Wind, light, water and fire storage day-ahead optimization scheduling method and system

Abstract

The invention discloses a day-ahead optimal scheduling method and system for wind, light, water and fire storage. The method comprises the following steps: constructing a day-ahead optimal scheduling model for wind, light, water and fire storage; establishing a multi-target bald eagle search algorithm, and solving the wind-light-water-fire storage day-ahead optimal scheduling model to obtain an optimal scheduling scheme; according to the multi-target bald eagle search algorithm, an archiving mechanism is added on the basis of a bald eagle search algorithm and used for storing a currently obtained Pareto optimal solution, an elite selection strategy is introduced, an individual is selected from an archive to serve as an elite or a leader, the elite in a group guides other individuals to conduct optimization search, and the multi-target optimization problem is solved. According to the invention, load and wind and light fluctuation can be stabilized, clean energy consumption can be promoted, system operation cost can be reduced, regional multi-energy complementation can be realized, and low-carbon development of a power grid can be promoted. The wind-solar-water-fire storage optimization scheduling system based on the method can be effectively applied to a multi-energy complementary engineering scene.

Description

Technical Field

[0001] The present invention relates to a wind, solar, water, thermal and storage joint optimization scheduling technology, and in particular to a wind, solar, water, thermal and storage day-ahead optimization scheduling method and system. Background Art

[0002] In recent years, the growth trend of new energy power generation represented by wind and solar has been significant. Due to the intermittent, random and volatile nature of wind and solar output, the pressure on peak and frequency regulation of the power grid has been greatly increased, limiting the power grid's ability to absorb wind and photovoltaic power, and causing a large number of wind and photovoltaic power abandonment problems. Based on the complementary characteristics of multiple energy sources, the multi-energy complementary optimization scheduling of hydropower with strong regulation flexibility and energy storage, thermal power and wind power, and photovoltaic power that undertake base load and peak regulation tasks can effectively deal with the new energy absorption and peak regulation problems brought about by the continuous growth of wind power and photovoltaic installed capacity.

[0003] Hydropower is the earliest clean energy to be developed and utilized. It is regarded as a high-quality regulating power source because of its ability to flexibly track load changes and quickly adjust its operating mode. It can effectively smooth out the fluctuations of wind power and photovoltaic power generation while providing power to the power system. The development of energy storage is a key link in solving the output fluctuations caused by large-scale wind and solar grid connection, which is conducive to increasing the proportion of renewable energy access to the grid and the grid's ability to absorb wind and solar power. In addition, the "base load" status and peak-shaving role of thermal power in the current power structure cannot be ignored.

[0004] The existing technical method adopts a hierarchical optimization scheduling scheme, and the specific method of hierarchical scheduling is as follows:

[0005] (1) The upper-level dispatching model uses the charging and discharging of energy storage to follow the fluctuations of wind, solar, and load, and optimizes the output of the wind, solar, and hydropower combined system with the goal of minimizing net load fluctuations and maximizing the operating benefits of the energy storage system, thereby reducing the peak-shaving and valley-filling pressure of thermal power units on the remaining load. That is, by optimizing the output of the energy storage system, the output of the wind, solar, and hydropower complementary system can track the load curve to the best effect. The upper-level model uses the particle swarm optimization algorithm to solve the model and obtains the optimal output of the wind, solar, and hydropower combined system with the minimum net load fluctuation and the maximum operating benefits of the energy storage system. The equivalent load curve is obtained based on the output of the energy storage system in the combined system combined with the load forecast curve. The actual wind power consumption needs to be determined according to the peak load regulation of the thermal power units in the combined system.

[0006] (2) The lower model uses the equivalent load curve transmitted by the upper model to minimize the operating cost of the thermal power units and minimize the amount of wind, solar, and water abandoned. Combined with the penalty cost of wind and solar abandonment, the peak-shaving cost, and the operating characteristics of each thermal power unit, the MATLAB platform is used to call CPLEX for solution to determine the most economically efficient scheduling strategy. Then, an alternating iterative solution method is adopted to determine the thermal power units involved in deep peak-shaving and output the final scheduling strategy.

[0007] The existing wind, solar, water, fire and storage layered scheduling technology has the following three defects:

[0008] 1. There are usually multiple objective functions in hierarchical scheduling, and multi-objective optimization is to achieve multiple objectives under certain circumstances. Usually, multiple objectives in multi-objective optimization problems are contradictory. The achievement of one objective often reduces another objective, that is, it is impossible to achieve the optimal multiple objectives at the same time. We can only find a balance and make compromises among multiple objectives, and keep multiple objectives as relatively optimal as possible. However, hierarchical optimization separates the optimization and solution of multiple objectives. After the upper model is solved, the output of a certain energy source, such as energy storage with strong regulation capability, has been fixed, and the lower model then performs optimal scheduling for redistribution of the remaining equivalent load. Although the obtained result simplifies the calculation, it is difficult to obtain the global optimal solution.

[0009] 2. Usually the expansion package that comes with the programming software is called for solution, which has limited solving capabilities and often leads to difficulties in convergence.

[0010] 3. Existing optimization scheduling schemes for wind, solar, water, fire and storage usually treat the output of energy such as hydropower as batteries, and only consider the maximum output constraint of a single hydropower station, ignoring the complex water storage control, water release flow constraints, cascade relationships, etc. of the hydropower station. Summary of the invention

[0011] Purpose of the invention: The purpose of the invention is to provide a method and system for optimizing the scheduling of wind, solar, water, thermal and energy storage systems.

[0012] Technical solution: A method for optimizing the dispatching of wind, solar, water, fire and energy storage systems on a day-ahead basis of the present invention comprises the following steps:

[0013] Construct a wind, solar, water, thermal and storage day-ahead optimization dispatching model, and establish a multi-objective vulture search algorithm; use the multi-objective vulture search algorithm to solve the wind, solar, water, thermal and storage day-ahead optimization dispatching model and obtain the optimal dispatching plan;

[0014] The multi-objective vulture search algorithm is as follows: the multi-objective vulture search algorithm adds an archive mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, introduces the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and the elites in the group guide other individuals to perform optimization search, which is used to deal with multi-objective optimization problems; the rules of the archive mechanism are as follows: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive, if the generated new solution is not dominated by all the existing solutions in the archive, then the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, then delete the solution with the least impact on the distribution of the solution set, and the solution set includes the existing solutions in the archive and the new non-dominated solutions.

[0015] Furthermore, the objective functions of the wind, solar, water, thermal and storage day-ahead optimization dispatch model include: minimizing net load variance, maximizing total clean energy power generation and minimizing the operating cost of thermal power units; specifically:

[0016] (1) The net load variance minimization objective function is:

[0017]

[0018] Where f1 is the variance of the net load, T is the dispatch period, P t is the net load at time t, is the average value of the net load, P load,t is the initial load of the system at time t, are the outputs of wind power, photovoltaic power, hydropower, and energy storage at time t respectively;

[0019] (2) The maximum objective function of total clean energy power generation is:

[0020]

[0021] Among them, f2 is the total power generation of wind, solar and hydropower;

[0022] (3) The minimum objective function of the thermal power unit operating cost is:

[0023]

[0024] Among them, f3, f mh 、f qt Respectively represent the thermal power operation cost, coal consumption cost and start-up and shutdown cost; u i,t is the state of the i-th thermal power unit in the t-th period, u i,t =1 means running, u i,t =0 means shutdown; a i 、b i 、c i are the consumption coefficients of the i-th thermal power unit, N i,t is the output of the i-th thermal power unit at time t, S i,t is the startup cost of the i-th thermal power unit, N G is the number of thermal power units.

