Water-wind-light complementary power generation system short-term optimization scheduling method, system, equipment and medium considering energy storage and source load matching

Abstract

The invention belongs to the technical field of wind-solar-water power generation optimization scheduling, and particularly discloses a water-wind-solar complementary power generation system short-term optimization scheduling method, system, equipment and medium considering energy storage and source load matching, and the method comprises the following steps: building a peak regulation-energy storage multi-target optimization scheduling model; carrying out constraint condition definition on the model; and calculating and solving the cascade peak regulation-energy storage short-term multi-target scheduling model by using an NSGA-II algorithm, and outputting a peak regulation-energy storage multi-target optimization scheduling result. According to the method, the two targets of peak regulation and energy storage can be considered at the same time, the peak regulation pressure faced by the power grid side can be effectively reduced on the basis of guaranteeing the hydropower generation efficiency and the end-of-period energy storage, and the multi-target demand response in the scheduling process is met. According to the method, the peak regulation demand and the power generation benefit of the power grid are comprehensively considered, the obvious game competition relationship between the hydropower peak regulation and the energy storage can be obviously seen, and the coordination between the hydropower peak regulation capacity and the future power generation benefit can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of wind-solar-hydropower generation optimization scheduling, and specifically to a short-term optimization scheduling method, system, equipment and medium for a wind-hydropower complementary power generation system that takes into account both energy storage and source-load matching. Background Art

[0002] my country is vigorously building a new power system with new energy as the main body, accelerating the construction of a new power system that is clean, low-carbon, safe, sufficient, economical, efficient, coordinated in supply and demand, flexible and intelligent. As the scale and proportion of new energy such as wind and solar power continue to expand and the proportion of fossil-based regulating power sources decreases, it has brought severe challenges to the large-scale consumption of new energy.

[0003] In recent years, the proportion of installed capacity of renewable energy in China has continued to rise. Two-thirds of China's new energy provinces are about to cross the 25% penetration threshold, and have and will face serious new scheduling problems. In 2023, the new installed capacity of wind power will reach 75.66 million kW, a record high, a year-on-year increase of 101.1%. By the end of 2023, the cumulative installed capacity of wind power in China will reach 441.34 million kW, a year-on-year increase of 20.8%. In 2023, the new installed capacity of photovoltaic power generation in my country will reach 216 million kW, a record high, a year-on-year increase of 147%. By the end of 2023, the cumulative installed capacity of photovoltaic power generation in China will reach 610 million kW, a year-on-year increase of 55%, accounting for 21% of the total installed capacity of power sources in the country, an increase of 5 percentage points year-on-year, becoming the second largest power source in the country.

[0004] With the gradual expansion of the development scale of wind power and photovoltaic power stations, new renewable energy power generation represented by the two has been connected to the power grid on a large scale, and the multi-energy complementary power generation system represented by photovoltaic water / wind water / wind photovoltaic water has become an important part of the modern power system. At present, there are many studies on the operation mode of integrated power generation systems such as "wind power-photovoltaic-pumped storage power station" and they are relatively mature. However, for the mode of direct grid connection of wind and photovoltaic power and using the main grid for regulation, due to the randomness and uncertainty of wind and photovoltaic output, the power grid is required to have a high stability and dispatching level, which brings huge challenges to grid dispatching. Different from the main grid dispatching to supplement wind and photovoltaic output, using basin hydropower to provide auxiliary supplements for wind and photovoltaic power, and establishing a comprehensive power generation system of "wind, photovoltaic, hydropower and comprehensive complementarity after 'bundling and external transmission'" is an effective way to absorb wind power and photovoltaic power, and can obtain a smooth output curve, effectively avoiding the impact of wind and photovoltaic output fluctuations on the power grid.

[0005] There are some technical solutions corresponding to the above problems in the existing technology, but the existing technology still has the following problems: only considering the power generation efficiency as a goal, not considering the peak-shaving demand of the power grid; or only considering the source-load matching but failing to take into account energy storage; the existing technology fails to balance the relationship between source-load matching, power generation efficiency and wind and solar power consumption; it is subject to many restrictions in the usage scenarios, the usage scenarios are single, and it cannot be applied to most scenarios.

[0006] Therefore, there is an urgent need for a short-term optimal scheduling method for cascade hydro-wind-solar multi-energy complementarity that can solve the above-mentioned problems, balance the relationship among source-load matching, power generation efficiency and wind-solar consumption, and be suitable for different regions and scales. Summary of the invention

[0007] In order to solve the problems existing in the prior art, the present invention provides a short-term optimization scheduling method, system, equipment and medium for a water-wind-solar complementary power generation system that takes into account energy storage and source-load matching, and adopts an improved genetic algorithm NSGA-Ⅱ to solve the peak-shaving-energy storage multi-objective optimization scheduling model, and has made progress in full reception of wind and light output, water level output process of power stations and power generation. The present invention comprehensively considers the peak-shaving demand of the power grid and the power generation efficiency, and it can be clearly seen that there is an obvious game competition relationship between hydropower peak-shaving and energy storage. The present invention can achieve coordination between hydropower peak-shaving capacity and future power generation income, and solves the problems mentioned in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a short-term optimization scheduling method for a water-wind-solar complementary power generation system taking into account both energy storage and source-load matching, comprising the following steps:

[0009] S1. Establish a peak load regulation-energy storage multi-objective optimization scheduling model;

[0010] S2, define constraints on the model;

[0011] S3. Use the NSGA-Ⅱ algorithm to calculate and solve the cascade peak-shaving-energy storage short-term multi-objective scheduling model, and output the peak-shaving-energy storage multi-objective optimization scheduling results.

