Power system scheduling method and device combined with new energy, and terminal equipment

Abstract

The invention discloses a new energy combined power system scheduling method, a new energy combined power system scheduling device and terminal equipment, and belongs to the technical field of power system scheduling. A first function model with the minimum thermal power output cost as the target, a second function model with the minimum pollution emission as the target and a third function model with the maximum new energy utilization rate as the target are established, and different weight coefficients are given to the first function model, the second function model and the third function model; obtaining a comprehensive target function model, solving the target function model to generate a target scheduling decision, and finally scheduling the power system according to the target scheduling decision. According to the method, the cost, the pollutant emission and the new energy utilization rate can be optimized, multi-target balance is achieved, the power generation cost and the pollutant emission are reduced, the new energy utilization rate is improved, and efficient utilization of new energy in a power system is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a power system dispatching method, device and terminal equipment combined with new energy. Background Art

[0002] In the power system dispatch, the joint dispatch of new energy is the key to improving the comprehensive benefits of the power system. In order to achieve the access and efficient use of new energy, it is necessary not only to regulate the operation of thermal power units, but also to access and dispatch new energy, so as to achieve a comprehensive balance of multiple goals, improve the utilization rate of new energy in the power system, and ensure the stable operation and power supply quality of the power system.

[0003] In the traditional power system dispatching method, power generation mainly relies on conventional energy such as thermal power, and focuses on meeting the basic needs of power load. Therefore, since only thermal power is considered, the low-cost advantage of new energy is not fully utilized, which will lead to higher overall power generation costs; and the advantages of new energy and clean power generation are not taken into consideration. Insufficient control of pollution emissions will increase the environmental burden and make it impossible to effectively utilize the abundant resources of new energy, resulting in waste of resources.

[0004] In the traditional power system dispatching method, not only can it not combine thermal power and new energy to dispatch the power grid, but it also lacks a comprehensive balance of multiple objectives. It is impossible to achieve efficient use of new energy in the power system, it is difficult to reduce the overall power generation cost, and it is impossible to effectively control pollution emissions, so it is impossible to provide high-quality power supply quality. Summary of the invention

[0005] The embodiments of the present invention provide a method, apparatus and terminal equipment for dispatching an electric power system in conjunction with new energy sources, which comprehensively consider the power generation conditions of thermal power and new energy sources, optimize multiple aspects such as cost, pollution emissions and new energy utilization rate, and achieve a balance of multiple objectives. It can effectively solve the problems in the prior art that it is impossible to achieve efficient utilization of new energy in the electric power system, it is difficult to reduce the overall power generation cost, and it is also difficult to effectively control pollution emissions.

[0006] An embodiment of the present invention provides a method for dispatching a power system in conjunction with new energy sources, wherein a first function model is constructed based on thermal power generation data and thermal power generation cost data with the goal of minimizing thermal power output cost; a second function model is constructed based on new energy generation data, new energy pollution emission data and thermal power pollution emission data with the goal of minimizing pollution emission; a third function model is constructed based on new energy generation data and preset power generation data with the goal of maximizing new energy utilization rate; wherein the new energy generation data includes: photovoltaic output data and wind power output data; Assigning different weight coefficients to the first function model, the second function model and the third function model to generate an objective function model with the goals of minimizing thermal power output cost, minimizing pollution emissions and maximizing new energy utilization rate; wherein the constraints corresponding to the objective function include: unit output constraints, power balance constraints and spare capacity constraints; Under the constraints of the unit output constraint, the power balance constraint and the reserve capacity constraint, the objective function model is solved to generate a target scheduling decision corresponding to the objective function model; wherein the target scheduling decision includes: the output of the thermal power unit, the output of the wind power unit and the output of the photovoltaic unit; According to the target dispatching decision, the output of thermal power units, wind power units and photovoltaic units in the power system is dispatched.

[0007] Preferably, solving the objective function model under the constraints of the unit output constraint, the power balance constraint and the spare capacity constraint to generate a target scheduling decision corresponding to the objective function model includes: An initial population including a number of different initial individuals is randomly generated; wherein each initial individual includes: the output of a thermal power unit, the output of a wind power unit, the output of a photovoltaic unit, and a number of initial weight coefficients; Repeat the following population update operation until the current number of iterations is the same as the preset number of iterations, and output the target scheduling decision corresponding to the individual with the highest function value in the current population, and the target scheduling decision satisfies the unit output constraint, power balance constraint, and spare capacity constraint: When the current number of iterations is less than the preset number of iterations, a number of current individuals randomly selected from the current population are subjected to crossover and mutation operations to generate updated individuals; wherein, initially, the initial population is used as the current population; Each current weight coefficient of each current individual in the current population is updated one by one, and a new individual derived from the current individual is generated during each update; wherein, initially, the initial weight coefficient is used as the current weight coefficient; Generate an updated population based on all current individuals and the newly added individuals corresponding to each current individual; According to the objective function model, the objective function value corresponding to each individual in the population after an update and the objective function value corresponding to the updated individual are calculated; the objective function value corresponding to each individual in the population after an update is compared with the objective function value of the updated individual, and the individual whose objective function value is greater than the objective function value of the updated individual is taken as the individual to be merged; Perform constraint verification on the updated individuals in several preset power grid operation scenarios to determine whether the updated individuals simultaneously meet the unit output constraints, power balance constraints and spare capacity constraints in all preset power grid operation scenarios. If so, generate a second updated population based on the updated individuals and each individual to be merged; otherwise, generate a second updated population based on each individual to be merged; Calculate the probability deviation of each individual in the population after the second update and all the historical output probability distribution scenarios, and then determine whether the probability deviation calculation result satisfies the first probability deviation constraint and the second probability deviation constraint at the same time. If so, use the population after the second update as the current population when the population update operation is performed next time; if not, randomly adjust the individuals in the population after the second update, and use the randomly adjusted population as the current population when the population update operation is performed next time; Add the current iteration count value to the preset iteration count increment; Among them, the first probability deviation constraint is used to characterize the probability distribution deviation degree of all historical output probability distribution scenarios; the second probability deviation constraint is used to characterize the probability distribution deviation degree of a single historical output probability distribution scenario.

[0008] Preferably, performing a crossover operation and a mutation operation on a number of current individuals randomly selected from the current population to generate updated individuals includes: Randomly select a number of current individuals from the current population; After performing crossover and mutation operations on each current individual, a mutated individual is generated; The sum of the outputs of the thermal power units, wind power units and photovoltaic units in the mutated individuals is compared with the preset total operating load of the power system; When it is determined that the total output is not less than the preset total operating load, the output of the thermal power unit is reduced according to the difference between the total output and the preset total operating load, and the reduced output of the thermal power unit is generated; and an updated individual is generated according to the reduced output of the thermal power unit and the unupdated data in the mutated individual; When it is determined that the total output is less than the preset total operating load, the mutated individual is regarded as an updated individual.

