A medium- and long-term scheduling method for water-solar hybridization based on improved particle swarm optimization algorithm

Abstract

The present invention discloses a medium- and long-term scheduling method for water-photovoltaic complementarity based on an improved particle swarm optimization algorithm, including: calculating the short-term power transmission of the water-photovoltaic complementarity system according to the maximum capacity of the power grid channel; constructing a medium- and long-term scheduling model for water-photovoltaic complementarity based on the short-term power transmission of the water-photovoltaic complementarity system and taking the maximum medium- and long-term power transmission of the water-photovoltaic complementarity system as the objective function; solving the medium- and long-term scheduling model for water-photovoltaic complementarity using an improved particle swarm optimization algorithm to obtain a medium- and long-term scheduling method for the water-photovoltaic complementarity system. The present invention defines the short-term power transmission of the water-photovoltaic complementarity system, quickly obtains the objective function value of the particle, introduces the constraint penalty degree of the particle, formulates a sorting rule to sort the population, formulates a water level variation rule, guides the particle variation evolution process, and forms an improved particle swarm optimization algorithm, which can overcome the problem of multiple constraints in the solution process and the difficulty in finding a feasible solution, and improves the solution efficiency in the process of calculating the medium- and long-term optimal scheduling of water-photovoltaic complementarity.

Description

Technical Field

[0001] The present invention relates to a water-photovoltaic complementary power generation technology, and in particular to a medium- and long-term scheduling method for water-photovoltaic complementary power generation based on an improved particle swarm optimization algorithm. Background Art

[0002] With the transformation of energy structure and the deepening of power system reform, photovoltaic power generation has been vigorously developed. Combined with the advantage of easy adjustment of hydropower output, by building a water-photovoltaic complementary system, the absorption of photovoltaic power generation can be improved, the amount of abandoned light can be reduced, and the power output of the water-photovoltaic complementary system can be maximized. However, in the long-term scheduling solution process of the water-photovoltaic complementary system, it is difficult to quickly find a feasible solution in the process of multiple constraints, and secondly, after entering the feasible domain, constraint violation is prone to occur in the process of adjusting the water level, which reduces the solution and optimization efficiency. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a medium- and long-term scheduling method for water-photovoltaic complementary system based on improved particle swarm optimization algorithm, to overcome the problem of low optimization efficiency in the medium- and long-term scheduling process of water-photovoltaic complementary system, and to optimize the medium- and long-term power transmission of water-photovoltaic complementary system.

[0004] Technical solution: A medium- and long-term scheduling method for water-photovoltaic complementarity based on an improved particle swarm optimization algorithm of the present invention comprises the following steps:

[0005] S1. The hydropower-photovoltaic complementary system includes cascade hydropower stations and distributed photovoltaic power stations. After photovoltaic power generation is converted into alternating current through inversion, it is bundled with hydropower and the two are connected to the same power grid channel to realize power transmission. According to the maximum capacity of the power grid channel, the short-term power transmission of the hydropower-photovoltaic complementary system is calculated. The short-term power transmission of the hydropower-photovoltaic complementary system is the sum of the hydropower generation and photovoltaic generation that the power grid channel can accommodate within the short-term scheduling period of the hydropower-photovoltaic complementary system. The short-term scheduling of the hydropower-photovoltaic complementary system takes the day as the scheduling period and the hour as the calculation step length.

[0006] S2. Based on the short-term power transmission of the hydro-solar complementary system, a medium- and long-term scheduling model for hydro-solar complementary is constructed. The model takes the maximum medium- and long-term power transmission of the hydro-solar complementary system as the objective function, and the constraints include power output balance, reservoir water balance, reservoir hydraulic connection, reservoir water level constraint, reservoir discharge flow constraint and hydropower station output constraint;

[0007] S3. Use the improved particle swarm optimization algorithm to solve the medium- and long-term scheduling model of water-solar complementary system and obtain the medium- and long-term scheduling method of water-solar complementary system.

