A scheduling rule optimization method for a water, wind and light integrated system

Abstract

The application discloses a kind of water wind light integration system scheduling rule optimization method, it is related to energy system scheduling technical field.Disclosed in the application includes: design water wind light multi-energy complementary long-term scheduling chart, initialize the up and down scheduling line on scheduling chart and decision output parameter, to calculate monthly total power generation;Design medium-term scheduling rule, monthly total power generation is distributed to each day, to obtain daily power generation;Design short-term scheduling rule, daily total power generation is distributed to each hour, to obtain day-ahead generation plan;Determine the load state of each hydropower station in cascade hydropower station, and the hydraulic connection and electric connection between different hydropower stations are constrained, to obtain real-time scheduling strategy;The optimization variable of multi-objective optimization scheduling model is updated and iterated to obtain pareto solution set, and water wind light integration scheduling rule is determined according to compromise solution.The application can minimize the risk of power rejection and load loss during operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy system scheduling, in particular to a scheduling rule optimization method of a water-wind-solar integrated system. BACKGROUND

[0002] Developing clean energy represented by hydropower, wind power and photovoltaic power is an important way to implement the "double carbon" goal, and is also an inevitable choice to solve the increasing energy demand and increasingly urgent ecological environment governance contradiction in China. With the gradual expansion of the installed capacity of new energy and the gradual increase of power generation proportion, the impact on the power grid is becoming more and more intense. The formation of a water-wind-solar multi-energy complementary system by combining the volatility, randomness and intermittency of wind power and photovoltaic power with the flexible start-stop of hydropower is an effective way to solve the problem of new energy grid connection and consumption.

[0003] Currently, for the water-wind-solar scheduling mode of multi-scale time nesting, a nested calculation model of hydropower medium-long term optimization scheduling-water-wind-solar short term optimization configuration is usually used, and scheduling is performed through different objective functions and constraint conditions, so as to realize the scheduling of multi-energy complementary system considering multi-time scale.

[0004] However, due to the mutual feedback relationship between long-term scheduling and short-term scheduling in the water-wind-solar multi-energy complementary system, whether the short-term is nested in the medium-long term scheduling or the medium-long term is considered in the short-term scheduling modeling method, it is difficult to form a closed-loop control, and the global optimality of the multi-time scale collaborative scheduling strategy cannot be guaranteed. SUMMARY

[0005] Therefore, it is necessary to provide a scheduling rule optimization method of a water-wind-solar integrated system in view of the above technical problems.

[0006] The embodiment of the present application provides a scheduling rule optimization method of a water-wind-solar integrated system, which comprises the following steps:

[0007] Obtaining hydrological data of cascade hydropower stations and long series of wind and solar output data, and constructing a water-wind-solar multi-energy complementary scheduling diagram according to the hydrological data and the wind and solar output data;

[0008] Initializing the up and down scheduling lines and the decision output parameters of the water-wind-solar multi-energy complementary scheduling diagram to determine the monthly total power generation, and obtaining a monthly scale long-term scheduling model;

[0009] Taking the daily power distribution curve as a medium-term scheduling rule to distribute the monthly total power generation to each day, and obtaining the daily power generation;

[0010] An initial power generation plan is generated based on hourly wind and solar power output data from long-term wind and solar power output data. The difference between the total power generation of the initial power generation plan and the daily power generation is taken as the remaining energy of the complementary system. The five-segment line of intraday energy allocation is used as the short-term dispatch rule to allocate the remaining energy of the complementary system to each hour, thus obtaining the day-ahead power generation plan.

[0011] The load status of each reservoir in the cascade hydropower station is determined by the discrimination coefficient and the relative storage rate, and the hydraulic and electrical connections between different hydropower stations are constrained to obtain the real-time scheduling strategy of different hydropower stations on the hourly scale.

[0012] A multi-objective optimization scheduling model is constructed with the objective functions of maximizing the total grid-connected electricity of the hydro-wind-solar multi-energy complementary system, minimizing the curtailment rate of wind and solar new energy, and minimizing the load loss rate of the complementary system. The upper and lower limits of the optimization variables of the hydro-wind-solar multi-energy complementary scheduling diagram, which are the non-intersection constraints of the upper and lower scheduling lines and the five-segment constraints of the daily energy distribution, are used as the optimization variables of the multi-objective optimization scheduling model.

[0013] Based on the parameter-simulation-optimization framework, the optimization variables are updated and iterated through a multi-objective cuckoo intelligent search algorithm to obtain the Pareto solution set; then, the Pareto solution set is filtered through a multi-attribute decision method to obtain compromise optimization variables, and the integrated water, wind and solar scheduling rules are determined based on the compromise optimization variables.