[0012] Preferably, in step S1, the following is specifically included:

[0013] S11. Peak load regulation target: The 15-minute runoff process of the downstream cascade power stations on different typical days and the wind and light output in different time periods are used as the input conditions of the peak load regulation-energy storage multi-objective short-term scheduling model, with the goal of minimizing the process variance of the residual load of the cascade hydropower station power grid. The formula is expressed as follows:

[0014]

[0015] Where: f is the process variance of the residual load of the cascade hydropower station grid; M is the number of hydropower stations; T is the total number of time periods; d t is the load faced by the power grid after deducting the wind and solar power output in the t period, MW; P t is the residual load value of the power grid after deducting the hydropower output in the t period, MW; is the output of power station k in period t, MW;

[0016] S12, energy storage target: select the maximum cascade energy storage at the end of the dispatch period as the target, the formula is as follows:

[0017]

[0018] Where: F is the total energy storage of the cascade at the end of the dispatch period, kM·h; V 1,T , V 2,T , …, V M,T is the reservoir water storage capacity at the end of each period of each cascade station, m 3 ; is the average water head of the kth power station, m; T is the end of the dispatch period;

[0019] S13. Considering the use of only the intraday regulation capacity of the daily regulation power station, keeping the reservoir water level equal at the beginning and end of the day, converting formula (1.2) into the maximum value of the total cascade energy storage increase, the formula is expressed as follows:

[0020]

[0021] Where: F kt is the energy storage increase of cascade power station k in time period t, (kW·h); K1,…,K m are the output coefficients of m power stations; H 1t , …, H mt is the water head of m power stations in time period t, (m); Qr 1t , Q 1t , …, Qr mt , Q mt are the inflow and outflow of m power stations, (m 3 / s); Δt is the length of a time period;

[0022] The above formula (1.3) is expressed as the sum of the energy storage added value of each cascade power station, among which the energy storage added value of the reservoir power station with daily regulation capacity at the end of the dispatch period is expressed as:

[0023]

[0024] Where: is the average water level during the power station dispatch period, m; ΔV is the increase in reservoir water storage, m 3 ;

[0025] Since the water level of the daily regulating reservoir is equal at the beginning and end of the period, the energy storage increase is zero, and ΔV = 0 at this time. Further transforming formula (1.4), the formula is expressed as follows:

[0026]

[0027] Preferably, in step S2, the constraint conditions are defined to include: water balance constraint, output constraint, reservoir capacity constraint, reservoir discharge flow constraint, reservoir operating water level constraint and non-negative constraint.

[0028] Preferably, the water balance constraint is expressed as follows:

[0029] QUR k,t =q k,t +Qr k-1,t-τ +(V k,t-1 -V k,t ) / Δt (1.6)

[0030] Where: Qr k,t is the inflow flow of the kth power station in time period t, m 3 / s;q k,t is the interval flow between the kth power station and the upstream reservoir in time period t, m 3 / s; τ is the water flow lag time between power station k and power station k-1; Qr k-1,t-τ is the outflow flow of power station k-1 in the period t-τ, m 3 / s, ;V k,t-1 and V k,t are the initial and final storage capacities of power station k at time period t, m 3 ; Δt is the length of a time period, min;

[0031] The output constraint is expressed as follows:

[0032]

[0033] Where: They represent the minimum and maximum output limits of power station k in time period t, N k,t represents the actual output of power station k in time period t;

[0034] The storage capacity constraint is expressed as follows:

[0035] V k min ≤V k,t ≤V k max (1.8)

[0036] Where: V k min、V k max are the minimum and maximum water storage capacity of power station k, m 3 ;

[0037] The reservoir discharge flow constraint is expressed as follows:

[0038]

[0039] Where: are the minimum and maximum discharge flow of power station k, m 3 / s;

[0040] The reservoir operating water level constraint is expressed as follows:

[0041]

[0042] Where: are the upper and lower limits of the water level in power station k, m respectively.

[0043] Preferably, in step S3, NSGA-II realizes population evolution through genetic operations, sorts individuals in combination with a fast non-dominated sorting algorithm, and introduces a congestion strategy to make the non-inferior solution set evenly distributed on the frontier of the Pareto domain, and obtains a peak-shaving-energy storage multi-objective optimization scheduling result, which specifically includes the following:

[0044] S31, population initialization:

[0045] Take 15 minutes as a time step and the water level Z at the end of each reservoir period t is the decision variable, and an initial population of 100 is randomly generated. One individual is the water level process of a group of cascade hydropower stations. The water level at the end of a period represents a gene, and the length of a chromosome is equal to the number of reservoir scheduling periods × the number of regulating reservoirs. The water level value contained in the individual is randomly generated by formula (1.11):

[0046]

[0047] Where: Z t is the water level at the end of the reservoir period; Z t,up , Z t,down are the upper and lower limits of the water level change of the reservoir at time t; r is a random number less than or equal to m; m is a constant determined by the decision accuracy δ;

[0048] S32, calculate the fitness function value:

[0049] According to the fitness function, the quality of individuals is judged, and the objective function formula (1.1) and formula (1.5) are used as the fitness function to perform non-dominated sorting and crowding calculation;

[0050] S33, fast non-dominated sorting and congestion calculation:

[0051] Starting from the second generation of evolution, the parent and child populations are merged to obtain a mixed population with twice the number of individuals in the initial population. After completing the fast non-dominated sort according to the individual fitness value, the crowding degree is calculated, and relatively better individuals are selected to form a new generation population to replace the initial population as the parent population. It is determined whether the maximum number of iterations has been reached. If so, the result is output in step S35, otherwise, the genetic operation is performed in step S34;

[0052] The crowding calculation introduced in NSGA-II can maintain population diversity and make individuals evenly distributed on the Pareto frontier. The crowding strategy is implemented by calculating the sum of the distance differences between different non-dominated individuals of the same level and their two adjacent individuals on each sub-objective function. The crowding calculation method is as follows:

[0053]

[0054] Where: I i is the crowding degree of individual i, N is the total number of non-dominated individuals at the same level, I i-1,m ,I i+1,m Represents the fitness function value of the mth target of the two individuals adjacent to individual i;

[0055] S34, genetic operations:

[0056] The core of genetic operations in NSGA-II are selection, crossover, and mutation;

[0057] Selection: The selection of parents is done by roulette wheel, and the mating pool size is set to 1 / 2 of the population size, i.e. 1 / 2N;

[0058] Crossover: Use simulated binary crossover and set the distribution index to 0.9;

[0059] Mutation: Mutation operation is realized by using non-uniform mutation, and the mutation probability is p m =0.05+1 / (gen+1), where gen is the current iteration number;

[0060] After completing the above genetic operations, a progeny population with the same size as the initial population is obtained;

[0061] S35. Output the scheduling result:

[0062] The above steps S32 to S34 are looped until the termination condition is met, that is, the number of iterations reaches 1000, the model is terminated, and the peak load regulation-energy storage multi-objective optimization scheduling result is output.

