A method, device and terminal equipment for dispatching a power system in conjunction with new energy

Abstract

The present invention discloses a method, device and terminal equipment for dispatching a power system in conjunction with new energy, and belongs to the technical field of power system dispatching. The method is as follows: a method for dispatching a power system in conjunction with new energy, respectively establishing a first function model with the minimum cost of thermal power output as the goal, a second function model with the minimum amount of pollution emissions as the goal, and a third function model with the maximum utilization rate of new energy as the goal, assigning different weight coefficients to the first function model, the second function model and the third function model, deriving a comprehensive objective function model, solving the objective function model to generate a target dispatching decision, and finally dispatching the power system according to the target dispatching decision. The present invention can optimize from the perspectives of cost, pollution emissions and utilization rate of new energy, achieve a balance of multiple objectives, not only reduce power generation costs and pollution emissions, but also improve utilization rate of new energy, and realize efficient utilization of new energy in the power system.

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, apparatus and terminal equipment combined with new energy. Background Art

[0002] In power system dispatch, integrating renewable energy with the system is key to improving the overall efficiency of the power system. To achieve the integration and efficient utilization of renewable energy, it is necessary not only to regulate the operation of thermal power units but also to integrate and dispatch renewable energy, thereby achieving a comprehensive balance of multiple objectives, improving the utilization rate of renewable energy in the power system, and ensuring the stable operation and power supply quality of the power system.

[0003] In traditional power system dispatching methods, 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 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 thermal power and new energy be combined to dispatch the power grid, but there is also a lack of comprehensive balance of multiple objectives. As a result, it is impossible to achieve efficient utilization 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, and 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 device for dispatching an electric power system in conjunction with new energy sources. These methods 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. These methods can effectively solve the problems in existing technologies of being unable to achieve efficient utilization of new energy in the electric power system, being difficult to reduce overall power generation costs, and being unable to effectively control pollution emissions.

[0006] An embodiment of the present invention provides a method for dispatching a power system in conjunction with renewable energy sources. 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 costs. A second function model is constructed based on renewable energy generation data, renewable energy pollution emission data, and thermal power pollution emission data, with the goal of minimizing pollution emissions. A third function model is constructed based on renewable energy generation data and preset power generation data, with the goal of maximizing renewable energy utilization. The renewable energy generation data includes photovoltaic output data and wind power output data.

[0007] 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; wherein the constraints corresponding to the objective function model include: unit output constraint, power balance constraint, and reserve capacity constraint;

[0008] 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;

[0009] According to the target dispatch decision, the output of thermal power units, wind power units and photovoltaic units in the power system is dispatched.

[0010] 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:

[0011] Randomly generate an initial population containing a number of different initial individuals; 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;

[0012] 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:

[0013] 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;

[0014] 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;

[0015] Generate an updated population based on all current individuals and the newly added individuals corresponding to each current individual;

[0016] According to the objective function model, the objective function value corresponding to each individual in the updated population and the objective function value corresponding to the updated individual are calculated; the objective function value corresponding to each individual in the updated population 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 regarded as the individual to be merged;

[0017] Perform constraint verification on the updated individuals in several preset grid operation scenarios to determine whether the updated individuals simultaneously meet the unit output constraints, power balance constraints, and reserve capacity constraints in all preset grid operation scenarios. If so, generate a secondary updated population based on the updated individuals and each individual to be merged; otherwise, generate a secondary updated population based on each individual to be merged;

[0018] Calculate the probability deviation of each individual in the twice-updated population with all historical output probability distribution scenarios, and then determine whether the probability deviation calculation result satisfies both the first probability deviation constraint and the second probability deviation constraint. If so, use the twice-updated population as the current population when the next population update operation is performed; if not, randomly adjust the individuals in the twice-updated population, and use the randomly adjusted population as the current population when the next population update operation is performed;

[0019] Add the current iteration count value to the preset iteration count increment;

[0020] The first probability deviation constraint is used to characterize the degree of probability distribution deviation of all historical output probability distribution scenarios; the second probability deviation constraint is used to characterize the degree of probability distribution deviation of a single historical output probability distribution scenario.

[0021] 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:

[0022] Randomly select several current individuals from the current population;

[0023] After performing crossover and mutation operations on each current individual, a mutated individual is generated;

[0024] Compare the sum of the outputs of the thermal power units, wind power units, and photovoltaic units in the mutated individuals with the preset total operating load of the power system;

[0025] 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 to generate a reduced output of the thermal power unit; and an updated individual is generated according to the reduced output of the thermal power unit and the unupdated data in the mutated individual;

[0026] 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.