[0025] Furthermore, the constraints of the day-ahead optimal dispatch model for wind, solar, water, thermal and energy storage include:

[0026] (1) Power balance constraints of wind, solar, hydro, thermal and energy storage combined output:

[0027]

[0028] (2) Wind and solar power output constraints:

[0029]

[0030]

[0031] in, are the predicted values ​​of wind power and photovoltaic output at time t, The maximum output of wind power and photovoltaic power respectively;

[0032] (3) Hydropower station constraints;

[0033] Output constraints:

[0034] Water storage capacity constraints:

[0035] Discharge flow constraints:

[0036] in, They are the minimum and maximum output of the hydropower station at time t respectively; is the water storage capacity of the hydropower station at time t, are the minimum and maximum water storage capacity of the hydropower station at time t respectively; is the water discharge flow of the hydropower station at time t; are the minimum and maximum water discharge of the hydropower station at time t respectively;

[0037] (4) Energy storage constraints, including energy storage system state of charge constraints and energy storage charging and discharging constraints;

[0038] Energy storage system charge state constraints:

[0039]

[0040] SOC min ≤SOC Storage,t ≤SOC max

[0041] Energy storage charging and discharging constraints:

[0042]

[0043]

[0044]

[0045] Among them, SOC Storage,t is the charge state of the energy storage system at time t, that is, the ratio of the remaining capacity of the energy storage device to the total capacity; η c , η d The charging and discharging efficiency of the energy storage system; is the charging and discharging power of energy storage at time t, are the minimum and maximum values ​​of energy storage charging and discharging power respectively; E s is the rated capacity of the energy storage system, SOC min , SOC max are the upper and lower limits of the state of charge of the energy storage system, respectively, and △t is the duration of each time period;

[0046] (5) Thermal power units; including output constraints and ramp constraints;

[0047] Output constraints:

[0048] Hill climbing constraints:

[0049] in, are the upper and lower limits of the ramp rate of thermal power unit i;

[0050] (6) Line transmission capacity constraints;

[0051] -P ij,max ≤B ij (θ i,t -θ j,t )≤P ij,max

[0052] Among them, B ij , P ij,max are the admittance and maximum transmission capacity between nodes i and j, respectively, i,t ,θ j,t are the voltage phase angles at nodes i and j respectively.

[0053] Furthermore, the roulette wheel method is used in the multi-objective vulture search algorithm to solve the archive overflow problem, where the probability formula for deleting the solution is: P i represents the probability that the i-th solution is deleted, N i represents the number of neighboring solutions of the i-th solution in the archive, and C is a constant greater than 1.

[0054] Furthermore, the number of neighboring solutions of a solution in the archive is calculated as follows: a distance d is selected, and the number of neighboring solutions of a solution within the distance is found as the crowding degree of a solution to evaluate the solution;

[0055] The distance formula is:

[0056] Among them, max and min represent the maximum and minimum fitness values ​​of each target in the archive respectively; Archive_size represents the size of the archive.

[0057] Furthermore, the elite selection strategy is: first calculate the new fitness of each individual, then sort them, and finally select all individuals with the lowest fitness as the elite;

[0058] Among them, the number of adjacent solutions is taken as the new fitness value of each individual; the new fitness value f i The calculation formula is: i =N i , N i Represents the number of adjacent solutions to the i-th solution in the archive.

[0059] Based on the same inventive concept, a wind, solar, water, thermal and energy storage day-ahead optimization dispatching system of the present invention comprises:

[0060] Model building unit, used to build a day-ahead optimization dispatching model for wind, solar, water, fire and energy storage;

[0061] The model solving unit is used to establish a multi-objective vulture search algorithm, and use the multi-objective vulture search algorithm to solve the day-ahead optimization scheduling model of wind, solar, water, fire and storage to obtain the optimal scheduling plan;

[0062] The multi-objective vulture search algorithm is as follows: the multi-objective vulture search algorithm adds an archive mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, introduces the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and the elites in the group guide other individuals to perform optimization search to obtain the optimal scheduling solution; the rules of the archive mechanism are as follows: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive, if the generated new solution is not dominated by all the existing solutions in the archive, then the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, then delete the solution with the smallest impact on the distribution of the solution set, and the solution set includes the existing solutions in the archive and the new non-dominated solutions.

[0063] Optionally, the objective function of the wind, solar, water, thermal and storage day-ahead optimization scheduling model includes: minimizing net load variance, maximizing total clean energy power generation and minimizing thermal power unit operating cost; specifically:

[0064] (1) The net load variance minimization objective function is:

[0065]

[0066] Where f1 is the variance of the net load, T is the dispatch period, P t is the net load at time t, is the average value of the net load, P load,t is the initial load of the system at time t, are the outputs of wind power, photovoltaic power, hydropower, and energy storage at time t respectively;

[0067] (2) The maximum objective function of total clean energy power generation is:

[0068]

[0069] Among them, f2 is the total power generation of wind, solar and hydropower;

[0070] (3) The minimum objective function of the thermal power unit operating cost is:

[0071]

[0072] Among them, f3, f mh 、f qt Respectively represent the thermal power operation cost, coal consumption cost and start-up and shutdown cost; u i,t is the state of the i-th thermal power unit in the t-th period, u i,t =1 means running, u i,t =0 means shutdown; a i 、b i 、c i are the consumption coefficients of the i-th thermal power unit, N i,t is the output of the i-th thermal power unit at time t, S i,t is the startup cost of the i-th thermal power unit, N G is the number of thermal power units.

[0073] Optionally, the constraints of the day-ahead optimization dispatch model for wind, solar, water, thermal and energy storage include:

[0074] (1) Power balance constraints of wind, solar, hydro, thermal and energy storage combined output:

[0075]

[0076] (2) Wind and solar power output constraints:

[0077]

[0078]

[0079] in, are the predicted values ​​of wind power and photovoltaic output at time t, The maximum output of wind power and photovoltaic power respectively;

[0080] (3) Hydropower station constraints;

[0081] Output constraints:

[0082] Water storage capacity constraints:

[0083] Discharge flow constraints:

[0084] in, They are the minimum and maximum output of the hydropower station at time t respectively; is the water storage capacity of the hydropower station at time t, are the minimum and maximum water storage capacity of the hydropower station at time t respectively; is the water discharge flow of the hydropower station at time t; are the minimum and maximum water discharge of the hydropower station at time t respectively;

[0085] (4) Energy storage constraints, including energy storage system state of charge constraints and energy storage charging and discharging constraints;

[0086] Energy storage system charge state constraints:

[0087]

[0088] SOC min ≤SOC Storage,t ≤SOC max

[0089] Energy storage charging and discharging constraints:

[0090]

[0091]

[0092]

[0093] Among them, SOC Storage,t is the charge state of the energy storage system at time t, that is, the ratio of the remaining capacity of the energy storage device to the total capacity; η c , η d The charging and discharging efficiency of the energy storage system; is the charging and discharging power of energy storage at time t, are the minimum and maximum values ​​of energy storage charging and discharging power respectively; E s is the rated capacity of the energy storage system, SOC min , SOC max are the upper and lower limits of the state of charge of the energy storage system, respectively, and △t is the duration of each time period;

[0094] (5) Thermal power units; including output constraints and ramp constraints;

[0095] Output constraints:

[0096] Hill climbing constraints:

[0097] in, are the upper and lower limits of the ramp rate of thermal power unit i;

[0098] (6) Line transmission capacity constraints;

[0099] -P ij,max ≤B ij (θ i,t -θ j,t )≤P ij,max

[0100] Among them, B ij , P ij,max are the admittance and maximum transmission capacity between nodes i and j, respectively, i,t ,θ j,t are the voltage phase angles at nodes i and j respectively.

[0101] Optionally, the roulette wheel method is used in the multi-objective vulture search algorithm to solve the archive overflow problem, where the probability formula for deleting the solution is: P i represents the probability that the i-th solution is deleted, N i represents the number of neighboring solutions of the i-th solution in the archive, and C is a constant greater than 1.

[0102] Optionally, the number of neighboring solutions of a solution in the archive is calculated by selecting a distance d and finding the number of neighboring solutions of a solution within this distance as the crowding degree of a solution to evaluate the solution;

[0103] The distance formula is:

[0104] Among them, max and min represent the maximum and minimum fitness values ​​of each target in the archive respectively; Archive_size represents the size of the archive.

[0105] Alternatively, the elite selection strategy is: first calculate the new fitness of each individual, then sort them, and finally select all individuals with the lowest fitness as the elite;

[0106] Among them, the number of adjacent solutions is taken as the new fitness value of each individual; the new fitness value f i The calculation formula is: i =N i , N i Represents the number of adjacent solutions to the i-th solution in the archive.

[0107] Based on the same inventive concept, the present invention provides a device for optimizing the day-ahead scheduling of wind, solar, water, fire and storage, wherein the device stores a constructed day-ahead optimization scheduling model for wind, solar, water, fire and storage, and a multi-objective vulture search algorithm for solving the day-ahead optimization scheduling model for wind, solar, water, fire and storage; wherein the multi-objective vulture search algorithm is: the multi-objective vulture search algorithm adds an archiving mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, introduces the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and the elites in the group guide other individuals to perform optimization search to obtain the optimal scheduling solution; the rule of the archiving mechanism is: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive, if the generated new solution is not dominated by all the existing solutions in the archive, then the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, then delete the solution with the smallest impact on the distribution of the solution set, and the solution set includes the existing solutions in the archive and the new non-dominated solutions.

[0108] Based on the same inventive concept, an electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the steps of the above-mentioned method for optimizing the scheduling of wind, solar, water, fire and storage systems in advance are implemented.

[0109] Based on the same inventive concept, the present invention provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps of the above-mentioned method for optimizing the day-ahead scheduling of wind, solar, water, thermal and storage.

[0110] Beneficial effects: Compared with the prior art, the advantages of the present invention are:

[0111] (1) In establishing the day-ahead optimization scheduling model for wind, solar, water, fire and storage, the present invention comprehensively considers the output characteristics and complementary relationships of wind, solar, water, fire and storage energy sources, sets multiple constraints such as power balance, power station output and line transmission, and sets optimization targets by comprehensively considering actual needs such as source-load coordination, clean energy consumption and power generation economy. The model is comprehensive and in line with reality.

[0112] (2) The present invention proposes a multi-objective vulture search algorithm, which obtains the optimal solution of the multi-objective optimization problem through repeated iterations of deterministic algorithms and heuristic random searches, and has good performance and global optimization convergence ability;

[0113] (3) The present invention applies the multi-objective vulture search algorithm to the day-ahead optimization scheduling problem of wind, solar, water, thermal and storage, and can obtain the optimal output allocation scheme for wind, solar, water, thermal and storage in each period;

[0114] (4) The present invention proposes a wind, solar, water, fire and storage optimization scheduling system based on a multi-objective vulture search algorithm, which can meet the relevant needs of multi-energy complementary engineering application scenarios and obtain the optimal scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 is a flow chart of the method of the present invention;

[0116] Figure 2 It is the flow chart of multi-objective vulture search algorithm;

[0117] Figure 3 is the wind power, photovoltaic output and load prediction curve in the embodiment of the present invention;

[0118] Figure 4 This is a schematic diagram of the day-ahead optimized dispatching of wind, solar, hydro, thermal and energy storage power in an embodiment of the present invention. DETAILED DESCRIPTION

[0119] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0120] Based on the complementary characteristics of multiple energy sources, the present invention optimizes the multi-energy complementarity of hydropower and energy storage with strong adjustment flexibility, thermal power and wind power, and photovoltaics that undertake base load and peak-shaving tasks, which can effectively deal with the new energy consumption and peak-shaving problems brought about by the continuous growth of wind power and photovoltaic installed capacity. The technical solution of the present invention mainly consists of four parts: establishment of a day-ahead optimization scheduling model for wind, solar, water, fire and storage, proposal of a multi-objective vulture search algorithm, application of the multi-objective vulture search algorithm in the day-ahead optimization scheduling problem of multiple energy complementarity, and development of a wind, solar, water, fire and storage optimization scheduling system.

[0121] In the establishment of the day-ahead optimization dispatching model of wind, solar, water, fire and storage, it is taken into account that wind power output is random and volatile, and the output is generally larger at night and smaller during the day; photovoltaic power generation is mainly affected by solar radiation, the output is concentrated in the daytime and is related to weather conditions, and the wind and solar output have certain complementarity but poor power quality. Hydropower is a high-quality peak-shaving power source with low cost and can effectively smooth out the fluctuations in wind and solar output. It is generally given priority in peak regulation. Energy storage has a fast response rate and can be adjusted in both directions, effectively supporting the development and grid connection of new energy, but the adjustment capacity is generally limited. The output of thermal power units is stable, which can better smooth out the fluctuations in wind and solar output and meet the load demand, but the environmental pollution is large, and frequent start and stop will cause economic losses and have certain safety hazards. The coordinated optimization of wind, solar, water, fire and storage is conducive to organically combining the output advantages of different power sources to achieve greater comprehensive benefits. Taking into account source-load coordination, grid constraints, new energy consumption demand, power generation economy, etc., a day-ahead optimization scheduling model for wind, solar, water, thermal and storage is established with the goals of minimizing net load variance, maximizing total clean energy power generation and minimizing operating costs of thermal power units.