[0008] Furthermore, the calculation method of the short-term power transmission of the water-solar complementary system in step S1 is:

[0009] S11, calculate the photovoltaic power G required to be adjusted and the channel adjustable capacity F;

[0010] Among them, the calculation formula for the photovoltaic power regulation G is:

[0011]

[0012] Among them, N h To provide cascade hydropower, is the output of the ith hydropower station, and L is the total number of cascade hydropower stations; is the photovoltaic output in the tth period; N T is the maximum load that the power grid channel can accommodate; T is the number of hours in a day;

[0013] The calculation formula of the channel adjustable capacity F is:

[0014]

[0015] Among them, N max It is the maximum output of cascade hydropower. is the maximum output of the ith hydropower station; △t is the short-term calculation step length;

[0016] S12. According to the relationship between the photovoltaic power G and the channel adjustable capacity F, the short-term external power W of the water-photovoltaic complementary system is calculated according to different situations;

[0017] Case ①: G≤F, indicating that the power grid channel can fully accommodate the power generation of the hydro-photovoltaic complementary system within the short-term dispatch cycle. The calculation formula for the short-term external power transmission W of the hydro-photovoltaic complementary system is:

[0018]

[0019] Case ②: G>F, indicating that the power grid channel cannot fully accommodate the power generation of the hydro-photovoltaic complementary system within the short-term dispatch cycle. The calculation formula for the short-term external power transmission W of the hydro-photovoltaic complementary system is:

[0020]

[0021] Furthermore, in step S2, the medium- and long-term scheduling of water-solar complementarity takes one year as the scheduling cycle and one day as the calculation step length, and the objective function is:

[0022]

[0023] Among them, W d is the short-term power transmission of the hydro-solar complementary system on the dth day in the medium- and long-term scheduling cycle, and D is the number of days in a year.

[0024] Furthermore, step S3 specifically includes the following steps:

[0025] S31, setting parameters of the improved particle swarm optimization algorithm, taking the days of the year as stage variables, taking the water level of each reservoir in the cascade reservoirs as a decision variable, and generating an initial population;

[0026] S32. Through the water level information of each particle at each stage, the runoff calculation is performed to obtain the short-term discharge flow Q of each particle at each stage. d,i , Short-term hydropower output Short-term power transmission W d information;

[0027] S33, the short-term external power W of each particle at each stage d , calculate the medium- and long-term external power transmission E, that is, the objective function value of each particle, according to the short-term downstream flow Q of each particle in each stage obtained in step S32 d,i , Short-term hydropower output Short-term power transmission W d Information calculation particle constraint penalty;

[0028] S34, sorting the population according to the population particle sorting rule; specifically:

[0029] According to the particle objective function value and the constraint penalty in step S33, a dual-objective sorting is performed, wherein the particle constraint penalty is the first objective and the particle objective function value is the second objective. The specific method is as follows: firstly, the particles are sorted from small to large according to the particle constraint penalty; if multiple particles have the same constraint penalty, the multiple particles are sorted from large to small according to the particle objective function value;

[0030] S35, performing individual variation according to the variation rule of the water level of the population particles;

[0031] S36, judging whether the evolution requirement is met, if the evolution requirement is met, proceeding to step S37, otherwise returning to step S33;

[0032] S37, the solution is completed, the optimal particle is determined according to the population particle sorting rule, and the medium- and long-term water level process, hydropower generation plan, and external power transmission information are extracted.

[0033] Furthermore, the particle constraint penalty degree in step S33 is calculated as follows:

[0034] S331. Calculate the value of the particle exceeding the downstream flow constraint and output constraint in each stage. The calculation formula is:

[0035]

[0036]

[0037] Among them, △Q d,i , are the absolute values ​​of the magnitude of the downstream flow constraint and output constraint violated by the i-th hydropower station on the d-th day, i = 1…L, L is the total number of cascade hydropower stations, d = 1…D, D is the number of days in a year;

[0038] S332, calculate the degree to which the particle exceeds the downstream flow constraint and output constraint in each stage, and the calculation formula is:

[0039]

[0040]

[0041] Among them, CQ d,i , CN d,i are the discharge flow and output constraint destruction degree of the i-th hydropower station on the dth day respectively;

[0042] S333. Calculate the particle constraint penalty, the calculation formula is:

[0043] CQ i =(CQ 1,i ,CQ 2,i ,…CQ d,i …,CQ D,i )

[0044] CN i =(CN 1,i ,CN 2,i ,…CN d,i …,CN D,i )

[0045] C i =δ Q ·CQ i +δ N ·CN i

[0046]

[0047] Among them, CQ i , CN i are the discharge flow and output constraint violation degree of the ith hydropower station on the dth day; C i is the constraint penalty of the i-th hydropower station; C is the constraint penalty of the cascade hydropower station, that is, the particle constraint penalty; δ Q ,δ N are the weight factors of the downstream flow and output constraint destruction degree, which are constants.

