Multi-energy complementary power grid joint scheduling optimization method considering wind-solar load uncertainty

Abstract

The invention discloses a multi-energy complementary power grid combined dispatching optimization method considering wind-solar load uncertainty, and belongs to the technical field of power grid dispatching optimization. The uncertainty is quantized by introducing a nonlinear fuzzy opportunity constraint to represent the wind-light load uncertainty in a scheduling scene, a dynamic multi-target genetic algorithm is provided, and a complex multi-target optimization model is solved by introducing a dynamic control and disturbance correction mechanism so as to realize a day-ahead optimal plan of joint scheduling. Example results show that compared with a scheme without uncertainty representation, the method has the advantages that optimization target values of low carbon, economical efficiency and stability are improved by 7%, 21% and 25% respectively, reliable guarantee can be provided for power grid operation, diversified requirements of complex scenes are met, and the solving efficiency of multi-target optimization is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid dispatching optimization, and specifically relates to a multi-energy complementary power grid joint dispatching optimization method considering the uncertainty of wind, solar and load. Background Art

[0002] The integration of a high proportion of renewable energy into the power grid poses challenges to maintaining power system power balance. While building a multi-energy grid with complementary benefits can improve power system reliability, the uncertainty of wind and solar loads still impacts grid stability and increases operating costs. Therefore, grid dispatch optimization technologies are needed to achieve multi-objective optimization within the grid and make optimal dispatch decisions.

[0003] In the early stages of new energy technology development, the proportion of wind and solar power installed capacity was low and the impact of grid connection was limited. Grid dispatch was centered around traditional energy sources, and deterministic optimization methods were mostly used, with economic dispatch and stable operation constraints as the core goals. However, traditional models do not fully consider the intermittent nature and uncertainty of renewable energy generation, resulting in insufficient adaptability of their dispatch strategies to fluctuating power sources. As the penetration rate of renewable energy increases, the impact of their output fluctuations on grid security increases, and the limitations of traditional models become more prominent. Single-energy dispatch is unable to smooth out wind and solar power fluctuations, and multi-energy complementary grid joint dispatch has become a solution, using flexible power sources and energy storage to cope with renewable energy fluctuations. Research methods include stochastic programming and robust optimization. Some methods use data-driven methods to improve prediction accuracy and optimize multi-timescale dispatch.

[0004] Current methods typically employ multi-objective linear optimization, which essentially transforms multiple objectives into a single one, reducing the decision-making dimension and making it difficult to achieve a trade-off between multiple conflicting objectives. Furthermore, uncertainty factors significantly impact dispatch optimization, yet traditional methods rarely consider them. While methods such as stochastic programming and fuzzy theory exist, they suffer from inaccurate feature fitting and overly conservative optimization. In the context of a multi-energy complementary power grid, dispatch models must balance conflicting optimization objectives such as environmental protection, economy, and stability. Both existing hierarchical solution strategies and intelligent solution algorithms suffer from poor solution performance. Summary of the Invention

[0005] Purpose of the invention: In order to solve the defects of the above-mentioned prior art, the present invention provides a multi-energy complementary power grid joint scheduling optimization method taking into account the uncertainty of wind, solar and load.

[0006] Technical solution: To achieve the above objectives, the present invention provides a multi-energy complementary power grid joint dispatch optimization method considering the uncertainty of wind, solar and load, which specifically includes the following steps: Step 1: Collect the parameters of each energy supply device in the multi-energy complementary power grid, as well as the day-ahead forecast results of wind power, photovoltaic power generation and power load demand in the regional power grid, as input data for subsequent models; Step 2: Leveraging the complementary and coordinated characteristics of wind, solar, thermal, and storage energy sources, combined with the power source composition and dispatch requirements of the multi-energy complementary power system, a multi-objective and multi-constrained day-ahead joint dispatch optimization model for wind, solar, thermal, and storage multi-energy complementary power systems is constructed from the perspective of day-ahead dispatch, taking into account the optimization goals of system operation economy, stability, and maximizing renewable energy consumption. Step 3: Based on the dispatch optimization model, nonlinear fuzzy chance constraints are used to characterize the uncertainty of wind and solar load in the system power balance constraint in the wind, solar, thermal and storage multi-energy complementary dispatch model; Step 4: A dynamic multi-objective genetic algorithm is used to solve the proposed multi-energy complementary power grid joint dispatch optimization model under the uncertainty of wind, solar and load. Step 5: Obtain the optimal day-ahead dispatch plan, i.e., the day-ahead joint dispatch plan of the power grid under the multi-energy complementary scenario, which includes the output plan of the thermal power units, the working plan of the energy storage battery, and the grid interactive purchase and sale plan.

[0007] Preferably, step 2 includes the following sub-steps: Step 2.1: Set the objective function of the dispatch optimization model. The multiple optimization objectives of the wind, solar, thermal and storage multi-energy complementary power system include wind and solar energy consumption, grid operation costs and net load volatility. Step 2.2: Set the constraints of the dispatch optimization model. The multiple constraints of the wind, solar, thermal and storage multi-energy complementary power system include system power balance constraints, thermal power unit operation constraints, energy storage battery operation constraints and grid interaction constraints, which are represented as constraints 1 to 4 respectively.

