Wind-solar-water-storage integrated medium-and-long-term optimization scheduling method for coupling short-term complementary operation

Abstract

The invention belongs to the technical field of multi-energy complementary optimization scheduling, and discloses a coupling short-term complementary operation wind-light-water-storage integrated medium-and-long-term optimization scheduling method, which comprises the following steps: S1, determining common and multi-type objective functions of a wind-light-water-storage integrated medium-and-long-term optimization scheduling model, and integrating the common and multi-type objective functions into the model; and S2, determining a decision variable of the model, and establishing a water balance calculation module, a scheduling period end water level adjustment module and a hydropower station output calculation module of the medium-and-long-term scale model according to the decision variable. And S3, establishing an electric quantity balance module, a load matching coefficient calculation module and a new energy real consumption rate calculation module which are used for coupling the short-term scale complementary operation model. And the short-term scale model is used for calculating a real target function fitness value from a short-term perspective by taking a solving result of the medium and long-term model module as an input condition and a boundary, and then returning the fitness value to the medium and long-term scale model. And S4, solving by using a heuristic algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-energy complementary optimization scheduling, and relates to a medium- and long-term optimization scheduling model for wind, solar, and water storage. More specifically, it relates to a medium- and long-term optimization scheduling method for integrated wind, solar, and water storage coupled with short-term complementary operation. Background Art

[0002] The integrated optimization dispatching of wind, solar, water and storage can improve energy utilization efficiency and reduce unnecessary resource waste such as wind and solar abandonment while ensuring stable power supply. However, due to the strong volatility and intermittency of wind and solar resources, and the complex spatiotemporal constraints involved in reservoir dispatching, the problem of joint optimization dispatching of wind, solar, water and storage is extremely challenging. Traditional dispatching technologies and integrated optimization dispatching models (such as linear programming, dynamic programming and their derivative algorithms) have great limitations in computational efficiency and solution capabilities when dealing with complex multi-stage and multi-dimensional integrated systems, especially when dealing with large-scale, multi-variable constrained integrated energy systems, which often lead to low computational efficiency and "dimensionality curse" problems. In addition, conventional multi-energy complementary optimization dispatching models are difficult to calculate the actual consumption of wind and solar renewable energy on a medium- and long-term scale. Therefore, developing a medium- and long-term complementary optimization dispatching model for wind, solar, water and storage integration that is efficient, robust, has good convergence, and can cope with the dispatching needs of complex and changeable multi-energy complementary systems is of great significance and practical value for improving the intelligent management level of energy systems. Summary of the invention

[0003] In response to the above defects or improvement needs of the prior art, the present invention provides a medium- and long-term optimal scheduling method for integrated wind, solar, and water storage systems with coupled short-term complementary operations. Its purpose is to solve the problems of medium- and long-term optimal scheduling of integrated wind, solar, and water storage systems, as well as the technical problems of poor model adaptability and difficulty in guiding the actual operation of the project.

[0004] To achieve the above object, according to one aspect of the present invention, a mid- and long-term optimization scheduling method for wind, solar, and water storage integration coupled with short-term complementary operation is provided, comprising the following steps:

[0005] S1, determine the objective function of the medium- and long-term optimization scheduling model for wind, solar, and hydropower integration. The optimization scheduling model for multi-energy complementary systems generally considers three types of objectives. One is the goal of focusing on the consumption of new energy, such as the consumption rate of wind and solar new energy, the amount of wind and solar new energy abandoned, the risk of wind and solar comprehensive abandoned power, etc.; the second is the goal of focusing on the situation at the power transmission end, such as optimal load matching, residual load variance, smoothness of the power transmission process, etc.; the third is the goal of focusing on the power generation efficiency, such as the maximum comprehensive power generation efficiency of hydropower, the maximum power generation efficiency of wind and solar new energy, the maximum system bundled power transmission efficiency, etc. The above three types of objective functions have different collaborative or competitive relationships in different systems and different power generation and transmission situations. The model of the present invention is adaptive, and any number of any type of objective functions can be integrated into this model.

[0006] S2, determine the decision variable of the model as the discharge flow of the hydropower station, and use it to establish the water balance calculation module of the medium and long-term scale model, the water level adjustment module at the end of the scheduling period, and the hydropower station output calculation module.

[0007] S3, establish the power balance module, load matching coefficient calculation module, and new energy real consumption rate calculation module for coupling the short-term scale complementary operation model. The role of the short-term scale model is to use the solution results of the medium- and long-term model module as input conditions and boundaries, calculate the real objective function fitness value from a short-term perspective, and then return it to the medium- and long-term scale model.

[0008] S4. The complexity of the medium- and long-term optimal scheduling method for wind, solar, and water storage integration with coupled short-term complementary operation proposed in the present invention depends on the dimension and number of decision variables. However, the complexity of model solution for general cascade hydropower systems is very high due to their numerous constraints and huge number of decision variables, and heuristic algorithms must be used to solve them.

[0009] Furthermore, S2 specifically includes the constraint expressions of each module and the output calculation formula as follows:

[0010] (1) Water balance constraints;

[0011] i,t = i,t-1 +(I i,t - i,t )·Δt

[0012] Where: V i,t-1 、V i,t are the water storage capacity of hydropower station i at the beginning and end of time period t, m 3 ;I i,t is the inflow of hydropower station i in period t, m 3 / s;Qo i,t is the outflow of hydropower station i in period t, m3 / s; other symbols have the same meanings as before.