[0122] The multi-objective vulture search algorithm can achieve multiple objectives under certain circumstances. Usually, multiple objectives in a multi-objective optimization problem are contradictory. The improvement of one objective often degrades the performance of another objective. It is impossible to achieve the optimal multiple objectives at the same time. We can only find a balance and make compromises among multiple objectives and keep multiple objectives relatively optimal as much as possible. The present invention proposes a multi-objective vulture search algorithm (MOBES) to solve multi-objective problems. First, MOBES adds an archive mechanism to store the obtained non-dominated Pareto solutions. When the archive is full, the most crowded solution will be deleted by the roulette method; secondly, the elite selection strategy is introduced into MOBES, and the elites in the group guide other individuals to perform optimization search.

[0123] Then, the multi-objective vulture search algorithm was applied to the day-ahead optimal scheduling problem of wind, solar, water, thermal and energy storage, demonstrating the feasibility and engineering value of the algorithm.

[0124] Finally, based on the day-ahead optimal scheduling method of wind, solar, water, thermal and storage using the multi-objective vulture search algorithm, a set of wind, solar, water, thermal and storage optimal scheduling system is developed and applied to the actual engineering of multi-energy complementary optimal scheduling.

[0125] Glossary of relevant technical terms:

[0126] Net load: The remaining load actually borne by the thermal power units in the system after deducting the output of wind, solar, and hydropower storage.

[0127] Pareto solution: also known as nondominated solutions, proposed by Pareto in 1986, its definition is: Assuming that for any two solutions S1 and S2, S1 is better than S2 for all objectives, then S1 is said to dominate S2. If S1 is not dominated by other solutions, then S1 is called a nondominated solution, also known as a Pareto solution.

[0128] like Figure 1 As shown, a method for optimizing the scheduling of wind, solar, water, fire and energy storage systems in advance according to the present invention comprises the following steps:

[0129] S1. Establish the optimization objective function of the wind, solar, water, thermal and storage day-ahead optimization dispatch model; the model includes three objective functions: minimum net load variance, maximum total clean energy power generation and minimum thermal power unit operation cost. Specifically:

[0130] (1) Minimum net load variance;

[0131] Net load refers to the remaining load borne by thermal power units in the power grid after removing the output of wind, solar, and hydropower storage. To reduce the frequent start and stop and adjustment of thermal power units, the fluctuation of the net load curve after the hydropower and energy storage output is smoothed should be as small as possible. The minimum net load variance is the first objective function of the wind, solar, and hydropower joint optimization scheduling model, that is,

[0132]

[0133] Where f1 is the variance of the net load, T is the dispatch period, P t is the net load at time t, is the average value of the net load, P load,t is the initial load of the system at time t, They are the output of wind power, photovoltaic power, hydropower and energy storage at time t respectively.

[0134] (2) The total clean energy generation is the largest;

[0135] In order to give full play to the advantages of new energy, low carbon and environmental protection, realize large-scale consumption of clean energy, and increase the wind, solar and hydroelectric power generation as much as possible to meet more load demands while smoothing the fluctuations of wind, solar and load, and reduce the abandonment of wind and solar, the total power generation of wind, solar and hydroelectric is maximized (that is, the total power generation of clean energy is maximized. Among wind, solar, hydro and fire storage, wind, solar and hydroelectric are clean energy) as the second objective function of the wind, solar, hydro and fire storage joint optimization scheduling model, that is,

[0136]

[0137] Among them, f2 is the total power generation of wind, solar and hydropower.

[0138] (3) The operating cost of thermal power units is minimal;

[0139] In order to ensure the economy of the optimized scheduling plan, the operating costs of thermal power units should be minimized as much as possible, including coal consumption costs and start-up and shutdown costs.

[0140]

[0141] Among them, f3, f mh 、f qt Respectively represent the thermal power operation cost, coal consumption cost and start-up and shutdown cost; u i,t is the state of the i-th thermal power unit in the t-th period, u i,t =1 means running, u i,t =0 means shutdown; a i 、b i 、c i are the consumption coefficients of the i-th thermal power unit, N i,t is the output of the i-th thermal power unit at time t, S i,t is the startup cost of the i-th thermal power unit, N G is the number of thermal power units.

[0142] S2. Establish the constraint conditions of the wind, solar, water, thermal and energy storage day-ahead optimization dispatch model; including wind, solar, water, thermal and energy storage combined output power balance constraint, wind and solar output constraint, hydropower station constraint, energy storage constraint, thermal power unit constraint and line transmission capacity constraint. Specifically:

[0143] (1) Power balance constraints of wind, solar, hydro, thermal and energy storage combined output:

[0144]

[0145] Among them, N t G is the total output of the thermal power unit at time t, P load,t is the initial load of the system at time t.

[0146] (2) Wind and solar power output constraints:

[0147] The model is a wind, solar, water, thermal and storage day-ahead optimization dispatching model. The predicted value of wind and solar output is input and must meet the following requirements:

[0148]

[0149]

[0150] in, are the predicted values ​​of wind power and photovoltaic output at time t, They are the maximum outputs of wind power and photovoltaic power respectively.

[0151] (3) Hydropower station constraints, including output constraints, water storage constraints, and water release flow constraints;

[0152] Output constraints:

[0153]

[0154] Water storage capacity constraints:

[0155]

[0156] Discharge flow constraints:

[0157]

[0158] in, They are the minimum and maximum output of the hydropower station at time t respectively; is the water storage capacity of the hydropower station at time t, are the minimum and maximum water storage capacity of the hydropower station at time t respectively; is the water discharge flow of the hydropower station at time t; are the minimum and maximum water discharge of the hydropower station at time t respectively.

[0159] (4) Energy storage constraints, including energy storage system state of charge constraints and energy storage charging and discharging constraints;

[0160] Energy storage system charge state constraints:

[0161]

[0162] SOC min ≤SOC Storage,t ≤SOC max (11)

[0163] Energy storage charging and discharging constraints:

[0164]

[0165]

[0166]

[0167] Among them, SOC Storage,t SOC is the state of charge of the energy storage system at time t, that is, the ratio of the remaining capacity of the energy storage device to the total capacity; Storage,t-1 is the charge state of the energy storage system at time t-1; η c , η d The charging and discharging efficiency of the energy storage system; is the charging and discharging power of energy storage at time t, are the minimum and maximum values ​​of energy storage charging and discharging power respectively; E s is the rated capacity of the energy storage system, SOC min , SOC max are the upper and lower limits of the state of charge of the energy storage system, respectively; Δt is the duration of each time period; in this embodiment, Δt=1h.

[0168] (5) Thermal power units; including output constraints and ramp constraints;

[0169] Output constraints:

[0170]

[0171] Hill climbing constraints:

[0172]

[0173] in, is the lower limit of the output of the i-th thermal power unit, is the upper limit of the output of the i-th thermal power unit, is the lower limit of the ramp rate of the i-th thermal power unit, is the upper limit of the ramp rate of the i-th thermal power unit.

[0174] (6) Line transmission capacity constraints;

[0175] -P ij,max ≤B ij (θ i,t -θ j,t )≤P ij,max (17)

[0176] Among them, B ij , P ij,max are the admittance and maximum transmission capacity between nodes i and j, respectively, i,t ,θ j,t are the voltage phase angles at nodes i and j respectively.