[0063] On the other hand, in order to achieve the above-mentioned purpose, the present invention also provides the following technical solution: a short-term optimization scheduling system for a water-wind-solar complementary power generation system taking into account both energy storage and source-load matching, the system comprising the following modules:

[0064] Model building module, establishing peak load regulation-energy storage multi-objective optimization scheduling model;

[0065] Constraint definition module, defines constraints for the model;

[0066] The optimization scheduling module uses the NSGA-Ⅱ algorithm to calculate and solve the cascade peak-shaving-energy storage short-term multi-objective scheduling model, and outputs the peak-shaving-energy storage multi-objective optimization scheduling results.

[0067] On the other hand, to achieve the above-mentioned purpose, the present invention also provides the following technical solution: an electronic device, the electronic device comprising: a processor; and a memory for storing one or more programs;

[0068] When the one or more programs are executed by the processor, the processor executes the short-term optimization scheduling method for the water-wind-solar complementary power generation system that takes into account both energy storage and source-load matching.

[0069] On the other hand, to achieve the above-mentioned purpose, the present invention also provides the following technical solutions: a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the short-term optimization scheduling method for the water-wind-solar complementary power generation system that takes into account both energy storage and source-load matching.

[0070] The beneficial effects of the present invention are as follows: in the context of the continuous increase in the scale of wind and photovoltaic grid connection, the use of cascade hydropower to complement the two and then "bundle and transmit" to form a "wind, photovoltaic and water" multi-energy complementary energy system is an effective way to maximize the utilization of clean energy. The present invention establishes a multi-objective short-term joint scheduling function model for cascade hydropower stations under wind and photovoltaic grid connection, and uses the NSGA-Ⅱ algorithm to solve the model to obtain the reservoir scheduling operation mode, which achieves the coordination between the hydropower peak-shaving capacity and future power generation revenue, and is consistent with the reservoir scheduling law, showing that the peak-shaving-energy storage multi-objective scheduling proposed by the present invention is reasonable, and provides theoretical and technical guidance for the actual operation of the reservoir. The present invention can take into account both peak-shaving and energy storage goals at the same time, and can effectively reduce the peak-shaving pressure faced by the power grid side on the basis of ensuring hydropower generation efficiency and end-of-period energy storage, and meet the multi-objective demand response in the scheduling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic flow chart of the steps of a short-term optimization scheduling method for a water-wind-solar hybrid power generation system taking into account both energy storage and source-load matching in an embodiment of the present invention;

[0072] Figure 2Schematic diagram of the NSGA-Ⅱ algorithm flow in an embodiment of the present invention;

[0073] Figure 3 A typical day in the dry season (a) Multi-objective Pareto frontier scatter plot;

[0074] Figure 4 (ii) Multi-objective Pareto frontier scatter plot for a typical day in the normal water period;

[0075] Figure 5 (iii) Multi-objective Pareto frontier scatter plot for a typical day in the flood season;

[0076] Figure 6 Schematic diagram of power station output and residual load process for dry season decision-making recommendations;

[0077] Figure 7 Schematic diagram of power station output and residual load process for decision-making proposals during normal water period;

[0078] Figure 8 Schematic diagram of power station output and residual load process for flood season decision-making proposal;

[0079] Fig. 9 The schematic diagram of the water level process of the power station on each typical day;

[0080] Fig.10 It is a schematic diagram of a short-term optimization dispatching system module of a water-wind-solar hybrid power generation system that takes into account both energy storage and source-load matching in an embodiment of the present invention;

[0081] Fig.11 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention;

[0082] In the figure, 110 - model building module; 120 - constraint definition module; 130 - optimization scheduling module; 210 - processor; 220 - storage. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] The present invention provides a technical solution: a short-term optimization scheduling method for a water-wind-solar complementary power generation system taking into account both energy storage and source-load matching, such as Figure 1 As shown, the following steps are included:

[0085] S1. Establish a peak load regulation-energy storage multi-objective optimization scheduling model;

[0086] After wind power and photovoltaic power are connected, the peak-valley distribution of the original load demand process will be affected, making hydropower face a new peak-shaving situation. In the short term, the present invention considers the peak-shaving benefits of cascade hydropower and the future benefits of reservoirs, and establishes a peak-shaving-energy storage multi-objective optimization scheduling model to explore the feedback relationship between the peak-shaving benefits of cascade hydropower and reservoir energy storage after wind and photovoltaic power are connected, and to seek a scheduling method that can well coordinate the two.