[0048] Furthermore, step S35 is specifically as follows:

[0049] According to the particle constraint destruction degree CQ d,i , CNd,i Perform individual mutation operations, in which the mutation probability and decision variable mutation range are determined according to the degree of constraint destruction on each day. The specific method is as follows:

[0050] If CQ d,i =0, and CN d,i =0, then pro = 0;

[0051] If CQ d,i >α1, and CN d,i =0, then pro = β1,

[0052] If CQ d,i =0, and CN d,i >α2, then pro=β2,

[0053] If CQ d,i >α3, and CN d,i >α4, then pro=β3,

[0054] In other cases, pro = β4,

[0055] Among them, pro is the mutation probability; are the maximum and minimum water levels of the ith hydropower station on the dth day; △Z d,i is the variation range of the decision variable, expressed as the increase in water level of the i-th hydropower station on the d-th day; △1, △2, △3, △4, α1, α2, α3, α4, β1, β2, β3, and β4 are all constants.

[0056] A medium- and long-term scheduling system for water-solar complementary power based on an improved particle swarm optimization algorithm of the present invention comprises:

[0057] The short-term module for hydro-photovoltaic complementary scheduling is based on the output of cascade hydropower stations and distributed photovoltaic power stations, starting from the overall short-term output process of the two, analyzing the relationship between the maximum capacity of the remaining power grid channels, and calculating the short-term external power transmission of the hydro-photovoltaic complementary system;

[0058] The medium- and long-term scheduling module of hydro-solar complementarity uses a random search algorithm, takes time as the stage variable, and water level as the decision variable. In the medium- and long-term calculation process, the short-term scheduling module of hydro-solar complementarity is called to calculate the short-term external power information of each stage, and further construct the medium- and long-term scheduling model of hydro-solar complementarity.

[0059] The model solving module uses an improved particle swarm optimization algorithm to solve the medium- and long-term scheduling model of water-photovoltaic complementary system and obtain the medium- and long-term scheduling method of the water-photovoltaic complementary system.

[0060] A device of the present invention includes a memory and a processor, wherein:

[0061] A memory for storing computer programs that can be run on the processor;

[0062] The processor is used to execute the steps of the above-mentioned medium- and long-term scheduling method of water-photovoltaic complementarity based on the improved particle swarm optimization algorithm when running the computer program.

[0063] A storage medium of the present invention stores a computer program, and when the computer program is executed by at least one processor, the steps of the above-mentioned medium- and long-term scheduling method of water-photovoltaic complementarity based on an improved particle swarm optimization algorithm are implemented.

[0064] Beneficial effect: Compared with the prior art, the method of the present invention constructs a medium- and long-term scheduling model for hydro-photovoltaic complementary systems based on the short-term power transmission of the hydro-photovoltaic complementary system, and forms a set of medium- and long-term optimization scheduling methods for hydro-photovoltaic complementary systems with good adaptability in combination with the random search algorithm. The process adopts the particle swarm algorithm in the random search algorithm as the basic framework. After initializing the medium- and long-term water levels of the cascade reservoirs, the particle objective function value is quickly obtained through the short-term power transmission of the hydro-photovoltaic complementary system. By introducing the constraint penalty associated with the constraint function, the particle swarm optimization algorithm is improved, and the population sorting and individual variation mechanism are further improved to overcome the problems of difficulty in finding feasible solutions and unsatisfied constraints in the solution process, thereby improving the optimization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flow chart of the method of the present invention;

[0066] Figure 2 It is a flow chart of the improved particle swarm optimization algorithm of the present invention;

[0067] Figure 3 This is a comparison chart of the performance of the method of the present invention and other algorithms;

[0068] Figure 4 It is a medium- and long-term scheduling method for the water-solar complementary system. DETAILED DESCRIPTION

[0069] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0070] A water-photovoltaic complementary system includes cascade hydropower stations and distributed photovoltaic power stations. After photovoltaic power generation is converted into AC power through inverters, it is bundled with hydropower and collected. The two are connected to the grid through the same power grid channel to realize power transmission. The maximum capacity of the power grid channel, the installed capacity of cascade hydropower, and the total installed capacity of photovoltaic power are all 10 million kW. The basic information of the cascade hydropower station is shown in Table 1. Reservoir ② has a strong control effect on the distribution of downstream cascade runoff. The water level process of reservoir ② is used below to show the scheduling plan of the cascade hydropower station. According to the photovoltaic output information of the multi-year runoff data of the address, in view of the stable total annual photovoltaic power generation, it is advisable to use the typical annual photovoltaic output process for example calculation.