[0008] Preferably, in step 2.1, taking into account the low-carbon optimization goal, the maximum wind and solar energy consumption is set as optimization goal 1. Since the overall operation economy of the system is related to fuel consumption costs and energy market transactions, the minimum grid operation cost is set as optimization goal 2. In order to ensure the stability of the system output, the minimum standard deviation of the net load fluctuation borne by thermal power is set as optimization goal 3. Among them, as a clean renewable energy source, wind and solar energy are used in the new energy grid to replace thermal power to reduce fossil energy consumption, improve system economics and environmental benefits, and maximize the absorption of wind and solar energy as optimization goal 1. The formula is as follows: in, Indicates the total amount of renewable energy absorbed in the regional new energy grid system; 、 They represent the amount of wind power and photovoltaic power consumed in the system, respectively. The formulas are as follows: in, 、 express The wind and photovoltaic power consumption of the system at all times, Represents the entire time period of optimized dispatch of regional new energy power grid system; In a wind, solar, thermal, and energy-storage integrated multi-energy grid, the economic efficiency of the power system operation mainly depends on the conventional operating costs of the grid, while other additional costs must also be considered. Minimizing the grid operating cost is taken as the optimization goal 2, which can be expressed by the formula: in, 、 、 、 、 、 They represent the fuel cost, operation and maintenance cost, grid interaction cost, charging and discharging depreciation cost of the energy storage system, pollution gas treatment cost of thermal power units, and deviation penalty cost within the power system respectively. The specific expressions are as follows: in, Indicates the number of thermal power generation units in the system, is the price of fuel consumed by thermal power generation units, express time The site's power generation capacity, To consume the lower calorific value of fuel, Express the unit power generation efficiency of thermal power generation; in, 、 、 、 Represent the unit power operation and maintenance costs of wind turbines, photovoltaic power, thermal power, and energy storage batteries, respectively. 、 、 、 express The output power of wind turbines, photovoltaic power, thermal power, and energy storage batteries at each moment; in, 、 They are The electricity purchase price and power at each moment, 、 are electricity selling price and electricity selling power respectively; in, is the depreciation cost coefficient of the energy storage battery, Indicates the charge and discharge adjustment power of the energy storage battery; in, Indicates the Unit treatment costs for such emissions, express Moment Emissions of similar emissions; in, 、 Indicates the penalty price for overestimation or underestimation of output. 、 express Constantly overestimating and underestimating the penalty for output; Thermal power units, as baseload power sources, provide fundamental power support in the power system. Frequent adjustments to the output of thermal power units directly impact the economic and environmental efficiency of the power system. Efforts must be made to mitigate net load fluctuations so that thermal power units can maintain a quasi-steady-state power output. Minimizing the standard deviation of the net load borne by thermal power generation is the optimization objective. The formula is as follows: in, represents the standard deviation of the net load borne by thermal power, express The net load borne by thermal power in the power system at any moment.

[0009] Preferably, four constraints are set in step 2.2; wherein, constraint 1 considers the balance between system power supply and total load power: in, 、 Respectively represent the charging and discharging power of the energy storage battery, Indicates the total load in the system; Among them, constraint 2 considers the power upper and lower limits and ramp constraints that need to be met during the operation of thermal power units: in, 、 for The upper and lower limits of the site's thermal power generation capacity, express Moment and The output difference at each moment, 、 Climb up and down for thermal power units; Among them, constraint 3 considers the upper and lower limits of energy storage battery power, capacitance, and working state transition constraints: in, Indicates the maximum output power of the energy storage battery, 、 Represent the initial and final states of the energy storage battery capacitor, 、 are the minimum and maximum capacitance of the energy storage battery, express Energy storage battery capacitance status at all times; Among them, constraint 4 considers the grid interaction power constraint: in, 、 Indicates the maximum power for interacting with the grid, purchasing electricity, and selling electricity.

[0010] Preferably, step 3 includes the following steps: Step 3.1: Construct a model based on fuzzy chance-constrained programming. The formula is as follows: in, is a parameter between 0 and 1, is the decision variable in the scheduling model, is the fuzzy variable in the scheduling model, The probability of an event occurring; Step 3.2: In the intraday optimal scheduling, the uncertainty problem of using normal distribution fuzzy parameters to characterize wind and solar load is: in, is the central value of the fuzzy number, corresponding to the predicted mean, is the ambiguity parameter, which is related to the prediction error; Step 3.3: Define fuzzy chance constraints based on system power balance, requiring that the probability of output meeting the load is not less than the fuzzy confidence level pre-specified by the chance constraint. : ; are fuzzy random variables corresponding to wind and solar loads, which obey the normal distribution membership function, and their mean and variance are fuzzy numbers: in, Indicates load The upper and lower bounds of the fuzzy interval of the mean, express The upper and lower bounds of the fuzzy interval of variance are defined similarly Related variables;

[0011] To make it easier to distinguish, Set as , Set as ; Define combination variables , whose mean and variance are: ; ; Using the properties of fuzzy normal distribution, the constraint equivalence is: in is the fuzzy confidence level, is the cumulative distribution function of the standard normal distribution; Step 3.4: Use the credibility measure clear equivalence theorem to transform the fuzzy constraint into a deterministic form. When ≥0.5, the clear equivalence class of chance constraints is: in, is the inverse function of the cumulative distribution function; the final result is the deterministic form of the fuzzy chance constraint on system power balance involving the uncertainty of wind and solar loads; Step 3.5: Introduce the fuzzy chance constraint into the above dispatch optimization model to construct a multi-energy complementary power grid joint dispatch optimization model under wind, solar and load uncertainty.