[0087] S11. Peak load regulation target: The model takes the daily 15-minute runoff process of the cascade power stations in the lower reaches of the Yalong River and the wind and light output in each period as the input conditions of the peak load regulation-energy storage multi-objective short-term scheduling model, with the goal of minimizing the process variance of the residual load of the cascade hydropower station power grid. The formula is expressed as follows:

[0088]

[0089] Where: f is the process variance of the residual load of the cascade hydropower station grid; M is the number of hydropower stations; T is the total number of time periods; d t is the load faced by the power grid after deducting the wind and solar power output in the t period, MW; P t is the residual load value of the power grid after deducting the hydropower output in the t period, MW; is the output of power station k in period t, MW;

[0090] S12. Energy storage target: Determining the operation mode of cascade power stations according to the load task given by the power grid is a typical "power-to-water" problem. In this mode, in order to ensure future power generation revenue, the maximum cascade energy storage at the end of the dispatch period is selected as the target. The formula is expressed as follows:

[0091]

[0092] Where: F is the total energy storage of the cascade at the end of the dispatch period, kM·h; V 1,T , V 2,T , …, V M,T is the reservoir water storage capacity at the end of each period of each cascade station, m 3 ; is the average water head of the kth power station, m; T is the end of the dispatch period;

[0093] S13. Among the power stations involved in the embodiment of the present invention, Guandi and Tongzilin are daily regulation power stations. Considering the use of only the intraday regulation capacity of the daily regulation power station, keeping the reservoir water level equal at the beginning and end of the day, converting formula (1.2) into the maximum added value of the total cascade energy storage, the formula is expressed as follows:

[0094]

[0095] Where: F ktis the energy storage increase of cascade power station k in time period t, (kW·h); K1,…,K m are the output coefficients of m power stations; H 1t , …, H mt is the water head of m power stations in time period t, (m); Qr 1t , Q 1t , …, Qr mt , Q mt are the inflow and outflow of m power stations, (m 3 / s); Δt is the length of a time period;

[0096] The above formula (1.3) is expressed as the sum of the energy storage added value of each cascade power station, among which the energy storage added value of the reservoir power station with daily regulation capacity at the end of the dispatch period is expressed as:

[0097]

[0098] Where: is the average water level during the power station dispatch period, m; ΔV is the increase in reservoir water storage, m 3 ;

[0099] Since the water level of the daily regulating reservoir is equal at the beginning and end of the period, the energy storage increase is zero, and ΔV = 0 at this time. Further transforming formula (1.4), the formula is expressed as follows:

[0100]

[0101] S2, define constraints on the model;

[0102] The constraint conditions include: water balance constraint, output constraint, reservoir capacity constraint, reservoir discharge flow constraint, reservoir operating water level constraint and non-negative constraint.

[0103] The water balance constraint is expressed as follows:

[0104] QUR k,t =q k,t +Qr k-1,t-τ +(V k,t-1 -V k,t ) / Δt (1.6)

[0105] Where: Qr k,t is the inflow flow of the kth power station in time period t, m 3 / s;q k,t is the interval flow between the kth power station and the upstream reservoir in time period t, m 3 / s; τ is the water flow lag time between power station k and power station k-1; Qr k-1,t-τ is the outflow flow of power station k-1 in the period t-τ, m 3 / s, ;V k,t-1 and V k,t are the initial and final storage capacities of power station k at time period t, m 3 ; Δt is the length of a time period, min;

[0106] Output constraint, the formula is expressed as follows:

[0107]

[0108] Where: They represent the minimum and maximum output limits of power station k in time period t, N k,t represents the actual output of power station k in time period t;

[0109] Storage capacity constraint, the formula is expressed as follows:

[0110] V k min ≤V k,t ≤V k max (1.8)

[0111] Where: V k min 、V k max are the minimum and maximum water storage capacity of power station k, m 3 ;

[0112] The reservoir discharge flow constraint is expressed as follows:

[0113]

[0114] Where: are the minimum and maximum discharge flow of power station k, m 3 / s;

[0115] The reservoir operating water level constraint is expressed as follows:

[0116]

[0117] Where: are the upper and lower limits of the water level in power station k, m respectively.

[0118] Non-negative constraint: All variables must be no less than 0.

[0119] S3. Use the NSGA-Ⅱ algorithm to calculate and solve the cascade peak-shaving-energy storage short-term multi-objective scheduling model, and output the peak-shaving-energy storage multi-objective optimization scheduling results.

[0120] Multi-objective optimization problems involve multiple single-objective optimization functions, and there are conflicting game relationships among them. Conventional GA algorithms are difficult to judge the pros and cons of individuals, resulting in the inability to perform genetic operations. The early NSGA algorithm has shortcomings in solving such problems, such as no elite retention mechanism, the need to manually determine shared parameters, and the high complexity of the construction of the non-dominated frontier, resulting in a large amount of calculation. Therefore, the NSGA-II algorithm was proposed by improving NSGA. Compared with the NSGA algorithm, the advantages of using NSGA-II to solve multi-objective problems are: dividing the evolutionary population hierarchy according to the dominance relationship between individuals and then sorting individuals using the fast non-dominated sorting method can reduce the computational complexity; introducing a crowding strategy to replace the fitness sharing strategy, while maintaining population diversity, and making the individuals in the Pareto domain evenly distributed and extended to the entire domain; combined with the elite strategy, it is maximized to ensure that the best individuals in the parent population enter the next generation evolutionary population, obtain a better next generation population, and improve the accuracy of the optimization results; finally, genetic operations are performed to solve multi-objective problems. The specific process is as follows: Figure 2 shown.

[0121] NSGA-II realizes population evolution through genetic operations, combines fast non-dominated sorting algorithm to sort individuals, and introduces crowding strategy to maintain population diversity, so that non-inferior solution sets can be evenly distributed on the Pareto frontier. Considering that NSGA-II has good adaptability in multi-objective optimization problems of reservoirs, the present invention uses this method to solve the multi-objective model of cascade peak load regulation-energy storage after wind power and photovoltaic power are connected. The specific solution steps and parameter settings involved are as follows:

[0122] S31, population initialization:

[0123] This example studies the optimization scheduling problem of cascade hydropower stations at 96 points in a day, with a time step of 15 minutes and the water level Z at the end of each reservoir period. t As decision variables, an initial population of 100 is randomly generated. One individual is a water level process of a group of cascade hydropower stations. The water level at the end of a period represents one gene, and the length of a chromosome is equal to the number of reservoir scheduling periods × the number of regulating reservoirs; that is, the chromosome in this embodiment consists of 96 × 4 genes. The water level value contained in the individual is randomly generated by formula (1.11):

[0124]

[0125] Where: Z t is the water level at the end of the reservoir period; Z t,up , Z t,down are the upper and lower limits of the water level change of the reservoir at time t, respectively; r is a random number less than or equal to m; m is a constant determined by the decision accuracy δ; in this embodiment, δ=0.1.