[0013] (2) Upper and lower limits of water level of hydropower station;

[0014]

[0015] Where: Z i,t is the initial water level of hydropower station i in time period t, m; Z i,t is the lower limit of the initial allowable water level of hydropower station i in time period t, m; is the upper limit of the initial allowable water level of hydropower station i in time period t, m. Generally, the normal high water level is selected in the non-flood season, and the flood limit water level corresponding to the flood control demand in the flood season; the meanings of other symbols are the same as before.

[0016] (3) Constraints on the initial and final water levels of the hydropower station;

[0017] Z i,1 =Z i,start

[0018]

[0019] Where: Z i,end , is the lower and upper limits of the water level at the end of the dispatching period of hydropower station i, m; Z i,start is the initial water level of hydropower station i during dispatching period; other symbols have the same meanings as before.

[0020] (4) Constraints on outflow from hydropower stations;

[0021]

[0022] Q i,t =Qe i,t +Qs i,t

[0023] Where: Q i,t and are the lower and upper limits of the discharge flow allowed by hydropower station i in time period t, m 3 / s;Qe i,t and Qs i,t are the power generation flow and abandoned water flow of hydropower station i in period t, respectively, 3 / s; other symbols have the same meanings as before.

[0024] (5) Upper and lower limits of hydropower station output;

[0025]

[0026] Where: They are respectively the lower and upper limits of the output allowed for hydropower station i in time period t, in 10,000 kW, where the maximum output is generally the expected output of the power station; the meanings of other symbols are the same as before.

[0027] (6) Constraints on water level fluctuation of hydropower stations;

[0028]

[0029] Where: is the upper limit of water level fluctuation, m. Due to the requirements of hydropower station environment and shipping, the water level fluctuation needs to be limited within a certain range. The meanings of other symbols are the same as before.

[0030] (7) Calculation of hydropower station output;

[0031] P=KQH=8.5·Q release ·((H begin +H end ) / 2-F H (Q release ))

[0032] Where: P is the output of the hydropower station; K is the output coefficient, which is equal to 8.5 by default; Q release is the discharge flow of the hydropower station during this period, H begin and H end are the initial and final water levels of the hydropower station during this period, and F H (Q release ) is the tailwater level of the hydropower station during this period, which is a function affected by the discharge flow, and its value is obtained by interpolating the discharge flow-tailwater level curve.

[0033] Furthermore, the S3 specifically includes the constraint expressions of each module of the model of the present invention and the output calculation formula as follows:

[0034] (1) Power balancing module

[0035] When the average hydropower generation in the medium and long term is known, the output process of cascade hydropower stations and pumped storage power stations is subjected to complementary nonlinear programming, which satisfies multiple constraints such as upper and lower limits of hydropower output, climbing constraints of hydropower units, efficiency constraints of pumped storage power stations, and capacity constraints of pumped storage power stations. That is, by adjusting the output of hydropower stations and the time and size of energy storage and power generation output of pumped storage power stations, the load matching coefficient is optimized, and then the output process of hydropower stations and energy storage and power generation processes of pumped storage power stations in the short term and hourly scale are obtained. Through the short-term correction process, the calculation of the real absorption rate of new energy and the calculation of the load matching process can be realized. The output of hydropower stations must first satisfy the constraint that the average output in the short term is equal to the output in the medium and long term:

[0036]

[0037] Where: P i hydro is equal to the output of the hydropower station at time i, T 0 Equal to the length of time for the entire short-term correction. For example, when the medium- and long-term scheduling time scale is 1 week, T 0 It is equal to 168h; Equal to the output of hydropower stations in the medium and long term.

[0038] (2) Wind power station output constraints

[0039]

[0040] Where: P t w is the average output of the wind power station in period t, kW; They are the minimum and maximum outputs allowed by the wind power station in the tth period, kW. The maximum output is generally the installed capacity of the power station.

[0041] (3) Output constraints of photovoltaic power stations

[0042]

[0043] Where: They are the minimum and maximum outputs allowed for the photovoltaic power station in period t, in kW. The maximum output is generally the installed capacity of the power station.

[0044] (4) Hydropower station output amplitude constraints

[0045]

[0046] Where: ΔP t h Indicates the variation of hydropower output in adjacent time periods, kW; Represents the upper limit of hydropower output variation, kW. This constraint indicates that the hydropower output cannot respond in time due to the limitation of power generation equipment.

[0047] (5) Output constraints of pumped storage power stations

[0048]

[0049] In the formula, the left side Indicates the cumulative output of the pumped storage power station for continuous energy storage or continuous power generation. C It represents the installed capacity of the pumped-storage power station. The continuous energy storage or cumulative power generation output of the pumped-storage power station does not exceed the capacity constraint, and the energy for pumping water comes from the wind and solar new energy power generation cluster connected to the pumped-storage power station.

[0050] (6) Load constraints at the transmission end

[0051] P t ≤D t

[0052] Where: D t is the grid load demand at time t, kW. When the system output exceeds the grid load, the excess electric energy cannot be used, and power abandonment occurs.

