Economic load distribution method, system and medium for new energy power system

Abstract

The present invention discloses a method, system, and medium for economic load distribution of a new energy power system. The method includes: constructing an objective function of the new energy power system; initializing a population according to preset constraints; calculating the fitness value of each particle according to the objective function and the position of each particle; calculating the individual optimal value of each particle and the global optimal value of the population according to the fitness value of each particle; updating the speed and position of each particle in the population according to a particle swarm algorithm; performing selection, crossover, and mutation operations on the population using a genetic algorithm to obtain a new population, and updating the crossover probability and mutation probability; when an end condition is met, outputting the particle corresponding to the global optimal value, and using the economic load parameter of each generator set at the position of the particle as the optimal distribution result. The present invention is planned according to the actual situation of the new energy power system, is more adaptable to the actual situation of the new energy power system, and can achieve optimal economic load distribution.

Description

Technical Field

[0001] The present invention relates to an economic load distribution method for an electric power system, and in particular to an economic load distribution method, system and medium for a new energy electric power system. Background Art

[0002] Security Constrained Economic Dispatch (SCED) involves formulating multi-period unit generation plans with the goal of minimizing the system's electricity purchase cost, while satisfying power system security constraints. The goal is to minimize power generation costs while meeting load and operational constraints, which is crucial for improving the economics and reliability of system operations. However, the valve point effect of thermal power units results in nonlinear and nondifferentiable consumption characteristics. Furthermore, operational constraints such as transmission capacity and system stability make the feasible domain of the problem nonconvex. Therefore, SCED is inherently a nonconvex, high-dimensional, nonlinear, and nondifferentiable optimization problem.

[0003] The particle swarm optimization (PSO) algorithm is simple to use and relies on few empirical parameters. It has been successfully applied to solving multidimensional nonlinear function optimization, neural network training, integer optimization, min-max problems, and a wide range of industrial optimization problems. In power systems, PSO has been used to solve problems such as reactive power optimization, dynamic safety boundary identification, distribution system state estimation, and optimal compensation capacitor configuration. However, standard PSO algorithms are prone to getting stuck in local optimal solutions, resulting in significant discrepancies between calculated results and actual results.

[0004] Against the backdrop of large-scale development of renewable energy generation, the connotations, objectives, models, and methods of grid planning will undergo significant changes. Different grid structures directly impact the system's ability to accommodate renewable energy. Furthermore, the grid structure influences the operation of other flexible power sources, indirectly impacting the system's ability to accommodate renewable energy. Therefore, achieving a reasonable compromise between renewable energy absorption capacity and system investment and operating costs is a key research question in renewable energy grid planning. Summary of the Invention

[0005] The technical problem to be solved by the present invention is as follows: In view of the above-mentioned problems of the prior art, a method, system and medium for economic load distribution of a new energy power system are provided, which are planned according to the actual situation of the new energy power system, and the optimal solution conforms to the actual situation, so as to achieve the optimal economic load distribution.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for economic load distribution of a new energy power system comprises the following steps:

[0008] S1) Constructing the objective function of the new energy power system;

[0009] S2) Initializing the population according to preset constraints, setting the position and velocity of each particle in the population, where the position of the particle is the economic load parameter of each generator set;

[0010] S3) calculating the fitness value of each particle according to the objective function and the position of each particle;

[0011] S4) calculating the individual optimal value of each particle and the global optimal value of the population according to the fitness value of each particle;

[0012] S5) updating the speed and position of each particle in the swarm according to the particle swarm algorithm;

[0013] S6) performing selection, crossover, and mutation operations of a genetic algorithm on the population to obtain a new population, and updating the crossover probability and mutation probability;

[0014] S7) Return to step S3) until the end condition is met, output the particle corresponding to the global optimal value, and use the economic load parameter of each generator set at the position of the particle as the optimal allocation result.