[0071] Table 1 Scale parameters of cascade hydropower stations

[0072]

[0073] like Figure 1 As shown, the medium- and long-term scheduling method of water-photovoltaic complementarity based on the improved particle swarm optimization algorithm of the present invention comprises the following steps:

[0074] S1. Calculate the short-term power transmission of the hydro-photovoltaic complementary system based on the maximum capacity of the power grid channel;

[0075] The short-term scheduling cycle of hydro-photovoltaic complementarity is based on days, and the calculation step is hours. The initial and final water levels of the short-term scheduling cycle are randomly selected according to the random search algorithm, and the hydropower output of each hydropower station in the cycle is obtained through runoff calculation in combination with the predicted runoff in the cycle. The predicted photovoltaic output process and hydropower output constraints in the cycle are introduced, and the short-term power transmission W of the hydro-photovoltaic complementary system is calculated in combination with the maximum capacity of the power grid channel. Among them, the short-term power transmission W of the hydro-photovoltaic complementary system is the sum of the hydropower generation and photovoltaic power generation that the power grid channel can accommodate in the short-term scheduling cycle of hydro-photovoltaic complementarity.

[0076] The calculation process of the short-term power transmission W of the water-solar hybrid system is as follows:

[0077] S11. Calculate the photovoltaic power G and the channel adjustable capacity F. The calculation formula is:

[0078]

[0079]

[0080]

[0081]

[0082] in, Provide power for the i-th hydropower station; is the maximum output of the i-th hydropower station; N h It is the output of cascade hydropower; N maxIt is the maximum output of cascade hydropower; is the photovoltaic output in the tth period; N T is the maximum load capacity of the power grid channel; t = 1…T, T is the number of hours in a day, i = 1…L, L is the total number of cascade hydropower stations, △t is the short-term calculation step length;

[0083] S12. The calculation of short-term power transmission W of the water-solar hybrid system is divided into the following two cases:

[0084] Case ①: G≤F, indicating that the power grid channel can fully accommodate the power generation of the hydro-photovoltaic complementary system within the short-term dispatch cycle. The short-term external power transmission of the hydro-photovoltaic complementary system is W, and the calculation formula is:

[0085]

[0086] Case ②: G>F, indicating that the power grid channel cannot fully accommodate the power generation of the hydro-photovoltaic complementary system within the short-term dispatch cycle. The short-term external power transmission W of the hydro-photovoltaic complementary system is calculated as:

[0087]

[0088] S2. According to the short-term power transmission of the hydro-photovoltaic complementary system, a medium- and long-term dispatching model of hydro-photovoltaic complementary is constructed. The model takes the maximum medium- and long-term power transmission of the hydro-photovoltaic complementary system as the objective function. The constraints include power output balance, reservoir water balance, reservoir hydraulic connection, reservoir water level constraint, reservoir discharge flow constraint and hydropower station output constraint; specifically:

[0089] S21, medium- and long-term scheduling of hydro-photovoltaic complementary system, with one year as the scheduling cycle and one day as the calculation step length; the maximum medium- and long-term power transmission E of the hydro-photovoltaic complementary system is taken as the objective function, specifically including:

[0090]

[0091] Among them, W d is the short-term power transmission of the hydro-solar complementary system on the dth day in the medium- and long-term dispatching cycle, d = 1…D, D is the number of days in a year;

[0092] Among them, W d The specific calculation method is:

[0093] The initial and final water levels of each day in the medium- and long-term scheduling period are randomly selected according to the random search algorithm and transferred into the short-term module of hydro-photovoltaic complementary scheduling, that is, the initial and final water levels of the short-term scheduling period. The short-term external power transmission W of the hydro-photovoltaic complementary system on that day is obtained through S1 calculation. d ;

[0094] S22. Medium- and long-term dispatch of hydropower-solar complementarity is based on the constraints of hydropower output balance, reservoir water volume balance, reservoir hydraulic connection, reservoir water level constraint, reservoir discharge flow constraint, and hydropower station output constraint. Specifically:

[0095] (1) Balance of hydropower output;

[0096]

[0097] in, For cascade hydropower, output on day d; is the output of the i-th hydropower station on day d, L is the number of hydropower stations in the cascade;

[0098] (2) Reservoir water balance;

[0099] V d+1,i =V d,i +(RQ d,i -Q d,i -qs d,i )△t

[0100] Among them, V d,i 、V d+1,i are the initial and final water storage of the ith hydropower station on day d; RQ d,i , Q d,i 、qs d,i They are the inflow, power generation and abandoned water flow of the i-th hydropower station on day d respectively;