[0053] According to another aspect of the present invention, a medium- and long-term optimal scheduling method for integrated wind, solar, water and storage coupled with short-term complementary operation is provided. When the program product is called and executed, the medium- and long-term optimal scheduling model and solution method for integrated wind, solar, water and storage coupled with short-term complementary operation as described in any of the preceding items are implemented.

[0054] In general, the above technical solutions conceived by the present invention can achieve the following advantages compared with the prior art:

[0055] Beneficial effects:

[0056] The medium- and long-term optimal scheduling method for wind, solar, and water storage integration coupled with short-term complementary operation proposed in the present invention can not only effectively improve the total bundled power transmission of the wind, solar, and water storage integrated system and increase the power generation efficiency when targeting multi-energy complementary optimal scheduling problems with high complexity, difficult constraints, and multi-objective combinations, but also exhibits excellent performance such as good convergence robustness, and produces a more reasonable complementary scheduling process while significantly increasing the power generation. It is an effective and superior method product for solving multi-energy complementary optimal scheduling problems, and is of great significance and practical value for improving the level of intelligent management of energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the process of the present invention.

[0058] Figure 2 It is a topological relationship diagram of the wind, solar, water and storage integrated multi-energy complementary system in the embodiment.

[0059] Figure 3 This is a graph showing the final population and Pareto front distribution results after iteration in the embodiment.

[0060] Figure 4 It is a medium- and long-term scale hydropower output process diagram of the maximum individual solution of comprehensive cascade hydropower generation in the Pareto front in the embodiment.

[0061] Figure 5 It is a diagram of the operation process of cascade hydropower stations on a medium- and long-term scale for the maximum individual solution of comprehensive cascade hydropower generation in the Pareto front in the embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0063] like Figure 1 The flowchart of the medium- and long-term optimization scheduling method of wind, solar, and water storage integration for coupled short-term complementary operation is shown in the present invention, which includes the following steps:

[0064] S1, determine the objective function of the medium- and long-term optimization scheduling model for wind, solar, and hydropower integration. The optimization scheduling model for multi-energy complementary systems generally considers three types of objectives. One is the goal of focusing on the consumption of new energy, such as the consumption rate of wind and solar new energy, the amount of wind and solar new energy abandoned, the risk of wind and solar comprehensive abandoned power, etc.; the second is the goal of focusing on the situation at the power transmission end, such as optimal load matching, residual load variance, smoothness of the power transmission process, etc.; the third is the goal of focusing on the power generation efficiency, such as the maximum comprehensive power generation efficiency of hydropower, the maximum power generation efficiency of wind and solar new energy, the maximum system bundled power transmission efficiency, etc. The above three types of objective functions have different collaborative or competitive relationships in different systems and different power generation and transmission situations. The model of the present invention is adaptive, and any number of any type of objective functions can be integrated into this model.

[0065] S2, determine the decision variable of the model as the discharge flow of the hydropower station, and use it to establish the water balance calculation module of the medium and long-term scale model, the water level adjustment module at the end of the scheduling period, and the hydropower station output calculation module.

[0066] S3, establish the power balance module, load matching coefficient calculation module, and new energy real consumption rate calculation module for coupling the short-term scale complementary operation model. The role of the short-term scale model is to use the solution results of the medium- and long-term model module as input conditions and boundaries, calculate the real objective function fitness value from a short-term perspective, and then return it to the medium- and long-term scale model.

[0067] S4, the complexity of the medium- and long-term optimization scheduling method for wind, solar, and water storage integration with coupled short-term complementary operation proposed in the present invention depends on the dimension and number of decision variables. The complexity of model solution for general cascade hydropower systems is very high due to the numerous constraints and huge number of decision variables, and a heuristic algorithm must be used to solve it. Therefore, the algorithm program product supporting the model solution of the present invention is the NSGA-III algorithm, which combines the decision variables of different time periods and different hydropower stations into individuals, simulates different feasible solutions by generating different individuals, and uses the reference point mechanism to determine the dominant relationship between individuals for multi-objective problems, while increasing the distribution uniformity of solutions to multi-objective problems. On this basis, crossover, mutation, and selection operators are used to realize the iteration of individuals in the optimization direction, and finally generate a set of multi-objective non-inferior solutions corresponding to different preference relationships, which reflects the relationship and overall trend between the objectives as a whole.

[0068] In this embodiment, step S1 specifically includes:

[0069] In the calculation of this embodiment, this model adopts three objective functions, namely, the maximum wind and solar energy new energy consumption rate, the optimal load tracking effect, and the maximum cascade hydropower comprehensive power generation. The calculation formula is as follows:

[0070] (1) Wind and solar energy have the highest absorption rate

[0071] The wind and solar energy consumption rate REUR refers to the ratio of the actual bundled electricity delivered by wind and solar energy in each period to the electricity generated by the power source itself. It can also be understood as the actual ratio of the output of wind and solar energy being actually utilized. Because the output of new energy is volatile and intermittent, the load constraints and channel capacity constraints on the power grid will lead to wind or solar abandonment in some situations. The integrated complementary optimization scheduling of new energy bases is to absorb as much wind and solar output as possible through the regulation and storage of hydropower, reduce power abandonment, and increase the wind and solar energy consumption rate.