[0012] Preferably, step 4 includes the following steps: Step 4.1, set the initial parameter population size N, the maximum number of iterations iter max , crossover probability p c , mutation probability p m , switching coefficient ct, threshold coefficient , generate an initial population P0 of size N, each individual is randomly generated, and evaluate the objective function value F(x) of each individual x in the initial population P0; Step 4.2: Select, cross, and mutate the t-th generation offspring population Q according to the dynamic control mechanism. t ; Step 4.3: Merge the t-th generation parent population P t-1 and the offspring population Q t Get the combined population R t ; Step 4.4: For the feasible solution set R t Perform non-dominated sorting and generate the frontier solution set Fronts; Step 4.5: Generate a new parent population P t ; Step 4.6: Apply the perturbation correction strategy to redistribute the candidate solutions into the potential solution space in the later iterations and repeat steps 4.4 and 4.5. Step 4.7: Output the solution set. The Pareto front solution set obtained by the algorithm is P final = {x ∈ Piter max}.

[0013] Preferably, in step 4.2, the dynamic control mechanism is as follows: the dynamic control mechanism adopts a dynamic control mechanism that adjusts the weight coefficient in stages to control the population selection behavior under the roulette strategy. The iterative process is divided into two stages, and the switching time between the two stages is determined by the variable Sure: in, represents the maximum value of iteration, is the switching coefficient; In the first stage, the fitness weight coefficient will be reduced in the early iteration process, and the scope of the population search space will be expanded with the crowding distance penalty, and then gradually approach the optimal solution; the cumulative probability function of the first stage is shown as follows: in, Represents an individual The fitness of For individuals The crowding distance, represents the sum of the fitness of all individuals in the population, It is the weight coefficient of the first iteration stage, which controls the weight of candidate individuals to reduce their fitness in the initial stage, improves population diversity, and avoids falling into local optimality; Among them, in the second stage, population selection is not affected by the early iterative parameter settings, and the selection pressure is adjusted according to the current individual fitness distribution; the cumulative probability function of the second stage is shown as follows: in, It is the weight coefficient of the second stage, which sets a higher parent selection pressure and enhances the selection opportunities of individuals with higher fitness by adjusting the probability formula, ensuring better convergence of the algorithm.

[0014] Preferably, the disturbance correction strategy in step 4.6 is as follows: In later iterations of NSGA-II, the disturbance correction strategy is triggered based on the following conditions: in, is the threshold coefficient; when the convergence speed slows down, based on the current non-dominated solution set , calculate the new boundaries of the individuals: ; in, 、 represents the upper and lower bounds of the potential solution space, For the solution set Inner individual The fitness of is the expansion coefficient, which is used to control the expansion range of the search space; Each individual in Perform Gaussian perturbation operation: ; ;in, is the perturbation intensity; after the operation, projection is performed to correct the boundary and generate a new solution set ,Will Merge with the original population and perform non-dominated sorting and environmental selection.

[0015] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0016] First, the present invention constructs a day-ahead joint dispatch optimization model for wind, solar, thermal, and energy storage to coordinate the dispatch of various power generation units in the power grid, providing reliable guarantees for grid operation and meeting the diverse needs of complex scenarios.

[0017] Second, the present invention introduces nonlinear fuzzy chance constraints to quantify the uncertainty and balance the risks and benefits in operation to cope with the uncertainty of wind and solar load in the scheduling scenario;

[0018] Third, the present invention proposes a dynamic multi-objective genetic algorithm, which accelerates the convergence speed of solving the optimization model, improves the quality of the solution set, and enhances the efficiency of solving multi-objective optimization by introducing dynamic control and disturbance correction mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flowchart of the method of the present invention.

[0020] Figure 2 Schematic diagram of the overall framework of the method of the present invention.

[0021] Figure 3 Schematic diagram of a multi-energy complementary power grid scenario for the method of the present invention.

[0022] Figure 4 : This is a diagram of the day-ahead scheduling results in an embodiment of the method of the present invention; in the figure, (a) is the optimized day-ahead scheduling plan, including energy storage, thermal power output, wind power output, and load power; (b) is the output plan curve of thermal power units with gas and coal as the main fuel sources; (c) is the output plan curve of the energy storage battery and the corresponding state of charge value; (d) is the load and wind and solar power absorption curve predicted day-ahead.

[0023] Figure 5 This is a diagram of actual scheduling results in an embodiment of the method of the present invention.