[0014] Optionally, the monthly-scale long-term scheduling model is determined based on the following formula:

[0015] ;

[0016] ;

[0017] in, For the first Time period The decision-making output of Reservoir A in the output area; , For the first Scheduling parameters within each output zone ; For the first Available energy of the time-phased hydro-wind-solar hybrid system; , , These are the Y, B, and C hydropower stations of the reservoir. Available energy during a given period; , The predicted power outputs are for wind power and solar power, respectively.

[0018] Optionally, the daytime power allocation curve is used as a medium-term dispatch rule to allocate the total monthly power generation requirement to each day, resulting in the daily power generation, specifically including:

[0019] The daily power distribution curve includes: average equal distribution, runoff size normalized distribution, wind-solar output normalized distribution, runoff x wind-solar output normalized distribution, runoff + wind-solar output normalized distribution, and runoff normalized + wind-solar output normalized post-normalized distribution;

[0020] A set of integrated scheduling rules is randomly generated by an intelligent optimization algorithm, and the daily power distribution curve is used as an optimization variable to perform integrated simulation scheduling and count the objective function value of each integrated simulation scheduling;

[0021] The daily power distribution curve with the largest objective function value of integrated simulation scheduling is used as the optimal daily power distribution curve, and the monthly total power generation demand is distributed to each day through the optimal daily power distribution curve to obtain the daily total power generation demand.

[0022] Optionally, the load state of each reservoir in the cascade hydropower station is determined by the discriminant coefficient and the relative storage rate, and the hydraulic connection and power connection between different hydropower stations are constrained to obtain the real-time scheduling strategy of different hydropower stations at the hourly scale, specifically including:

[0023] The output of the nth cascade hydropower station in the mth period is determined based on the following formula:

[0024] ;

[0025] The output of the nth cascade hydropower station in the mth period according to the inflow is determined based on the following formula:

[0026] ;

[0027] Wherein, is the output of the nth cascade hydropower station in the mth period; is the power generation plan of the water-wind-solar multi-energy complementary system in the mth period; is the output of the nth cascade hydropower station in the mth period according to the inflow; is the comprehensive output coefficient of the nth hydropower station; is the inflow of the nth hydropower station in the mth period; is the power generation head of the nth hydropower station in the mth period; is the total number of cascade hydropower stations; is the output of the nth cascade hydropower station in the mth period; is the output of the nth cascade hydropower station in the mth period according to the inflow; is the inflow of the nth hydropower station in the mth period; is the power generation head of the nth hydropower station in the mth period; is the total number of cascade hydropower stations; is the output of the nth cascade hydropower station in the mth period; is the output of the nth cascade hydropower station in the mth period according to the inflow; is the inflow of the nth hydropower station in the mth period;

[0028] is the power generation head of the nth hydropower station in the mth period; ​​The theoretical compensation output of the cascade hydropower station in the time period and the first A comparative analysis of the output of cascade hydropower stations based on the inflow rate was conducted to derive a load allocation strategy, which specifically includes:

[0029] The cascade reservoir impoundment specifically includes: determining the discrimination coefficient for each reservoir, and marking the reservoir with the highest discrimination coefficient as... Determine in sequence The relative water storage rate with other reservoirs is used to determine the relative water storage rate and the discrimination coefficient to determine the water storage order; water is stored in the order of water storage until the plan is met or the reservoir is full.

[0030] The cascade reservoirs release water until the incoming water output reaches the load demand;

[0031] The cascade reservoirs release water according to the natural inflow, without storing or releasing water.

[0032] The cascade reservoirs release water at the minimum discharge rate.

[0033] Optionally, the discrimination coefficient and relative water storage rate are determined based on the following formula:

[0034] ;

[0035] ;

[0036] ;

[0037] in, for Hydropower station Discriminant coefficient for the time period; for Hydropower station The amount of water entering the reservoir during a given time period; for The period initially located at The sum of available water volume in the reservoirs upstream of the hydropower station; for Hydropower station The water surface area during a given time period; for The period initially located at The sum of the hydropower head generated by the reservoirs downstream of the hydropower station; for Hydropower station Water storage rate during a given period; for hydroelectric power station Reservoir capacity during a given period; , respectively dead storage and normal storage of the hydropower station; is the storage rate of the hydropower station, i.e., the relative storage rate; is the control value of the relative storage rate.

[0038] Optionally, the storage sequence is determined, specifically including:

[0039] If , the cascade reservoirs are sequentially stored in the order of the relative storage rate from large to small, and when it is the turn of the reservoir , the reservoir is stored in the order of the discrimination coefficient from large to small, until the total output of the cascade hydropower station is equal to or each reservoir is stored to the allowed highest water level;

[0040] If , the cascade reservoirs are directly stored in the order of the discrimination coefficient from large to small, until the plan is met or all reservoirs are full;

[0041] The discharge sequence is determined, specifically including:

[0042] If , the cascade reservoirs are sequentially discharged in the order of the relative storage rate from large to small, and when it is the turn of the reservoir , the reservoir is discharged in the order of the discrimination coefficient from small to large, until the total output of the cascade hydropower station is equal to or each reservoir reaches the dead water level;

[0043] If , the cascade reservoirs are directly discharged in the order of the discrimination coefficient from small to large, until the power generation plan is met or all reservoirs are discharged to the dead water level.