[0009] Preferably, each current weight coefficient of each current individual in the current population is updated one by one, and a new individual derived from the current individual is generated during each update, including: For each current weight coefficient of each current individual in the current population, taking the current weight coefficient as the center and according to the preset value range, a coefficient value range corresponding to the current weight coefficient is generated; Extracting a number of coefficients whose similarity with the current weight coefficient is greater than a preset similarity threshold from the coefficient value range as the current adjacent weight coefficient corresponding to the current weight coefficient; The current weight coefficients of the current individual are replaced one by one according to the current adjacent weight coefficients, and a new individual derived from the current individual is generated during each replacement.

[0010] Preferably, the generation of the preset power grid operation scenario includes: Under the constraints of the first probability deviation constraint and the second probability deviation constraint, several different new energy output scenarios are extracted from the operating scenarios of the power system; wherein the new energy output scenario is used to characterize the scenario where the wind power output or photovoltaic output is in an unstable state.

[0011] Each renewable energy output scenario is used as a preset grid operation scenario for constraint verification.

[0012] Preferably, the probability deviation calculation is performed on each individual in the second updated population and all historical output probability distribution scenarios, and then it is determined whether the probability deviation calculation result satisfies both the first probability deviation constraint and the second probability deviation constraint. If so, the second updated population is used as the current population when the population update operation is performed next time; if not, the individuals in the second updated population are randomly adjusted, and the randomly adjusted population is used as the current population when the population update operation is performed next time, including: Extracting wind power output probability distribution and photovoltaic output probability distribution from each historical output probability distribution scenario; wherein the wind power output probability distribution includes: different wind power output intervals correspond to different wind power output probability values, and different photovoltaic output intervals correspond to different photovoltaic output probability values; According to the wind power output interval corresponding to the output of the wind turbine group in the updated individual in the wind power output probability distribution, a target wind power output probability value corresponding to the updated individual is determined; According to the photovoltaic output interval corresponding to the output of the photovoltaic unit in the updated individual in the photovoltaic output probability distribution, the target photovoltaic output probability value corresponding to the updated individual is determined; When it is determined that both the target wind power output probability value and the target photovoltaic output probability value are not less than the first probability deviation constraint, and both the target wind power output probability value and the target photovoltaic output probability value are not greater than the second probability deviation constraint, the second updated population is used as the current population when the population update operation is performed next time; When it is determined that the target wind power output probability value is less than the first probability deviation constraint or the target photovoltaic output probability value is less than the first probability deviation constraint, the individuals in the population after the second update are randomly adjusted, and the randomly adjusted population is used as the current population when the population update operation is performed next time; When it is determined that the target wind power output probability value is greater than the second probability deviation constraint or the target photovoltaic output probability value is greater than the second probability deviation constraint, the individuals in the population after the second update are randomly adjusted, and the randomly adjusted population is used as the current population when the next population update operation is performed.

[0013] Preferably, the objective function model includes: ; ; In the formula, It means taking the maximum value among the three absolute value items; It means minimizing the maximum value of three absolute value terms; Indicates thermal power output cost and cost reference value Deviation value of Indicates the pollution emission and the reference value of pollution emission Deviation value of Represents the new energy utilization rate and the ideal value of new energy utilization rate Deviation value of It is the first function model with the goal of minimizing the thermal power output cost. is the second function model with the goal of minimizing pollution emissions. It is the third function model with the goal of maximizing the utilization rate of new energy. is the weight coefficient corresponding to the first function model, is the weight coefficient corresponding to the second function model, is the weight coefficient corresponding to the third function model; For the The output of thermal power units, is the number of thermal power units, For the The quadratic cost coefficient of each thermal power unit is: For the The linear cost coefficient of each thermal power unit is: For the The fixed cost of a thermal power unit is For the The output of a wind turbine, is the number of wind turbines, is the output cost coefficient of the wind turbine, For the The output of each photovoltaic unit, is the number of photovoltaic units, is the output cost coefficient of the photovoltaic unit; , Respectively Different pollution emission coefficients of thermal power units, is the pollution emission factor of the wind turbine, is the pollution emission factor of the photovoltaic unit, For the The preset reference output of each wind turbine is For the The preset reference output of each PV unit.

[0014] Preferably, the unit output constraint includes: ; ; ; in, For the The output limit of each thermal power unit is For the The lower limit of the output of each thermal power unit is is the upper limit of wind turbine output, The output limit of the photovoltaic unit; The power balance constraint includes: ; in, is the network loss value, is the total load demand of the system.

[0015] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0016] An embodiment of the present invention provides a power system dispatching device combined with new energy, including: a sub-model construction module, an objective function model construction module, a model solving module and an output dispatching module; The sub-model construction module is used to construct a first function model with the goal of minimizing the thermal power output cost according to the thermal power generation data and the thermal power generation cost data; to construct a second function model with the goal of minimizing the pollution emission according to the new energy power generation data, the new energy pollution emission data and the thermal power pollution emission data; and to construct a third function model with the goal of maximizing the new energy utilization rate according to the new energy power generation data and the preset power generation data; wherein the new energy power generation data includes: photovoltaic output data and wind power output data; The objective function model building module is used to assign different weight coefficients to the first function model, the second function model and the third function model to generate an objective function model with the objectives of minimizing the thermal power output cost, minimizing the pollution emission and maximizing the utilization rate of new energy; wherein the constraint conditions corresponding to the objective function include: unit output constraint, power balance constraint and spare capacity constraint; The model solving module is used to solve the objective function model under the constraints of the unit output constraint, the power balance constraint and the spare capacity constraint, and generate a target scheduling decision corresponding to the objective function model; wherein the target scheduling decision includes: the output of the thermal power unit, the output of the wind power unit and the output of the photovoltaic unit; The output dispatching module is used to dispatch the output of thermal power generation units, wind power generation units and photovoltaic units in the power system according to the target dispatching decision.

[0017] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0018] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the power system scheduling method combined with new energy as described in the above-mentioned embodiment of the invention.

[0019] The following beneficial effects are achieved by implementing the present invention: The embodiment of the present invention provides a method, device and terminal equipment for dispatching a power system in conjunction with new energy. The present invention respectively establishes a first function model with the minimum cost of thermal power output as the goal, a second function model with the minimum pollution emission as the goal, and a third function model with the maximum utilization rate of new energy as the goal. Different weight coefficients are assigned to the first function model, the second function model and the third function model, and a comprehensive objective function model can be obtained. The constraint conditions corresponding to the objective function model include unit output constraints, power balance constraints and spare capacity constraints. Then, the objective function model is solved under these constraints to generate a target dispatching decision, and finally the unit output in the power system is dispatched according to the target dispatching decision. Compared with the prior art, the present invention can comprehensively consider the power generation of thermal power and new energy, optimize from multiple aspects such as cost, pollution emission and new energy utilization rate, and achieve a balance of multiple objectives under the premise of satisfying multiple constraints of power system operation. Therefore, the present invention realizes the joint scheduling of thermal power and renewable energy, and the constructed multi-objective function model enables the comprehensive consideration of the costs, pollution emissions and utilization rate of thermal power and renewable energy during scheduling, which not only reduces the cost of power generation and pollution emissions, but also improves the utilization rate of renewable energy, realizes the efficient utilization of renewable energy in the power system, and improves the power supply quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a method for dispatching a power system in conjunction with new energy sources provided by an embodiment of the present invention.