[0027] Preferably, the updating of each current weight coefficient of each current individual in the current population one by one, and generating a new individual derived from the current individual during each update, includes:

[0028] 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, generate the coefficient value range corresponding to the current weight coefficient;

[0029] Extracting a number of coefficients from the coefficient value range whose similarity to the current weight coefficient is greater than a preset similarity threshold as the current adjacent weight coefficients corresponding to the current weight coefficient;

[0030] 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.

[0031] Preferably, the generation of the preset power grid operation scenario includes:

[0032] 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 scenarios are used to characterize scenarios where wind power output or photovoltaic output is in an unstable state.

[0033] Each renewable energy output scenario is used as a preset grid operation scenario for constraint verification.

[0034] Preferably, 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:

[0035] Extracting a wind power output probability distribution and a photovoltaic output probability distribution from each historical output probability distribution scenario; wherein the wind power output probability distribution includes: different wind power output probability values ​​corresponding to different wind power output intervals, and different photovoltaic output probability values ​​corresponding to different photovoltaic output intervals;

[0036] Determine the target wind power output probability value corresponding to the updated individual 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;

[0037] Determine the target photovoltaic output probability value corresponding to the updated individual according to the photovoltaic output interval corresponding to the output of the photovoltaic unit in the updated individual in the photovoltaic output probability distribution;

[0038] 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 population after the second update is used as the current population when the population update operation is performed next time;

[0039] 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;

[0040] 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 population update operation is performed next time.

[0041] Preferably, the objective function model includes:

[0042] ;

[0043] ;

[0044] Where, Indicates taking the maximum value of the three absolute value items;

[0045] It means minimizing the maximum value of the three absolute value terms;

[0046] Represents thermal power output cost and cost reference value Deviation value of

[0047] Indicates pollution emissions and pollution emission reference values Deviation value of Represents the new energy utilization rate and the ideal value of new energy utilization rate Deviation value of

[0048] 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;

[0049] 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, For the The fixed cost of a thermal power unit, 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;

[0050] 、 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, For the The preset reference output of each PV unit.

[0051] Preferably, the unit output constraint includes:

[0052] ;

[0053] ;

[0054] ;

[0055] in, For the The output limit of each thermal power unit, For the The lower limit of the output of a thermal power unit, is the upper limit of wind turbine output, The output limit of the photovoltaic unit;

[0056] The power balance constraint includes:

[0057] ;

[0058] in, is the network loss value, is the total load demand of the system.

[0059] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0060] An embodiment of the present invention provides a power system dispatching device combined with new energy, comprising: a sub-model construction module, an objective function model construction module, a model solving module, and an output dispatching module;

[0061] The sub-model construction module is used to construct a first function model with the goal of minimizing thermal power output cost based on thermal power generation data and thermal power generation cost data; construct a second function model with the goal of minimizing pollution emissions based on renewable energy generation data, renewable energy pollution emission data, and thermal power pollution emission data; and construct a third function model with the goal of maximizing renewable energy utilization rate based on renewable energy generation data and preset power generation data; wherein the renewable energy generation data includes: photovoltaic output data and wind power output data;

[0062] The objective function model construction module is configured 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 thermal power output cost, minimizing pollution emissions, and maximizing new energy utilization; wherein the constraints corresponding to the objective function model include: unit output constraint, power balance constraint, and reserve capacity constraint;

[0063] The model solving module is configured to solve the objective function model under the constraints of the unit output constraint, the power balance constraint, and the reserve 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;

[0064] 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.

[0065] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0066] 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. When the processor executes the computer program, it implements the power system scheduling method of combined new energy as described in the above-mentioned embodiment of the invention.

[0067] The following beneficial effects are achieved by implementing the present invention:

[0068] The embodiment of the present invention provides a method, apparatus, and terminal device for dispatching a power system in conjunction with new energy sources. The present invention establishes a first function model with the goal of minimizing thermal power output costs, a second function model with the goal of minimizing pollution emissions, and a third function model with the goal of maximizing the utilization rate of new energy sources. Different weight coefficients are assigned to the first function model, the second function model, and the third function model, so that a comprehensive objective function model can be derived. The constraints corresponding to the objective function model include unit output constraints, power balance constraints, and spare capacity constraints. The objective function model is then solved under these constraints to generate a target dispatch decision. Finally, the unit output in the power system is dispatched according to the target dispatch decision. Compared with the prior art, the present invention can comprehensively consider the power generation of thermal power and new energy sources, optimize from multiple aspects such as cost, pollution emissions, and new energy utilization, and achieve a balance of multiple objectives while satisfying multiple constraints on the operation of the power system. Therefore, the present invention realizes the joint scheduling of thermal power and new energy, and the constructed multi-objective function model enables the comprehensive consideration of the costs, pollution emissions and utilization rate of thermal power and new energy during scheduling, which not only reduces the power generation cost and pollution emissions, but also improves the utilization rate of new energy, realizes the efficient utilization of new energy in the power system, and improves the power supply quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The present invention provides a flowchart of a method for dispatching a power system in conjunction with new energy sources.