[0024] Figure 6 This is a comparison chart of the optimization algorithm iterations of the method of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] like Figure 1 As shown, the present invention relates to a multi-energy complementary power grid joint dispatch optimization method considering the uncertainty of wind and solar loads. The overall process framework of the present invention is as follows: Figure 2 As shown, the method process includes inputting data, establishing a multi-energy complementary scheduling model and a model based on a dynamic multi-objective genetic algorithm. The method specifically includes the following steps:

[0027] Step 1: Collect the parameters of each energy supply device in the multi-energy complementary power grid, as well as the day-ahead forecast results of wind power, photovoltaic power generation and power load demand in the regional power grid, as input data for subsequent models;

[0028] The multi-energy complementary power grid mentioned in the method of the present invention is a multi-energy complementary power grid with coordinated complementarity of wind, solar, thermal and storage, such as Figure 3 As shown, its main energy supply methods include wind power generation, photovoltaic power generation, thermal power output, battery discharge, and external grid interactive power purchase, and it can store electricity through energy storage batteries. Its electricity demand mainly comes from user load, energy storage charging, and grid interactive power sales.

[0029] Step 2: Utilizing the complementary and coordinated characteristics of wind, solar, thermal, and storage energy sources, combined with the power source composition and dispatching needs of the multi-energy complementary power system, a multi-objective and multi-constrained day-ahead joint dispatch optimization model for wind, solar, thermal, and storage multi-energy complementary power systems is constructed from the perspective of day-ahead dispatch, taking into account the optimization goals of system operation economy, stability, and maximization of renewable energy consumption. The model is as follows:

[0030] Step 2.1. Set the objective function of the dispatch optimization model. The multiple optimization objectives of the wind, solar, thermal, and storage hybrid power system include wind and solar energy consumption, grid operating costs, and net load volatility. Considering the low-carbon optimization goal, maximizing wind and solar energy consumption is set as optimization objective 1. Because the overall system operation economy involves fuel consumption costs and energy market transactions, minimizing grid operating costs is optimization objective 2. To ensure the stability of system output, minimizing the standard deviation of net load volatility borne by thermal power is optimization objective 3. Among them, as a clean and renewable energy source, wind and solar energy are used in new energy grids to replace thermal power to reduce fossil energy consumption and improve system economics and environmental benefits. Therefore, maximizing the absorption of wind and solar energy is taken as optimization goal 1, and the formula is as follows: in, It represents the total amount of renewable energy absorbed in the regional new energy grid system. 、 They represent the amount of wind power and photovoltaic power consumed in the system, respectively. The formulas are as follows: in, 、 express The wind and photovoltaic power consumption of the system at all times, Represents the entire time period of optimized dispatch of regional new energy power grid system; In a wind, solar, thermal, and energy storage integrated multi-energy grid, the economic efficiency of the power system operation mainly depends on the conventional operating costs of the grid, while other additional costs must also be considered. Therefore, minimizing the grid operating cost is taken as optimization goal 2, expressed by the formula: in, 、 、 、 、 、 They represent the fuel cost, operation and maintenance cost, grid interaction cost, charging and discharging depreciation cost of the energy storage system, pollution gas treatment cost of thermal power units and deviation penalty cost within the power system respectively.

[0031] The specific expression is as follows: in, Indicates the number of thermal power generation units in the system, is the price of fuel consumed by thermal power generation units, express time The site's power generation capacity, To consume the lower calorific value of fuel, Express the unit power generation efficiency of thermal power generation; in, 、 、 、 Represent the unit power operation and maintenance costs of wind turbines, photovoltaic power, thermal power, and energy storage batteries, respectively. 、 、 、 express The output power of wind turbines, photovoltaic power, thermal power, and energy storage batteries at each moment; in, 、 They are The electricity purchase price and power at each moment, 、 are electricity selling price and electricity selling power respectively; in, is the depreciation cost coefficient of the energy storage battery, Indicates the charge and discharge adjustment power of the energy storage battery; in, Indicates the Unit treatment costs for such emissions, express Moment Emissions of similar emissions; in, 、 Indicates the penalty price for overestimation or underestimation of output. 、 express Constantly overestimating and underestimating the penalty for output; Thermal power units, as baseload power sources, provide fundamental power support within the power system. They have high startup and shutdown costs and low ramp rates, resulting in relatively poor operational flexibility. Frequent adjustments to thermal power unit output directly impact the economic and environmental efficiency of the power system. Therefore, efforts must be made to mitigate net load fluctuations, enabling thermal power units to maintain a quasi-steady-state operating state of power output. Minimizing the standard deviation of the net load borne by thermal power generation is the optimization objective3, as shown in the following formula: in, represents the standard deviation of the net load borne by thermal power, express The net load borne by thermal power in the power system at any moment.

[0032] Step 2.2: Set the constraints of the dispatch optimization model. The multiple constraints of the wind, solar, thermal and storage multi-energy complementary power system mainly include system power balance constraints, thermal power unit operation constraints, energy storage battery operation constraints, and grid interaction constraints, which are represented as constraints 1 to 4 respectively. Among them, constraint 1 considers the balance between system power supply and total load power: in, 、 Respectively represent the charging and discharging power of the energy storage battery, Indicates the total load in the system; Among them, constraint 2 considers the power upper and lower limits and ramp constraints that need to be met during the operation of thermal power units: in, 、 for The upper and lower limits of the site's thermal power generation capacity, express Moment and The output difference at each moment, 、 Climb up and down for thermal power units; Among them, constraint 3 considers the upper and lower limits of energy storage battery power, capacitance, and working state transition constraints: in, Indicates the maximum output power of the energy storage battery, 、 Represent the initial and final states of the energy storage battery capacitor, 、 are the minimum and maximum capacitance of the energy storage battery, express Energy storage battery capacitance status at all times; Among them, constraint 4 considers the grid interaction power constraint: in, 、 Indicates the maximum power for interacting with the grid, purchasing electricity, and selling electricity.