[0015] Furthermore, the objective function of the new energy power system in step S1) is:

[0016]

[0017] In the above formula, j is the serial number of a single unit, Y j t is a binary variable, indicating the startup status of the jth unit at time t. 1 indicates that the unit is starting, and 0 indicates that the unit is not in the startup state. is a binary variable, indicating the shutdown status of the jth unit at time t, 1 indicates that the unit is shutting down, and 0 indicates that the unit is not shutting down. j t is the output of the jth unit at time t, N j To optimize the number of units of each type, T is the preset time length, α j is the startup coal consumption of the jth unit, β j is the shutdown coal consumption of the jth unit, a j is the slope of coal consumption of a single unit changing with power, b j is the coal consumption constant of a single unit, and γ is the carbon dioxide emission coefficient.

[0018] Furthermore, the position expression of each particle in the particle swarm in step S2) is:

[0019] X i =(x1, x2, ..., x Nj )

[0020] In the above formula, i is the particle number, N j is the total number of generators in the system, x1, x2, ..., x Nj For the 1st to Nth j The economic load parameters of the unit include the start and stop conditions and output conditions of the current unit at each moment within time T.

[0021] Furthermore, in step S6), the step of performing the selection, crossover, and mutation operations of the genetic algorithm on the population to obtain a new population specifically includes:

[0022] The particle corresponding to the global optimal value is used as the parent generation, and m particles in the population are selected as the parent generation;

[0023] At the crossover probability P c Next, cross each parent generation with the mother generation to obtain m offspring, so that the offspring contains some elements of both the parent generation and the mother generation;

[0024] In the mutation probability P m Next, the offspring particles are mutated to obtain new offspring particles;

[0025] Particles in the original population are selected in descending order of fitness value, and the selected particles and m offspring particles are combined into a new population, so that the number of particles in the new population is the same as that in the original population.

[0026] Furthermore, the mutation of the offspring particle to obtain a new offspring particle specifically includes: for the current offspring particle, randomly selecting a unit, and changing the start state of the economic load parameter of the selected unit at a random moment within time T to a stop state.

[0027] Furthermore, the step of updating the crossover probability and the mutation probability in step S6) specifically includes: calculating the crossover probability P in the next iteration according to the preset cooling coefficient c ′ and mutation probability P m ′, the function expression is as follows:

[0028]

[0029] In the above formula, λ represents the cooling coefficient, P c is the crossover probability, P m is the mutation probability.

[0030] Furthermore, the termination condition in step S7) is that the maximum number of iterations is reached, or the difference between the global optimal values ​​of five consecutive iterations is less than a preset value.

[0031] The present invention also proposes an economic load distribution system for a new energy power system, comprising:

[0032] An objective function construction program unit is used to construct an objective function of a new energy power system;

[0033] An initialization program unit is used to initialize the population according to preset constraints and set the position and speed of each particle in the population, where the position of the particle is the economic load parameter of each generator set;

[0034] A fitness value calculation program unit is used to calculate the fitness value of each particle according to the objective function and the position of each particle;

[0035] An optimal value calculation program unit is used to calculate the individual optimal value of each particle and the global optimal value of the population according to the fitness value of each particle;

[0036] A particle updater unit, used to update the velocity and position of each particle in the swarm according to the particle swarm algorithm;

[0037] The population update program unit is used to perform genetic algorithm selection, crossover, and mutation operations on the population to obtain a new population, and to update the crossover probability and mutation probability;

[0038] The iterative calculation program unit is used to control the fitness value calculation program unit, the optimal value calculation program unit, the particle update program unit and the population update program unit to perform iterative calculations until the end condition is met, output the particle corresponding to the global optimal value, and use the economic load parameter of each generator set at the position of the particle as the optimal allocation result.

[0039] The present invention also proposes an economic load distribution system for a new energy power system, comprising a computer device, wherein the computer device includes at least a microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the steps of the economic load distribution method for the new energy power system, and the memory stores a computer program programmed or configured to execute the economic load distribution method for the new energy power system.

[0040] The present invention also provides a computer-readable storage medium, in which a computer program programmed or configured to execute the economic load distribution method for the new energy power system is stored.