[0126] S32, calculate the fitness function value:

[0127] Genetic algorithms usually judge the quality of individuals based on the fitness function, which can directly affect the convergence speed of the algorithm and the acquisition of the optimal solution. Therefore, the objective function formula (1.1) and formula (1.5) are used as the fitness function to perform non-dominated sorting and congestion calculation;

[0128] S33, fast non-dominated sorting and congestion calculation:

[0129] Starting from the second generation of evolution, the parent and child populations are merged to obtain a mixed population with twice the number of individuals in the initial population. After completing the fast non-dominated sort according to the individual fitness value, the crowding degree is calculated, and relatively better individuals are selected to form a new generation population to replace the initial population as the parent population. It is determined whether the maximum number of iterations has been reached. If so, the result is output in step S35, otherwise, the genetic operation is performed in step S34;

[0130] The crowding calculation introduced in NSGA-II can maintain population diversity and make individuals evenly distributed on the Pareto frontier. The crowding strategy is implemented by calculating the sum of the distance differences between different non-dominated individuals of the same level and their two adjacent individuals on each sub-objective function. The crowding calculation method is as follows:

[0131]

[0132] Where: I i is the crowding degree of individual i, N is the total number of non-dominated individuals at the same level, I i-1,m ,I i+1,m Represents the fitness function value of the mth target of the two individuals adjacent to individual i;

[0133] S34, genetic operations:

[0134] The core of genetic operations in NSGA-II are selection, crossover, and mutation;

[0135] Selection: The selection of parents is done by roulette wheel, and the mating pool size is set to 1 / 2 of the population size, i.e. 1 / 2N;

[0136] Crossover: Use simulated binary crossover and set the distribution index to 0.9;

[0137] Mutation: Mutation operation is realized by using non-uniform mutation, and the mutation probability is p m =0.05+1 / (gen+1), where gen is the current iteration number; p m It changes as the search process progresses. In the initial stage of evolution, its value is relatively large, and then gradually decreases to about 0.05 in the later stage.

[0138] After completing the above genetic operations, a progeny population with the same size as the initial population is obtained;

[0139] S35. Output the scheduling result:

[0140] The above steps S32 to S34 are looped until the termination condition is met, that is, the number of iterations reaches 1000, the model is terminated, and the peak load regulation-energy storage multi-objective optimization scheduling result is output.

[0141] Example verification analysis

[0142] In this embodiment, a certain day in April, May and August 2020 is selected as a typical day, representing three different scenarios of the dry season, normal water season and flood season of the year, respectively. Combined with the daily power grid load demand and the actual operation of the cascade, the day is used as the scheduling cycle and 15 minutes is a scheduling period. The NSGA-Ⅱ algorithm is used to optimize the peak-shaving-energy storage short-term multi-objective scheduling model of the cascade to obtain the scheduling results under different typical days, and the individuals with a non-dominated sorting level of "0" are selected to obtain the optimal non-dominated solution set, that is, the Pareto frontier.

[0143] Typical Day Pareto Results Analysis

[0144] Table 1 shows the set of non-inferior solutions for cascade peak regulation and energy storage under the scenario of a typical day (I) in the dry season; Figure 3 It is the Pareto scatter plot of the multi-objective model of cascade peak regulation and energy storage under the scenario of a typical day (one) in the dry season. Figure 3 It can be seen that the Pareto frontier is roughly distributed on a curve, and there is an obvious game competition relationship between the two proposed objectives (minimum variance of grid residual load process and maximum cascade energy storage), which is manifested in that when the added value of energy storage of cascade hydropower stations increases, the variance of grid residual load process increases accordingly. Figure 3 The distribution of non-inferior solutions shows that the non-inferior solutions are distributed in the reservoir with an increase in energy storage value of 5×10 3 kW·h to 65×10 3 kW·h, the variance of the grid residual load process is 212.56×10 4 MW 2 to 213.13×10 4 MW 2 As shown in Table 1, in the scenario of a typical day in the dry season (one), among the non-inferior solutions obtained by solving the model, the maximum variance of the grid residual load process is 213.13×10 4 MW 2 , the minimum is 212.56×10 4 MW 2, the maximum value of the cascade reservoir energy storage increase is 63073.6kW·h, the minimum is 17865.5kW·h, and the average value is 52243.3kW·h. When the reservoir energy storage increase is small, as the variance of the grid residual load process increases, the reservoir energy storage increase increases significantly, and the rate of increase gradually decreases with the increase of the grid residual load process variance; after the reservoir energy storage increase increases to 58169.3kW·h, the grid residual load process variance continues to increase significantly, and the reservoir energy storage increase can only be slowly increased to 63073.6kW·h; This shows that in the scenario of a typical day (one) in the dry season, when the grid residual load process variance reaches 212.88×10 4 MW 2 After the added value of reservoir energy storage reaches 58169.3kW·h, the growth of added value of reservoir energy storage obtained at the expense of the peak-shaving benefits of cascade hydropower is very limited.