[0072]

[0073] In the formula, the first, second and third terms of REUR represent the photovoltaic energy consumption rate, wind power energy consumption rate, and the consumption rate of the three photovoltaic clusters connected to the pumped storage power station; Indicates the expected output of photovoltaic energy, Represents the abandoned power of photovoltaic energy, and their difference is equal to the actual output of photovoltaic energy; Indicates the expected output of wind power energy, Represents the abandoned power of wind power energy, and their difference is equal to the actual output of wind power energy; Represents the electricity absorbed and stored by the pumped storage power station. represents the expected output of the photovoltaic cluster connected to the pumped storage power station; t represents a certain moment in the short-term correction process; T long-term Indicates the length of time for medium- and long-term scheduling. For example, when the medium- and long-term scheduling time scale is 1 week and the time span is 1 year, T long-term is equal to 53 weeks; T short-term Indicates the length of time for short-term corrections. For example, when the medium- and long-term scheduling time scale is 1 week, T short-term It is equal to 168h; the units of short-term and above outputs nested in the medium and long-term scale are all capacity units. It should be noted that even if the purpose of the model is to optimize the complementary dispatch of the wind, solar, and water storage integrated system from a medium and long-term time scale, in order to calculate the real wind and solar absorption rate, it is necessary to consider the short-term time scale. This is because the volatility and intermittency of renewable energy output are mainly reflected in the short-term time scale. If it is simply averaged, it will lead to the loss of renewable energy output characteristics and thus the real wind and solar absorption rate cannot be calculated.

[0074] (2) Load tracking effect is optimal

[0075] The load matching coefficient LMC can quantitatively evaluate the effect of load tracking. The calculation of LMC mainly examines the proportion of the residual load:

[0076]

[0077] In the formula, It indicates the expected output of all energy sources in the channel during the time period, and Load indicates the load value of the time period.

[0078] (3) Cascade hydropower stations have the largest comprehensive power generation capacity

[0079]

[0080] Where: E hydro It is the comprehensive power generation of the hydropower station during the entire dispatch period. is the average comprehensive power generation of the ith hydropower station in the tth period, in units of electricity.

[0081] In this embodiment, step S2 specifically includes:

[0082] (1) Water balance constraints;

[0083] i,t = i,t-1 +(I i,t - i,t )·Δt

[0084] Where: V i,t-1 、V i,t are the water storage capacity of hydropower station i at the beginning and end of time period t, m3 ;I i,t is the inflow of hydropower station i in period t, m 3 / s;Qo i,t is the outflow of hydropower station i in period t, m 3 / s; other symbols have the same meanings as before.

[0085] (2) Upper and lower limits of water level of hydropower station

[0086]

[0087] Where: Z i,t is the initial water level of hydropower station i in time period t, m; Z i,t is the lower limit of the initial allowable water level of hydropower station i in time period t, m; is the upper limit of the initial allowable water level of hydropower station i in time period t, m. Generally, the normal high water level is selected in the non-flood season, and the flood limit water level corresponding to the flood control demand in the flood season; the meanings of other symbols are the same as before.

[0088] (3) Constraints on the initial and final water levels of the hydropower station;

[0089] Z i,1 =Z i,start

[0090]

[0091] Where: Z i,end , is the lower and upper limits of the water level at the end of the dispatching period of hydropower station i, m; Z i,start is the initial water level of hydropower station i during dispatching period; other symbols have the same meanings as before.

[0092] (4) Constraints on outflow from hydropower stations;

[0093]

[0094] Q i,t =Qe i,t +Qs i,t

[0095] Where: Q i,t and are the lower and upper limits of the discharge flow allowed by hydropower station i in time period t, m 3 / s;Qe i,t and Qs i,t are the power generation flow and abandoned water flow of hydropower station i in period t, respectively, 3 / s; other symbols have the same meanings as before.

[0096] (5) Upper and lower limits of hydropower station output;

[0097]

[0098] Where: They are respectively the lower and upper limits of the output allowed for hydropower station i in time period t, in 10,000 kW, where the maximum output is generally the expected output of the power station; the meanings of other symbols are the same as before.

[0099] (6) Constraints on water level fluctuation of hydropower stations;

[0100]

[0101] Where: is the upper limit of water level fluctuation, m. Due to the requirements of hydropower station environment and shipping, the water level fluctuation needs to be limited within a certain range. The meanings of other symbols are the same as before.

[0102] (7) Calculation of hydropower station output;

[0103] P=KQH=8.5·Q release ·((H begin +H end ) / 2-F H (Q release ))

[0104] Where: P is the output of the hydropower station; K is the output coefficient, which is equal to 8.5 by default; Q release is the discharge flow of the hydropower station during this period, H begin and H end are the initial and final water levels of the hydropower station during this period, and F H (Q release ) is the tailwater level of the hydropower station during this period, which is a function affected by the discharge flow, and its value is obtained by interpolating the discharge flow-tailwater level curve.

[0105] In this embodiment, step S3 specifically includes:

[0106] (1), power balancing module;

[0107] When the average hydropower generation in the medium and long term is known, the output process of cascade hydropower stations and pumped storage power stations is subjected to complementary nonlinear programming, which satisfies multiple constraints such as upper and lower limits of hydropower output, climbing constraints of hydropower units, efficiency constraints of pumped storage power stations, and capacity constraints of pumped storage power stations. That is, by adjusting the output of hydropower stations and the time and size of energy storage and power generation output of pumped storage power stations, the load matching coefficient is optimized, and then the output process of hydropower stations and energy storage and power generation processes of pumped storage power stations in the short term and hourly scale are obtained. Through the short-term correction process, the calculation of the real absorption rate of new energy and the calculation of the load matching process can be realized. The output of hydropower stations must first satisfy the constraint that the average output in the short term is equal to the output in the medium and long term:

[0108]

[0109] Where: P i hydro is equal to the output of the hydropower station at time i, T 0 Equal to the length of time for the entire short-term correction. For example, when the medium- and long-term scheduling time scale is 1 week, T 0 It is equal to 168h; Equal to the output of hydropower stations in the medium and long term.