[0021] Figure 2 It is a flow chart of a multi-objective distributed robust power dispatching method provided by another embodiment of the present invention.

[0022] Figure 3 It is a structural schematic diagram of a power system dispatching device combined with new energy sources provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] like Figure 1 FIG. 1 is a flow chart of a method for dispatching a power system in combination with new energy sources provided by an embodiment of the present invention. The method for dispatching a power system in combination with new energy sources comprises: Step S1: constructing a first function model with the goal of minimizing the thermal power output cost according to the thermal power generation data and the thermal power generation cost data; constructing a second function model with the goal of minimizing the pollution emission according to the new energy power generation data, the new energy pollution emission data and the thermal power pollution emission data; constructing a third function model with the goal of maximizing the new energy utilization rate according to the new energy power generation data and the preset power generation data; wherein the new energy power generation data includes: photovoltaic output data and wind power output data; Step S2: assigning different weight coefficients to the first function model, the second function model and the third function model to generate an objective function model with the goal of minimizing thermal power output cost, minimizing pollution emissions and maximizing new energy utilization rate; wherein the constraint conditions corresponding to the objective function include: unit output constraint, power balance constraint and spare capacity constraint; Step S3: Under the constraints of the unit output constraint, the power balance constraint and the reserve capacity constraint, the objective function model is solved to generate a target scheduling decision corresponding to the objective function model; wherein the target scheduling decision includes: the output of the thermal power unit, the output of the wind power unit and the output of the photovoltaic unit; Step S4: according to the target dispatching decision, dispatch the output of the thermal power generation units, wind power generation units and photovoltaic units in the power system.

[0025] For step S1, in a preferred embodiment, the thermal power generation data mainly includes the output of the thermal power unit, that is, the power generation or power generation of the thermal power unit in different time periods or working conditions, reflecting the operating status and power generation capacity of the thermal power unit. The thermal power generation cost data mainly includes power generation cost data including fuel cost, environmental governance cost and fixed cost, etc. The thermal power generation data and the thermal power generation cost data are used as input parameters of the function model, and then a first function model for calculating the output cost of the thermal power unit and minimizing the thermal power output cost can be constructed.

[0026] Furthermore, when constructing a second function model with the goal of minimizing pollution emissions, the present invention can combine the new energy power generation data with its pollution emission data, which can solve the limitation of traditional scheduling that only focuses on the single pollution source of thermal power, thereby improving the calculation accuracy of pollution emissions, so that in the subsequent process of making the optimal target scheduling decision and performing scheduling according to the target scheduling decision, pollution emissions can be further reduced.

[0027] It is understandable that the new energy power generation data mainly refers to the power output data generated by new energy power generation facilities (such as photovoltaic power stations and wind farms), and the preset power generation data refers to the power generation target that the new energy power generation facilities should achieve within a specific time period, which is pre-set based on factors such as the power generation capacity of the new energy power generation facilities, historical power generation data, and weather forecasts.

[0028] When constructing the third function model with the goal of maximizing the utilization rate of new energy, by comparing and analyzing the preset power generation data with the new energy power generation data, the power generation efficiency of new energy power generation facilities and the level of new energy utilization can be evaluated, so that when the dispatching strategy of the power system is subsequently optimized, the utilization rate of new energy and the stability of the power system can be improved.

[0029] For step S2, in a preferred embodiment, the objective function model includes: ; ; In the formula, It means taking the maximum value among the three absolute value items; It means minimizing the maximum value of three absolute value terms; Indicates thermal power output cost and cost reference value Deviation value of Indicates the pollution emission and the reference value of pollution emission Deviation value of Represents the new energy utilization rate and the ideal value of new energy utilization rate Deviation value of It is the first function model with the goal of minimizing the thermal power output cost. It is the second function model with the goal of minimizing pollution emissions. It is the third function model with the goal of maximizing the utilization rate of new energy. is the weight coefficient corresponding to the first function model, is the weight coefficient corresponding to the second function model, is the weight coefficient corresponding to the third function model; For the The output of thermal power units, is the number of thermal power units, For the The quadratic cost coefficient of each thermal power unit is: For the The linear cost coefficient of each thermal power unit is: For the The fixed cost of a thermal power unit is For the The output of a wind turbine, is the number of wind turbines, is the output cost coefficient of the wind turbine, For the The output of each photovoltaic unit, is the number of photovoltaic units, is the output cost coefficient of the photovoltaic unit; , Respectively Different pollution emission coefficients of thermal power units, is the pollution emission factor of the wind turbine, is the pollution emission factor of the photovoltaic unit, For the The preset reference output of each wind turbine is For the The preset reference output of each PV unit.

[0030] It can be understood that the purpose of taking the maximum value in the present invention is to comprehensively consider the three objectives and take the objective with the largest deviation as the object that needs to be optimized currently. Because in multi-objective optimization, it is often necessary to balance various objectives, and this maximum value represents the aspect that most needs improvement at present.

[0031] As the outermost layer, it means minimizing the maximum value obtained. By adjusting the weight coefficient and decision variables (such as thermal power, wind power, and photovoltaic output), the one with the largest deviation among the three objectives can be made as small as possible, thereby balancing the three objectives as a whole and avoiding the situation where a certain objective has a large deviation, thus achieving the optimization effect of multi-objective balance.

[0032] Illustratively, the embodiment of the present invention can be The setting of dynamic trade-offs among multiple objectives. The specific meanings are as follows: When the economic cost orientation (i.e. The weight of the thermal power output cost is large), the weighted difference between the thermal power output cost and the reference value can be minimized. Under the constraint conditions, the total operating costs of thermal power, wind power, and photovoltaic power can be reduced first. In addition, the output of low-cost new energy (wind power, photovoltaic power) can be increased, and the output of high-cost thermal power can be reduced. Optimize the unit combination and select units with lower marginal costs for priority dispatch.

[0033] When pollution emission is the guide (i.e. The weight of the reference value is large), then the weighted difference between the pollution emissions and the reference value can be minimized. For example, priority should be given to reducing the carbon emissions of thermal power units, increasing the proportion of clean energy, limiting the output of high-emission thermal power units, and giving priority to dispatching low-emission units. Maximize the output of wind power and photovoltaic power, and reduce dependence on thermal power.

[0034] When the utilization rate of new energy is taken as the guide (i.e. The weight of the reference value is large), the weighted difference between the utilization rate of new energy and the reference value can be minimized, thereby maximizing the consumption of new energy and reducing the phenomenon of wind and solar power abandonment. For example, priority is given to the consumption of wind power and photovoltaic forecast output, and the energy storage charging and discharging strategy is dynamically adjusted to smooth the fluctuation of new energy.