[0145] Table 1 Multi-objective non-inferior solution set for a typical day in the dry season (I)

[0146]

[0147] Table 2 Figure 4 They are the non-inferior solution set and Pareto frontier scatter points of cascade peak load regulation and energy storage under the scenario of typical day (II) in normal water period. Figure 4 It can be seen that the Pareto frontier is evenly distributed on a curve, and the game competition relationship between the two objectives (minimum grid residual load process variance and maximum cascade energy storage) is obvious, which is still manifested in that when the energy storage added value of the cascade hydropower station increases, the grid residual load process variance increases accordingly. Figure 4 It can be seen that the non-inferior solution is mainly distributed in the reservoir energy storage increase value of 5×10 3 kW·h to 35×10 3 kW·h, the variance of the grid residual load process is 221×10 4 MW 2 to 221.5×10 4 MW 2 As shown in Table 2, in the scenario of a typical day (second) during the normal water period, among the non-inferior solutions obtained by solving the model, the maximum variance of the grid residual load process is 221.42×10 4 MW 2 , the minimum is 221.03×10 4 MW 2, the maximum added value of cascade reservoir energy storage is 31890.8kW·h, the minimum is 7095.9kW·h, and the average added value is 26012.4kW·h. When the added value of reservoir energy storage is small, as the variance of the grid residual load process increases, the added value of reservoir energy storage increases significantly; when the added value of reservoir energy storage increases to 29793.1kW·h, the variance of the grid residual load process continues to increase significantly, but the growth rate of the added value of reservoir energy storage is very slow; this shows that in the scenario of a typical day (II) in the normal water period, when the variance of the grid residual load process reaches 221.16×10 4 MW 2 After the added value of reservoir energy storage reaches 29793.1kW·h, sacrificing the peak-shaving benefits of larger cascade hydropower stations can only result in a smaller increase in the added value of reservoir energy storage.

[0148] Table 2 Multi-objective non-inferior solution set for typical day (II) during normal water period

[0149]

[0150] Table 3 Figure 5 They represent the non-inferior solution set and Pareto frontier scatter plot of cascade peak-shaving-energy storage multi-objective under the scenario of typical day (three) in flood season. It can be seen that the Pareto frontier is relatively evenly distributed on a curve, and there is an obvious game competition relationship between the two objectives, which is consistent with the changing trends under the scenarios of typical day (one) and typical day (two). It is manifested that when the variance of the grid residual load process increases, the water level increase value of the cascade hydropower station increases accordingly. Figure 5 The distribution of non-inferior solutions shows that the non-inferior solutions are mainly distributed in the reservoir energy storage increment of 45×10 3 kW·h to 70×10 3 kW·h, the variance of the grid residual load process is 233.5×10 4 MW 2 to 235.5×10 4 MW 2 As shown in Table 2, in the scenario of a typical day (Wednesday) in the flood season, among the non-inferior solutions obtained by solving the model, the maximum variance of the grid residual load process is 235.02×10 4 MW 2 , the minimum is 233.84×10 4 MW 2, the maximum value of the cascade reservoir energy storage increase is 66593.4kW·h, the minimum is 49360.5kW·h, and the average value is 60911.4kW·h. When the reservoir energy storage increase is small, with the increase of the grid residual load process variance, the reservoir energy storage increase shows a significant increase trend; when the reservoir energy storage increase rises to 65994.1kW·h, the grid residual load process variance continues to increase at a large scale, at this time the reservoir energy storage increase growth rate is very small, and its increase rate changes very little; this shows that in the scenario of a typical day (Wednesday) in the flood season, when the grid residual load process variance reaches 234.67×10 4 MW 2 After the added value of reservoir energy storage reaches 65994.1kW·h, the peak-shaving benefits of cascade hydropower stations will be greatly sacrificed and only a relatively small increase in the added value of reservoir energy storage can be obtained.

[0151] Table 3 Multi-objective non-inferior solution set for typical day (three) in flood season

[0152]

[0153] Optimizing scheduling analysis

[0154] The Pareto scatter plots under different typical daily scenarios were analyzed, among which the individual numbered 10 in the dry season, the individual numbered 14 in the normal water season, and the individual numbered 7 in the flood season are the growth inflection points under each scenario, indicating that in this state, the cascade power station takes into account both the reservoir energy storage and peak regulation benefits when operating, that is, the solution has a high optimization level in both objective functions. Therefore, the reservoir operation process corresponding to the inflection point can be used as a decision-making recommendation for actual scheduling reference.

[0155] Figure 6 The output process of the water-wind-solar complementary power generation system and the grid load process in the selected decision-making proposal under the scenario of a typical day in the dry season (I). Due to the non-adjustability of wind power and photovoltaic power, it is chosen to give priority to the two, and then use hydropower scheduling to supplement. It can be seen that in the complementary scheduling process, the cascade hydropower can adjust its own output according to the load process, so that the complementary output and the load process have a relatively consistent change trend, making the remaining load process smoother and reducing the load fluctuation amplitude. The peak load borne by the water-wind-solar complementary power generation system helps to improve the stability of the power grid, and the stable remaining load is conducive to the access of other power sources, optimizing the power configuration structure of the regional power grid, and improving the utilization rate of various power sources. The above results show that under the current wind and solar installed capacity, the wind and solar in the system can be fully absorbed.

[0156] Figure 7The output process of the water-wind-solar complementary power generation system and the grid load process in the selected decision-making proposal under the scenario of a typical day in the normal water season (II). It can be seen that the load output trend is relatively consistent with that in the dry season, but because the output of wind power and photovoltaic power generation is small in the normal water season, it is necessary to arrange the power station to increase the output during the peak load period, and use the peak-shaving capacity of hydropower to a certain extent to reduce the peak and flatten the valley, so as to facilitate the subsequent preparation of the unit power generation plan. In addition, since the runoff in the normal water season is more than that in the dry water season, the hydropower output has a certain degree of increase under the condition of meeting the water level change requirements. By adjusting the hydropower output to achieve peak load reduction, the remaining load process is smoother, which effectively reduces the peak-shaving pressure of the power grid and can better meet the needs of peak-shaving target optimization.