[0044] Optionally, a multi-objective optimization scheduling model is constructed based on the following formula, with the maximum total on-grid power of the water-wind-sight multi-energy complementary system, the minimum wind-sight new energy curtailment rate, and the minimum complementary system loss load rate as the objective function:

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049]

[0050] wherein, Total online power of water, wind and light complementary system; Abandoned power rate of wind and light new energy; Loss load rate of water, wind and light complementary system; Indicates the online power; 、 、 Respectively indicates the cascade hydropower station, wind power plant and photovoltaic power plant; The photovoltaic power plant The power generation of the time period; The power generation of the time period; The power generation of the time period; The online power of the time period; The online power of the time period; The power generation plan of the water, wind and light complementary system in the first The loss load rate of the water, wind and light complementary system in the first

[0051] Optionally, the non-intersection constraint of the up and down scheduling lines in the water, wind and light complementary scheduling diagram is determined based on the following formula:

[0052] ;

[0053] The upper and lower limit constraints of the optimization variables of the five-section line of the daily energy distribution are determined based on the following formula:

[0054] ;

[0055] Wherein, The water level of the first Reservoir in the time period; 、The dead water level and the normal storage level of the first Reservoir; The daily distribution coefficient of the five-section line of the daily energy distribution in the first Time period; 、The upper and lower limits of the distribution coefficient.

[0056] Optionally, based on the parameter-simulation-optimization framework, the optimization variables are updated and iteratively optimized by the multi-objective cuckoo intelligent search algorithm to obtain a Pareto solution set, which specifically includes:

[0057] The hydrological data of the cascade reservoir and the long series of wind and light output data are taken as input data, and the initial scheduling rule is generated by the multi-objective cuckoo intelligent search algorithm;

[0058] ​​​The water-wind-solar integrated simulation scheduling coupled by the initial scheduling rule is performed to obtain a series of scheduling decisions, and the online power of the water-wind-solar complementary system, the wind-solar curtailment rate and the complementary system loss load rate under each scheduling decision are determined.

[0059] The different scheduling decisions are iteratively screened through the multi-objective cuckoo intelligent search algorithm to obtain the current optimal scheduling rule; it is judged whether the iteration number reaches the target number, if not, the scheduling rule is updated through the multi-objective cuckoo intelligent search algorithm; if the target number is reached, the iteration is stopped to obtain the Pareto solution set of the water-wind-solar integrated scheduling rule coupled by multi-scale and a series of scheduling decisions.

[0060] The scheduling rule optimization method of the water-wind-solar integrated system provided by the embodiment of the application has the following beneficial effects compared with the prior art.

[0061] The application combines the advantages of the short-term scheduling model in considering the risk conditions caused by the grid connection of wind and solar power and the advantages of the long-term and medium-term scheduling model in ensuring the overall benefits of the complementary system in multi-year operation through the rule interaction and intelligent optimization of multi-time scale rules, realizes the coordinated closed-loop control of long-term planning and short-term operation, can significantly improve the adaptability of the system to wind and solar uncertainty, and maximizes the reduction of the curtailment and loss load risks in the operation process of the water-wind-solar complementary system on the premise of ensuring the power generation benefits of the water-wind-solar complementary system. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A flowchart of a scheduling rule optimization method of a water-wind-solar integrated system provided in an embodiment;

[0063] Figure 2 A cascade hydropower load distribution diagram of a scheduling rule optimization method of a water-wind-solar integrated system provided in an embodiment;

[0064] Figure 3 A PSO flowchart of a scheduling rule optimization method of a water-wind-solar integrated system provided in an embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0066] The embodiment of the application provides a scheduling rule optimization method of a water-wind-solar integrated system, which comprises:

[0067] Hydrological data and long series of wind-solar output data of cascade hydropower stations are acquired, and a water-wind-solar multi-energy complementary dispatching graph is constructed according to the hydrological data and the wind-solar output data.

[0068] The upper and lower dispatching lines of the water-wind-solar multi-energy complementary dispatching graph and decision output parameters are initialized to obtain a long-term monthly dispatching model to determine the monthly total power generation.

[0069] The daily power generation is obtained by distributing the monthly total power generation to each day by using the daily power distribution curve as a medium-term dispatching rule.

[0070] The initial power generation plan is generated according to the hourly wind-solar output data in the long series of wind-solar output data, and the difference between the total power generation of the initial power generation plan and the daily power generation is taken as the complementary system residual energy. The intra-day energy distribution five-segment line is taken as a short-term dispatching rule to distribute the complementary system residual energy to each hour to obtain a day-ahead power generation plan.