[0035] The embodiment of the present invention introduces weight coefficients to dynamically adjust the priorities of the three goals of economic cost, pollution emission, and new energy consumption according to scheduling requirements. Dominant, can minimize the deviation of economic cost from reference value, when The deviation of pollution emission from the reference value is minimized. Dominant, then minimize the deviation of the new energy utilization rate from the ideal value (1).

[0036] In a preferred embodiment, the unit output constraint includes: thermal power unit output constraint, wind power unit output constraint and photovoltaic unit output constraint; The output constraints of the thermal power units include: ; The wind turbine output constraint includes: ; The photovoltaic unit output constraint includes: ; in, For the The output limit of each thermal power unit is For the The lower limit of the output of each thermal power unit is is the upper limit of wind turbine output, The output limit of the photovoltaic unit; Furthermore, the power balance constraint includes: ; in, is the network loss value, is the total load demand of the system.

[0037] Furthermore, the reserve capacity constraint includes: positive and negative spinning reserve machine constraints; the positive spinning reserve capacity constraint is: ; The negative spinning reserve capacity constraint is: ; Among them, U SR For the backup demand of the conventional system, the output of the largest thermal power unit in the current dispatch period can be taken. , , , are the demand coefficients of wind farm and photovoltaic farm output for positive and negative spinning reserves respectively.

[0038] Therefore, the embodiment of the present invention constructs unit output constraints (including thermal power units, wind power units and photovoltaic units) for the objective function model, thereby ensuring that various types of units do not exceed their physical and technical limitations during operation, thereby avoiding system instability or safety accidents caused by unit overload.

[0039] The power balance constraint ensures the balance between supply and demand of the system, that is, the total amount of electricity generated is equal to the system load demand plus network losses. Furthermore, the reserve capacity constraint (including positive spinning reserve and negative spinning reserve) can provide the power system with a buffering capacity to cope with emergencies (such as a sudden drop in renewable energy output or a sudden increase in load), thereby improving the flexibility and reliability of the power system.

[0040] For step S3, in a preferred embodiment, solving the objective function model under the constraints of the unit output constraint, the power balance constraint and the spare capacity constraint to generate a target scheduling decision corresponding to the objective function model includes: An initial population including a number of different initial individuals is randomly generated; wherein each initial individual includes: the output of a thermal power unit, the output of a wind power unit, the output of a photovoltaic unit, and a number of initial weight coefficients; Repeat the following population update operation until the current number of iterations is the same as the preset number of iterations, and output the target scheduling decision corresponding to the individual with the highest function value in the current population, and the target scheduling decision satisfies the unit output constraint, power balance constraint, and spare capacity constraint: When the current number of iterations is less than the preset number of iterations, a number of current individuals randomly selected from the current population are subjected to crossover and mutation operations to generate updated individuals; wherein, initially, the initial population is used as the current population; Each current weight coefficient of each current individual in the current population is updated one by one, and a new individual derived from the current individual is generated during each update; wherein, initially, the initial weight coefficient is used as the current weight coefficient; Generate an updated population based on all current individuals and the newly added individuals corresponding to each current individual; According to the objective function model, the objective function value corresponding to each individual in the population after an update and the objective function value corresponding to the updated individual are calculated; the objective function value corresponding to each individual in the population after an update is compared with the objective function value of the updated individual, and the individual whose objective function value is greater than the objective function value of the updated individual is taken as the individual to be merged; Perform constraint verification on the updated individuals in several preset power grid operation scenarios to determine whether the updated individuals simultaneously meet the unit output constraints, power balance constraints and spare capacity constraints in all preset power grid operation scenarios. If so, generate a second updated population based on the updated individuals and each individual to be merged; otherwise, generate a second updated population based on each individual to be merged; Calculate the probability deviation of each individual in the population after the second update and all the historical output probability distribution scenarios, and then determine whether the probability deviation calculation result satisfies the first probability deviation constraint and the second probability deviation constraint at the same time. If so, use the population after the second update as the current population when the population update operation is performed next time; if not, randomly adjust the individuals in the population after the second update, and use the randomly adjusted population as the current population when the population update operation is performed next time; Add the current iteration count value to the preset iteration count increment; Among them, the first probability deviation constraint is used to characterize the probability distribution deviation degree of all historical output probability distribution scenarios; the second probability deviation constraint is used to characterize the probability distribution deviation degree of a single historical output probability distribution scenario.

[0041] In a preferred embodiment, the initialization step of the present invention may be: first, the external population is set to an empty set, which is used to store the undominated solutions in the current solution set (i.e., the solutions whose function values ​​are greater than the objective function values ​​corresponding to the updated individuals), that is, to retain the optimal solution in each iteration; According to the Euclidean distance between different coefficients, find the T weight vectors closest to each weight coefficient and get the closest weight coefficient of Adjacent weight coefficients; Generate the initial population by random method , and set its function value , among which The function corresponds to the objective function of the objective function model with the goal of minimizing the thermal power output cost, minimizing the pollution emission and maximizing the utilization rate of new energy, which can be: ; initialization , are reference values, corresponding to the ideal values ​​of the three objective functions, where the parameters can be initialized and selected based on historical data: : The minimum expected value of economic cost.

[0042] : The lowest pollution emission in history.

[0043] : The maximum theoretical value of new energy utilization rate (usually set to 1).

[0044] In a preferred embodiment, Figure 2 The schematic diagram of the multi-objective distributed robust power dispatching method shown in the figure includes the following processes: Step 1: Obtain grid operation-related data and new energy prediction-related data from the grid platform and new energy prediction platform, and classify and label the data to divide the data into multiple optimization targets for input into the multi-target decomposition model and grid data and new energy prediction data for input into the comprehensive model, so as to facilitate the subsequent decomposition of multiple targets; Step 2: Establish a multi-objective model that can be used for the MOEA / D decomposition algorithm; for example, the first function model, the second function model, and the third function model; Step 3: Decompose the input multi-objective model (i.e., the first function model, the second function model, and the third function model) into a single optimization objective (i.e., the objective function model with the objectives of minimizing the thermal power output cost, minimizing the pollution emission, and maximizing the utilization rate of new energy) to facilitate the comprehensive model to optimize the single optimization objective and find the global optimal solution; Step 4: By verifying the worst case scenario using the Pareto global optimal strategy and distributed robustness model of the MOEA / D evolutionary algorithm, a global optimal solution search is performed for the single optimization objective; Step 5: The grid new energy data and the decomposed single objectives are integrated and input into the MOEA / D evolutionary algorithm for optimization and solution, and the multi-objective Pareto global optimal solution that meets the relevant constraints is searched, that is, the target scheduling decision corresponding to the objective function model; Step 6: Perform robust performance analysis on the evolved global optimal dispatch result (target dispatch decision corresponding to the objective function model), analyze the result in the 1-norm and ∞-norm probability sets, and judge whether the robust performance meets the grid requirements. If there is a situation that does not meet the requirements, the evolutionary algorithm optimization needs to be performed again; Step seven, comprehensively calculate the dispatch optimization results that meet the requirements, calculate the specific operations of the relevant power grid dispatch and display the economic losses, wind power abandonment data and other results of the optimized dispatch, so as to dispatch the output of thermal power units, wind power units and photovoltaic units in the power system according to the target dispatch decision corresponding to the objective function model.