[0033] Step 3: Based on the dispatch optimization model, nonlinear fuzzy chance constraints are used to characterize the uncertainty of wind and solar load in the system power balance constraint in the wind, solar, thermal and storage multi-energy complementary dispatch model, including:

[0034] Step 3.1: Construct a model based on fuzzy chance-constrained programming. The formula is as follows: in, is a parameter between 0 and 1, is the decision variable in the scheduling model, is the fuzzy variable in the scheduling model, The probability of an event occurring;

[0035] Step 3.2: In the intraday optimal scheduling, the uncertainty problem of using normal distribution fuzzy parameters to characterize wind and solar load is: in, is the central value of the fuzzy number, corresponding to the predicted mean, is the ambiguity parameter, which is related to the prediction error. are fuzzy random variables corresponding to wind and solar loads, which obey the normal distribution membership function, and their mean and variance are fuzzy numbers: in, Indicates load The upper and lower bounds of the fuzzy interval of the mean, express The upper and lower bounds of the fuzzy interval of variance are defined similarly Related variables;

[0036] Step 3.3: Define fuzzy chance constraints based on system power balance, requiring that the probability of output meeting the load is not less than the fuzzy confidence level pre-specified by the chance constraint. : To make it easier to distinguish, Set as , Set as . Define combination variables , whose mean and variance are: ; ;

[0037] Using the properties of fuzzy normal distribution, the constraint equivalence is: in is the fuzzy confidence level, is the cumulative distribution function of the standard normal distribution;

[0038] Step 3.4: Use the credibility measure clear equivalence theorem to transform the fuzzy constraint into a deterministic form. When ≥0.5, the clear equivalence class of chance constraints is: in, is the inverse function of the cumulative distribution function. The final result is the deterministic form of the fuzzy chance constraint on system power balance involving the uncertainty of wind and solar loads.

[0039] Step 3.5: Introduce the fuzzy chance constraint into the above dispatch optimization model to construct a multi-energy complementary power grid joint dispatch optimization model under wind, solar and load uncertainty.

[0040] Step 4: A dynamic multi-objective genetic algorithm is used to solve the proposed multi-energy complementary power grid joint dispatch optimization model under the uncertainty of wind, solar and load, as follows:

[0041] Step 4.1, set the initial parameter population size N, the maximum number of iterations iter max , crossover probability p c , mutation probability p m , switching coefficient ct, threshold coefficient , generate an initial population P0 of size N, each individual is randomly generated, and evaluate the objective function value F(x) of each individual x in the initial population P0;

[0042] Step 4.2: Select, cross, and mutate the t-th generation offspring population Q according to the dynamic control mechanism. t , the dynamic control mechanism is as follows:

[0043] The dynamic control mechanism uses a dynamic control mechanism that adjusts the weight coefficient in stages to control the population selection behavior under the roulette strategy. The iterative process is divided into two stages, and the switching time between the two stages is determined by the variable Sure: in, represents the maximum value of iteration, is the switching coefficient.

[0044] In the first stage, the fitness weight coefficient will be reduced in the early iteration process, and the scope of the population search space will be expanded with the crowding distance penalty, and then gradually approach the optimal solution. The cumulative probability function of the first stage is shown as follows: in, Represents an individual The fitness of For individuals The crowding distance, represents the sum of the fitness of all individuals in the population, It is the weight coefficient of the first iteration stage, which controls the weight of candidate individuals to reduce fitness in the initial stage, improves population diversity, and avoids falling into local optimality.

[0045] In the second stage, population selection is not affected by the early iteration parameter settings, and the selection pressure is adjusted according to the current individual fitness distribution. The cumulative probability function of the second stage is shown as follows: in, It is the weight coefficient of the second stage, which sets a higher parent selection pressure and enhances the selection opportunities of individuals with higher fitness by adjusting the probability formula, ensuring better convergence of the algorithm.

[0046] Step 4.3: Merge the t-th generation parent population P t-1 and the offspring population Q t Get the combined population R t ;

[0047] Step 4.4: For the feasible solution set R t Perform non-dominated sorting and generate the frontier solution set Fronts;

[0048] Step 4.5: Generate a new parent population P t , the specific operations are as follows:

[0049] Let P t is an empty set, i = 1; P t Merge with the i-th non-dominated frontier Fronts[i] and increment i by 1. Repeat this operation until P t The size of the collection of Fronts[i] is greater than or equal to N; if at this time |P t |< N, then assign congestion to Fronts[i] and sort Fronts[i] in descending order of congestion, and put P t = P t ∪ Fronts[i] [1 : (N - |P t |)];

[0050] Step 4.6: Apply the perturbation correction strategy to redistribute the candidate solutions to the potential solution space in the later iterations and repeat steps 4.4 and 4.5. The perturbation correction strategy is as follows:

[0051] In later iterations of NSGA-II, the disturbance correction strategy is triggered based on the following conditions: in, is the threshold coefficient. When the number of iterations reaches a certain number or the convergence speed slows down, based on the current non-dominated solution set , calculate the new boundaries of the individuals: ; in, 、 represents the upper and lower bounds of the potential solution space, For the solution set Inner individual The fitness of is the expansion coefficient, which is used to control the expansion range of the search space. Each individual in Perform Gaussian perturbation operation: ; ; is the perturbation intensity. After the operation, projection is performed to correct the boundary and generate a new solution set. ,Will Merge with the original population and perform non-dominated sorting and environmental selection.