[0071] The load state of each hydropower station in the cascade hydropower station is determined by the discrimination coefficient and the relative storage rate, and the hydraulic connection and the electric connection between different hydropower stations are constrained to obtain the real-time dispatching strategy of different hydropower stations under the hourly scale.

[0072] A multi-objective optimization dispatching model is constructed with the maximum total on-grid power of the water-wind-solar multi-energy complementary system, the minimum wind-solar new energy curtailment rate and the minimum complementary system loss of load rate as objective functions. The non-crossing constraint of the upper and lower dispatching lines in the water-wind-solar multi-energy complementary dispatching graph and the upper and lower limit constraints of the optimization variables of the intra-day energy distribution five-segment line are taken as the optimization variables of the multi-objective optimization dispatching model.

[0073] Based on the parameter-simulation-optimization framework, the optimization variables are updated and iteratively optimized by the multi-objective cuckoo intelligent search algorithm to obtain a Pareto solution set. The compromise optimization variables are obtained by screening the Pareto solution set by the multi-attribute decision method, and the water-wind-solar integrated dispatching rule is determined according to the compromise optimization variables.

[0074] The specific implementation is as follows:

[0075] 1. The hydrological data of the hydropower station, such as the inflow data of the hydropower station, the reservoir characteristic curve and the installed capacity of the hydropower station, are acquired from the cascade hydropower station, and the long series of wind-solar output data are collected from the meteorological station, relevant websites, software and platforms.

[0076] 2. In the long-term dispatching, the monthly total power generation is determined by the water-wind-solar multi-energy complementary dispatching graph, the monthly scale power generation of the water-wind-solar integrated system is constrained, and the optimization variables of the upper and lower dispatching lines in the dispatching graph and the optimization parameters of the decision output are calculated. The long-term dispatching model is established based on the monthly scale, and the dispatching decision is determined by the water-wind-solar multi-energy complementary dispatching graph.

[0077] ;

[0078] ;

[0079] In the formula: is the decision-making output of the reservoir A in the first time period in the first output area; , is the scheduling parameter in the first output area ; is the available energy of the water-wind-solar complementary system in the first time period; , , are the available energies of the reservoirs Y, B, and C in the first time period, respectively; , are the predicted outputs of the wind power and photovoltaic power, respectively.

[0080] 3. In the medium-term scheduling, the total monthly power generation demand is accurately allocated to each day through the daily power allocation curve, so as to obtain the daily power generation.

[0081] The medium-term scheduling model is established based on the daily scale, and the scheduling decision is determined by using the daily power allocation curve. The total monthly power generation demand can be obtained through the water-wind-solar multi-energy complementary scheduling diagram of the long-term scheduling model, and the daily power allocation curve is used to allocate it to each day.

[0082] As shown in Table 1, a strategy is proposed: and are calculated and then added, and then normalized, which is to avoid the runoff / wind-solar output being too large or too small, so that the daily total power generation demand allocated is more consistent with the water-wind-solar resource situation, and the deviation of the monthly total power generation allocation strategy is avoided.

[0083] Table 1 Daily power allocation strategy table

[0084]

[0085] In view of the multi-scale coupled water-wind-solar integrated optimization scheduling involving different time scales of long, medium and short, with many optimization variables, long scheduling time sequence, and complex hydraulic / electricity contact, an intelligent optimization algorithm is used to randomly generate a set of integrated scheduling rules, only the daily power allocation curve in the integrated model is changed, the integrated simulation scheduling is carried out, and the allocation scheme of the medium-term scheduling model is comprehensively screened according to the water-wind-solar power output, wind-solar new energy curtailment rate and complementary system loss load rate, to obtain the optimal scheme.

[0086] 4. In short-term scheduling, the grid-connection order of each hydropower station is determined, and the initial generation plan is generated using hourly wind and solar output data. The total generation of the initial generation plan is subtracted from the daily generation to obtain the remaining energy of the complementary system. The remaining energy is then allocated to each hour using the five-segment line for intra-day energy allocation. The time coordinates of the five-segment line are used to divide different time zones, and the average wind and solar output in the corresponding time zone is used to generate an initial generation plan. Then, the difference between the total generation of the initial generation plan and the daily total generation is calculated to obtain the remaining energy. The intra-day allocation coefficient of the five-segment line is used to allocate the remaining energy, and the initial generation plan is added to obtain the generation plan of the water-wind-solar multi-energy complementary system for each hour. In the case of wind and solar priority grid-connection, the load state of each reservoir in the cascade hydropower station is determined using the combination of the discriminant coefficient and the relative storage rate, and the hydraulic connection between hydropower stations and the power connection between different hydropower stations are constrained to realize the real-time scheduling of different hydropower stations at the hourly scale.

[0087] The theoretical compensation output of the cascade hydropower station after excluding wind and solar output is as follows: Then, the cascade hydropower station discharges according to the incoming water to generate power, thereby obtaining By comparing and , the load allocation strategy is determined.