[0101] (3) Reservoir hydraulic connection;

[0102] RQ d,i =Q d,i-1 +qs d,i-1 +QJ d,i

[0103] Among them, QJ d,i is the interval flow of the i-1th hydropower station on day d;

[0104] (4) Reservoir water level constraints;

[0105]

[0106] Among them, Z d,i is the water level of the i-th hydropower station on day d; are the lowest and highest operating water levels of the ith hydropower station on day d, respectively;

[0107] (5) Reservoir discharge flow constraints;

[0108]

[0109] Among them, Qd,i is the discharge flow of the i-th hydropower station on day d; are the maximum and minimum discharge flows of the ith hydropower station on day d, respectively;

[0110] (6) Hydropower station output constraints;

[0111]

[0112] in, The output of the ith hydropower station on day d; are the maximum and minimum outputs of the ith hydropower station on the dth day respectively.

[0113] S3. Use the improved particle swarm optimization algorithm to solve the medium- and long-term scheduling model of water-solar complementary system and obtain the medium- and long-term scheduling mode of water-solar complementary system; Figure 2 As shown, the specific steps include:

[0114] S31, call in the improved particle swarm optimization algorithm, set parameters, use the days of the year as stage variables, use the water level of each reservoir in the cascade reservoirs as a decision variable, generate an initial population, and enter step S32;

[0115] In this embodiment, 100 particles are set in each population;

[0116] S32. Through the water level information of each particle at each stage, the runoff calculation is performed to obtain the short-term discharge flow Q of each particle at each stage. d,i , Short-term hydropower output Short-term power transmission W d Information, go to step S33;

[0117] S33, short-term external power W transmitted by each particle d , calculate the medium- and long-term external power transmission E, that is, the objective function value of each particle, calculate the constraint penalty degree according to the information in S32, and enter step S34; specifically:

[0118] S331. Calculate the value of the particle exceeding the downstream flow constraint and output constraint in each stage. The calculation formula is:

[0119]

[0120]

[0121] in, are the absolute values ​​of the magnitude of the downstream flow constraint and output constraint violated by the i-th hydropower station on the d-th day, i = 1…L, L is the total number of cascade hydropower stations, d = 1…D, D is the number of days in a year;

[0122] S332, calculate the degree to which the particle exceeds the downstream flow constraint and output constraint in each stage, and the calculation formula is:

[0123]

[0124]

[0125] Among them, CQ d,i , CN d,i are the discharge flow and output constraint destruction degree of the i-th hydropower station on the dth day respectively;

[0126] S333. Calculate the particle constraint penalty, the calculation formula is:

[0127] CQ i =(CQ 1,i ,CQ 2,i ,…CQ d,i …,CQ D,i )

[0128] CN i =(CN 1,i ,CN 2,i ,…CN d,i …,CN D,i )

[0129] C i =δ Q ·CQ i +δ N ·CN i

[0130]

[0131] Among them, CQ i , CN i are the discharge flow and output constraint violation degree of the ith hydropower station on the dth day; C i is the constraint penalty of the i-th hydropower station; C is the constraint penalty of the cascade hydropower station, that is, the particle constraint penalty; δ Q ,δ N are the weight factors of the downstream flow and output constraint damage degree, respectively. The weight factors can be determined by a variety of evaluation methods. In this embodiment, they are 2.4×10 5 , 1.0;

[0132] S34, sort the population according to the population particle sorting rule, and proceed to step S35; specifically:

[0133] According to the particle objective function value and constraint penalty in S33, a dual-objective sorting is performed, wherein the particle constraint penalty is the first objective and the particle objective function value is the second objective. The specific method is as follows: firstly, the particles are sorted from small to large according to the particle constraint penalty; if multiple particles have equal constraint penalties, the multiple particles are sorted from large to small according to the particle objective function value; the above sorting method helps to drive the particle information to quickly approach the value that satisfies the constraint conditions, thereby improving the efficiency of searching for feasible solutions;

[0134] S35, according to the population particle water level variation rule, individual variation is performed, and the process goes to step S36; specifically:

[0135] According to the particle constraint destruction degree set CQ in S34 i , CN i Perform individual mutation operations, in which the mutation probability and decision variable mutation range are determined according to the degree of constraint destruction on each day. The greater the degree of constraint destruction, the greater the probability of occurrence and the greater the mutation range. The specific method is:

[0136] If CQ d,i =0, and CN d,i =0, then pro = 0;