[0110] (2) Wind power station output constraints;

[0111]

[0112] Where: P t w is the average output of the wind power station in period t, kW; They are the minimum and maximum outputs allowed by the wind power station in the tth period, kW. The maximum output is generally the installed capacity of the power station.

[0113] (3) PV power station output constraints;

[0114]

[0115] Where: They are the minimum and maximum outputs allowed for the photovoltaic power station in period t, in kW. The maximum output is generally the installed capacity of the power station.

[0116] (4) Constraints on the output amplitude of hydropower stations;

[0117]

[0118] Where: ΔP t h Indicates the variation of hydropower output in adjacent time periods, kW; Represents the upper limit of hydropower output variation, kW. This constraint indicates that the hydropower output cannot respond in time due to the limitation of power generation equipment.

[0119] (5) Output constraints of pumped storage power stations;

[0120]

[0121] In the formula, the left side Indicates the cumulative output of the pumped storage power station for continuous energy storage or continuous power generation. C It represents the installed capacity of the pumped-storage power station. The continuous energy storage or cumulative power generation output of the pumped-storage power station does not exceed the capacity constraint, and the energy for pumping water comes from the wind and solar new energy power generation cluster connected to the pumped-storage power station.

[0122] (6) Load constraints at the transmission end;

[0123] P t ≤D t

[0124] Where: D t is the grid load demand at time t, kW. When the system output exceeds the grid load, the excess electric energy cannot be used, and power abandonment occurs.

[0125] In this embodiment, step S4 specifically includes:

[0126] S41, predefined reference points. The NSGA-III algorithm ensures the diversity of solutions by predefined reference points. The reference points are distributed in the [0,1] interval of each target, that is, "1" on each coordinate axis represents the maximum value of the corresponding target, and [0,1] represents the distribution range of the target value. For each target, the reference point distribution of the target can be obtained by equally dividing the [0,1] interval according to the set number of segments; the reference points of different targets can be combined to obtain the reference points of the corresponding target space dimension. The predefined reference points will serve as the basis for subsequent screening of individuals.

[0127] S42, initialization and generation of feasible solutions. According to the dispatching requirements of the cascade reservoir group, n individuals are generated to form the initial population, and each individual corresponds to a set of dispatching schemes. In the medium- and long-term dispatching process, the discharge flow of the hydropower station is selected as the decision variable, then each individual in the population contains (T+1)×N decision variables, where T corresponds to the number of time periods in the dispatching period. Since the storage capacity at the end of the dispatching period needs to be considered, each power station corresponds to T+1 decision variables, and N corresponds to the number of hydropower stations that need to be regulated. The corresponding population matrix C pop As shown in the following formula, the population is used as the initial value of the iteration, that is, the first generation parent population.

[0128]

[0129] In the formula, ind represents the set of decision variables of individuals in the population, for example, for the nth individual ind n ; The decision variable set of each individual is a (T+1)×N matrix, It represents the discharge flow of the Nth hydropower station of the kth individual in the population at the T+1th time, in m 3 / s.

[0130] S43, crossover and mutation operators generate new individuals. The crossover operator forms a new individual by randomly combining some decision variables of two individuals to seek local optimality. This paper uses a simulated binary crossover algorithm for calculation. The calculation methods are shown in the following formulas:

[0131]

[0132] Where: c l 、c u are the parent population individuals l and u respectively; c l ' l and c u ' u are the new population individuals ll and uu generated by crossover; β is the recombination coefficient corresponding to the simulated binary crossover algorithm, and the randomness of its calculation corresponds to the random crossover process of genes.

[0133] The mutation operator changes the values ​​of some random variables within the feasible range to make them deviate from the original sequence in order to achieve the purpose of global random optimization. In this paper, the polynomial mutation algorithm is used for calculation. The calculation method is shown in the following formula:

[0134] c v ' v =c v +δ·(UL)

[0135] Where: δ is the polynomial coefficient of variation, corresponding to the random variation process of the gene; U and L are the upper and lower limits of the decision variables respectively; c v is the parent population individual v; c v ' v is the new population individual vv generated by the mutation. After random crossover and mutation calculation, a new population with the same size of n can be obtained. The parent population and the new population are merged to obtain an alternative population with a size of 2n.

[0136] After the above crossover, mutation and merging, 2n candidate individuals can be obtained, but only n individuals will eventually enter the next generation population, so the candidate population needs to be sorted and screened. The screening in the NSGA-III algorithm is divided into two stages: first, the non-dominated hierarchy is divided according to the Pareto dominance principle, and non-inferior solutions and inferior solutions with relatively good performance are retained first; for the remaining inferior solutions, the reference point method is used for further screening to ensure the uniformity of individual distribution.