[0041] Compared with the prior art, the present invention has the following advantages:

[0042] 1. The present invention adopts a genetic algorithm to update the population for the next iteration in each iteration, thus avoiding the dilemma of falling into local optimality when using the standard particle swarm algorithm for optimization. It breaks through the limitation of candidate results and is more conducive to finding the global optimal solution. At the same time, an annealing algorithm is used to calculate the crossover probability value and mutation probability value required by the genetic algorithm in the next iteration, so that the crossover probability value and mutation probability value gradually decrease with the increase of the number of iterations, thereby converging to the optimal solution. This avoids the problem that the crossover probability and mutation probability remain unchanged, which may destroy the particle structure with high fitness and tend to random search, and ultimately reduces the error between the optimal result and the actual situation.

[0043] 2. The present invention constructs an objective function based on the actual situation of the new energy power system. Since the composition of the generator sets in the new energy power system is complex and includes not only conventional units but also new energy units, the existing model cannot be applied to the new energy power system. The objective function constructed by the present invention fully considers the working status of different types of units at different times, reduces the carbon dioxide emissions of the new energy power system while maintaining output, and is more adapted to the actual situation of the new energy power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Schematic diagram of a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the scope of protection of the present invention is not limited thereby.

[0046] The algorithm flow of standard PSO is as follows:

[0047] Step 1: Initialize a group of particles (group size is N), including random positions and velocities;

[0048] Step 2: Evaluate the fitness of each particle;

[0049] Step 3: For each particle, compare its fitness value with the best position pbest it has ever experienced. If it is better, take it as the current best position pbest;

[0050] Step 4: For each particle, compare its fitness value with the best position gbest experienced globally. If it is better, reset gbest.

[0051] Step 5: Change the velocity and position of the particle according to the following equations:

[0052] V id =w×V id +c1×rand()×(pid -x id )+c2×Rand()×(p gd -x id ) (1)

[0053] x id =x id +V id (2)

[0054] Where: rand() and rand() are two random functions that vary in the range [0, 1], w is the inertia weight, c1 and c2 are acceleration constants;

[0055] Step 6: If the end condition is not met (usually a sufficiently good fitness value or reaching a preset maximum number of generations T), return to Step 2.

[0056] PSO parameters include: swarm size N, inertia weight w, acceleration constants c1 and c2, maximum speed Vmax, and maximum number of iterations T. The maximum speed Vmax determines the resolution (or accuracy) of the region between the current position and the best solution. If it is too high, particles may fly over good solutions; if it is too low, the search speed is slow. The PSO algorithm uses three weighting factors: the inertia weight w, and the acceleration constants c1 and c2. The inertia weight w maintains the particles' inertia, giving them a tendency to expand the search space and explore new areas. The acceleration constants c1 and c2 represent the weights of the statistical acceleration term that pushes each particle toward the pbest and gbest positions. Introducing the inertia weight w eliminates the need for Vmax, as both serve to maintain a balance between global and local search capabilities. Thus, as Vmax increases, a balanced search can be achieved by reducing w. Reducing w reduces the number of required iterations. In this sense, Vmax can be fixed within the range of each dimension, with only w being adjusted.

[0057] In order to avoid the problem of standard PSO falling into local optimality, we improved the standard PSO and added the following steps in Step 6:

[0058] First, a new population is obtained through the selection, crossover, and mutation operations of the genetic algorithm;

[0059] The selection operation of the genetic algorithm is generally completed through the roulette method. According to the fitness of each particle in the original population, the probability of each particle being selected is calculated, and the set of selected particles is used as the parent generation, and the global optimal solution of PSO is used as the parent generation;

[0060] The crossover operation of the genetic algorithm refers to exchanging chromosomes between the parent generation and the parent generation at the crossover probability, thereby obtaining offspring with some chromosomes of the parent generation and the parent generation;

[0061] The mutation operation of the genetic algorithm refers to replacing one or more values ​​in the offspring with random numbers according to the mutation probability.