[0045] Illustratively, in the process of optimizing and solving the problem in the MOEA / D evolutionary algorithm module, the embodiment of the present invention may use the Chebyshev decomposition method, and the decomposition and optimization process is as follows: Set weight vectors and reference points: Assume a set of uniformly distributed weight vectors. Since there are no assigned weights between the initially established multi-objective functions, when considering changing the multi-objectives to a single objective, the desired weights can be preliminarily set according to the needs of the power grid. For example, if new energy resources are abundant, the weights of the new energy with high utilization rates will be set to larger values, but there will not be too much difference between the overall weights.

[0046] * is the reference point. The goal is to approximate the Pareto frontier (PF) of the MOP into N scalar subproblems by decomposing it. The objective function of the subproblem is as follows: in , represents the objective function, where x is a variable parameter including the output of thermal power units, the output of wind power units, the utilization rate of new energy, the single objective function and the reserve capacity. right is continuous.

[0047] The above case is decomposed using Chebyshev decomposition, and the three goals of minimum thermal power output, minimum emission pollution, and maximum utilization of new energy are combined into a single goal according to the weights: ; Finally, the weight vector The setting of the target scheduling decision is carried out through dynamic trade-offs among multiple targets to obtain the optimal solution that satisfies the multi-target balance.

[0048] Illustratively, when solving the objective function model, the present invention can use the target scheduling decisions corresponding to each objective function model and the weight coefficients of each sub-function model (such as the first, second, and third function models) as individuals of the population, and generate new individuals through crossover, mutation and other operations in subsequent iterations. This makes it possible to search for the optimal solution in the entire solution space and avoid falling into the local optimum.

[0049] Moreover, by introducing weight coefficients and adjacent weight sets, the present invention can dynamically weigh the weights among multiple objectives, find a solution that meets the requirements, and search for the optimal solution in the local space to avoid falling into the global optimum.

[0050] Specifically, in the process of iterative optimization, after the updated individuals are obtained, the present invention can mark some current individuals in the current population whose current function values ​​are greater than the objective function values ​​of the updated individuals in the objective function model as individuals to be merged, thereby extracting local solutions in the adjacent weight set that are more advantageous than the updated individuals.

[0051] Therefore, the iterative optimization process of the present invention can be summarized as: crossover mutation operation, labeling of individuals to be merged, constraint verification, and probability deviation calculation and adjustment; In a preferred embodiment, the present invention can correct the mutated individuals after performing the crossover mutation operation, and then output the updated individuals; the present invention can also perform a solution correction operation before marking the individuals to be merged, specifically: The step of performing a crossover operation and a mutation operation on a number of current individuals randomly selected from the current population to generate updated individuals includes: Randomly select a number of current individuals from the current population; After performing crossover and mutation operations on each current individual, a mutated individual is generated; The sum of the outputs of the thermal power units, wind power units and photovoltaic units in the mutated individuals is compared with the preset total operating load of the power system; When it is determined that the total output is not less than the preset total operating load, the output of the thermal power unit is reduced according to the difference between the total output and the preset total operating load, and the reduced output of the thermal power unit is generated; and an updated individual is generated according to the reduced output of the thermal power unit and the unupdated data in the mutated individual; When it is determined that the total output is less than the preset total operating load, the mutated individual is regarded as an updated individual.

[0052] It can be understood that the embodiment of the present invention can ensure that the final total output of the updated individuals meets the load demand of the power system by comparing the total output of the mutated individuals with the preset total operating load of the power system and reducing the output of the thermal power units according to the difference.

[0053] Moreover, by reducing the output of high-cost and high-emission thermal power, the optimization of the first and second objective functions (reducing costs and emissions) can be implicitly promoted, and by giving priority to retaining wind and solar output and only adjusting the thermal power part, it can also form synergy with the third objective function (maximizing the utilization rate of new energy), thereby achieving the coordinated realization of multi-objective optimization.

[0054] Schematically, the mutated individuals generated by cross mutation have outputs of thermal power units, wind power units, and photovoltaic units of 550MW, 220MW, and 120MW respectively; If the total load is 800MW, the current output of the mutated individual is 550+200+120=870MW, which exceeds 70MW. The present invention can reduce thermal power first according to priority, and the output of the thermal power unit after reduction can be: PG=550-70=480MW.

[0055] At this time, it can be concluded that among the updated individuals, the output of the corresponding generator group, wind turbine group and photovoltaic group are 480MW, 200MW and 120MW respectively.

[0056] In a preferred embodiment, when obtaining the updated individuals, the present invention can also extract the local solution in the adjacent weight set that is more advantageous than the updated individuals, that is, the individuals to be merged can be calculated and extracted according to the current adjacent weight coefficients corresponding to each individual in the population. The process of marking the individuals to be merged is: The current weight coefficient of each current individual in the current population is updated one by one, and a new individual derived from the current individual is generated during each update, including: For each current weight coefficient of each current individual in the current population, taking the current weight coefficient as the center and according to the preset value range, a coefficient value range corresponding to the current weight coefficient is generated; Extracting a number of coefficients whose similarity with the current weight coefficient is greater than a preset similarity threshold from the coefficient value range as the current adjacent weight coefficient corresponding to the current weight coefficient; The current weight coefficients of the current individual are replaced one by one according to the current adjacent weight coefficients, and a new individual derived from the current individual is generated during each replacement.

[0057] Illustratively, the present invention takes the current weight coefficient as the center and generates a coefficient value range according to a preset numerical range, thereby expanding the search space of the weight coefficient to increase population diversity.

[0058] By extracting adjacent weight coefficients from the coefficient value range and replacing them with the current individual to generate a replaced individual, the composition of the population can be further enriched. Since each replaced individual only contains one current adjacent weight coefficient, this local change method not only ensures the correlation between the new individual and the original individual, but also introduces new features, which helps the algorithm to jump out of the local optimum and is more likely to find the global optimal solution.

[0059] Therefore, by marking individuals whose objective function values ​​are greater than those of the updated individuals as individuals to be merged, local solutions that are more advantageous than the updated individuals can be screened out from the current population.

[0060] The embodiment of the present invention can enable the algorithm to perform a more detailed search in the local space by focusing on the adjacent weight coefficients and their corresponding function values. The adjacent weight coefficients reflect a group of weights that are highly similar to the current weight coefficients. By mining these local areas, some high-quality solutions that may be overlooked in the global search can be found, avoiding the algorithm from falling into the global optimal solution too early, and improving the ability to search for the optimal solution in the local space.