[0088] ;

[0089] ;

[0090] In the formula: is the theoretical compensation output of the cascade hydropower station in the time period; is the generation plan of the water-wind-solar multi-energy complementary system in the time period; is the output of the cascade hydropower station discharging according to the incoming water in the time period; is the comprehensive output coefficient of the th hydropower station; is the incoming water of the th hydropower station in the time period; is the generation water head of the th hydropower station in the time period; is the total number of cascade hydropower stations.

[0091] ① This means that the cascade reservoirs can fully meet the load requirements based on the inflow, and there is still excess inflow. It is necessary to store water to reduce the reservoir's output for use during water supply.

[0092] ② This indicates that the cascade reservoirs cannot meet the load requirements based on the inflow, and water needs to be released to increase the system output.

[0093] ③ This means that the cascade reservoirs can meet the load requirements by releasing natural water flow, and the cascade reservoirs neither store nor release water.

[0094] ④ This indicates that the output of wind power and photovoltaic power can fully meet the requirements, and at this time the cascade reservoirs release water at a minimum discharge flow rate of 200 m³ / s.

[0095] The formulas for calculating the discrimination coefficient and the relative water storage rate are as follows:

[0096] ;

[0097] ;

[0098] ;

[0099] In the formula: for Hydropower station Discriminant coefficient for the time period; for Hydropower station The amount of water entering the reservoir during a given time period; for The period initially located at The sum of available water volume in the reservoirs upstream of the hydropower station; for Hydropower station The water surface area during a given time period; for The period initially located at The sum of the hydropower head generated by the reservoirs downstream of the hydropower station; for Hydropower station Water storage rate during a given period; for hydroelectric power station Reservoir capacity during a given period; , They are respectively Dead storage capacity and storage capacity corresponding to normal water level at a hydropower station; for Hydropower station relative to The water storage rate of a hydropower station, also known as the relative water storage rate; The control value for the relative water storage rate is set to 0.4.

[0100] After assessing the water storage and release status of cascade reservoirs, the order of water storage and release needs to be determined using a combination of discrimination coefficients and relative storage rates. Figure 2 As shown.

[0101] when At that time, the cascade reservoirs utilize their remaining capacity to store water and reduce power output, thereby meeting the power generation plan. First, the discrimination coefficients for each reservoir are calculated separately, and then... The reservoir with the largest value is marked as And calculate the reservoir in sequence. The relative water storage rate with other reservoirs is then used to determine the relative water storage rate and the discrimination coefficient, thereby determining the water storage order. Finally, water is stored in the order until the plan is met or the reservoir is full.

[0102] Determining the water storage order involves two scenarios. The first scenario: If The cascade reservoirs first store water in order of decreasing relative water storage ratio, and then it's the turn of the next reservoir to fill the next one. The reservoirs are filled with water in descending order of their discrimination coefficients, until the total output of the cascade hydropower stations equals... Or all reservoirs are filled to their maximum permissible water level; Second scenario: if The cascade reservoirs directly store water in descending order of their discrimination coefficients until the plan is met or all reservoirs are full.

[0103] when At that time, the cascade reservoirs release their stored water to increase reservoir output and meet power generation plans. The method is basically the same as that of cascade reservoirs, but there are some differences in the water supply determined based on the relative storage rate and the criteria for determining storage.

[0104] Determining the water supply sequence involves two scenarios. The first scenario: If The cascade reservoirs supply water in descending order of their relative water storage ratio, and then it is the turn of the reservoirs to supply water to the next reservoir. The reservoirs supply water in ascending order of their discrimination coefficients until the total output of the cascade hydropower stations equals... Or, the reservoirs may reach their dead water levels; Second scenario: if The cascade reservoirs supply water directly in ascending order of the discrimination coefficient until the power generation plan is met or all reservoirs release water to the dead water level.

[0105] 5. Establish a multi-objective optimization scheduling model that maximizes the total grid-connected electricity of the hydro-wind-solar multi-energy complementary system, minimizes the curtailment rate of wind and solar renewable energy, and minimizes the load failure rate of the complementary system. The objective function is as follows:

[0106] The hydro-wind-solar multi-energy complementary system has the largest total on-grid power consumption.

[0107] ;

[0108] ;

[0109] ;

[0110] The minimum wind and light new energy curtailment rate:

[0111] ;

[0112] ;

[0113] ;

[0114] The minimum load loss rate of the water, wind and light multi-energy complementary system:

[0115] ;

[0116] ;

[0117] In the formula: is the total on-grid power of the water, wind and light multi-energy complementary system; is the wind and light new energy curtailment rate; is the load loss rate of the water, wind and light multi-energy complementary system; subscript represents the on-grid power; superscript , , respectively represent the cascade hydropower station, wind power plant and photovoltaic power plant; is the power generation of the photovoltaic power plant in the time period; is the power generation of the wind power plant in the time period; is the on-grid power (power generation) of the cascade hydropower station in the time period; is the power generation plan of the water, wind and light multi-energy complementary system in the time period; is the load loss rate of the water, wind and light multi-energy complementary system in the time period.