[0177] S3, establish a multi-target vulture search algorithm;

[0178] (1) Buzzard Search (BES) algorithm;

[0179] The Buzzard Search (BES) algorithm is a new metaheuristic algorithm proposed in 2020. Its main idea is to simulate the behavior of vultures foraging and has strong global search capabilities. The algorithm is divided into three parts: selection phase, search phase, and dive phase. The individual vulture has only a position attribute, and the quality of the position is calculated by the fitness function F(x).

[0180] 1) Selection stage:

[0181] In the selection phase, the vulture first identifies and selects the best area (based on the amount of food) in the selected search space for hunting. The vulture will fly near the current best individual, and the distance to the best individual is determined by the average position of all individuals. The mathematical model of this phase is:

[0182] p i,new =p best +α*r(p mean -p i ) (18)

[0183] Among them, p i,new represents the updated position of the ith vulture in the selection phase; p best represents the current best position of the vulture; α represents the position change parameter, α∈(1.5,2); r represents a random number, r∈(0,1); p mean represents the average position of vultures; p i is the position of the i-th vulture.

[0184] If the new location is better than the original one, the vulture will fly to the new location, otherwise it will stay where it is.

[0185] 2) Search phase:

[0186] Entering the search phase, the vulture flies in a spiral shape in the space determined in the previous phase to search for prey, accelerate the search process, and find the best dive capture position. The spiral flight mathematical model uses polar coordinate equations to update the position. The formula for the search phase is:

[0187] p′ i,new =p i +x(i)*(p i -p mean )+y(i)*(p i -p i+1 ) (19)

[0188]

[0189]

[0190] θ(i)=a*π*rand and r(i)=θ(i)+R*rand (22)

[0191] Among them, p′ i,new represents the updated position of the ith vulture during the search phase; x(i) and y(i) represent the vulture's position in polar coordinates, both ranging from -1 to 1; p i+1 is the position of the i+1th vulture; v(i) and w(i) are intermediate variables; θ(i) and r(i) are the polar angle and polar radius of the spiral equation in the search phase respectively; a and R are spiral parameters, and the value ranges of a and R are (5, 10) and (0.5, 2) respectively; rand is a random number from 0 to 1.

[0192] It can be seen that the vulture flies around itself, and the distance it flies is determined by the distance between itself and the center of the group and the distance between itself and the next vulture. (If the current vulture is the last vulture in the group, its next vulture is the first vulture in the group.) If the new position found is better than the original position, the vulture will fly to the new position, otherwise it will stay where it is.

[0193] 3) Diving stage:

[0194] During the dive phase, the vulture dives to prey that is locked in the search space. The equation of motion is described using polar coordinates. The following equation explains the vulture's hunting behavior during this phase.

[0195] p″ i,new =rand*p best +m(i)*(p i -c1p mean )+n(i)*(p i -c2*p best ) (twenty three)

[0196]

[0197]

[0198] θ'(i)=a*π*rand and r'(i)=θ'(i) (26)

[0199] Among them, p″ i,new represents the updated position of the ith vulture during the dive phase; c1 and c2 are enhancement coefficients, which are generally 2, and rand is a uniform random number in [0,1]. m(i), n(i), v'(i), and w'(i) are all intermediate variables. θ'(i) and r'(i) are the polar angle and polar radius of the spiral equation during the dive phase, respectively.

[0200] Vulture search algorithm process:

[0201] Step 1: Initialize the vulture algorithm parameters and the population;

[0202] step2: calculate the fitness value;

[0203] Step 3: The vulture selects the search space and updates its position using equation (18);

[0204] Step 4: The vulture searches for prey in the search space and updates its position using equation (19);

[0205] Step 5: The vulture dives and updates its position using equation (23);

[0206] Step 6: Determine whether the end condition is met. If so, output the optimal result. Otherwise, repeat steps 2-step 6.

[0207] (2) Establish a multi-objective vulture search algorithm (MOBES); Based on the vulture search algorithm (BES), the multi-objective vulture search algorithm (MOBES) is improved. The day-ahead optimal scheduling model of wind, solar, water, thermal and storage is solved by the multi-objective vulture search algorithm to obtain the optimal output distribution of wind, solar, water, thermal and storage in each period.

[0208] The multi-objective vulture search algorithm MOBES adds an archive mechanism to store the currently obtained Pareto optimal solution (non-dominated solution), and then puts the elite selection strategy into MOBES, which selects individuals from the archive as elites or leaders.

[0209] The main function of the MOBES archiving mechanism is to store the currently obtained Pareto optimal solution, that is, the non-dominated solution. This archiving mechanism also has some rules for storing and deleting solutions: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive. If the generated new solution is not dominated by all the existing solutions in the archive, the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, delete the solution with the least impact on the distribution of the solution set, which includes the existing solutions in the archive and the new non-dominated solutions.

[0210] This paper uses the roulette wheel method to understand the distribution of solutions in the archive. It is used to remove more crowded solutions. The method first selects a distance d and finds the number of neighboring solutions within this distance as its crowdedness to evaluate the solution. The distance formula is:

[0211]

[0212] Among them, max and min represent the maximum and minimum fitness values ​​of each target in the archive respectively; Archive_size represents the size of the archive.

[0213] For MOBES, the roulette wheel method is used to solve the problem that the number of non-dominated solutions exceeds the archive capacity. The probability formula for deleting a solution is shown in equation (28):

[0214]

[0215] Among them, P i represents the probability that the i-th solution is deleted. N i represents the number of neighboring solutions of the ith solution in the archive, and C is a constant greater than 1. As can be seen from formula (28), the higher the congestion of a solution, the easier it is to be deleted.

[0216] Another important strategy of MOBES is the elite selection strategy. This strategy is to select a suitable elite or leader (global optimal solution) to guide other individuals to search for optimization in the search space. Since the fitness of each objective function is different, each objective function cannot be used directly. Therefore, individuals can be directly selected from the archives as elites. For each individual, it is converted into a fitness value that can be directly compared through the following equation.

[0217] f i =N i (29)

[0218] The number of adjacent solutions is used as the new fitness value of each individual. Therefore, for the minimization problem, the new fitness of each individual is first calculated, then sorted, and finally all individuals with the lowest fitness are selected as elites. The elite selection strategy can quickly find the Pareto optimal solution while ensuring the convergence and diversity of the solution.

[0219] like Figure 2 As shown, the multi-objective vulture search algorithm includes the following steps:

[0220] Step 1: Initialize the vulture algorithm parameters, initialize the population, and initialize the archive;

[0221] step2: calculate the fitness value;

[0222] Step 3: The vulture selects the search space and updates its position using equation (18);

[0223] Step 4: The vulture searches for prey in the search space and updates its position using equation (19);

[0224] Step 5: The vulture dives and updates its position using equation (23);

[0225] Step 6: Save the non-dominated solutions in the archive. If the archive overflows, use the roulette method to solve the problem that the number of non-dominated solutions exceeds the archive capacity;

[0226] Step7: Select the best solution in the archive as the elite;

[0227] Step8: Determine whether the end condition is met. If so, output the optimal result. Otherwise, repeat steps step2-step8.