[0137] If CQ d,i >0.1, and CN d,i =0, then pro = 5%,

[0138] If CQ d,i =0, and CN d,i >0.1, then pro=5%,

[0139] If CQ d,i >0.5, and CN d,i >0.5, then pro=20%,

[0140] In other cases, pro = 10%,

[0141] Among them, pro is the probability of mutation; △Z d,i is the variation range of the decision variable, expressed as the water level increase of the i-th hydropower station on the dth day;

[0142] S36, judging whether the evolution requirement is met, if the evolution requirement is met, proceeding to step S37, otherwise returning to step S33;

[0143] S37. The solution is completed. The optimal particle is determined according to the population particle sorting rule, that is, the medium- and long-term scheduling method of the hydro-photovoltaic complementary system is obtained, and the key scheduling information in the scheduling method is extracted: medium- and long-term water level process, hydropower generation plan, and external power transmission information.

[0144] The present invention is based on the above method and a medium- and long-term scheduling system for water-photovoltaic complementarity based on an improved particle swarm optimization algorithm, comprising:

[0145] The short-term module for hydro-photovoltaic complementary scheduling is based on the output of cascade hydropower stations and distributed photovoltaic power stations, starting from the overall short-term output process of the two, analyzing the relationship between the maximum capacity of the remaining power grid channels, and calculating the short-term external power transmission of the hydro-photovoltaic complementary system;

[0146] The medium- and long-term scheduling module of hydro-solar complementarity uses a random search algorithm, takes time as the stage variable, and water level as the decision variable. In the medium- and long-term calculation process, the short-term scheduling module of hydro-solar complementarity is called to calculate the short-term external power information of each stage, and further construct the medium- and long-term scheduling model of hydro-solar complementarity.

[0147] The model solving module uses an improved particle swarm optimization algorithm to solve the medium- and long-term scheduling model of water-photovoltaic complementary system and obtain the medium- and long-term scheduling method of the water-photovoltaic complementary system.

[0148] like Figure 3 As shown, through the performance comparison of various optimization algorithms, compared with other GA, PSO, IPSO, and SPSO optimization algorithms, the improved particle swarm optimization algorithm can meet the requirements of feasible solutions in 2000 iterations, and has a significant advantage in searching for feasible solutions. By continuing to iterate and optimize the medium- and long-term external power transmission objective function value of the complementary system, the medium- and long-term scheduling method of the water-light complementary system is obtained. Through the above steps S33 to S35, the constraint destruction degree of each particle in each stage during the medium- and long-term scheduling cycle can be screened, and the water level fluctuation amplitude of this stage can be increased through mutation operations, and the water level variables that meet the constraint function can be quickly selected to overcome the difficulty in finding feasible solutions and the problem of unsatisfied constraints in the solution process, thereby improving the optimization efficiency.

[0149] like Figure 4 As shown in the figure, the improved particle swarm optimization algorithm is used to solve the medium- and long-term scheduling model of water-photovoltaic complementary system, and the medium- and long-term scheduling mode of the water-photovoltaic complementary system is obtained, which is demonstrated through the medium- and long-term water level process of reservoir ②.

[0150] A device of the present invention includes a memory and a processor, wherein:

[0151] A memory for storing computer programs that can be run on the processor;

[0152] The processor is used to execute the steps of the above-mentioned medium- and long-term scheduling method of water-photovoltaic complementarity based on the improved particle swarm optimization algorithm when running the computer program.

[0153] A storage medium of the present invention stores a computer program, and when the computer program is executed by at least one processor, the steps of the above-mentioned medium- and long-term scheduling method of water-photovoltaic complementarity based on an improved particle swarm optimization algorithm are implemented.

[0154] In summary, the present invention calculates the short-term power transmission of the hydro-photovoltaic complementary system, constructs a medium- and long-term scheduling model for the hydro-photovoltaic complementary system, and combines the random search algorithm to form a set of medium- and long-term optimization scheduling methods for the hydro-photovoltaic complementary system with good adaptability. The process adopts the particle swarm algorithm in the random search algorithm as the basic framework. After initializing the water levels of the cascade reservoirs in each medium- and long-term time stage, the short-term power transmission of the hydro-photovoltaic complementary system is used to quickly obtain the objective function value of the particle. In this process, the short-term output characteristics of hydropower and photovoltaics are fully considered; by introducing the constraint penalty associated with the constraint function, the particle swarm optimization algorithm is improved, and the population sorting and individual variation mechanism are further improved to overcome the problems of difficulty in finding feasible solutions and unsatisfied constraints in the solution process, and to improve the optimization efficiency. By comparing the optimization process, compared with the existing multiple optimization algorithms, the search efficiency and optimization efficiency of feasible solutions are significantly improved, and the optimized medium- and long-term scheduling method of the hydro-photovoltaic complementary system has engineering reliability and applicability.