[0118] The constraints of the optimization variables are considered in the optimization scheduling model, including the non-crossing constraints of the on-line and off-line of the scheduling diagram, and the upper and lower limit constraints of the optimization variables of the five-section line of the intra-day energy distribution.

[0119] (1) Water balance constraint:

[0120] ;

[0121] (2) Water level constraint:

[0122] ;

[0123] (3) Storage capacity constraints:

[0124] ;

[0125] (4) Downflow constraint:

[0126] ;

[0127] (5) Output constraints:

[0128] ;

[0129] (6) No crossing constraint for dispatch lines:

[0130] ;

[0131] (7) Upper and lower limits of the five-segment line distribution coefficient:

[0132] ;

[0133] In the formula: , , , The first reservoir Reservoir capacity, inflow, outflow, and water level for the specified time period; The scheduling time interval; For the first One power supply Efforts during a specific time period; , The first Water levels at the upper and lower control lines during the specified time period; For the first Intraday energy distribution coefficient of the five-segment line during a given time period; , The first The dead water level and normal water storage level of the reservoir; , For the first The dead storage capacity and the storage capacity corresponding to the normal water level of the reservoir; , These are the upper and lower limits of the allocation coefficient, respectively.

[0134] 0. Take the parameter-simulation-optimization (PSO) framework, use multi-objective cuckoo search (MOCS) algorithm to update and iterate optimization variables, and then obtain the Pareto solution set.

[0135] As shown in Figure 3 , the following are the main steps of the PSO method:

[0136] Step 1: Use the cascade reservoir runoff data, wind and light output, etc. as input data, and use MOCS to generate initial scheduling rules, including the key nodes (water level + time) of the upper and lower scheduling lines involved in the medium and long-term scheduling diagram, the decision output optimization parameters, a total of 26 optimization variables, and a total of 108 intra-day distribution parameters of the total of 12 intra-day energy distribution five-section lines, thereby determining a total of 134 optimization variables;

[0137] Step 2: Based on the scheduling rules generated by MOCS, carry out multi-scale coupled water, wind and light integrated simulation scheduling, and generate a series of scheduling decisions (such as power station output, grid-connected power, etc.), and then calculate the grid-connected power of the water, wind and light complementary system, the wind and light abandoned power rate, and the complementary system loss load rate according to the scheduling decisions;

[0138] Step 3: Use MOCS to screen different water, wind and light integrated scheduling rules, thereby updating the current optimal scheduling rule;

[0139] Step 4: Determine whether the iteration number has reached the target number. If not, update the scheduling rule using MOCS, repeat steps 2 and 3 until the target number is reached; if the termination condition is reached, output the multi-scale coupled water, wind and light integrated scheduling rule (Pareto solution set) and a series of scheduling decisions.

[0140] The main process steps of the multi-objective cuckoo search algorithm are as follows:

[0141] Step 1: Initialize the required parameters of the algorithm, such as population size , number of objective functions , number of iterations , upper limit and lower limit of the search domain, probability of being discovered by the host , etc.; randomly generate initial parent solutions (water, wind and light integrated optimization scheduling rules) according to the upper and lower limits of the search domain, and calculate the objective functions;

[0142] Step 2: Simulate the process of parent cuckoo reproducing offspring by using Levy flight principle and local random walk, and calculate the objective function corresponding to the offspring nest;

[0143] Step 3: Perform fast non-dominated sorting on all objective values of the parent and offspring, calculate their crowding degree, and use the elite strategy to select the parent and offspring in order of non-dominated order from low to high and crowding degree from large to small, until the population size is met, and the selected population is the latest parent;

[0144] Step 4: Repeat steps 2 and 3 until the iteration reaches the set iteration number, and output the parent nest, i.e. the population size of Multi-scale coupled water, wind and light integrated scheduling rules (Pareto solution set).

[0145] Then, a multi-attribute decision method is used to screen the solution set, and finally the water, wind and light integrated scheduling rules are obtained. The multi-objective cuckoo search algorithm is used to obtain a multi-scale coupled water, wind and light integrated scheduling rule (Pareto solution set) with a population size of In order to select a balanced solution (compromise optimization variable) from the Pareto solution set and further extract the water, wind and light integrated scheduling rules, a multi-attribute decision method is used, which is as follows:

[0146] According to the attributes of different solutions, the membership of each solution in the Pareto solution set is calculated and normalized. The solution with the maximum normalized membership value in the solution set is considered as the balanced solution.

[0147] When the objective function is smaller, the membership value calculation formula is:

[0148] ;

[0149] When the objective function is larger, the membership value calculation formula is:

[0150] ;

[0151] When the objective function is larger, the membership value calculation formula is:

[0152] ;

[0153] In the formula: is the membership of the th objective function; is the th objective function value; , is the maximum and minimum value of the th objective function; is the number of Pareto solutions, i.e. the population size of cuckoo; the number of objective functions; the normalized membership value of the the normalized membership value of the

[0154] The above embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application.