[0070] Figure 2 It is a flowchart of a multi-objective distributed robust power dispatching method provided by another embodiment of the present invention.

[0071] Figure 3It is a structural diagram of a power system dispatching device combined with new energy provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0073] like Figure 1 FIG. 1 is a flow chart of a method for dispatching a power system in conjunction with new energy sources according to an embodiment of the present invention. The method for dispatching a power system in conjunction with new energy sources includes:

[0074] Step S1: constructing a first function model with the goal of minimizing thermal power output cost based on thermal power generation data and thermal power generation cost data; constructing a second function model with the goal of minimizing pollution emissions based on renewable energy generation data, renewable energy pollution emission data, and thermal power pollution emission data; and constructing a third function model with the goal of maximizing renewable energy utilization rate based on renewable energy generation data and preset power generation data; wherein the renewable energy generation data includes: photovoltaic output data and wind power output data;

[0075] 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 goals of minimizing thermal power output cost, minimizing pollution emissions, and maximizing new energy utilization; wherein the constraints corresponding to the objective function model include: unit output constraint, power balance constraint, and reserve capacity constraint;

[0076] Step S3: Solving the objective function model under the constraints of the unit output constraint, power balance constraint, and reserve capacity constraint 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 turbine unit, and the output of the photovoltaic unit;

[0077] Step S4: dispatching the output of thermal power generation units, wind power generation units and photovoltaic generation units in the power system according to the target dispatching decision.

[0078] In step S1, in a preferred embodiment, the thermal power generation data primarily includes the output of the thermal power units, i.e., the power generation or power generated by the thermal power units in different time periods or operating conditions, reflecting the operating status and power generation capacity of the thermal power units. The thermal power generation cost data primarily includes power generation cost data, including fuel costs, environmental governance costs, and fixed costs. Using the thermal power generation data and thermal power generation cost data as input parameters of the function model, a first function model can be constructed for calculating the output cost of the thermal power units, with the goal of minimizing the thermal power output cost.

[0079] Furthermore, when constructing a second function model with the goal of minimizing pollution emissions, the present invention can combine new energy power generation data with its pollution emission data, which can solve the limitation of traditional scheduling that only focuses on a single pollution source of thermal power, thereby improving the accuracy of calculating pollution emissions, so that pollution emissions can be further reduced when the optimal target scheduling decision is made and scheduling is performed according to the target scheduling decision.

[0080] It can be understood that 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.

[0081] When constructing a 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 subsequently optimizing the dispatching strategy of the power system, the utilization rate of new energy and the stability of the power system can be improved.

[0082] For step S2, in a preferred embodiment, the objective function model includes:

[0083] ;

[0084] ;

[0085] Where, Indicates taking the maximum value of the three absolute value items;

[0086] It means minimizing the maximum value of the three absolute value terms;

[0087] Represents thermal power output cost and cost reference value Deviation value of

[0088] Indicates pollution emissions and pollution emission reference values Deviation value of Represents the new energy utilization rate and the ideal value of new energy utilization rate Deviation value of

[0089] 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;

[0090] 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, For the The fixed cost of a thermal power unit, 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;

[0091] 、 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, For the The preset reference output of each PV unit.

[0092] 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 at present. 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.

[0093] 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 is made as small as possible, thereby balancing the three objectives as a whole, avoiding the situation where a certain objective has a large deviation, and achieving the optimization effect of multi-objective balance.

[0094] Schematically, the embodiment of the present invention can be The setting of , dynamically balances multiple goals. The specific meaning is as follows:

[0095] When the economic cost orientation (i.e. When the weight of the thermal power output cost is high (the weight is large), the weighted difference between the thermal power output cost and the reference value can be minimized. Under the constraints, the total operating costs of thermal power, wind power, and photovoltaic power can be prioritized. Furthermore, the output of low-cost renewable energy (wind power and photovoltaic power) can be increased, while the output of high-cost thermal power can be reduced. The unit mix can be optimized, prioritizing the dispatch of units with lower marginal costs.