[0062] Add the particle with the highest fitness in the original group to the new group so that the number of particles in the new group is the same as that in the original group;

[0063] Then, the crossover probability value and mutation probability value at the next iteration are calculated through the annealing algorithm.

[0064] By adding the genetic algorithm, we can help us escape the dilemma of falling into local optimality when using the standard PSO, break through the restrictions on candidate results in the particle swarm, and be more conducive to finding the global optimal solution. At the same time, the annealing algorithm is used to calculate the crossover probability value and mutation probability value in the genetic algorithm, which can gradually slow down the search speed for the optimal solution as the number of iterations increases, and finally converge to the optimal solution, thereby ensuring the accuracy of the optimal solution and reducing the error between the calculation results and the actual situation.

[0065] like Figure 1 As shown, this embodiment proposes an economic load distribution method for a new energy power system, comprising the following steps:

[0066] S1) Constructing the objective function of the new energy power system;

[0067] S2) Initializing the population according to preset constraints, setting the position and velocity of each particle in the population, where the position of the particle is the economic load parameter of each generator set;

[0068] S3) calculating the fitness value of each particle according to the objective function and the position of each particle;

[0069] S4) calculating the individual optimal value of each particle and the global optimal value of the population according to the fitness value of each particle;

[0070] S5) updating the speed and position of each particle in the swarm according to the particle swarm algorithm;

[0071] S6) performing selection, crossover, and mutation operations of a genetic algorithm on the population to obtain a new population, and updating the crossover probability and mutation probability;

[0072] S7) Return to step S3) until the end condition is met, output the particle corresponding to the global optimal value, and use the economic load parameter of each generator set at the location of the particle as the optimal allocation result. Use the economic load parameter of each generator set at the location of the particle as the optimal allocation result.

[0073] In step S1) of this embodiment, in order to accept as much wind and solar power as possible, reduce system carbon dioxide emissions, and improve the energy conservation and emission reduction benefits of the power grid, the objective function is:

[0074]

[0075] In the above formula, j is the serial number of a single unit, Y j t is a binary variable, indicating the startup status of the jth unit at time t. 1 indicates that the unit is starting, and 0 indicates that the unit is not in the startup state. is a binary variable, indicating the shutdown status of the jth unit at time t, 1 indicates that the unit is shutting down, and 0 indicates that the unit is not shutting down. j t is the output of the jth unit at time t, and P j t are all independent variables, N j To optimize the number of units of each type, T is the preset time length, α j is the startup coal consumption of the jth unit, β j is the shutdown coal consumption of the jth unit, a j is the slope of coal consumption of a single unit changing with power, b j is the coal consumption constant of a single unit, γ is the carbon dioxide emission coefficient, where N j , T, α j , β j 、a j 、b j , γ and other parameters are known constants.

[0076] In step S2 of this embodiment, the population is initialized according to the preset constraints, so that each particle in the population contains the economic load parameters of all generator sets in the new energy power system, and when two particles are compared, if there is a difference in the economic load parameters of at least one generator set, let X i =(x1, x2, ..., x Nj ) is the position of the i-th particle in the particle swarm, where N j is the total number of generators in the system, including conventional generators as well as new energy generators such as wind and solar power, x1, x2, ..., x Nj For the 1st to Nth j The economic load parameters of the units, including the start and stop conditions, output conditions, etc. of the i-th unit at each moment within the time T, can be obtained by the economic load parameters and unit types of all units in the i-th particle. The startup status of the j-th unit at time t The jth unit is shut down at time t And the output P of the jth unit at time t j t At the same time, in step S2 of this embodiment, the inertia weight w, acceleration constants c1 and c2, maximum speed Vmax, and maximum number of iterations T are also initialized.

[0077] Different constraint functions can be set based on actual conditions. In this embodiment, the constraints include: the proportion of renewable energy generators is greater than 80%, the output of the renewable energy power system is no less than 1000MW, the proportion of units shut down at any given moment is less than 20%, and the operating duty cycle of each unit is greater than 80%. By using these constraints, it is expected that the proportion of renewable energy generators in the renewable energy power system will be increased while maintaining output, and the downtime of each unit will be reduced, thereby improving power generation efficiency and reducing carbon dioxide emissions.