[0061] After marking the individuals to be merged, when generating the updated population, the evolutionary direction of the population can be guided according to these individuals to be merged, avoiding the over-reproduction of inferior individuals in the population, thereby maintaining the diversity of the population and increasing the possibility of finding the global optimal solution.

[0062] Furthermore, after obtaining the updated individuals, the constraint verification process of the present invention is as follows: The updated individuals can be constrained and verified in the preset grid operation scenarios to determine whether the updated individuals are feasible in any scenario; If feasible, the feasible updated individuals and the individuals to be merged are used to generate an updated population; If it is not feasible, the updated individuals are eliminated, and the updated population is generated based on those local solutions (i.e., individuals to be merged) that are more advantageous than the updated individuals in the adjacent weight set.

[0063] Specifically, in a preferred embodiment, the present invention can first extract several different new energy output scenarios from the operating scenarios of the power system under the constraints of the first probability deviation constraint and the second probability deviation constraint; wherein the new energy output scenario is used to characterize the scenario where the wind power output or photovoltaic output is in an unstable state.

[0064] Each renewable energy output scenario is used as a preset grid operation scenario for constraint verification.

[0065] Next, after the preset power grid operation scenario is obtained, constraint verification is performed on the updated individuals in several preset power grid operation scenarios to determine whether the updated individuals simultaneously meet the unit output constraint, power balance constraint, and reserve capacity constraint in all preset power grid operation scenarios; If the updated individuals meet the operating constraints of each scenario, an updated population is generated based on the updated individuals and the individuals to be merged; If the updated individual cannot meet the operating constraints of each scenario, it means that the updated individual has certain safety risks. In this case, the updated individual is removed and the updated population is directly generated based on the individuals to be merged.

[0066] The above-mentioned constraint verification process of the present invention ensures the feasibility and safety of the solution (i.e., the updated individual) generated during the optimization process in practical applications, that is, by simulating different power grid operation scenarios, the updated individuals are more rigorously verified, thereby screening out solutions that can operate stably under various conditions.

[0067] Therefore, when generating the population after the second update, the result of constraint verification will directly affect which individuals are retained. By eliminating individuals that do not meet the constraints and retaining the advantageous local solutions (i.e., individuals to be merged), the population can be guided to evolve in a direction that better meets the needs of actual applications.

[0068] Specifically, during the constraint verification process, the following constraint judgment classification and robust scenario verification can be performed: Generate K extreme scenarios (such as the lowest 10% wind power output and the lowest 10% photovoltaic output) based on the 1-norm and ∞-norm constraints corresponding to the historical data of renewable energy output or the predicted data; In extreme scenarios, check whether the updated individual y' satisfies multiple constraints (such as unit output constraints, power balance constraints, and spare capacity constraints). If y' is not feasible in any scenario, it is determined that y' is dominated by the scenario and is directly eliminated, that is, an updated population is generated based on each individual to be merged.

[0069] Furthermore, in a preferred embodiment, after obtaining the population after the second update, the embodiment of the present invention can also evaluate the performance of the population after the second update in different historical scenarios by calculating the probability deviation with the historical output probability distribution scenario, and setting the first probability deviation constraint and the second probability deviation constraint, so as to ensure that the population after the second update will not deviate too much from expectations due to the occurrence of certain specific scenarios in actual operation, thereby enhancing the robustness of the understanding and enabling it to remain stable under various uncertain conditions. The probability deviation calculation and adjustment process of the present invention is specifically as follows: Extracting wind power output probability distribution and photovoltaic output probability distribution from each historical output probability distribution scenario; wherein the wind power output probability distribution includes: different wind power output intervals correspond to different wind power output probability values, and different photovoltaic output intervals correspond to different photovoltaic output probability values; According to the wind power output interval corresponding to the output of the wind turbine group in the updated individual in the wind power output probability distribution, a target wind power output probability value corresponding to the updated individual is determined; According to the photovoltaic output interval corresponding to the output of the photovoltaic unit in the updated individual in the photovoltaic output probability distribution, the target photovoltaic output probability value corresponding to the updated individual is determined; When it is determined that both the target wind power output probability value and the target photovoltaic output probability value are not less than the first probability deviation constraint, and both the target wind power output probability value and the target photovoltaic output probability value are not greater than the second probability deviation constraint, the second updated population is used as the current population when the population update operation is performed next time; When it is determined that the target wind power output probability value is less than the first probability deviation constraint or the target photovoltaic output probability value is less than the first probability deviation constraint, the individuals in the population after the second update are randomly adjusted, and the randomly adjusted population is used as the current population when the population update operation is performed next time; When it is determined that the target wind power output probability value is greater than the second probability deviation constraint or the target photovoltaic output probability value is greater than the second probability deviation constraint, the individuals in the population after the second update are randomly adjusted, and the randomly adjusted population is used as the current population when the next population update operation is performed.

[0070] It is understandable that after obtaining the second updated population, the embodiment of the present invention calculates the probability deviation of wind power and photovoltaic output by comparing with the historical output probability distribution scenario, and evaluates and adjusts the second updated population according to the preset probability deviation constraint.

[0071] By comparing and adjusting with historical output probability distribution scenarios, it can be ensured that the population after the second update will not deviate too much from expectations due to the emergence of certain specific scenarios in actual operation, thereby enhancing the robustness of the understanding, optimizing the quality of the population, and improving the optimization efficiency of the algorithm.

[0072] Schematically, the first probability deviation constraint is a 1-norm constraint, and the second probability deviation constraint is an ∞-norm constraint. It can be understood that the 1-norm constraint focuses on the degree of probability distribution deviation of all historical output probability distribution scenarios, which is the sum of the absolute values ​​of the deviations between the actual probability distribution and the historical probability distribution in each scenario. In this way, the deviation of the probability distribution of new energy output relative to the historical distribution can be grasped as a whole, and the deviations of all scenarios can be comprehensively considered. The ∞-norm constraint focuses on the degree of probability distribution deviation of a single historical output probability distribution scenario, and it takes the maximum value of the absolute value of the deviation between the actual probability distribution and the historical probability distribution in each scenario.

[0073] In a preferred embodiment, the embodiment of the present invention can verify the distribution robustness of the updated population through the 1-norm constraint and the ∞-norm constraint, that is, considering the set restrictions of the 1-norm and the ∞-norm, we have: in, is the value of the scene probability, The initial probability value of the i-th discrete scenario in the historical data, and is the limit value of the probability allowable deviation under the 1-norm and ∞-norm constraints. In the case of a successful probability set, the global optimal solution in the updated population is tested to determine whether it meets the constraints. The process of testing whether the constraints are met is as follows: The first step is to extract the probability distribution of scenarios, and extract the probability distribution of wind power and photovoltaic output from the new energy forecast data. For example, the wind power output is divided into 10 intervals between 100MW and 200MW, and the probability of each interval is p1, p2, ..., p10; Next, the probability deviation is calculated: the target wind power output probability value corresponding to the output of the wind turbine group in the updated individual is calculated, and the target photovoltaic output probability value corresponding to the output of the photovoltaic group in the updated individual is determined, so as to obtain the deviation from the probability distribution of wind power and photovoltaic output; Finally, constraint verification is performed: if the 1-norm deviation ≤ the target wind power output probability value and the target photovoltaic output probability value, and the ∞-norm deviation ≤ the target wind power output probability value and the target photovoltaic output probability value, then it is determined that the solution meets the distributed robustness constraint, and the population after the second update can be directly used as the current population when the next population update operation is performed.