[0137] S44, use the Pareto dominance principle to perform non-dominated sorting. t The Pareto levels of all solutions can be obtained through non-dominated sorting. According to the order of Pareto levels from low to high, the entire layer of population with low levels is preferentially placed in the new parent population P. t+1 , until all individuals in a certain layer cannot be put into the new parent population P t+1 .

[0138] S45, further screening using reference points. For the last level that cannot be fully placed in S44, it is still necessary to further screen better individuals from it and put them into the offspring population. The NSGA-III algorithm uses the reference point method for screening. This method associates individuals in the candidate population with predefined reference points, and screens them according to the density of individual distribution. Individuals in sparse areas are preferentially selected for retention to ensure the uniformity of the offspring population distribution. It is mainly divided into the following steps:

[0139] First, normalize the target space: To eliminate the magnitude differences between different targets, the target values ​​need to be standardized. Take the minimum value of each target as the ideal point, and subtract the ideal value from the target value of all individuals to get the standard target value, that is, measure the quality of the target value by the distance from the ideal point, as shown in the following formula.

[0140]

[0141] Where: f m (c l ) is the original target value of individual l target m; f m ′(c l ) is the standardized target value of individual l target m; is the minimum value of the target m. On this basis, the scalarization function (ASF) is used to traverse the target values ​​of all individuals and find the extreme point individuals that reflect the maximum value of each target. Connecting each extreme point and extending it to the coordinate axis can get the intercept a m (the intersection of the hyperplane and the coordinate axis), the intercept reflects the possible maximum value of each target and is used for normalization, as shown in the following formula.

[0142]

[0143] Where: w m is the weight corresponding to the target m direction, which is used to obtain the extreme point closer to the coordinate axis of the target direction; f m ″(c l ) is the normalized target value of individual I target m; f has the same meaning as before. Through normalization, the individual target value can be converted into the same calculation scale as the reference point, and each target intercept corresponds to the reference point boundary value "1"; this is used to carry out subsequent calculations.

[0144] Then associate reference points and individual screening: According to the normalized individual target value, calculate the Euclidean distance of all individuals to the line connecting each reference point and the origin. The shortest distance indicates that the solution is associated with the reference point. In this way, all individuals can be assigned to different reference points, which is equivalent to partitioning the population. Count the number of individuals associated with each reference point ρ j , ρ j The larger the value, the denser the individuals are in the area, and vice versa. To ensure the globality and uniformity of the optimization results, the algorithm is based on ρ j Select reference points in order from small to large, and keep the individuals associated with them until the required The individuals retained in steps (4) and (5) constitute the offspring population.

[0145] S46, convergence condition determination. Determine whether the difference between the target value of the offspring population and the parent population is less than 1% or reaches a predetermined number of iterations. If so, the optimization is completed. Otherwise, the screened offspring population is used as a new parent population, and the above steps are repeated until the above convergence conditions are met, thereby obtaining a set of mutually non-dominant multi-energy complementary scheduling schemes.

[0146] Example: Take a wind, solar, water and storage integrated system as an example. The system and power transmission structure are as follows: Figure 2 As shown. In order to compare and verify the effectiveness and superiority of the present invention in the mid- and long-term optimal scheduling of multi-energy complementarity, this example designs two control groups, namely the conventional scheduling method using inflow and outflow balance in the same multi-energy complementary system (control group No. 1) and the hydropower separate optimization scheduling method (control group No. 2); the model calculation rule of control group No. 1 is to make the outflow of the hydropower station equal to the inflow, and the wind, solar, and hydropower complementary optimization is not performed when the wind, solar, and hydropower are transmitting electricity. The model calculation rule of control group No. 2 is to optimize the operation mode of the cascade hydropower station with the goal of maximizing the total power generation of cascade hydropower, and the wind, solar, and hydropower complementary optimization is not performed during short-term scale power transmission. The specific scheduling methods and solution information of the experimental group and the control group are shown in Table 1:

[0147] Table 1. Comparison of scheduling methods between the experimental group and the control group in the example design

[0148]

[0149] The scheduling period of the experimental group is set to one year, the scheduling time step of the medium- and long-term scale model is weekly (52 weeks in total), the scheduling time step of the short-term scale model is hourly (168 hours a week), the population size is set to 4000, the number of individuals for crossover and mutation is set to 2000, and the maximum number of iterations is 500. The inflow runoff of the cascade hydropower station selects the measured monthly runoff data of the dam site of Hydropower Station No. 1 in 2010. The photovoltaic and wind power outputs are selected from the hourly scale simulated output data from 0:00 on January 1, 2010 to 23:00 on December 31, 2010, and the load process uses a typical two-stage step curve. In this embodiment, the iteration termination condition in step S46 is to reach the maximum number of iterations. The basic information and constraints of the other related instance wind, solar and water storage integrated bases are shown in Tables 2 to 4:

[0150] Table 2. Basic information of the wind, solar, water and storage integrated base

[0151]

[0152]

[0153] Table 3. Instance constraint set information table

[0154]