[0052] Step 4.7: Output the solution set. The Pareto front solution set obtained by the algorithm is P final = {x ∈ Piter max}.

[0053] Step 5: Obtain the optimal day-ahead dispatch plan, i.e., the day-ahead joint dispatch plan of the power grid under the multi-energy complementary scenario, which includes the output plan of the thermal power units, the working plan of the energy storage battery, and the grid interactive purchase and sale plan.

[0054] In this specific embodiment of the present invention, a multi-energy complementary power grid in the study area is used as the research object. The power production scenario on December 1, 2023, is used as a simulation example. The scheduling cycle is 24 hours, and the unit scheduling time is 15 minutes. The system includes 6563MW of photovoltaic power generation capacity, 5212MW of wind power generation capacity, a total thermal power generation capacity of 10,400MW, and a maximum power limit of 3000MW for the energy storage battery system. The output equipment involved in the example is the same as mentioned above. Table 1 shows the abbreviations and operating parameters of each device.

[0055] Table 1

[0056] The actual data of time-of-use electricity prices interacting with the external power grid are shown in Table 2.

[0057] Table 2

[0058] The proposed wind-solar-thermal-storage multi-energy complementary scheduling model under wind-solar uncertainty is solved based on the dynamic multi-objective genetic algorithm. The day-ahead scheduling results are as follows: Figure 4 The day-ahead dispatch plan is the result of optimization based on the day-ahead forecast of the multi-energy complementary power grid day-ahead joint dispatch optimization method taking into account the uncertainty of wind and solar power. The actual dispatch results in the actual scenario on the day are as follows: Figure 5 shown.

[0059] In order to intuitively reflect the optimization target values ​​of the day-ahead plan and the actual results, the comparison between the day-ahead scheduling plan and the actual scheduling results is shown in Table 3.

[0060] Table 3

[0061] To compare and analyze the differences between single-objective and multi-objective scenarios, three additional scenarios were set up in addition to the example scenario. Different objective functions were used as the optimization targets, and the multi-objective optimization results were compared. The results of each single-objective algorithm in each scenario and the results of the multi-objective algorithm in the standard scenario are shown in Table 4.

[0062] Table 4

[0063] When other model conditions are the same, NSGAII, SPEA2, and MOPSO are selected as comparative optimization algorithms to compare the performance of each optimization algorithm in the day-ahead dispatch optimization of multi-energy complementary power grid. The optimization results are shown in Table 5. At the same time, the optimization efficiency in the iterative process is shown in Table 5. Figure 6 .

[0064] Table 5

[0065] The results of the comparative experiments between the day-ahead dispatch plan and the actual dispatch results, the comparative experiments between single-objective and multi-objective scenarios, and the comparative experiments on optimization algorithms further verified the effectiveness and superiority of the method of the present invention in the dispatch optimization problem of wind-solar-thermal-storage multi-energy complementary power grid under the uncertainty of wind and solar power. The results of the case analysis show that the day-ahead dispatch optimization model of wind-solar-thermal-storage multi-energy complementary constructed by this method can coordinate the dispatch of various power sources and construct a multi-objective and multi-constrained dispatch model, which meets the needs of the target scenario. Through nonlinear chance fuzzy constraints, the uncertainty of wind and solar power in the dispatch scenario can be reasonably quantified, and the decision can be optimized to reduce the adverse effects of errors, thereby obtaining better actual dispatch results. The function values ​​of each optimization objective were increased by 7%, 21%, and 25%, respectively. At the same time, the dynamic multi-objective genetic algorithm is superior to other multi-objective optimization algorithms in terms of optimization results and optimization efficiency.

[0066] The present invention provides a method for optimizing the joint dispatch of a multi-energy complementary power grid that considers the uncertainties of wind, solar, and load. It should be noted that the specific technical features described in the above embodiments may be combined in any suitable manner, provided they are not contradictory. To avoid unnecessary repetition, the present invention will not further describe the various possible combinations.

Claims

1. A multi-energy complementary power grid joint dispatch optimization method considering the uncertainty of wind and solar loads, characterized by: The steps include: S1. Collect parameters of various energy supply devices in the multi-energy complementary power grid, as well as day-ahead forecast results of wind power, photovoltaic power generation and electricity load demand in the regional power grid, as input data for subsequent models; S2. Leveraging the complementary and coordinated characteristics of wind, solar, thermal, and storage energy sources, combined with the power source composition and dispatch requirements of the multi-energy complementary power system, a multi-objective and multi-constrained day-ahead joint dispatch optimization model for wind, solar, thermal, and storage multi-energy complementary power systems is constructed, taking into account the optimization goals of system operation economy, stability, and maximizing renewable energy consumption from a day-ahead dispatch perspective. S3. Based on the dispatch optimization model, nonlinear fuzzy chance constraints are used to characterize the uncertainty of wind and solar load in the system power balance constraint of the wind, solar, thermal and storage multi-energy complementary dispatch model; S4. A dynamic multi-objective genetic algorithm is used to solve the proposed multi-energy complementary power grid joint dispatch optimization model under the uncertainty of wind, solar and load. S5. Obtain the optimal day-ahead dispatch plan, that is, the day-ahead joint dispatch plan of the power grid under the multi-energy complementary scenario, including the output plan of thermal power units, the working plan of energy storage batteries, and the grid interactive purchase and sale plan.

