Indicates the power change ratio, which is used to measure the change trend of load resource power over time; t1 indicates the start time, and t2 indicates the end time; (1c) Calculate the maximum regulation capacity P of all load resources in the distribution network max : Where N is the total number of load resource types in the distribution network; P i,max and P i,min are the maximum power and minimum power of the i-th type of load resource respectively; is the average regulation rate of the i-th resource in the time period, which is used to balance the regulation capabilities of resources with different loads.

3. The method for evaluating the carbon reduction potential of distribution network load resources according to claim 1 is characterized in that: The step (2) specifically refers to: calculating the carbon emission E of the i-th load resource in the distribution network at time t i (t): Where, EF i is the carbon emission factor of the i-th load resource, indicating the carbon emission per unit power; P i (t) is the output power of the i-th load resource at time t before adjustment; δ i It represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emission with the adjustment rate; represents the power regulation rate of the i-th type of load resource before regulation; γ i Represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output; α i represents the carbon emission fluctuation amplitude coefficient, indicating the impact of load fluctuation on carbon emissions; ω represents the fluctuation frequency, simulating the periodic carbon emission characteristics; φ represents the phase angle, which is used to adjust the time position of the periodic impact; Calculate the total carbon emissions E of the distribution network before regulation at time t total (t): In the formula, N represents the number of types of load resources; Calculate the total carbon emissions E of the distribution network after regulation at time t t ' otal (t): Where P i ′(t) represents the output power of the i-th load resource at time t after adjustment; represents the power regulation rate of the i-th type of load resource after regulation; Calculate the total change in carbon emissions ΔE total :

4. The method for evaluating the carbon reduction potential of distribution network load resources according to claim 1 is characterized in that: The step (3) specifically refers to: Calculation of instantaneous carbon reduction potential of a single load resource: Calculate the instantaneous carbon reduction potential ΔE of the i-th load resource at time t i (t): Where: P i (t) is the output power of the i-th load resource at time t before adjustment; P i ′(t) represents the output power of the i-th load resource at time t after adjustment; EF i represents the carbon emission factor of the i-th type of load resource; δ i represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emissions with the adjustment rate; γ i It represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output; and The term describes the dynamic response of carbon emissions during the regulation process, reflecting the impact of the regulation rate on the carbon reduction effect; γ i ·P i (t) 2 and γ i ·(P i ′(t)) 2 The term is used to represent the nonlinear carbon emission characteristics under high load conditions, capturing the additional carbon reduction potential brought by higher power regulation; Calculation of the cumulative carbon reduction potential of load resources: In the entire time interval [t1, t2], the cumulative carbon reduction potential ΔE of the i-th type of load resources accum Calculated by integration: In the formula, (P i (t)-P i ′(t))·EF i Indicates the carbon reduction effect caused directly by power regulation; represents the additional carbon reduction caused by the dynamic adjustment rate; γ i (P i (t) 2 -(P i ′(t)) 2 ) represents the nonlinear carbon reduction effect under high power regulation; Calculation of the total carbon reduction potential of the distribution network: The cumulative carbon reduction potential of all load resources is summed up to obtain the total carbon reduction potential E of the distribution network. accum Carbon reduction assessment model: Where N is the total number of types of load resources in the distribution network; Total carbon reduction potential of distribution network E accum The larger it is, the better the carbon reduction effect is.

5. The method for evaluating the carbon reduction potential of distribution network load resources according to claim 1 is characterized in that: The step (4) specifically comprises the following steps in order: (4a) Define the optimization objective function: The optimization objective function is to maximize the total carbon reduction potential E of the distribution network. accum : Where: N is the total number of load resource types in the distribution network; t1 represents the start time, t2 represents the end time; P i (t) is the output power of the i-th load resource at time t before adjustment; P i ′(t) represents the output power of the i-th load resource at time t after adjustment; EF i represents the carbon emission factor of the i-th type of load resource; δ i represents the instantaneous emission rate adjustment factor, reflecting the change of carbon emissions with the adjustment rate; γ i It represents the nonlinear adjustment coefficient, which is used to reflect the nonlinear relationship between carbon emissions and power output; By maximizing the total carbon reduction potential E accum , find the optimal P for all load resources i (t); (4b) Initialize the population: Definition of chromosome: In the genetic algorithm, a chromosome represents a candidate solution, and each chromosome contains the power output of each load resource in different time periods: chromosome=[P1(t1),P2(t1),...,P N (t1),P1(t2),...,P N (t n )] Where: P i (t j ) represents the i-th type of load resource at time t j Power output; t n It is the final time period; Initialize the population: Generate multiple random chromosomes, each chromosome represents a different resource scheduling scheme, and the population size is between 50 and 200; (4c) Construct a fitness function, which is used to evaluate the quality of each chromosome. The fitness function is calculated based on the carbon reduction potential objective function, and the formula is: fitness(chromosome)=E accum The fitness value is equal to the total carbon reduction potential E accum , the individuals, i.e., chromosomes, with higher fitness values ​​indicate that the carbon reduction effect of the regulation scheme is better; (4d) Use the roulette wheel selection method to select individuals with higher fitness from the population for mating. Individuals with higher fitness refer to the individuals with the top 30% fitness values: Where: P select (i) is the probability of the i-th individual being selected; fitness(i) is the fitness value of the i-th individual, fitness(j) is the fitness value of the j-th individual, and N′ represents the number of individuals in the population; (4e) Perform a crossover operation, using the single-point crossover method to generate new offspring by exchanging some genes of the chromosome: Child1=[Parent1 part1 ,Parent2 part2 ] In the formula, Child1 represents the new offspring chromosome generated by the crossover operation; Parent1 represents the first parent chromosome; Parent2 represents the second parent chromosome; (4f) The genetic algorithm iterates until the fitness value increase is less than the threshold Δfitness min After the iteration, the genetic algorithm outputs the chromosome with the highest fitness value. The regulation strategy corresponding to the chromosome is the optimal load regulation scheme. The optimal load regulation scheme includes the optimal power regulation scheme and the maximum cumulative carbon reduction potential. The optimal power regulation scheme refers to the power output of each load resource at different times; the maximum cumulative carbon reduction potential refers to the E corresponding to the optimal chromosome. accum , that is, the maximized total carbon reduction potential.

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