[0155] Table 4 gives the comparative analysis of the results between the experimental group and the control group. Figure 3The three-dimensional drawing results of the three-objective optimization scheduling calculated by the example of the present invention are given. The dot set is a population of 4,000 individuals, and the stars are the Pareto front distribution in the population. It can be seen from the chart results that there are 61 individuals in the Pareto front, and the objective function values ​​of each individual have their own advantages and disadvantages. Among them, the optimal value of the maximum objective function of the new energy consumption rate REUR is equal to 0.8689 (the average value throughout the year, the same below), which is 5.83% and 9.31% higher than the REUR values ​​of the conventional scheduling solutions of the control group No. 1 and the control group No. 2, respectively; the optimal value of the maximum objective function of the load matching coefficient LMC is equal to 0.8099, which is 8.34% and 4.42% higher than the LMC values ​​of the conventional scheduling solutions of the control group No. 1 and the control group No. 2, respectively; the optimal value of the maximum objective function of the cascade hydropower comprehensive power generation Ehydropower is equal to 24.71646 billion kWh, which is 28.91% higher than the Ehydropower value of the conventional scheduling solution of the control group No. 1. Judging from the average results of the overall Pareto frontier, the average REUR value of all individuals in the frontier is equal to 0.8425, which is 2.61% and 5.98% higher than the REUR values ​​of the conventional dispatch solutions of the control groups No. 1 and No. 2, respectively; the average LMC value of all individuals in the frontier is equal to 0.7965, which is 6.55% and 2.69% higher than the LMC values ​​of the conventional dispatch solutions of the control groups No. 1 and No. 2, respectively; the average cascade hydropower comprehensive power generation Ehydropower of all individuals in the frontier is equal to 22.12748 billion kWh, which is 15.41% higher than the Ehydropower value of the conventional dispatch solution of the control group No. 1; the average annual cumulative bundled power output of the entire Pareto frontier solution set is equal to 49.53694 billion kWh, which is 5.84% and 2.18% higher than the control groups No. 1 and No. 2, respectively. Obviously, the medium- and long-term optimization scheduling model of wind, solar, and water storage integration coupled with short-term complementary operation proposed in the present invention can not only improve the new energy consumption rate, but also make the power transmission process more matching the load process, thereby realizing the increase of the cumulative bundled power output of the multi-energy complementary base throughout the year.

[0156] Table 4. Comparative analysis of the results between the experimental group and the control group

[0157]

[0158] Judging from the objective function results of individuals in the Pareto frontier, the optimal individual with the maximum REUR objective function value has an average LMC and comprehensive Ehydropower equal to 0.7882 and 20.37526 billion kWh, respectively, which are 2.75% and 21.31% smaller than the corresponding values ​​of the optimal individuals with the optimal LMC and comprehensive Ehydropower maximum objective functions; the optimal individual with the optimal LMC objective function value has an annual average REUR and comprehensive Ehydropower equal to 0.8092 and 24.53189 billion kWh, respectively, which are 7.38% and 0.75% smaller than the individual values ​​with the maximum objective functions; the optimal individual with the maximum comprehensive Ehydropower objective function value has an annual average REUR and annual average LMC equal to 0.8055 and 0.8067, respectively, which are 7.88% and 0.39% smaller than the individual values ​​with the maximum objective functions. From the two-dimensional projection diagram, it can be seen that with the increase of individual average REUR in the Pareto front, the average LMC and comprehensive Ehydropower will decrease. Although the degree of reduction and the rate of change are inconsistent, it can reflect the overall competitive relationship between REUR, LMC and Ehydropower. In the Pareto front, with the increase of individual average LMC, the overall comprehensive Ehydropower shows an increasing trend. However, when LMC increases to a certain extent, the increasing trend of comprehensive Ehydropower slows down or even gradually decreases, which reflects the overall synergy and local competition between LMC and Ehydropower. In general, the Pareto front solution set shows the synergy and competition between the three goals of load matching, consumption of new energy, and power generation efficiency of hydropower itself in the process of wind, solar, water and storage integrated scheduling. In the specific scheduling process, the optimal solution can be selected according to the priority of different scheduling goals. The above results show that the method proposed in the present invention can effectively improve the total power transmission of the wind, solar, water and storage integrated system, increase the power generation efficiency, and has strong stability. The case analysis also fully proves the applicability and superiority of the present invention in the complementary optimization scheduling problem. In addition, the model of the present invention will also output the medium- and long-term hydropower station operation process of all individuals in the optimal frontier and the short-term power output process of each power source, such as Figure 4 and Figure 5 As shown, the output results are saved in Excel format for easy inspection by decision makers.

[0159] In summary, when solving complex engineering problems such as wind, solar, hydro and storage multi-energy complementary optimization scheduling, the present invention can demonstrate superior performance such as good convergence robustness, and produce a more reasonable complementary scheduling process while significantly increasing power generation. This shows that the wind, solar, hydro and storage integrated medium- and long-term optimization scheduling method coupled with short-term complementary operation proposed in the present invention is an effective and superior method for solving multi-energy complementary optimization scheduling problems.

[0160] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A mid- to long-term optimization dispatching method for wind, solar, and water storage integration coupled with short-term complementary operation, characterized in that: The steps include: S1, determine the objective function of the medium- and long-term optimization dispatch model of wind, solar, water and storage integration. The optimization dispatch model of the multi-energy complementary system considers three types of objectives: one is the goal of focusing on the consumption of new energy; the second is the goal of focusing on the situation at the power transmission end; the third is the goal of focusing on the power generation efficiency; S2, determine the decision variable of the model as the discharge flow of the hydropower station, and use it to establish the water balance calculation module of the medium and long-term scale model, the water level adjustment module at the end of the dispatch period, and the hydropower station output calculation module; S3, establish a power balance module, a load matching coefficient calculation module, and a new energy real consumption rate calculation module for coupling the short-term scale complementary operation model; The role of the short-term scale model is to use the solution results of the medium- and long-term model module as input conditions and boundaries, calculate the true fitness value of the objective function from a short-term perspective, and then return it to the medium- and long-term scale model; S4, uses a heuristic algorithm to solve the medium- and long-term optimal scheduling model of wind, solar, water and storage integration with coupled short-term complementary operation.