[0096] When pollution emission is the main When the weight of the reference value is high, the weighted difference between pollution emissions and the reference value can be minimized. For example, priority can be given to reducing carbon emissions from thermal power units, increasing the proportion of clean energy, limiting the output of high-emission thermal power units, and prioritizing low-emission units. Maximizing the output of wind power and photovoltaic power generation can reduce reliance on thermal power.

[0097] When the new energy utilization rate is taken as the guide (i.e. When the weight of the renewable energy utilization rate is large, the weighted difference between the renewable energy utilization rate and the reference value can be minimized, thereby maximizing renewable energy consumption and reducing wind and solar power curtailment. For example, priority can be given to the predicted output of wind power and photovoltaic power, and energy storage charging and discharging strategies can be dynamically adjusted to smooth out fluctuations in renewable energy.

[0098] 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 the scheduling requirements. Dominant, can minimize the deviation of economic cost from reference value, when The deviation of pollution emissions from the reference value is minimized. If the energy consumption is dominant, the deviation of the new energy utilization rate from the ideal value (1) is minimized.

[0099] In a preferred embodiment, the unit output constraint includes: thermal power unit output constraint, wind power unit output constraint and photovoltaic unit output constraint;

[0100] The output constraints of the thermal power units include:

[0101] ;

[0102] The wind turbine output constraint includes:

[0103] ;

[0104] The photovoltaic unit output constraint includes:

[0105] ;

[0106] in, For the The output limit of each thermal power unit, For the The lower limit of the output of a thermal power unit, is the upper limit of wind turbine output, The output limit of the photovoltaic unit;

[0107] Furthermore, the power balance constraint includes:

[0108] ;

[0109] in, is the network loss value, is the total load demand of the system.

[0110] Furthermore, the reserve capacity constraint includes positive and negative spinning reserve machine constraints; the positive spinning reserve capacity constraint is:

[0111] ;

[0112] The negative spinning reserve capacity constraint is:

[0113] ;

[0114] Among them, U SR For the backup demand of the conventional system, we can take the output of the largest thermal power unit in the current dispatch period. 、 、 、 are the demand coefficients of wind farm and photovoltaic farm output for positive and negative spinning reserves, respectively.

[0115] Therefore, the embodiment of the present invention ensures that various types of units do not exceed their physical and technical limitations during operation by constructing unit output constraints (including thermal power units, wind power units, and photovoltaic units) for the objective function model, thereby avoiding system instability or safety accidents caused by unit overload.

[0116] Power balance constraints ensure a balanced supply and demand in the system, meaning the total amount of power generated equals the system's load demand plus network losses. Furthermore, reserve capacity constraints (including positive and negative spinning reserves) provide the power system with a buffer against unexpected situations (such as a sudden drop in renewable energy output or a sudden increase in load), thereby improving the power system's flexibility and reliability.

[0117] Regarding 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 reserve capacity constraint to generate a target scheduling decision corresponding to the objective function model includes:

[0118] Randomly generate an initial population containing a number of different initial individuals; 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;

[0119] 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:

[0120] 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;

[0121] 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;

[0122] Generate an updated population based on all current individuals and the newly added individuals corresponding to each current individual;

[0123] According to the objective function model, the objective function value corresponding to each individual in the updated population and the objective function value corresponding to the updated individual are calculated; the objective function value corresponding to each individual in the updated population is compared with the objective function value of the updated individual, and the individual with an objective function value greater than the objective function value of the updated individual is regarded as the individual to be merged;

[0124] Perform constraint verification on the updated individuals in several preset grid operation scenarios to determine whether the updated individuals simultaneously meet the unit output constraints, power balance constraints, and reserve capacity constraints in all preset grid operation scenarios. If so, generate a secondary updated population based on the updated individuals and each individual to be merged; otherwise, generate a secondary updated population based on each individual to be merged;

[0125] Calculate the probability deviation of each individual in the twice-updated population with all historical output probability distribution scenarios, and then determine whether the probability deviation calculation result satisfies both the first probability deviation constraint and the second probability deviation constraint. If so, use the twice-updated population as the current population when the next population update operation is performed; if not, randomly adjust the individuals in the twice-updated population, and use the randomly adjusted population as the current population when the next population update operation is performed;

[0126] Add the current iteration count value to the preset iteration count increment;

[0127] The first probability deviation constraint is used to characterize the degree of probability distribution deviation of all historical output probability distribution scenarios; the second probability deviation constraint is used to characterize the degree of probability distribution deviation of a single historical output probability distribution scenario.