[0078] In step S3 of this embodiment, the function expression for calculating the fitness value of each particle is as follows:

[0079] J=minF(Xi) (4)

[0080] The fitness value of each particle is the result obtained by substituting the economic load parameters of all generators in the particle into formula (3), which is the carbon dioxide emissions of the new energy power system. The subsequent steps update the optimization direction according to the size of the fitness value.

[0081] Determining the individual optimal value and the global optimal value in step S4) of this embodiment is basically the same as that in the existing solution, and will not be described in detail here.

[0082] In step S5) of this embodiment, the speed of the next iteration can be calculated according to formula (1) based on the current economic load parameter of each particle, the individual optimal value and the global optimal value. The economic load parameter of the next iteration can be calculated according to formula (2) based on the current economic load parameter of each particle and the speed of the next iteration. These are also existing technical solutions and will not be described in detail here.

[0083] In step S6 of this embodiment, the following steps are included:

[0084] The particle corresponding to the global optimal value is used as the parent generation. Through the selection operation in the genetic algorithm, m particles in the population are screened out as the parent generation. The selection operation specifically includes roulette selection method, random competition selection method, best retention selection method, etc. These are all conventional methods in the genetic algorithm and will not be described in detail here.

[0085] Through the crossover operation in the genetic algorithm, the crossover probability P cNext, cross each parent generation with the mother generation to obtain m offspring, so that the offspring contains some elements of both the parent generation and the mother generation. For example, let X' i =(x'1, x'2, ..., x' Nj ) is the parent particle, X” i =(x”1,x”2,…,x” Nj ) is the parent particle, and the two have a crossover probability P c The descendant particles (x'1, x'2, ..., x' Nj ) includes both the economic load parameters of some generators in the parent generation particles and the economic load parameters of some generators in the mother generation particles. Compared with the simple particle swarm algorithm, the crossover operation opens up a new search area for finding the optimal solution. The crossover operation is a conventional method in the genetic algorithm and will not be described in detail here.

[0086] For m offspring particles, we use the mutation operation in the genetic algorithm to generate m Under this condition, the offspring particles are mutated with a certain probability to obtain new offspring particles. In this embodiment, the mutation method is specifically as follows: for the current offspring particles, the start and stop status of the economic load parameter of the i-th unit within the time T is randomly changed, and the start state at some moments is randomly changed to the stop state. The mutation operation avoids the situation where the crossover operation falls into the local optimal solution due to the lack of key elements for constructing the optimal solution. The mutation operation is a conventional method in the genetic algorithm and will not be described in detail here.

[0087] Select particles from the original population in descending order of fitness, and form a new population with the selected particles and m offspring particles, so that the number of particles in the new population is the same as that in the original population;

[0088] Calculate the crossover probability P in the next iteration c ′ and mutation probability P m ', in this embodiment, the annealing algorithm is used to calculate the crossover probability P in the next iteration according to the preset cooling coefficient c ′ and mutation probability P m ′, the function expression is as follows:

[0089]

[0090] In the above formula, λ represents the cooling coefficient, which ranges from 0 to 1, and the crossover probability P c The value range is 0.4~0.99, and the mutation probability P m The value range is 0.001~0.1. Through this step, the crossover probability P in each iteration is gradually reduced. c and mutation probability P m, so that the calculation results of each iteration gradually converge to the optimal value, avoiding the crossover probability P c and mutation probability P m If the number of particles remains unchanged, it may destroy the particle structure with high fitness and tend to the problem of random search.

[0091] In this embodiment, λ is 0.5, P c The initial value is 1, P m The initial value is 0.1. If the number of iterations is not reached and the crossover probability P in the next iteration is c and mutation probability P m is less than the lower limit of their respective value ranges, using the crossover probability P c and mutation probability P m The lower limit of each value range is used as the crossover probability P in the next iteration c and mutation probability P m .