[0074] For step S4, the embodiment of the present invention can achieve a multi-objective optimization effect of minimizing the cost of thermal power output, minimizing pollution emissions, and maximizing the utilization rate of new energy by scheduling according to the target scheduling decision. For example, through the target scheduling decision, the output of wind power and photovoltaic power is increased in the scheduling of the power system, and the use of thermal power is reduced, which can reduce costs and pollution and increase the proportion of new energy consumption.

[0075] like Figure 3 As shown, based on the above-mentioned various embodiments of the power system dispatching method combined with new energy, the present invention provides a corresponding device embodiment; An embodiment of the present invention provides a power system dispatching device combined with new energy, including: a sub-model construction module, an objective function model construction module, a model solving module and an output dispatching module; The sub-model construction module is used to construct a first function model with the goal of minimizing the thermal power output cost according to the thermal power generation data and the thermal power generation cost data; to construct a second function model with the goal of minimizing the pollution emission according to the new energy power generation data, the new energy pollution emission data and the thermal power pollution emission data; and to construct a third function model with the goal of maximizing the new energy utilization rate according to the new energy power generation data and the preset power generation data; wherein the new energy power generation data includes: photovoltaic output data and wind power output data; The objective function model building module is used to assign different weight coefficients to the first function model, the second function model and the third function model to generate an objective function model with the objectives of minimizing the thermal power output cost, minimizing the pollution emission and maximizing the utilization rate of new energy; wherein the constraint conditions corresponding to the objective function include: unit output constraint, power balance constraint and spare capacity constraint; The model solving module is used to solve the objective function model under the constraints of the unit output constraint, the power balance constraint and the spare capacity constraint, and generate a target scheduling decision corresponding to the objective function model; wherein the target scheduling decision includes: the output of the thermal power unit, the output of the wind power unit and the output of the photovoltaic unit; The output dispatching module is used to dispatch the output of thermal power units, wind power units and photovoltaic units in the power system according to the target dispatching decision.

[0076] It should be noted that the device embodiments described above are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without paying creative labor.

[0077] Those skilled in the art can clearly understand that, for the sake of convenience and simplicity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0078] Based on the above-mentioned various embodiments of the power system dispatching method combined with new energy sources, the present invention provides corresponding embodiments of terminal equipment items.

[0079] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for dispatching a power system in conjunction with new energy sources as described in any method embodiment of the present invention.

[0080] The terminal device may be a computing terminal device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0081] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0082] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0083] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for dispatching a power system in conjunction with new energy, characterized in that: include: A first function model is constructed based on thermal power generation data and thermal power generation cost data with the goal of minimizing thermal power output cost; a second function model is constructed based on new energy power generation data, new energy pollution emission data and thermal power pollution emission data with the goal of minimizing pollution emission; a third function model is constructed based on new energy power generation data and preset power generation data with the goal of maximizing new energy utilization rate; wherein the new energy power generation data includes: photovoltaic output data and wind power output data; Assigning different weight coefficients to the first function model, the second function model and the third function model to generate an objective function model with the goals of minimizing thermal power output cost, minimizing pollution emissions and maximizing new energy utilization rate; wherein the constraints corresponding to the objective function include: unit output constraints, power balance constraints and spare capacity constraints; Under the constraints of the unit output constraint, the power balance constraint and the reserve capacity constraint, the objective function model is solved to generate a target scheduling decision corresponding to the objective function model; wherein the target scheduling decision includes: the output of the thermal power unit, the output of the wind power unit and the output of the photovoltaic unit; According to the target dispatching decision, the output of thermal power units, wind power units and photovoltaic units in the power system is dispatched.

2. A method for dispatching a power system in conjunction with new energy sources as claimed in claim 1, characterized in that: The objective function model is solved under the constraints of the unit output constraint, the power balance constraint and the spare capacity constraint to generate a target scheduling decision corresponding to the objective function model, including: An initial population including a number of different initial individuals is randomly generated; wherein each initial individual includes: the output of a thermal power unit, the output of a wind power unit, the output of a photovoltaic unit, and a number of initial weight coefficients; Repeat the following population update operation until the current number of iterations is the same as the preset number of iterations, and output the target scheduling decision corresponding to the individual with the highest function value in the current population, and the target scheduling decision satisfies the unit output constraint, power balance constraint, and spare capacity constraint: When the current number of iterations is less than the preset number of iterations, a number of current individuals randomly selected from the current population are subjected to crossover and mutation operations to generate updated individuals; wherein, initially, the initial population is used as the current population; Each current weight coefficient of each current individual in the current population is updated one by one, and a new individual derived from the current individual is generated during each update; wherein, initially, the initial weight coefficient is used as the current weight coefficient; Generate an updated population based on all current individuals and the newly added individuals corresponding to each current individual; According to the objective function model, the objective function value corresponding to each individual in the population after an update and the objective function value corresponding to the updated individual are calculated; the objective function value corresponding to each individual in the population after an update is compared with the objective function value of the updated individual, and the individual whose objective function value is greater than the objective function value of the updated individual is taken as the individual to be merged; Perform constraint verification on the updated individuals in several preset power grid operation scenarios to determine whether the updated individuals simultaneously meet the unit output constraints, power balance constraints and spare capacity constraints in all preset power grid operation scenarios. If so, generate a second updated population based on the updated individuals and each individual to be merged; otherwise, generate a second updated population based on each individual to be merged; Calculate the probability deviation of each individual in the population after the second update and all the historical output probability distribution scenarios, and then determine whether the probability deviation calculation result satisfies the first probability deviation constraint and the second probability deviation constraint at the same time. If so, use the population after the second update as the current population when the population update operation is performed next time; if not, randomly adjust the individuals in the population after the second update, and use the randomly adjusted population as the current population when the population update operation is performed next time; Add the current iteration count value to the preset iteration count increment; Among them, the first probability deviation constraint is used to characterize the probability distribution deviation degree of all historical output probability distribution scenarios; the second probability deviation constraint is used to characterize the probability distribution deviation degree of a single historical output probability distribution scenario.