[0128] In a preferred embodiment, the initialization step of the present invention may be as follows: 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;

[0129] 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;

[0130] 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 thermal power output cost, minimizing pollution emissions and maximizing new energy utilization, which can be: ;

[0131] 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:

[0132] : The minimum expected value of economic cost.

[0133] : Pollution emissions are at their lowest level in history.

[0134] : The maximum theoretical value of new energy utilization rate (usually set to 1).

[0135] In a preferred embodiment, Figure 2 The flowchart of the multi-objective distributed robust power dispatching method shown in FIG. 1 includes the following steps:

[0136] Step 1: Obtain grid operation-related data and new energy forecast data from the grid platform and the new energy forecast platform, classify and label the data, and divide the data into multiple optimization targets for input into the multi-objective decomposition model and grid data and new energy forecast data for input into the comprehensive model, so as to facilitate the subsequent decomposition processing of the multiple targets;

[0137] 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;

[0138] 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 mentioned above with the goals of minimizing thermal power output cost, minimizing pollution emissions, and maximizing renewable energy utilization), so that the integrated model can optimize the single optimization objective and find the global optimal solution.

[0139] Step 4: By verifying the worst case scenario using the Pareto global optimal strategy of the MOEA / D evolutionary algorithm and the distributed robust model, a global optimal solution search is performed for the single optimization objective.

[0140] Step 5: The grid's new energy data and the decomposed single objectives are integrated and input into the MOEA / D evolutionary algorithm for optimization and solution, searching for a multi-objective Pareto global optimal solution that satisfies relevant constraints, i.e., the target scheduling decision corresponding to the objective function model.

[0141] Step 6: Perform robust performance analysis on the evolved global optimal dispatch result (the target dispatch decision corresponding to the objective function model). Analyze the results in the 1-norm and ∞-norm probability sets to determine whether the robust performance meets the grid requirements. If it does not meet the requirements, re-optimize the evolutionary algorithm.

[0142] 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 curtailment data and other results of the optimized dispatch, so as to dispatch the output of the 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.

[0143] 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:

[0144] 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-objective 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 weight of the high new energy utilization rate will be set to a larger value, but there will not be a large gap between the overall weights.

[0145] * is the reference point. The goal is to approximate the Pareto front (PF) of the MOP into N scalar subproblems by decomposing it. The objective function of the subproblem is as follows:

[0146]

[0147] in , represents the objective function, where x is a variable parameter including the output of thermal power units, the output of wind turbines, the utilization rate of new energy, the single objective function, and the reserve capacity. right is continuous.

[0148] The above case is decomposed using Chebyshev decomposition, and the three goals of minimizing thermal power output, minimizing emission pollution, and maximizing renewable energy utilization are combined into a single goal based on weights:

[0149] ;

[0150] Finally, the weight vector The setting of the target scheduling is carried out, and a dynamic trade-off is made among multiple objectives to obtain the optimal solution that satisfies the multi-objective balance, that is, the target scheduling decision.

[0151] 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 operations such as crossover and mutation in subsequent iterations, thereby searching for the optimal solution in the entire solution space and avoiding falling into local optimality.

[0152] Moreover, by introducing weight coefficients and adjacent weight sets, the present invention can dynamically balance 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.

[0153] Specifically, in the process of iterative optimization, after obtaining the updated individuals, 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.

[0154] 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;

[0155] In a preferred embodiment, the present invention can correct the mutated individuals after performing the crossover mutation operation and then output the updated individuals. Therefore, the present invention can also perform a solution correction operation before marking the individuals to be merged, specifically:

[0156] 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:

[0157] Randomly select several current individuals from the current population;

[0158] After performing crossover and mutation operations on each current individual, a mutated individual is generated;

[0159] Compare the sum of the outputs of the thermal power units, wind power units, and photovoltaic units in the mutated individuals with the preset total operating load of the power system;

[0160] 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 to generate a reduced output of the thermal power unit; and an updated individual is generated according to the reduced output of the thermal power unit and the unupdated data in the mutated individual;

[0161] 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.

[0162] 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 requirements 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.

[0163] 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. 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.

[0164] Schematically, the mutated individuals generated by cross-mutation have outputs of thermal power units, wind turbine units, and photovoltaic units of 550MW, 220MW, and 120MW, respectively.

[0165] 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.

[0166] 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.