[0157] Figure 8 The output process of the water-wind-solar complementary power generation system and the grid load process in the selected decision-making proposal for a typical day (three) in the flood season. It can be seen that compared with the dry season and the normal water season, the overall output of wind power and photovoltaic power generation in the flood season is relatively small, and because the runoff in the flood season is large, the overall output of hydropower is generally larger. In the given flood season decision proposal, by adjusting the output of the cascade power stations in different periods of load demand, peak shaving and valley flattening can be achieved, and a relatively smooth load demand curve can be obtained. Comparison Figure 6 , Figure 7 and Figure 8 It can be found that the regulation performance of the water-wind-solar complementary power generation system is stronger in the dry season. Specifically, the average daily power generation in the dry season is lower, but the water-wind-solar complementary power generation process is more stable than that in the flood season and normal water season, and has a more consistent change trend with the load curve.

[0158] Fig. 9 The following are the water level changes of the four regulating power stations in the lower reaches of the Yalong River during the dispatching period under three typical scenarios. It can be seen that on typical days in different periods, the water levels of Jinping I and Ertan Hydropower Stations both showed a weak and gradual upward trend, while the water levels of Guandi and Tongzilin Hydropower Stations first decreased and then increased during the dispatching period. This is because in the peak regulation process, it is mainly these two power stations that undertake the peak regulation task to achieve peak reduction and valley flattening. At the same time, the daily regulating reservoir water level is set to remain unchanged at the beginning and end of the dispatching period, so the water level gradually rises to the normal water storage level in the later period.

[0159] The method of the present invention comprehensively considers the peak-shaving demand and power generation efficiency of the power grid, takes the daily runoff process at the dam sites of Jinping I, Guandi, Ertan and Tongzilin reservoirs on different typical days and the wind and light output calculation results of each period in Chapter 2 as the input of the peak-shaving-energy storage multi-objective short-term scheduling model, and uses the NSGA-II algorithm to solve the model. The non-inferior solution set of each typical day is analyzed as a whole, and the growth inflection point is used as a decision-making recommendation plan and its scheduling results are analyzed, and the following conclusions are obtained:

[0160] (1) Taking the minimization of the variance of the residual load process of the power grid and the maximization of the cascade energy storage at the end of the dispatch period as the objective function, a short-term multi-objective optimization dispatching model for the Jinping I to Tongzilin cascade power stations was constructed, which achieved the coordination between the hydropower peak-shaving capacity and the future power generation revenue.

[0161] (2) In order to avoid the problem of uneven distribution of non-inferior solutions on the Pareto front, the model is solved by using the NSGA-II algorithm that introduces congestion calculation. The results show that the non-inferior solutions of each typical day can be distributed relatively evenly on the Pareto front, and there is an obvious game competition relationship between the two proposed objectives.

[0162] (3) The dispatch plan corresponding to the growth inflection point was selected and analyzed as a decision-making recommendation. The results show that the selected plan can take into account both peak load regulation and energy storage at the same time, and can effectively reduce the peak load regulation pressure faced by the power grid while ensuring the hydropower generation efficiency and end-of-period energy storage, and meet the multi-objective demand response in the dispatch process.

[0163] Based on the same inventive concept as the above method embodiment, the embodiment of the present application also provides a short-term optimization scheduling system for a water-wind-solar complementary power generation system that takes into account both energy storage and source-load matching. The system can realize the functions provided by the above method embodiment, such as Fig.10 As shown, the system includes the following modules:

[0164] Model building module 110, establishing a peak load regulation-energy storage multi-objective optimization scheduling model;

[0165] The constraint definition module 120 defines the constraint conditions for the model;

[0166] The optimization scheduling module 130 uses the NSGA-Ⅱ algorithm to calculate and solve the cascade peak-shaving-energy storage short-term multi-objective scheduling model, and outputs the peak-shaving-energy storage multi-objective optimization scheduling results.

[0167] Based on the same inventive concept as the above method embodiment, the present application embodiment also provides an electronic device, such as Fig.11 As shown, the device includes: a processor 210; and a memory 220, for storing one or more programs;

[0168] When the one or more programs are executed by the processor 210, the processor executes the short-term optimization scheduling method for the water-wind-solar complementary power generation system that takes into account both energy storage and source-load matching.

[0169] A short-term optimal dispatching method for a hydro-wind-solar hybrid power generation system taking into account both energy storage and source-load matching includes the following steps:

[0170] Establish a peak load regulation-energy storage multi-objective optimization scheduling model;

[0171] Define constraints on the model;

[0172] The NSGA-Ⅱ algorithm is used to calculate and solve the cascade peak-shaving-energy storage short-term multi-objective scheduling model, and the peak-shaving-energy storage multi-objective optimization scheduling results are output.

[0173] Based on the same inventive concept as the above-mentioned method embodiment, the embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor 210, it implements the short-term optimization scheduling method of the water-wind-solar complementary power generation system that takes into account both energy storage and source-load matching.

[0174] A short-term optimal dispatching method for a hydro-wind-solar hybrid power generation system taking into account both energy storage and source-load matching includes the following steps:

[0175] Establish a peak load regulation-energy storage multi-objective optimization scheduling model;

[0176] Define constraints on the model;

[0177] The NSGA-Ⅱ algorithm is used to calculate and solve the cascade peak-shaving-energy storage short-term multi-objective scheduling model, and the peak-shaving-energy storage multi-objective optimization scheduling results are output.

[0178] In several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, method and computer program product according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0179] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0180] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk. It should be noted that, in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0181] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0182] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0183] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0184] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0185] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A short-term optimization scheduling method for a hydro-wind-solar hybrid power generation system that takes into account both energy storage and source-load matching, characterized in that: The steps include: S1. Establish a peak load regulation-energy storage multi-objective optimization scheduling model; S2, define constraints on the model; S3. Use the NSGA-Ⅱ algorithm to calculate and solve the cascade peak-shaving-energy storage short-term multi-objective scheduling model, and output the peak-shaving-energy storage multi-objective optimization scheduling results.