Claims

1. A method for optimizing scheduling rules of a water, wind and light integrated system, characterized in that, The method comprises the following steps: obtaining hydrological data and long series wind-solar output data of the cascade hydropower station, and constructing a water-wind-solar multi-energy complementary scheduling diagram according to the hydrological data and the wind-solar output data; initializing upper and lower scheduling lines of the water-wind-solar multi-energy complementary scheduling diagram and decision output parameters to obtain a monthly scale long-term scheduling model to determine a monthly total power generation; distributing a daily power generation amount according to a daily power distribution curve as a medium-term scheduling rule to distribute the monthly total power generation to each day to obtain a daily power generation amount; generating an initial power generation plan according to hourly wind-solar output data in the long series wind-solar output data, taking a difference between a total power generation of the initial power generation plan and the daily power generation amount as a complementary system residual energy, and distributing an intra-day energy distribution five-segment line as a short-term scheduling rule to distribute the complementary system residual energy to each hour to obtain a day-ahead power generation plan; determining a load state of each reservoir in the cascade hydropower station by a discriminant coefficient and a relative storage rate, and performing constraint on a water power connection and an electric power connection between different hydropower stations to obtain a real-time scheduling strategy of the different hydropower stations at an hourly scale; taking maximum total grid-connected power of the water-wind-solar multi-energy complementary system, minimum wind-solar new energy curtailment rate and minimum complementary system loss of load rate as objective functions to construct a multi-objective optimization scheduling model, and taking non-crossing constraints of the upper and lower scheduling lines in the water-wind-solar multi-energy complementary scheduling diagram and upper and lower limit constraints of optimization variables of the intra-day energy distribution five-segment line as optimization variables of the multi-objective optimization scheduling model; updating and iteratively optimizing the optimization variables by a multi-objective cuckoo intelligent search algorithm based on a parameter-simulation-optimization framework to obtain a Pareto solution set, and screening the Pareto solution set by a multi-attribute decision method to obtain compromise optimization variables, and determining a water-wind-solar integrated scheduling rule according to the compromise optimization variables. 2.The scheduling rule optimization method of a water, wind and light integrated system according to claim 1, wherein, The monthly scale long-term scheduling model is determined based on the following formula: ； ； wherein, is the decision output of the reservoir A for the time period, is the decision output of the reservoir A for the , is the scheduling parameter in the output area, ; is the available energy of the water-wind-solar complementary system for the time period; , , are the available energies of the reservoirs Y, B, C for the time period, respectively; , are the predicted outputs of the wind power and the photovoltaic power, respectively.

3. The method of claim 1, wherein the scheduling rule is optimized based on a water, wind, and light integration system. The daily power distribution curve as the medium-term scheduling rule to distribute the monthly total power generation to each day to obtain the daily power generation amount specifically comprises: The daily power distribution curve comprises: average equal distribution, runoff size normalization distribution, wind-solar output normalization distribution, runoff x wind-solar output normalization distribution, runoff + wind-solar output normalization distribution, and runoff normalization + wind-solar output normalization post-normalization distribution; a set of integrated scheduling rules is randomly generated by an intelligent optimization algorithm, the daily power distribution curve is taken as an optimization variable, integrated simulation scheduling is performed, and a target function value of each integrated simulation scheduling is counted; the daily power distribution curve with the maximum target function value of the integrated simulation scheduling is taken as an optimal daily power distribution curve, and the monthly total power generation is distributed to each day by the optimal daily power distribution curve to obtain the daily power generation amount.

4. The method of claim 1, wherein the scheduling rule is optimized for a water, wind, and light integrated system. The load state of each reservoir in the cascade hydropower station is determined by the discriminant coefficient and the relative storage rate, and the water power connection and the electric power connection between different hydropower stations are constrained to obtain the real-time scheduling strategy of the different hydropower stations at the hourly scale, specifically comprising: The output of the theoretical compensation of the cascade hydropower station is determined based on the following formula period ； The first time period is determined based on the following formula: The output of the cascade hydropower station according to the inflow is determined. ； in, For the first The theoretical compensation output of cascade hydropower stations during specific time periods; For the first Power generation plan for a multi-energy complementary system of hydropower, wind power, and solar power during specific periods; For the first The power output of a cascade hydropower station is determined by the amount of incoming water. For the first The overall output coefficient of the hydropower station; for Time period The incoming water to the hydroelectric power station; for Time period The generating head of the hydroelectric power station; This represents the total number of cascade hydropower stations. For photovoltaic power plants Electricity generation during a given period; For wind power plants Electricity generation during a given period; The first The theoretical compensation output of the cascade hydropower station in the time period and the first A comparative analysis of the output of cascade hydropower stations based on the inflow rate was conducted to derive a load allocation strategy, which specifically includes: , the stepped reservoirs are impounded, specifically comprising: determining a discrimination coefficient of each reservoir respectively, marking the reservoir with the largest discrimination coefficient as , determining the relative impoundment rates between the reservoir and other reservoirs in sequence; , judging the size of the relative impoundment rate and the discrimination coefficient to determine the impoundment order; and impounding according to the impoundment order until the plan is met or the reservoir is full. , the stepped reservoirs discharge water until the incoming water output reaches the load demand; The stepped reservoirs release water according to natural inflow without storing water or releasing water. The cascade reservoirs are discharged according to the minimum discharge.