[0228] In order to verify the effectiveness of the wind, solar, water, thermal and energy storage day-ahead optimization scheduling model and multi-objective vulture search algorithm proposed in this invention, the IEEE30-node system is used for simulation analysis. The system includes a wind farm with a total capacity of 350MW, a photovoltaic power station with a total capacity of 100MW, a 100MW hydropower unit, a 50MW / 200MWh energy storage power station and 5 thermal power units, with a rotating reserve of 10% of the load. The scheduling cycle is 24h, and the time interval is 1h; the output and load forecast curves of wind and photovoltaic power stations are as follows Figure 3 The multi-objective vulture search algorithm proposed in this invention is used to solve the problem and obtain the output distribution of wind, solar, water, fire and storage in each period. The solution result is shown in Figure 4 shown.

[0229] The wind, solar, water, fire and storage day-ahead optimization dispatching method based on the multi-objective vulture search algorithm not only helps to smooth the load and wind and solar fluctuations, but also promotes the consumption of clean energy, reduces the system operation cost, and is conducive to achieving regional multi-energy complementarity and promoting the low-carbon development of the power grid. The wind, solar, water, fire and storage optimization dispatching system based on this method can be effectively applied in multi-energy complementary engineering scenarios.

[0230] Based on the same inventive concept, a wind, solar, water, thermal and energy storage day-ahead optimization dispatching system of the present invention comprises:

[0231] Model building unit, used to build a day-ahead optimization dispatching model for wind, solar, water, fire and energy storage;

[0232] The model solving unit is used to establish a multi-objective vulture search algorithm, and use the multi-objective vulture search algorithm to solve the day-ahead optimization scheduling model of wind, solar, water, fire and storage to obtain the optimal scheduling plan;

[0233] The multi-objective vulture search algorithm is as follows: the multi-objective vulture search algorithm adds an archive mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, introduces the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and the elites in the group guide other individuals to perform optimization search to obtain the optimal scheduling solution; the rules of the archive mechanism are as follows: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive, if the generated new solution is not dominated by all the existing solutions in the archive, then the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, then delete the solution with the smallest impact on the distribution of the solution set, and the solution set includes the existing solutions in the archive and the new non-dominated solutions.

[0234] Optionally, the objective function of the wind, solar, water, thermal and storage day-ahead optimization scheduling model includes: minimizing net load variance, maximizing total clean energy power generation and minimizing thermal power unit operating cost; specifically:

[0235] (1) The net load variance minimization objective function is:

[0236]

[0237] Where f1 is the variance of the net load, T is the dispatch period, P t is the net load at time t, is the average value of the net load, P load,t is the initial load of the system at time t, are the outputs of wind power, photovoltaic power, hydropower, and energy storage at time t respectively;

[0238] (2) The maximum objective function of total clean energy power generation is:

[0239]

[0240] Among them, f2 is the total power generation of wind, solar and hydropower;

[0241] (3) The minimum objective function of the thermal power unit operating cost is:

[0242]

[0243] Among them, f3, f mh 、f qt Respectively represent the thermal power operation cost, coal consumption cost and start-up and shutdown cost; u i,t is the state of the i-th thermal power unit in the t-th period, u i,t =1 means running, u i,t =0 means shutdown; a i 、b i 、c i are the consumption coefficients of the i-th thermal power unit, N i,t is the output of the i-th thermal power unit at time t, S i,t is the startup cost of the i-th thermal power unit, N G is the number of thermal power units.

[0244] Optionally, the constraints of the day-ahead optimization dispatch model for wind, solar, water, thermal and energy storage include:

[0245] (1) Power balance constraints of wind, solar, hydro, thermal and energy storage combined output:

[0246]

[0247] (2) Wind and solar power output constraints:

[0248]

[0249]

[0250] in, are the predicted values ​​of wind power and photovoltaic output at time t, The maximum output of wind power and photovoltaic power respectively;

[0251] (3) Hydropower station constraints;

[0252] Output constraints:

[0253]

[0254] Water storage capacity constraints:

[0255]

[0256] Discharge flow constraints:

[0257]

[0258] in, They are the minimum and maximum output of the hydropower station at time t respectively; is the water storage capacity of the hydropower station at time t, are the minimum and maximum water storage capacity of the hydropower station at time t respectively; is the water discharge flow of the hydropower station at time t; are the minimum and maximum water discharge of the hydropower station at time t respectively;

[0259] (4) Energy storage constraints, including energy storage system state of charge constraints and energy storage charging and discharging constraints;

[0260] Energy storage system charge state constraints:

[0261]

[0262] SOC min ≤SOC Storage,t ≤SOC max (11)

[0263] Energy storage charging and discharging constraints:

[0264]

[0265]

[0266]

[0267] Among them, SOC Storage,t is the charge state of the energy storage system at time t, that is, the ratio of the remaining capacity of the energy storage device to the total capacity; η c , η d The charging and discharging efficiency of the energy storage system; is the charging and discharging power of energy storage at time t, are the minimum and maximum values ​​of energy storage charging and discharging power respectively; E s is the rated capacity of the energy storage system, SOC min , SOC max are the upper and lower limits of the state of charge of the energy storage system, respectively, and △t is the duration of each time period;

[0268] (5) Thermal power units; including output constraints and ramp constraints;

[0269] Output constraints:

[0270]

[0271] Hill climbing constraints:

[0272]

[0273] in, are the upper and lower limits of the ramp rate of thermal power unit i;

[0274] (6) Line transmission capacity constraints;

[0275] -P ij,max ≤B ij (θ i,t -θ j,t )≤P ij,max (17)

[0276] Among them, B ij , P ij,max are the admittance and maximum transmission capacity between nodes i and j, respectively, i,t ,θ j,t are the voltage phase angles at nodes i and j respectively.

[0277] Optionally, the roulette wheel method is used in the multi-objective vulture search algorithm to solve the archive overflow problem, where the probability formula for deleting the solution is: P i represents the probability that the i-th solution is deleted, N i represents the number of neighboring solutions of the i-th solution in the archive, and C is a constant greater than 1.

[0278] Optionally, the number of neighboring solutions of a solution in the archive is calculated by selecting a distance d and finding the number of neighboring solutions of a solution within this distance as the crowding degree of a solution to evaluate the solution;

[0279] The distance formula is:

[0280] Among them, max and min represent the maximum and minimum fitness values ​​of each target in the archive respectively; Archive_size represents the size of the archive.

[0281] Alternatively, the elite selection strategy is: first calculate the new fitness of each individual, then sort them, and finally select all individuals with the lowest fitness as the elite;

[0282] Among them, the number of adjacent solutions is taken as the new fitness value of each individual; the new fitness value f i The calculation formula is: i =N i , N i Represents the number of adjacent solutions to the i-th solution in the archive.

[0283] Based on the same inventive concept, the present invention provides a device for optimizing the day-ahead scheduling of wind, solar, water, fire and storage, wherein the device stores a constructed day-ahead optimization scheduling model for wind, solar, water, fire and storage, and a multi-objective vulture search algorithm for solving the day-ahead optimization scheduling model for wind, solar, water, fire and storage; wherein the multi-objective vulture search algorithm is: the multi-objective vulture search algorithm adds an archiving mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, introduces the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and the elites in the group guide other individuals to perform optimization search to obtain the optimal scheduling solution; the rule of the archiving mechanism is: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive, if the generated new solution is not dominated by all the existing solutions in the archive, then the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, then delete the solution with the smallest impact on the distribution of the solution set, and the solution set includes the existing solutions in the archive and the new non-dominated solutions.