2. A multi-energy complementary power grid joint dispatch optimization method considering the uncertainty of wind and solar loads according to claim 1, characterized in that: Step S2 includes the following sub-steps: S2.

1. Set the objective function of the dispatch optimization model. The multiple optimization objectives of the wind, solar, thermal and storage multi-energy complementary power system include wind and solar energy consumption, grid operation costs and net load volatility. S2.

2. Set the constraints of the dispatch optimization model. The multiple constraints of the wind, solar, thermal and storage multi-energy complementary power system include system power balance constraints, thermal power unit operation constraints, energy storage battery operation constraints and grid interaction constraints, which are represented as constraints 1 to constraints 4 respectively.

3. A multi-energy complementary power grid joint dispatch optimization method considering the uncertainty of wind and solar loads according to claim 2, characterized in that: In step S2.1, considering the low-carbon optimization goal, maximizing wind and solar energy consumption is set as optimization goal 1. Since the overall system operation economy involves fuel consumption costs and energy market transactions, minimizing grid operation costs is set as optimization goal 2. To ensure the stability of system output, minimizing the standard deviation of the net load fluctuation borne by thermal power is set as optimization goal 3. Among them, as a clean renewable energy source, wind and solar energy are used in the new energy grid to replace thermal power to reduce fossil energy consumption, improve system economics and environmental benefits, and maximize the absorption of wind and solar energy as optimization goal 1. The formula is as follows: in, Indicates the total amount of renewable energy absorbed in the regional new energy grid system; 、 They represent the amount of wind power and photovoltaic power consumed in the system, respectively. The formulas are as follows: in, 、 express The wind and photovoltaic power consumption of the system at all times, Represents the entire time period of optimized dispatch of regional new energy power grid system; In a wind, solar, thermal, and energy storage integrated multi-energy grid, the economic efficiency of the power system operation depends on the conventional grid operation costs, while also considering other additional costs. Taking the minimum grid operation cost as the optimization goal 2, it can be expressed as follows: in, 、 、 、 、 、 They represent the fuel cost, operation and maintenance cost, grid interaction cost, charging and discharging depreciation cost of the energy storage system, pollution gas treatment cost of thermal power units, and deviation penalty cost within the power system respectively. The specific expressions are as follows: in, Indicates the number of thermal power generation units in the system, is the price of fuel consumed by thermal power generation units, express time The site's power generation capacity, To consume the lower calorific value of fuel, Express the unit power generation efficiency of thermal power generation; in, 、 、 、 Represent the unit power operation and maintenance costs of wind turbines, photovoltaic power, thermal power, and energy storage batteries, respectively. 、 、 、 express The output power of wind turbines, photovoltaic power, thermal power, and energy storage batteries at each moment; in, 、 They are The electricity purchase price and power at each moment, 、 are electricity selling price and electricity selling power respectively; in, is the depreciation cost coefficient of the energy storage battery, Indicates the charge and discharge adjustment power of the energy storage battery; in, Indicates the Unit treatment costs for such emissions, express Moment Emissions of similar emissions; in, 、 Indicates the penalty price for overestimation or underestimation of output. 、 express Constantly overestimating and underestimating the penalty for output; Thermal power units, as baseload power sources, provide fundamental power support in the power system. Frequent adjustments to the output of thermal power units directly impact the economic and environmental efficiency of the power system. Efforts must be made to mitigate net load fluctuations so that thermal power units can maintain a quasi-steady-state power output. Minimizing the standard deviation of the net load borne by thermal power generation is the optimization objective. The formula is as follows: in, represents the standard deviation of the net load borne by thermal power, express The net load borne by thermal power in the power system at any moment.

4. The method for optimizing joint dispatch of a multi-energy complementary power grid considering the uncertainty of wind, solar and load according to claim 2 is characterized in that: Step S2.2 sets four constraints; Among them, constraint 1 considers the balance between system power supply and total load power: in, 、 Respectively represent the charging and discharging power of the energy storage battery, Indicates the total load in the system; Among them, constraint 2 considers the power upper and lower limits and ramp constraints that need to be met during the operation of thermal power units: in, 、 for The upper and lower limits of the site's thermal power generation capacity, express Moment and The output difference at each moment, 、 Climb up and down for thermal power units; Among them, constraint 3 considers the upper and lower limits of energy storage battery power, capacitance, and working state transition constraints: in, Indicates the maximum output power of the energy storage battery, 、 Represent the initial and final states of the energy storage battery capacitor, 、 are the minimum and maximum capacitance of the energy storage battery, express Energy storage battery capacitance status at all times; Among them, constraint 4 considers the grid interaction power constraint: in, 、 Indicates the maximum power for interacting with the grid, purchasing electricity, and selling electricity.