5. The scheduling rule optimization method of claim 4, wherein, The discriminant coefficient and the relative storage rate are determined based on the following formula: ； ； ； in, for Hydropower station Discriminant coefficient for the time period; for Hydropower station The amount of water entering the reservoir during a given time period; for The period initially located at The sum of available water volume in the reservoirs upstream of the hydropower station; for Hydropower station The water surface area during a given time period; for The period initially located at The sum of the hydropower head generated by the reservoirs downstream of the hydropower station; for Hydropower station Water storage rate during a given period; for hydroelectric power station Reservoir capacity during a given period; , They are respectively Dead storage capacity and storage capacity corresponding to normal water level at a hydropower station; for Hydropower station relative to The water storage rate of a hydropower station, also known as the relative water storage rate; This is the control value for the relative water storage rate.

6. The scheduling rule optimization method of claim 5, wherein The storage sequence is determined, specifically comprising: If , the cascade reservoirs are successively impounded in the order of the relative storage rates from large to small, and the reservoir is impounded in the order of the discrimination coefficients from large to small until the total output of the cascade hydropower stations equals or each reservoir is impounded to the highest allowable water level. If , the cascade reservoirs directly store water according to the order of the discrimination coefficients from large to small until the plan or all reservoirs are full. The drainage sequence is determined, specifically comprising: If , the cascade reservoirs are supplied water in turn from large to small relative storage rate, and the reservoirs are supplied water in turn from small to large discriminant coefficient until the total output of the cascade hydropower stations equals or each reservoir reaches the dead water level. If , the cascade reservoirs directly supply water in the order of discriminant coefficient from small to large until the power generation plan is met or all reservoirs are discharged to the dead water level.

7. The method of claim 1, wherein the scheduling rule is optimized for a water, wind, and light integrated system. A multi-objective optimization scheduling model is constructed based on the following formula with the total online power of the water-wind-solar complementary system, the wind-solar new energy curtailment rate and the complementary system loss load rate as objective functions: ； ； ； ； ； ； ； ； wherein, is the total on-grid power of the water-wind-solar complementary system; is the abandoned power rate of the new energy of wind and solar; is the load loss rate of the water-wind-solar complementary system; represents the on-grid power; , , respectively represent the cascade hydropower station, the wind power plant, and the photovoltaic power plant; is the power generation of the photovoltaic power plant in the time period; is the power generation of the wind power plant in the time period; is the on-grid power of the cascade hydropower station in the time period; is the power generation plan of the water-wind-solar complementary system in the time period; is the load loss rate of the water-wind-solar complementary system in the time period. 8.The method of claim 1, wherein The water-wind-solar complementary scheduling graph is determined based on the following formula to ensure that the upper and lower scheduling lines do not cross: ； The upper and lower limit constraints of the optimization variables of the five-section line of the intra-day energy distribution are determined based on the following formula: ； in, For the first reservoir Water level during a given period; , The first The dead water level and normal water storage level of the reservoir; For the first Intraday energy distribution coefficient of the five-segment line during a given time period; , These are the upper and lower limits of the allocation coefficient, respectively; , The first Water levels at the upper and lower control lines during the specified time period.

9. The method of claim 1, wherein the scheduling rule is optimized for a water, wind, and light integrated system. The parameter-simulation-optimization framework is used to update and iteratively optimize the optimization variables through the multi-objective cuckoo intelligent search algorithm to obtain a Pareto solution set, which specifically includes: The hydrological data of the cascade reservoirs and the long series of wind-solar output data are used as input data, and the initial scheduling rules are generated through the multi-objective cuckoo intelligent search algorithm; The multi-scale coupled water-wind-solar integrated simulation scheduling is performed through the initial scheduling rules to obtain a series of scheduling decisions and determine the online power of the water-wind-solar complementary system, the wind-solar curtailment rate and the complementary system loss load rate under each scheduling decision; The different scheduling decisions are iteratively screened through the multi-objective cuckoo intelligent search algorithm to obtain the current optimal scheduling rule; it is determined whether the iteration number has reached the target number, if not, the scheduling rule is updated through the multi-objective cuckoo intelligent search algorithm; if the target number is reached, the iteration is stopped, and the Pareto solution set and a series of scheduling decisions representing the multi-scale coupled water-wind-solar integrated scheduling rules are obtained.

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