[0167] In a preferred embodiment, when obtaining the updated individual, the present invention can also extract a local solution in the adjacent weight set that is more advantageous than the updated individual. That is, the individual to be merged can be calculated and extracted based on the current adjacent weight coefficients corresponding to each individual in the population. The process of marking the individual to be merged is:

[0168] The method of updating each current weight coefficient of each current individual in the current population one by one and generating a new individual derived from the current individual during each update includes:

[0169] 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, generate the coefficient value range corresponding to the current weight coefficient;

[0170] Extracting a number of coefficients from the coefficient value range whose similarity to the current weight coefficient is greater than a preset similarity threshold as the current adjacent weight coefficients corresponding to the current weight coefficient;

[0171] 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.

[0172] 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.

[0173] Extracting adjacent weight coefficients from the coefficient value range and replacing them with the current individual to generate a replaced individual further enriches the population composition. Because each replaced individual only contains one of the current adjacent weight coefficients, this localized variation ensures the relevance of the new individual to the original while introducing new features, helping the algorithm escape local optima and increase the likelihood of finding the global optimal solution.

[0174] 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.

[0175] By focusing on adjacent weight coefficients and their corresponding function values, the present invention enables the algorithm to perform a more detailed search within the local space. Adjacent weight coefficients reflect a set of weights that are highly similar to the current weight coefficient. By mining these local regions, high-quality solutions that might be overlooked in a global search can be discovered, preventing the algorithm from prematurely falling into the global optimal solution and improving its ability to search for the optimal solution within the local space.

[0176] 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.

[0177] Furthermore, after obtaining the updated individual, the constraint verification process of the present invention is as follows:

[0178] 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;

[0179] If feasible, the updated individuals and individuals to be merged will be used to generate an updated population;

[0180] 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.

[0181] 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.

[0182] Each renewable energy output scenario is used as a preset grid operation scenario for constraint verification.

[0183] Then, after obtaining the preset grid operation scenario, constraint verification is performed on the updated individuals in several preset 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 grid operation scenarios;

[0184] 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;

[0185] 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 eliminated and the updated population is directly generated based on the individuals to be merged.

[0186] The constraint verification process described above in the present invention ensures the feasibility and safety of the solutions (i.e., updated individuals) 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.

[0187] Therefore, when generating the second updated population, the results of constraint verification will directly influence which individuals are retained. By removing individuals that do not meet the constraints and retaining 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 practical applications.

[0188] Specifically, during the constraint verification process, the following constraint judgment and robust scenario verification can be performed:

[0189] Generate K extreme scenarios (e.g., wind power output at the lowest 10%, photovoltaic output at the lowest 10%) based on the 1-norm and ∞-norm constraints corresponding to the historical or predicted renewable energy output data.

[0190] In extreme scenarios, the updated individual y' is checked to see whether it meets 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.

[0191] Furthermore, in a preferred embodiment, after obtaining the second updated population, the embodiment of the present invention can also evaluate the performance of the second updated population under 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, thereby ensuring that the second updated population 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:

[0192] Extracting a wind power output probability distribution and a photovoltaic output probability distribution from each historical output probability distribution scenario; wherein the wind power output probability distribution includes: different wind power output probability values ​​corresponding to different wind power output intervals, and different photovoltaic output probability values ​​corresponding to different photovoltaic output intervals;

[0193] Determine the target wind power output probability value corresponding to the updated individual 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;

[0194] Determine the target photovoltaic output probability value corresponding to the updated individual according to the photovoltaic output interval corresponding to the output of the photovoltaic unit in the updated individual in the photovoltaic output probability distribution;

[0195] 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 population after the second update is used as the current population when the population update operation is performed next time;

[0196] 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;

[0197] 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 population update operation is performed next time.

[0198] 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.

[0199] By comparing and adjusting with historical output probability distribution scenarios, we can ensure 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.

[0200] 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, it is possible to grasp the deviation of the new energy output probability distribution from the historical distribution as a whole and comprehensively consider the deviations of all scenarios. 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.

[0201] 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:

[0202]

[0203] 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 deviation allowed under the 1-norm and ∞-norm constraints. When the successful probability set is constructed, 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:

[0204] The first step is to extract the scenario probability distribution, which extracts the probability distribution of wind power and photovoltaic power output from the renewable energy forecast data. For example, the wind power output between 100MW and 200MW is divided into 10 intervals, and the probability of each interval is p1, p2, ..., p10;

[0205] Next, the probability deviation is calculated: the target wind power output probability value corresponding to the output of the wind turbine in the updated individual is calculated, and the target photovoltaic output probability value corresponding to the output of the photovoltaic unit in the updated individual is determined, thereby obtaining the deviation from the probability distribution of wind power and photovoltaic output;

[0206] 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 distribution-robust constraint, and the population after the second update can be directly used as the current population when the next population update operation is performed.