2. The short-term optimization scheduling method for a water-wind-solar hybrid power generation system taking into account both energy storage and source-load matching according to claim 1 is characterized in that: In step S1, the specific steps include: S11. Peak load regulation target: The 15-minute runoff process of the downstream cascade power stations on different typical days and the wind and light output in different time periods are used as the input conditions of the peak load regulation-energy storage multi-objective short-term scheduling model, with the goal of minimizing the process variance of the residual load of the cascade hydropower station power grid. The formula is expressed as follows: S12, energy storage target: select the maximum cascade energy storage at the end of the dispatch period as the target, the formula is as follows: S13. Considering the use of only the intraday regulation capacity of the daily regulation power station, keeping the reservoir water level equal at the beginning and end of the day, converting formula (1.2) into the maximum value of the total cascade energy storage increase, the formula is expressed as follows: The above formula (1.3) is expressed as the sum of the energy storage added value of each cascade power station, among which the energy storage added value of the reservoir power station with daily regulation capacity at the end of the dispatch period is expressed as: Since the water level of the daily regulating reservoir is equal at the beginning and end of the period, the energy storage increase is zero, and ΔV = 0 at this time. Further transforming formula (1.4), the formula is expressed as follows:

3. The short-term optimization scheduling method for a water-wind-solar hybrid power generation system taking into account both energy storage and source-load matching according to claim 1 is characterized in that: In step S2, the constraint conditions are defined including: water balance constraint, output constraint, reservoir capacity constraint, reservoir discharge flow constraint, reservoir operating water level constraint and non-negative constraint.

4. The short-term optimization scheduling method for a water-wind-solar hybrid power generation system taking into account both energy storage and source-load matching according to claim 3 is characterized in that: The water balance constraint is expressed as follows: Qr k,t =q k,t +Qr k-1,t-τ +(V k,t-1 -V k,t ) / Δt(1.6) The output constraint is expressed as follows: The storage capacity constraint is expressed as follows: The reservoir discharge flow constraint is expressed as follows: The reservoir operating water level constraint is expressed as follows:

5. The short-term optimization scheduling method for a water-wind-solar hybrid power generation system taking into account both energy storage and source-load matching according to claim 1 is characterized in that: In step S3, NSGA-II realizes population evolution through genetic operations, sorts individuals in combination with the fast non-dominated sorting algorithm, and introduces the congestion strategy to make the non-inferior solution set evenly distributed on the frontier of the Pareto domain, and obtains the peak load-shaving-energy storage multi-objective optimization scheduling results, which are as follows: S31, population initialization: Take 15 minutes as a time step and the water level Z at the end of each reservoir period t is the decision variable, and an initial population of 100 is randomly generated. One individual is the water level process of a group of cascade hydropower stations. The water level at the end of a period represents a gene, and the length of a chromosome is equal to the number of reservoir scheduling periods × the number of regulating reservoirs. The water level value contained in the individual is randomly generated by formula (1.11): S32, calculate the fitness function value: According to the fitness function, the quality of individuals is judged, and the objective function formula (1.1) and formula (1.5) are used as the fitness function to perform non-dominated sorting and crowding calculation; S33, fast non-dominated sorting and congestion calculation: Starting from the second generation of evolution, the parent and child populations are merged to obtain a mixed population with twice the number of individuals in the initial population. After completing the fast non-dominated sort according to the individual fitness value, the crowding degree is calculated, and relatively better individuals are selected to form a new generation population to replace the initial population as the parent population. It is determined whether the maximum number of iterations has been reached. If so, the result is output in step S35, otherwise, the genetic operation is performed in step S34; The crowding calculation introduced in NSGA-II can maintain population diversity and make individuals evenly distributed on the Pareto frontier. The crowding strategy is implemented by calculating the sum of the distance differences between different non-dominated individuals of the same level and their two adjacent individuals on each sub-objective function. The crowding calculation method is as follows: Where: I i is the crowding degree of individual i, N is the total number of non-dominated individuals at the same level, I i-1,m ,I i+1,m Represents the fitness function value of the mth target of the two individuals adjacent to individual i; S34, genetic operations: The core of genetic operations in NSGA-II are selection, crossover, and mutation; Selection: The selection of parents is done by roulette wheel, and the mating pool size is set to 1 / 2 of the population size, i.e. 1 / 2N; Crossover: Use simulated binary crossover and set the distribution index to 0.9; Mutation: Mutation operation is realized by using non-uniform mutation, and the mutation probability is p m =0.05+1 / (gen+1), where gen is the current iteration number; After completing the above genetic operations, a progeny population with the same size as the initial population is obtained; S35. Output the scheduling result: The above steps S32 to S34 are looped until the termination condition is met, that is, the number of iterations reaches 1000, the model is terminated, and the peak load regulation-energy storage multi-objective optimization scheduling result is output.

6. A system for short-term optimization scheduling of a water-wind-solar hybrid power generation system taking into account both energy storage and source-load matching according to any one of claims 1 to 5, characterized in that: The system includes the following modules: A model building module (110) is used to build a peak load regulation-energy storage multi-objective optimization scheduling model; A constraint definition module (120) defines constraint conditions for the model; The optimization scheduling module (130) uses the NSGA-II algorithm to calculate and solve the cascade peak-shaving-energy storage short-term multi-objective scheduling model, and outputs the peak-shaving-energy storage multi-objective optimization scheduling result.

7. An electronic device, characterized in that: The electronic device comprises: a processor (210); and a memory (220) for storing one or more programs; When the one or more programs are executed by the processor (210), the processor executes the short-term optimization scheduling method for a water-wind-solar complementary power generation system taking into account both energy storage and source-load matching as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by the processor (210), a short-term optimization scheduling method for a water-wind-solar complementary power generation system taking into account both energy storage and source-load matching as described in any one of claims 1 to 5 is implemented.

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