5. The method for optimizing joint dispatch of a multi-energy complementary power grid considering the uncertainty of wind, solar and load according to claim 1 is characterized in that: Step S3 includes the following steps: Step 3.1: Construct a model based on fuzzy chance-constrained programming. The formula is as follows: in, is a parameter between 0 and 1, is the decision variable in the scheduling model, is the fuzzy variable in the scheduling model, The probability of an event occurring; Step 3.2: In the intraday optimal scheduling, the uncertainty problem of using normal distribution fuzzy parameters to characterize wind and solar load is: in, is the central value of the fuzzy number, corresponding to the predicted mean, is the ambiguity parameter, which is related to the prediction error; Step 3.3: Define fuzzy chance constraints based on system power balance, requiring that the probability of output meeting the load is not less than the fuzzy confidence level pre-specified by the chance constraint. : ; are fuzzy random variables corresponding to wind and solar loads, which obey the normal distribution membership function, and their mean and variance are fuzzy numbers: in, Indicates load The upper and lower bounds of the fuzzy interval of the mean, express The upper and lower bounds of the fuzzy interval of variance are defined similarly Related variables; for easy distinction, Set as , Set as ; Define combination variables , whose mean and variance are: ; ; Using the properties of fuzzy normal distribution, the constraint equivalence is: in is the fuzzy confidence level, is the cumulative distribution function of the standard normal distribution; Step 3.4: Use the credibility measure clear equivalence theorem to transform the fuzzy constraint into a deterministic form. When ≥0.5, the clear equivalence class of chance constraints is: in, is the inverse function of the cumulative distribution function, and its final result is the deterministic form of the fuzzy chance constraint on system power balance involving the uncertainty of wind and solar loads; Step 3.5: Introduce the fuzzy chance constraint into the above dispatch optimization model to construct a multi-energy complementary power grid joint dispatch optimization model under wind, solar and load uncertainty.

6. The method for optimizing joint dispatch of a multi-energy complementary power grid considering the uncertainty of wind, solar and load according to claim 1 is characterized in that: Step S4 includes the following steps: Step 4.1, set the initial parameter population size N, the maximum number of iterations iter max , crossover probability p c , mutation probability p m , switching coefficient ct, threshold coefficient , generate an initial population P0 of size N, each individual is randomly generated, and evaluate the objective function value F(x) of each individual x in the initial population P0; Step 4.2: Select, cross, and mutate the t-th generation offspring population Q according to the dynamic control mechanism. t ; Step 4.3: Merge the t-th generation parent population P t-1 and the offspring population Q t Get the combined population R t ; Step 4.4: For the feasible solution set R t Perform non-dominated sorting and generate the frontier solution set Fronts; Step 4.5: Generate a new parent population P t ; Step 4.6: Apply the perturbation correction strategy to redistribute the candidate solutions into the potential solution space in the later iterations and repeat steps 4.4 and 4.

5. Step 4.7: Output the solution set. The Pareto front solution set obtained by the algorithm is P final = {x ∈ Piter max }.

7. A multi-energy complementary power grid joint dispatch optimization method considering the uncertainty of wind, solar and load according to claim 6, characterized in that: In step S4.2, the dynamic control mechanism is as follows: The dynamic control mechanism uses a dynamic control mechanism that adjusts the weight coefficient in stages to control the population selection behavior under the roulette strategy. The iterative process is divided into two stages, and the switching time between the two stages is determined by the variable Sure: in, represents the maximum value of iteration, is the switching coefficient; In the first stage, the fitness weight coefficient will be reduced in the early iteration process, and the scope of the population search space will be expanded with the crowding distance penalty, and then gradually approach the optimal solution; the cumulative probability function of the first stage is shown as follows: in, Represents an individual The fitness of For individuals The crowding distance, represents the sum of the fitness of all individuals in the population, It is the weight coefficient of the first iteration stage, which controls the weight of candidate individuals to reduce their fitness in the initial stage, improves population diversity, and avoids falling into local optimality; Among them, in the second stage, population selection is not affected by the early iterative parameter settings, and the selection pressure is adjusted according to the current individual fitness distribution; the cumulative probability function of the second stage is shown as follows: in, It is the weight coefficient of the second stage, which sets a higher parent selection pressure and enhances the selection opportunities of individuals with higher fitness by adjusting the probability formula, ensuring better convergence of the algorithm.

8. A multi-energy complementary power grid joint dispatch optimization method considering the uncertainty of wind, solar and load according to claim 6, characterized in that: The disturbance correction strategy in step S4.6 is as follows: In later iterations of NSGA-II, the disturbance correction strategy is triggered based on the following conditions: in, is the threshold coefficient; when the convergence speed slows down, based on the current non-dominated solution set , calculate the new boundaries of the individuals: ; ;in, 、 represents the upper and lower bounds of the potential solution space, For the solution set Inner individual The fitness of is the expansion coefficient, which is used to control the expansion range of the search space; Each individual in Perform Gaussian perturbation operation: ; ;in, is the perturbation intensity; after the operation, projection is performed to correct the boundary and generate a new solution set ,Will Merge with the original population and perform non-dominated sorting and environmental selection.

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