[0284] Based on the same inventive concept, an electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the steps of the above-mentioned method for optimizing the scheduling of wind, solar, water, fire and storage systems in advance are implemented.

[0285] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment. The program modules typically perform the functions and / or methods in the embodiments described herein.

[0286] The processor executes various functional applications and data processing by running the program stored in the memory, such as implementing the method provided in the first embodiment of the present invention.

[0287] Based on the same inventive concept, the present invention provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps of the above-mentioned day-ahead optimization scheduling method for wind, solar, water, thermal and storage.

[0288] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0289] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0290] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0291] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0292] Of course, the storage medium containing computer executable instructions provided by an embodiment of the present invention, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the method provided by any embodiment of the present invention.

[0293] In summary, in order to cope with the new energy consumption and peak-shaving problems brought about by the continuous growth of wind power and photovoltaic installed capacity, the present invention comprehensively considers the actual needs such as source-load coordination, clean energy consumption, and power generation economy, takes the minimum net load variance, the maximum clean energy power generation, and the minimum operating cost of thermal power units as the optimization goals, and considers various constraints such as grid constraints and power station output to construct a wind, solar, water, fire, and storage multi-energy complementary day-ahead optimization scheduling model; the multi-objective vulture search algorithm proposed in the present invention adds an archive mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, and then puts the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and is used to deal with multi-objective optimization problems; the present invention applies the multi-objective vulture search algorithm to the multi-energy complementary day-ahead optimization scheduling problem, reflecting the feasibility and advancement of the model and algorithm. The wind, solar, water, and fire storage optimization scheduling system based on the multi-objective vulture search algorithm can meet the relevant needs of multi-energy complementary application scenarios and obtain the optimal scheduling plan.

Claims

1. A method for optimizing the dispatching of wind, solar, water, fire and energy storage systems on a day-ahead basis, characterized in that: The following steps are involved: Construct a wind, solar, water, thermal and storage day-ahead optimization dispatching model, and establish a multi-objective vulture search algorithm; use the multi-objective vulture search algorithm to solve the wind, solar, water, thermal and storage day-ahead optimization dispatching model and obtain the optimal dispatching plan; The multi-objective vulture search algorithm is as follows: the multi-objective vulture search algorithm adds an archive mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, introduces the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and the elites in the group guide other individuals to perform optimization search, which is used to deal with multi-objective optimization problems; the rules of the archive mechanism are as follows: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive, if the generated new solution is not dominated by all the existing solutions in the archive, then the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, then delete the solution with the smallest impact on the distribution of the solution set, and the solution set includes the existing solutions in the archive and the new non-dominated solutions.

2. The method for optimizing the dispatching of wind, solar, water, fire and energy storage according to claim 1, characterized in that: The objective functions of the wind, solar, water, thermal and storage day-ahead optimization dispatch model include: minimizing net load variance, maximizing total clean energy power generation and minimizing the operating cost of thermal power units; specifically: (1) The net load variance minimization objective function is: Where f1 is the variance of the net load, T is the dispatch period, P t is the net load at time t, is the average value of the net load, P load,t is the initial load of the system at time t, are the outputs of wind power, photovoltaic power, hydropower, and energy storage at time t respectively; (2) The maximum objective function of total clean energy power generation is: Among them, f2 is the total power generation of wind, solar and hydropower; (3) The minimum objective function of the thermal power unit operating cost is: Among them, f3, f mh 、f qt Respectively represent the thermal power operation cost, coal consumption cost and start-up and shutdown cost; u i,t is the state of the i-th thermal power unit in the t-th period, u i,t =1 means running, u i,t =0 means shutdown; a i 、b i 、c i are the consumption coefficients of the i-th thermal power unit, N i,t is the output of the i-th thermal power unit at time t, S i,t is the startup cost of the i-th thermal power unit, N G is the number of thermal power units.

3. The method for optimizing the dispatching of wind, solar, water, fire and energy storage according to claim 1, characterized in that: The constraints of the day-ahead optimization dispatch model for wind, solar, water, thermal and energy storage include: (1) Power balance constraints of wind, solar, hydro, thermal and energy storage combined output: (2) Wind and solar power output constraints: in, are the predicted values ​​of wind power and photovoltaic output at time t, The maximum output of wind power and photovoltaic power respectively; (3) Hydropower station constraints; Output constraints: Water storage capacity constraints: Discharge flow constraints: in, are the minimum and maximum output of the hydropower station at time t; V t Hydro is the water storage capacity of the hydropower station at time t, are the minimum and maximum water storage capacity of the hydropower station at time t respectively; is the water discharge flow of the hydropower station at time t; are the minimum and maximum water discharge of the hydropower station at time t respectively; (4) Energy storage constraints, including energy storage system state of charge constraints and energy storage charging and discharging constraints; Energy storage system charge state constraints: SOC min ≤SOC Storage,t ≤SOC max Energy storage charging and discharging constraints: Among them, SOC Storage,t is the charge state of the energy storage system at time t, that is, the ratio of the remaining capacity of the energy storage device to the total capacity; η c , η d The charging and discharging efficiency of the energy storage system; is the charging and discharging power of energy storage at time t, are the minimum and maximum values ​​of energy storage charging and discharging power respectively; E s is the rated capacity of the energy storage system, SOC min , SOC max are the upper and lower limits of the state of charge of the energy storage system, respectively, and △t is the duration of each time period; (5) Thermal power units; including output constraints and ramp constraints; Output constraints: Hill climbing constraints: Among them, δ i G are the upper and lower limits of the ramp rate of thermal power unit i; (6) Line transmission capacity constraints; -P ij,max ≤B ij (i i,t -θ j,t )≤P ij,max Among them, B ij , P ij,max are the admittance and maximum transmission capacity between nodes i and j, respectively, i,t ,θ j,t are the voltage phase angles at nodes i and j respectively.

4. The method for optimizing the dispatching of wind, solar, water, fire and energy storage according to claim 1, characterized in that: The roulette wheel method is used in the multi-objective vulture search algorithm to solve the archive overflow problem, where the probability formula for deleting the solution is: P i represents the probability that the i-th solution is deleted, N i represents the number of neighboring solutions of the i-th solution in the archive, and C is a constant greater than 1.

5. The method for optimizing the dispatching of wind, solar, water, fire and energy storage according to claim 4 is characterized in that: The number of neighboring solutions of a solution in the archive is calculated as follows: choose a distance d and find the number of neighboring solutions of a solution within this distance as the congestion degree of a solution to evaluate the solution; The distance formula is: Among them, max and min represent the maximum and minimum fitness values ​​of each target in the archive respectively; Archive_size represents the size of the archive.