[0207] In step S4, the present invention implements scheduling according to target scheduling decisions, achieving a multi-objective optimization effect: minimizing thermal power output costs, minimizing pollution emissions, and maximizing renewable energy utilization. For example, by implementing target scheduling decisions, increasing wind and photovoltaic power output and reducing thermal power use in power system scheduling can reduce costs and pollution while increasing the proportion of renewable energy consumption.

[0208] 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 corresponding device embodiments;

[0209] An embodiment of the present invention provides a power system dispatching device combined with new energy, comprising: a sub-model construction module, an objective function model construction module, a model solving module, and an output dispatching module;

[0210] The sub-model construction module is used to construct a first function model with the goal of minimizing thermal power output cost based on thermal power generation data and thermal power generation cost data; construct a second function model with the goal of minimizing pollution emissions based on renewable energy generation data, renewable energy pollution emission data, and thermal power pollution emission data; and construct a third function model with the goal of maximizing renewable energy utilization rate based on renewable energy generation data and preset power generation data; wherein the renewable energy generation data includes: photovoltaic output data and wind power output data;

[0211] The objective function model construction module is configured 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 thermal power output cost, minimizing pollution emissions, and maximizing new energy utilization; wherein the constraints corresponding to the objective function model include: unit output constraint, power balance constraint, and reserve capacity constraint;

[0212] The model solving module is configured to solve the objective function model under the constraints of the unit output constraint, the power balance constraint, and the reserve 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;

[0213] 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.

[0214] It should be noted that the device embodiments described above are merely illustrative, 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 across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the 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. A person of ordinary skill in the art can understand and implement the present invention without paying any creative effort.

[0215] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, 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.

[0216] Based on the above-mentioned various embodiments of the power system dispatching method combined with new energy, the present invention provides corresponding embodiments of terminal equipment items.

[0217] An embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for dispatching a power system with combined new energy as described in any method embodiment of the present invention.

[0218] The terminal device may be a computing terminal device such as a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0219] 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), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device and connects various parts of the entire terminal device using various interfaces and lines.

[0220] The memory can be used to store the computer program. The processor implements the various functions of the terminal device by running or executing the computer program stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system, at least one application required for a function, and the data storage area may store data generated based on the use of the mobile phone. Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0221] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles 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 renewable energy generation data, renewable energy pollution emission data, and thermal power pollution emission data, with the goal of minimizing pollution emissions; and a third function model is constructed based on renewable energy generation data and preset power generation data, with the goal of maximizing renewable energy utilization rate; wherein the renewable 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; wherein the constraints corresponding to the objective function model include: unit output constraint, power balance constraint, and reserve capacity constraint; 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; dispatching the output of thermal power units, wind power units, and photovoltaic units in the power system according to the target dispatch decision; Solving the objective function model under the constraints of the unit output constraint, the power balance constraint, and the reserve capacity constraint to generate a target scheduling decision corresponding to the objective function model includes: Randomly generate an initial population containing a number of different initial individuals; 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 updated population and the objective function value corresponding to the updated individual are calculated; the objective function value corresponding to each individual in the updated population 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 regarded as the individual to be merged; Perform constraint verification on the updated individuals in several preset grid operation scenarios to determine whether the updated individuals simultaneously meet the unit output constraints, power balance constraints, and reserve capacity constraints in all preset grid operation scenarios. If so, generate a secondary updated population based on the updated individuals and each individual to be merged; otherwise, generate a secondary updated population based on each individual to be merged; Calculate the probability deviation of each individual in the twice-updated population with all historical output probability distribution scenarios, and then determine whether the probability deviation calculation result satisfies both the first probability deviation constraint and the second probability deviation constraint. If so, use the twice-updated population as the current population when the next population update operation is performed; if not, randomly adjust the individuals in the twice-updated population, and use the randomly adjusted population as the current population when the next population update operation is performed; Add the current iteration count value to the preset iteration count increment; The first probability deviation constraint is used to characterize the degree of probability distribution deviation of all historical output probability distribution scenarios; the second probability deviation constraint is used to characterize the degree of probability distribution deviation of a single historical output probability distribution scenario.

2. The method for dispatching a power system in conjunction with new energy sources according to claim 1, 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 several current individuals from the current population; After performing crossover and mutation operations on each current individual, a mutated individual is generated; Compare the sum of the outputs of the thermal power units, wind power units, and photovoltaic units in the mutated individuals 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 to generate a reduced output of the thermal power unit; 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.