[0092] Finally, in step S7 of this embodiment, after the preset termination condition is met, the termination condition in this embodiment is that the maximum number of iterations is reached, or the difference between the global optimal values ​​of 5 consecutive iterations is less than the preset value, and the particle corresponding to the global optimal value is output. The operation of each unit of the new energy power system is configured according to the start and stop conditions of the conventional power generation unit and the new energy power generation unit in the particle at each moment within time T. This can reduce the carbon dioxide emissions of the new energy power system while maintaining the output power, thereby improving the energy conservation and emission reduction benefits of the power grid.

[0093] In addition, this embodiment also provides an economic load distribution system for a new energy power system, including:

[0094] An objective function construction program unit is used to construct an objective function of a new energy power system;

[0095] An initialization program unit is used to initialize the population according to preset constraints and set the position and speed of each particle in the population, where the position of the particle is the economic load parameter of each generator set;

[0096] A fitness value calculation program unit is used to calculate the fitness value of each particle according to the objective function and the position of each particle;

[0097] An optimal value calculation program unit is used to calculate the individual optimal value of each particle and the global optimal value of the population according to the fitness value of each particle;

[0098] A particle updater unit, used to update the velocity and position of each particle in the swarm according to the particle swarm algorithm;

[0099] The population update program unit is used to perform genetic algorithm selection, crossover, and mutation operations on the population to obtain a new population, and to update the crossover probability and mutation probability;

[0100] The iterative calculation program unit is used to control the fitness value calculation program unit, the optimal value calculation program unit, the particle update program unit and the population update program unit to perform iterative calculations until the end condition is met, output the particle corresponding to the global optimal value, and use the economic load parameter of each generator set at the position of the particle as the optimal allocation result.

[0101] In addition, this embodiment further provides an economic load dispatch system for a new energy power system, comprising a computer device including at least a microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the steps of the economic load dispatch method for the new energy power system. In this embodiment, the memory stores a computer program programmed or configured to execute the economic load dispatch method for the new energy power system.

[0102] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute the economic load distribution method of the new energy power system.

[0103] It should be understood by those skilled in the art that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is based on the flowcharts of the methods, devices (systems), and computer program products according to the embodiments of the present application and / or the instructions executed by the processor to generate instructions for implementing the flow charts. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0104] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed above with reference to the preferred embodiment, it is not intended to limit the present invention. Therefore, any simple modifications, equivalent variations, and modifications to the above embodiment that do not depart from the technical solution of the present invention and are based on the technical essence of the present invention shall fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for economic load distribution of a new energy power system, characterized in that: The following steps are involved: S1) Construct the objective function of the new energy power system; S2) Initializing the population according to preset constraints, setting the position and velocity of each particle in the population, where the position of the particle is the economic load parameter of each generator set; S3) calculating the fitness value of each particle according to the objective function and the position of each particle; S4) calculating the individual optimal value of each particle and the global optimal value of the population according to the fitness value of each particle; S5) updating the speed and position of each particle in the swarm according to the particle swarm algorithm; S6) performing selection, crossover, and mutation operations on the population using a genetic algorithm to obtain a new population, and updating the crossover probability and mutation probability. If the updated crossover probability or mutation probability is less than the lower limit of the respective value range, the corresponding lower limit of the value range is used as the updated crossover probability or mutation probability. The steps of updating the crossover probability and mutation probability specifically include: calculating the crossover probability P in the next iteration according to the preset cooling coefficient. c ′ and mutation probability P m ′, the function expression is as follows: In the above formula, λ represents the cooling coefficient, P c is the crossover probability, P m is the mutation probability; S7) Return to step S3) until the end condition is met, output the particle corresponding to the global optimal value, and use the economic load parameter of each generator set at the position of the particle as the optimal allocation result.