2. The mid- and long-term optimization scheduling method for wind, solar, and water storage integration with coupled short-term complementary operation according to claim 1 is characterized in that: The S2 specifically includes: (1) Water balance constraints; i,t = i,t-1 +(I i,t - i,t )·Δt Where: V i,t-1 、V i,t are the water storage capacity of hydropower station i at the beginning and end of time period t, m 3 ;I i,t is the inflow of hydropower station i in period t, m 3 / s;Qo i,t is the outflow of hydropower station i in period t, m 3 / s; other symbols have the same meaning as before; (2) Upper and lower limits of water level of hydropower station; Where: Z i,t is the initial water level of hydropower station i in time period t, m; Z i,t is the lower limit of the initial allowable water level of hydropower station i in time period t, m; is the upper limit of the initial allowable water level of hydropower station i in time period t, m, which is the normal high water level in the non-flood season and the flood limit water level corresponding to the flood control demand in the flood season; (3) Constraints on the initial and final water levels of the hydropower station; WITH i,1 =Z i,start Where: Z i,end , is the lower and upper limits of the water level at the end of the dispatching period of hydropower station i, m; Z i,start is the initial water level of hydropower station i during dispatch period; (4) Constraints on outflow from hydropower stations; Qo i,t =What? i,t +Qs i,t Where: Q i,t and are the lower and upper limits of the discharge flow allowed by hydropower station i in time period t, m 3 / s;Qe i,t and Qs i,t are the power generation flow and abandoned water flow of hydropower station i in period t, respectively, 3 / s; (5) Constraints on upper and lower limits of hydropower station output; Where: are the lower and upper limits of the output allowed for hydropower station i in time period t, 10,000 kW, where the maximum output is the expected output of the power station; (6) Constraints on water level fluctuation of hydropower stations; Where: is the upper limit of water level fluctuation, m; (7), calculation of hydropower station output; P=KQH=8.5·Q release ·((H begin +H end ) / 2-F H (Q release )) Where: P is the output of the hydropower station; K is the output coefficient, which is equal to 8.5 by default; Q release is the discharge flow of the hydropower station during this period, H begin and H end are the initial and final water levels of the hydropower station during this period, and F H (Q release ) is the tailwater level of the hydropower station during this period, which is a function affected by the discharge flow, and its value is obtained by interpolating the discharge flow-tailwater level curve.

3. The mid- and long-term optimization scheduling method for wind, solar, and water storage integration with coupled short-term complementary operation according to claim 1 is characterized in that: The S3 specifically includes: (1), power balancing module; Under the condition that the average hydropower generation in the medium and long term is known, the multiple constraints of upper and lower limits of hydropower output, climbing constraints of hydropower units, efficiency constraints of pumped storage power stations, and capacity constraints of pumped storage power stations are met, and the output process of cascade hydropower stations and pumped storage power stations is subjected to complementary nonlinear programming, that is, by adjusting the time and size of the output of hydropower stations and the storage and power generation output of pumped storage power stations, the optimal load matching coefficient is achieved, and then the output process of hydropower stations and the storage and power generation process of pumped storage power stations on a short-term hourly scale are obtained, and the calculation of the real consumption rate of new energy and the calculation of the load matching process are realized through a short-term correction process; the output of hydropower stations first meets the constraint that the average output in the short term is equal to the output in the medium and long term: Where: is equal to the output of the hydropower station at time i, and T0 is equal to the duration of the entire short-term correction; Equal to the hydropower station output on a medium- to long-term scale; (2) Wind power station output constraints; Where: is the average output of the wind power station in period t, kW; are the minimum and maximum outputs allowed by the wind power station in the tth period, kW; the maximum output is the installed capacity of the power station; (3) PV power station output constraints; Where: are the minimum and maximum outputs allowed for the photovoltaic power station in period t, kW; the maximum output is the installed capacity of the power station; (4) Constraints on the output amplitude of hydropower stations; Where: Indicates the variation of hydropower output in adjacent time periods, kW; represents the upper limit of hydropower output variation, kW; this constraint indicates that the hydropower output cannot respond in time due to the limitation of power generation equipment; (5) Output constraints of pumped storage power stations; In the formula, the left side Indicates the cumulative output of the pumped storage power station for continuous energy storage or continuous power generation. C It represents the installed capacity of the pumped-storage power station. The continuous energy storage or cumulative power generation output of the pumped-storage power station does not exceed the capacity constraint, and the energy for pumping water comes from the wind and solar power generation cluster connected to the pumped-storage power station. (6) Load constraints at the transmission end; P t ≤D t Where: D t is the grid load demand at time t, kW. When the system output exceeds the grid load, the excess electric energy cannot be used, and power abandonment occurs.

4. 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 processor executes the program, the steps of the method according to any one of claims 1 to 3 are implemented.

5. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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