Claims

1. A medium- and long-term scheduling method for water-solar complementarity based on an improved particle swarm optimization algorithm, characterized in that: The following steps are involved: S1. The hydropower-photovoltaic complementary system includes cascade hydropower stations and distributed photovoltaic power stations. After photovoltaic power generation is converted into alternating current through inversion, it is bundled with hydropower and the two are connected to the grid through the same power grid channel to realize power transmission. According to the maximum capacity of the power grid channel, the short-term power transmission of the hydropower-photovoltaic complementary system is calculated. The short-term power transmission of the hydropower-photovoltaic complementary system is the sum of the hydropower generation and photovoltaic generation accommodated in the power grid channel during the short-term scheduling period of the hydropower-photovoltaic complementary system. The short-term scheduling of the hydropower-photovoltaic complementary system takes days as the scheduling period and hours as the calculation step length. S2. Based on the short-term power transmission of the hydro-solar complementary system, a medium- and long-term scheduling model for hydro-solar complementary is constructed. The model takes the maximum medium- and long-term power transmission of the hydro-solar complementary system as the objective function, and the constraints include power output balance, reservoir water balance, reservoir hydraulic connection, reservoir water level constraint, reservoir discharge flow constraint and hydropower station output constraint; S3, using the improved particle swarm optimization algorithm to solve the medium- and long-term scheduling model of water-solar complementary system, and obtain the medium- and long-term scheduling mode of water-solar complementary system; specifically including the following steps: S31, setting parameters of the improved particle swarm optimization algorithm, taking the days of the year as stage variables, taking the water level of each reservoir in the cascade reservoirs as a decision variable, and generating an initial population; S32, through the water level information of each particle at each stage, the runoff calculation is performed to obtain the short-term discharge flow Q of each particle at each stage d,i , Short-term hydropower output Short-term power transmission W d information; S33, the short-term external power W of each particle at each stage d , calculate the medium- and long-term external power transmission E, that is, the objective function value of each particle, according to the short-term downstream flow Q of each particle in each stage obtained in step S32 d,i , Short-term hydropower output Short-term power transmission W d The particle constraint penalty is calculated by: S331. Calculate the value of the particle exceeding the downstream flow constraint and output constraint in each stage. The calculation formula is: Among them, △Q d,i , are the absolute values ​​of the magnitude of the downstream flow constraint and output constraint violated by the i-th hydropower station on the d-th day, i = 1…L, L is the total number of cascade hydropower stations, d = 1…D, D is the number of days in a year; S332, calculate the degree to which the particle exceeds the downstream flow constraint and output constraint in each stage, and the calculation formula is: Among them, CQ d,i , CN d,i are the discharge flow and output constraint destruction degree of the i-th hydropower station on the dth day respectively; S333. Calculate the particle constraint penalty, the calculation formula is: CQ i =(CQ 1,i ,CQ 2,i ,…CQ d,i …,CQ D,i ) CN i =(CN 1,i ,CN 2,i ,…CN d,i …,CN D,i ) C i =δ Q ·CQ i +δ N ·CN i Among them, CQ i , CN i are the discharge flow and output constraint violation degree of the ith hydropower station on the dth day; C i is the constraint penalty of the i-th hydropower station; C is the constraint penalty of the cascade hydropower station, that is, the particle constraint penalty; δ Q , δ N are the weight factors of the downstream flow and output constraint destruction degree, which are constants; S34, sorting the population according to the population particle sorting rule; specifically: According to the particle objective function value and the constraint penalty in step S33, a dual-objective sorting is performed, wherein the particle constraint penalty is the first objective and the particle objective function value is the second objective. The specific method is as follows: firstly, the particles are sorted from small to large according to the particle constraint penalty; if multiple particles have the same constraint penalty, the multiple particles are sorted from large to small according to the particle objective function value; S35. Perform individual variation according to the population particle water level variation rule; specifically: According to the particle constraint destruction degree CQ d,i , CN d,i Perform individual mutation operations, in which the mutation probability and decision variable mutation range are determined according to the degree of constraint destruction on each day. The specific method is as follows: If CQ d,i =0, and CN d,i =0, then pro=0; If CQ d,i >α1, and CN d,i =0, then pro = β1, If CQ d,i =0, and CN d,i >α2, then pro=β2, If CQ d,i >α3, and CN d,i >α4, then pro=β3, In other cases, pro = β4, Among them, pro is the mutation probability; are the maximum and minimum water levels of the ith hydropower station on the dth day; △Z d,i is the variation range of the decision variable, expressed as the water level increase of the ith hydropower station on the dth day; △1, △2, △3, △4, α1, α2, α3, α4, β1, β2, β3, β4 are all constants; S36, judging whether the evolution requirement is met, if the evolution requirement is met, proceeding to step S37, otherwise returning to step S33; S37, the solution is completed, the optimal particle is determined according to the population particle sorting rule, and the medium- and long-term water level process, hydropower generation plan, and external power transmission information are extracted.