2. The economic load distribution method of the new energy power system according to claim 1 is characterized in that: The objective function of the new energy power system in step S1) is: In the above formula, j is the serial number of a single unit, is a binary variable, indicating the startup status of the jth unit at time t. 1 indicates that the unit is starting, and 0 indicates that the unit is not in the startup state. is a binary variable, indicating the shutdown status of the jth unit at time t. 1 indicates that the unit is shutting down, and 0 indicates that the unit is not shutting down. is the output of the jth unit at time t, N j To optimize the number of units of each type, T is the preset time length, α j is the startup coal consumption of the jth unit, β j is the shutdown coal consumption of the jth unit, a j is the slope of coal consumption of a single unit changing with power, b j is the coal consumption constant of a single unit, and γ is the carbon dioxide emission coefficient.

3. The economic load distribution method of the new energy power system according to claim 1, characterized in that: The position expression of each particle in the particle swarm in step S2) is: X i =(x1,x2,…,x Nj ) In the above formula, i is the particle number, N j is the total number of generators in the system, x1, x2, ..., x Nj For the 1st to Nth j The economic load parameters of the unit include the start and stop conditions and output conditions of the current unit at each moment within time T.

4. The economic load distribution method of the new energy power system according to claim 1, characterized in that: In step S6), the steps of performing genetic algorithm selection, crossover, and mutation operations on the population to obtain a new population specifically include: The particle corresponding to the global optimal value is used as the parent generation, and m particles in the population are selected as the parent generation; At the crossover probability P c Next, cross each parent generation with the mother generation to obtain m offspring, so that the offspring contains some elements of both the parent generation and the mother generation; In the mutation probability P m Next, the offspring particles are mutated to obtain new offspring particles; Particles in the original population are selected in descending order of fitness value, and the selected particles and m offspring particles are combined into a new population, so that the number of particles in the new population is the same as that in the original population.

5. The economic load distribution method of the new energy power system according to claim 4 is characterized in that: The method of obtaining a new offspring particle by mutation of the offspring particle specifically includes: for the current offspring particle, randomly selecting a unit, and changing the start state of the economic load parameter of the selected unit at a random moment within time T to the stop state.

6. The economic load distribution method of the new energy power system according to claim 1, characterized in that: The termination condition in step S7) is that the maximum number of iterations is reached, or the difference between the global optimal values ​​of 5 consecutive iterations is less than a preset value.

7. An economic load distribution system for a new energy power system, characterized in that: include: An objective function construction program unit is used to construct an objective function of a new energy power system; An initialization program unit is used to initialize the population according to preset constraints and set the position and speed of each particle in the population, where the position of the particle is the economic load parameter of each generator set; A fitness value calculation program unit is used to calculate the fitness value of each particle according to the objective function and the position of each particle; An optimal value calculation program unit is used to calculate the individual optimal value of each particle and the global optimal value of the population according to the fitness value of each particle; A particle updater unit, used to update the velocity and position of each particle in the swarm according to the particle swarm algorithm; The population update program unit is used to perform genetic algorithm selection, crossover, and mutation operations on the population to obtain a new population, and update the crossover probability and mutation probability. If the updated crossover probability or mutation probability is less than the lower limit of the respective value range, the corresponding lower limit of the value range is used as the value of the updated crossover probability or mutation probability. The steps of updating the crossover probability and mutation probability specifically include: calculating the crossover probability P in the next iteration according to the preset cooling coefficient c ′ and mutation probability P m ′, the function expression is as follows: In the above formula, λ represents the cooling coefficient, P c is the crossover probability, P m is the mutation probability; The iterative calculation program unit is used to control the fitness value calculation program unit, the optimal value calculation program unit, the particle update program unit and the population update program unit to perform iterative calculations until the end condition is met, output the particle corresponding to the global optimal value, and use the economic load parameter of each generator set at the position of the particle as the optimal allocation result.

8. An economic load distribution system for a new energy power system, comprising a computer device, wherein the computer device comprises at least a microprocessor and a memory, characterized in that: The microprocessor is programmed or configured to execute the steps of the economic load distribution method for the new energy power system described in any one of claims 1 to 6, and the memory stores a computer program programmed or configured to execute the economic load distribution method for the new energy power system described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program that is programmed or configured to execute the economic load dispatching method for the new energy power system according to any one of claims 1 to 6.

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