3. A method for dispatching a power system in conjunction with new energy sources as claimed in claim 2, characterized in that: The step of performing a crossover operation and a mutation operation on a number of current individuals randomly selected from the current population to generate updated individuals includes: Randomly select a number of current individuals from the current population; After performing crossover and mutation operations on each current individual, a mutated individual is generated; The sum of the outputs of the thermal power units, wind power units and photovoltaic units in the mutated individuals is compared with the preset total operating load of the power system; When it is determined that the total output is not less than the preset total operating load, the output of the thermal power unit is reduced according to the difference between the total output and the preset total operating load, and the reduced output of the thermal power unit is generated; and an updated individual is generated according to the reduced output of the thermal power unit and the unupdated data in the mutated individual; When it is determined that the total output is less than the preset total operating load, the mutated individual is regarded as an updated individual.

4. A method for dispatching a power system in conjunction with new energy sources as claimed in claim 3, characterized in that: The current weight coefficient of each current individual in the current population is updated one by one, and a new individual derived from the current individual is generated during each update, including: For each current weight coefficient of each current individual in the current population, taking the current weight coefficient as the center and according to the preset value range, a coefficient value range corresponding to the current weight coefficient is generated; Extracting a number of coefficients whose similarity with the current weight coefficient is greater than a preset similarity threshold from the coefficient value range as the current adjacent weight coefficient corresponding to the current weight coefficient; The current weight coefficients of the current individual are replaced one by one according to the current adjacent weight coefficients, and a new individual derived from the current individual is generated during each replacement.

5. A method for dispatching a power system in conjunction with new energy sources as claimed in claim 4, characterized in that: The generation of the preset power grid operation scenario includes: Under the constraint conditions of the first probability deviation constraint and the second probability deviation constraint, a number of different renewable energy output scenarios are extracted from the operation scenarios of the power system; wherein the renewable energy output scenario is used to characterize a scenario in which the wind power output or the photovoltaic output is in an unstable state; Each renewable energy output scenario is used as a preset grid operation scenario for constraint verification.

6. A method for dispatching a power system in conjunction with new energy sources as claimed in claim 5, characterized in that: The method performs probability deviation calculation on each individual in the second updated population and all historical output probability distribution scenarios, and then determines whether the probability deviation calculation result satisfies both the first probability deviation constraint and the second probability deviation constraint. If so, the second updated population is used as the current population when the population update operation is performed next time; if not, the individuals in the second updated population are randomly adjusted, and the randomly adjusted population is used as the current population when the population update operation is performed next time, including: Extracting wind power output probability distribution and photovoltaic output probability distribution from each historical output probability distribution scenario; wherein the wind power output probability distribution includes: different wind power output intervals correspond to different wind power output probability values, and different photovoltaic output intervals correspond to different photovoltaic output probability values; According to the wind power output interval corresponding to the output of the wind turbine group in the updated individual in the wind power output probability distribution, a target wind power output probability value corresponding to the updated individual is determined; According to the photovoltaic output interval corresponding to the output of the photovoltaic unit in the updated individual in the photovoltaic output probability distribution, the target photovoltaic output probability value corresponding to the updated individual is determined; When it is determined that both the target wind power output probability value and the target photovoltaic output probability value are not less than the first probability deviation constraint, and both the target wind power output probability value and the target photovoltaic output probability value are not greater than the second probability deviation constraint, the second updated population is used as the current population when the population update operation is performed next time; When it is determined that the target wind power output probability value is less than the first probability deviation constraint or the target photovoltaic output probability value is less than the first probability deviation constraint, the individuals in the population after the second update are randomly adjusted, and the randomly adjusted population is used as the current population when the population update operation is performed next time; When it is determined that the target wind power output probability value is greater than the second probability deviation constraint or the target photovoltaic output probability value is greater than the second probability deviation constraint, the individuals in the population after the second update are randomly adjusted, and the randomly adjusted population is used as the current population when the next population update operation is performed.

7. A method for dispatching a power system in conjunction with new energy sources as claimed in claim 6, characterized in that: The objective function model comprises: ； ； In the formula, It means taking the maximum value among the three absolute value items; It means minimizing the maximum value of three absolute value terms; Indicates thermal power output cost and cost reference value Deviation value of Indicates the pollution emission and the reference value of pollution emission Deviation value of Represents the new energy utilization rate and the ideal value of new energy utilization rate Deviation value of It is the first function model with the goal of minimizing the thermal power output cost. It is the second function model with the goal of minimizing pollution emissions. It is the third function model with the goal of maximizing the utilization rate of new energy. is the weight coefficient corresponding to the first function model, is the weight coefficient corresponding to the second function model, is the weight coefficient corresponding to the third function model; For the The output of thermal power units, is the number of thermal power units, For the The quadratic cost coefficient of each thermal power unit is: For the The linear cost coefficient of each thermal power unit is: For the The fixed cost of a thermal power unit is For the The output of a wind turbine, is the number of wind turbines, is the output cost coefficient of the wind turbine, For the The output of each photovoltaic unit, is the number of photovoltaic units, is the output cost coefficient of the photovoltaic unit; , Respectively Different pollution emission coefficients of thermal power units, is the pollution emission factor of the wind turbine, is the pollution emission factor of the photovoltaic unit, For the The preset reference output of each wind turbine is For the The preset reference output of each PV unit.

8. A method for dispatching a power system in conjunction with new energy sources as claimed in claim 7, characterized in that: The unit output constraints include: ； ； ； in, For the The output limit of each thermal power unit is For the The lower limit of the output of each thermal power unit is is the upper limit of wind turbine output, is the output upper limit of the photovoltaic unit; The power balance constraint includes: ； in, is the network loss value, is the total load demand of the system.

9. A power system dispatching device combined with new energy, characterized in that: include: Sub-model construction module, objective function model construction module, model solving module and output scheduling module; The sub-model construction module is used to construct a first function model with the goal of minimizing the thermal power output cost according to the thermal power generation data and the thermal power generation cost data; to construct a second function model with the goal of minimizing the pollution emission according to the new energy power generation data, the new energy pollution emission data and the thermal power pollution emission data; and to construct a third function model with the goal of maximizing the new energy utilization rate according to the new energy power generation data and the preset power generation data; wherein the new energy power generation data includes: photovoltaic output data and wind power output data; The objective function model building module is used to assign different weight coefficients to the first function model, the second function model and the third function model to generate an objective function model with the objectives of minimizing the thermal power output cost, minimizing the pollution emission and maximizing the utilization rate of new energy; wherein the constraint conditions corresponding to the objective function include: unit output constraint, power balance constraint and spare capacity constraint; The model solving module is used to solve the objective function model under the constraints of the unit output constraint, the power balance constraint and the spare capacity constraint, and generate a target scheduling decision corresponding to the objective function model; wherein the target scheduling decision includes: the output of the thermal power unit, the output of the wind power unit and the output of the photovoltaic unit; The output dispatching module is used to dispatch the output of thermal power generation units, wind power generation units and photovoltaic units in the power system according to the target dispatching decision.

10. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a power system dispatching method combined with new energy as described in any one of claims 1 to 8.

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