6. The method for optimizing the dispatching of wind, solar, water, fire and energy storage in advance according to claim 1, characterized in that: The elite selection strategy is: first calculate the new fitness of each individual, then sort them, and finally select all individuals with the lowest fitness as the elite; Among them, the number of adjacent solutions is taken as the new fitness value of each individual; the new fitness value f i The calculation formula is: i =N i , N i Represents the number of adjacent solutions to the i-th solution in the archive.

7. A wind, solar, water, thermal and energy storage day-ahead optimization dispatching system, characterized in that: include: Model building unit, used to build a day-ahead optimization dispatching model for wind, solar, water, fire and energy storage; The model solving unit is used to establish a multi-objective vulture search algorithm, and use the multi-objective vulture search algorithm to solve the day-ahead optimization scheduling model of wind, solar, water, fire and storage to obtain the optimal scheduling plan; The multi-objective vulture search algorithm is as follows: the multi-objective vulture search algorithm adds an archive mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, introduces the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and the elites in the group guide other individuals to perform optimization search to obtain the optimal scheduling solution; the rules of the archive mechanism are as follows: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive, if the generated new solution is not dominated by all the existing solutions in the archive, then the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, then delete the solution with the smallest impact on the distribution of the solution set, and the solution set includes the existing solutions in the archive and the new non-dominated solutions.

8. The wind, solar, water, fire and energy storage day-ahead optimization dispatching system according to claim 7 is characterized in that: The objective functions of the wind, solar, water, thermal and storage day-ahead optimization dispatch model include: minimizing net load variance, maximizing total clean energy power generation and minimizing the operating cost of thermal power units; specifically: (1) The net load variance minimization objective function is: Where f1 is the variance of the net load, T is the dispatch period, P t is the net load at time t, is the average value of the net load, P load,t is the initial load of the system at time t, are the outputs of wind power, photovoltaic power, hydropower, and energy storage at time t respectively; (2) The maximum objective function of total clean energy power generation is: Among them, f2 is the total power generation of wind, solar and hydropower; (3) The minimum objective function of the thermal power unit operating cost is: Among them, f3, f mh 、f qt Respectively represent the thermal power operation cost, coal consumption cost and start-up and shutdown cost; u i,t is the state of the i-th thermal power unit in the t-th period, u i,t =1 means running, u i,t =0 means shutdown; a i 、b i 、c i are the consumption coefficients of the i-th thermal power unit, N i,t is the output of the i-th thermal power unit at time t, S i,t is the startup cost of the i-th thermal power unit, N G is the number of thermal power units.

9. The wind, solar, water, fire and energy storage day-ahead optimization dispatching system according to claim 7 is characterized in that: The constraints of the day-ahead optimization dispatch model for wind, solar, water, thermal and energy storage include: (1) Power balance constraints of wind, solar, hydro, thermal and energy storage combined output: (2) Wind and solar power output constraints: in, are the predicted values ​​of wind power and photovoltaic output at time t, The maximum output of wind power and photovoltaic power respectively; (3) Hydropower station constraints; Output constraints: Water storage capacity constraints: Discharge flow constraints: in, are the minimum and maximum output of the hydropower station at time t; V t Hydro is the water storage capacity of the hydropower station at time t, are the minimum and maximum water storage capacity of the hydropower station at time t respectively; is the water discharge flow of the hydropower station at time t; are the minimum and maximum water discharge of the hydropower station at time t respectively; (4) Energy storage constraints, including energy storage system state of charge constraints and energy storage charging and discharging constraints; Energy storage system charge state constraints: SOC min ≤SOC Storage,t ≤SOC max Energy storage charging and discharging constraints: Among them, SOC Storage,t is the charge state of the energy storage system at time t, that is, the ratio of the remaining capacity of the energy storage device to the total capacity; η c , η d The charging and discharging efficiency of the energy storage system; is the charging and discharging power of energy storage at time t, are the minimum and maximum values ​​of energy storage charging and discharging power respectively; E s is the rated capacity of the energy storage system, SOC min , SOC max are the upper and lower limits of the state of charge of the energy storage system, respectively, and △t is the duration of each time period; (5) Thermal power units; including output constraints and ramp constraints; Output constraints: Hill climbing constraints: Among them, δ i G are the upper and lower limits of the ramp rate of thermal power unit i; (6) Line transmission capacity constraints; -P ij,max ≤B ij (i i,t -θ j,t )≤P ij,max Among them, B ij , P ij,max are the admittance and maximum transmission capacity between nodes i and j, respectively, i,t ,θ j,t are the voltage phase angles at nodes i and j respectively.

10. The wind, solar, water, fire and energy storage day-ahead optimization dispatching system according to claim 7, characterized in that: The roulette wheel method is used in the multi-objective vulture search algorithm to solve the archive overflow problem, where the probability formula for deleting the solution is: P i represents the probability that the i-th solution is deleted, N i represents the number of neighboring solutions of the i-th solution in the archive, and C is a constant greater than 1.

11. The wind, solar, water, fire and energy storage day-ahead optimization dispatching system according to claim 10, characterized in that: The number of neighboring solutions of a solution in the archive is calculated as follows: choose a distance d and find the number of neighboring solutions of a solution within this distance as the congestion degree of a solution to evaluate the solution; The distance formula is: Among them, max and min represent the maximum and minimum fitness values ​​of each target in the archive respectively; Archive_size represents the size of the archive.

12. The wind, solar, water, fire and energy storage day-ahead optimization dispatching system according to claim 7, characterized in that: The elite selection strategy is: first calculate the new fitness of each individual, then sort them, and finally select all individuals with the lowest fitness as the elite; Among them, the number of adjacent solutions is taken as the new fitness value of each individual; the new fitness value f i The calculation formula is: i =N i , N i Represents the number of adjacent solutions to the i-th solution in the archive.

13. A wind, solar, water, fire and storage day-ahead optimization dispatching device, characterized in that: The device stores a constructed day-ahead optimization scheduling model for wind, solar, water, fire and storage, and a multi-objective vulture search algorithm for solving the day-ahead optimization scheduling model for wind, solar, water, fire and storage; wherein the multi-objective vulture search algorithm is: the multi-objective vulture search algorithm adds an archive mechanism on the basis of the vulture search algorithm to store the currently obtained Pareto optimal solution, introduces the elite selection strategy into the multi-objective vulture search algorithm, selects individuals from the archive as elites or leaders, and the elites in the group guide other individuals to perform optimization search to obtain the optimal scheduling solution; the rule of the archive mechanism is: compare the new solution generated by the multi-objective vulture search algorithm with the existing solutions in the archive, if the generated new solution is not dominated by all the existing solutions in the archive, then the generated new solution is called a non-dominated solution and stored in the archive; otherwise, the generated new solution will be discarded; when the archive overflows, if there are new non-dominated solutions, then delete the solution with the smallest impact on the distribution of the solution set, and the solution set includes the existing solutions in the archive and the new non-dominated solutions.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into the processor, the steps of the method for optimizing the day-ahead scheduling of wind, solar, water, thermal and energy storage systems according to any one of claims 1 to 5 are implemented.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when called, are used to execute the steps of the day-ahead optimization scheduling method for wind, solar, water, thermal and energy storage as described in any one of claims 1 to 5.

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