3. The method for dispatching a power system in conjunction with new energy sources as claimed in claim 2, characterized in that: The method of updating each current weight coefficient of each current individual in the current population one by one and generating a new individual derived from the current individual during each update includes: 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, generate the coefficient value range corresponding to the current weight coefficient; Extracting a number of coefficients from the coefficient value range whose similarity to the current weight coefficient is greater than a preset similarity threshold as the current adjacent weight coefficients 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.

4. The method for dispatching a power system in conjunction with new energy sources as claimed in claim 3, wherein: 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, extracting a plurality of different renewable energy output scenarios from the operation scenarios of the power system; wherein the renewable energy output scenarios are used to represent scenarios in which wind power output or photovoltaic output is in an unstable state; Each renewable energy output scenario is used as a preset grid operation scenario for constraint verification.

5. The method for dispatching a power system in conjunction with new energy sources as claimed in claim 4, 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 a wind power output probability distribution and a photovoltaic output probability distribution from each historical output probability distribution scenario; wherein the wind power output probability distribution includes: different wind power output probability values ​​corresponding to different wind power output intervals, and different photovoltaic output probability values ​​corresponding to different photovoltaic output intervals; Determine the target wind power output probability value corresponding to the updated individual 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; Determine the target photovoltaic output probability value corresponding to the updated individual according to the photovoltaic output interval corresponding to the output of the photovoltaic unit in the updated individual in the photovoltaic output probability distribution; 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 population after the second update 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 population update operation is performed next time.

6. The method for dispatching a power system in conjunction with new energy sources as claimed in claim 5, characterized in that: The objective function model includes: ； ； Where, Indicates taking the maximum value of the three absolute value items; It means minimizing the maximum value of the three absolute value terms; Represents thermal power output cost and cost reference value Deviation value of Indicates pollution emissions and pollution emission reference values 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, For the The fixed cost of a thermal power unit, 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, For the The preset reference output of each PV unit.

7. The method for dispatching a power system in conjunction with new energy sources as claimed in claim 6, characterized in that: The unit output constraints include: ； ； ； in, For the The output limit of each thermal power unit, For the The lower limit of the output of a thermal power unit, 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.

8. 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 thermal power output cost based on thermal power generation data and thermal power generation cost data; construct a second function model with the goal of minimizing pollution emissions based on renewable energy generation data, renewable energy pollution emission data, and thermal power pollution emission data; and construct a third function model with the goal of maximizing renewable energy utilization rate based on renewable energy generation data and preset power generation data; wherein the renewable energy generation data includes: photovoltaic output data and wind power output data; The objective function model construction module is configured 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 thermal power output cost, minimizing pollution emissions, and maximizing new energy utilization; wherein the constraints corresponding to the objective function model include: unit output constraint, power balance constraint, and reserve capacity constraint; The model solving module is used to solve the objective function model under the constraints of the unit output constraint, power balance constraint and 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 solving of the objective function model under the constraints of the unit output constraint, power balance constraint and spare capacity constraint, and generating a target scheduling decision corresponding to the objective function model includes: randomly generating an initial population containing a number of different initial individuals; wherein, each initial individual includes: the output of the thermal power unit, the output of the wind power unit, the output of the photovoltaic unit and a number of initial weights Coefficient; 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 at each update, a new individual derived from the current individual is generated; wherein, initially, the initial weight coefficient is used as the current weight coefficient; according to all current The objective function value corresponding to each individual in the updated population and the objective function value corresponding to the updated individual are calculated according to the objective function model; the objective function value corresponding to each individual in the updated population 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 used as the individual to be merged; the constraint verification of the updated individual is performed in several preset power grid operation scenarios to determine whether the updated individual satisfies the unit output constraint, power balance constraint and spare capacity constraint in all preset power grid operation scenarios. If so, a new individual to be merged is generated according to the updated individual and the individuals to be merged. The population after the second update; if not, generate the second updated population according to each individual to be merged; calculate the probability deviation of each individual in the second updated population with all 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 second updated population as the current population when the population update operation is performed next time; if not, randomly adjust the individuals in the second updated population, and use the randomly adjusted population as the current population when the population update operation is performed next time; add the preset iteration number increment to the current iteration number value; wherein, 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; 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.

9. A terminal device, characterized in that: The method comprises 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, the method for dispatching a power system in combination with new energy sources as described in any one of claims 1 to 7 is implemented.

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