2. According to claim 1, a medium- and long-term scheduling method for water-photovoltaic complementarity based on an improved particle swarm optimization algorithm is characterized in that: The calculation method of the short-term power transmission of the water-solar complementary system in step S1 is: S11, calculate the photovoltaic power G required to be adjusted and the channel adjustable capacity F; Among them, the calculation formula for the photovoltaic power regulation G is: Among them, N h To provide cascade hydropower, is the output of the ith hydropower station, and L is the total number of cascade hydropower stations; is the photovoltaic output in the tth period; N T is the maximum load that the power grid channel can accommodate; T is the number of hours in a day; The calculation formula of the channel adjustable capacity F is: Among them, N max It is the maximum output of cascade hydropower. is the maximum output of the ith hydropower station; △t is the short-term calculation step length; S12. According to the relationship between the photovoltaic power G and the channel adjustable capacity F, the short-term external power W of the water-photovoltaic complementary system is calculated according to different situations; Case ①: G≤F, indicating that the power grid channels in the short-term dispatching cycle all accommodate the power generation of the hydro-photovoltaic complementary system. The calculation formula for the short-term external power transmission W of the hydro-photovoltaic complementary system is: Case ②: G>F, indicating that the power grid channel cannot fully accommodate the power generation of the hydro-photovoltaic complementary system within the short-term dispatch cycle. The calculation formula for the short-term external power transmission W of the hydro-photovoltaic complementary system is:

3. According to the method of claim 1, the method is characterized in that: In step S2, the medium- and long-term scheduling of water-solar complementarity takes one year as the scheduling cycle and one day as the calculation step length. The objective function is: Among them, W d is the short-term power transmission of the hydro-solar complementary system on the dth day in the medium- and long-term scheduling cycle, and D is the number of days in a year.

4. A medium- and long-term scheduling system for water-solar complementary power based on an improved particle swarm optimization algorithm, characterized in that: include: The short-term module for hydro-photovoltaic complementary scheduling is based on the output of cascade hydropower stations and distributed photovoltaic power stations, starting from the overall short-term output process of the two, analyzing the relationship between the maximum capacity of the remaining power grid channels, and calculating the short-term external power transmission of the hydro-photovoltaic complementary system; The medium- and long-term scheduling module of hydro-solar complementarity uses a random search algorithm, takes time as the stage variable, and water level as the decision variable. In the medium- and long-term calculation process, the short-term scheduling module of hydro-solar complementarity is called to calculate the short-term external power information of each stage, and further construct the medium- and long-term scheduling model of hydro-solar complementarity. The model solving module uses the improved particle swarm optimization algorithm to solve the medium- and long-term scheduling model of water-solar complementary system and obtain the medium- and long-term scheduling mode of water-solar complementary system; specifically: S31, setting parameters of the improved particle swarm optimization algorithm, taking the days of the year as stage variables, taking the water level of each reservoir in the cascade reservoirs as a decision variable, and generating an initial population; S32, through the water level information of each particle at each stage, the runoff calculation is performed to obtain the short-term discharge flow Q of each particle at each stage d,i , Short-term hydropower output Short-term power transmission W d information; S33, the short-term external power W of each particle at each stage d , calculate the medium- and long-term external power transmission E, that is, the objective function value of each particle, according to the short-term downstream flow Q of each particle in each stage obtained in step S32 d,i , Short-term hydropower output Short-term power transmission W d The particle constraint penalty is calculated by: S331. Calculate the value of the particle exceeding the downstream flow constraint and output constraint in each stage. The calculation formula is: Among them, △Q d,i , are the absolute values ​​of the magnitude of the downstream flow constraint and output constraint violated by the i-th hydropower station on the d-th day, i = 1…L, L is the total number of cascade hydropower stations, d = 1…D, D is the number of days in a year; S332, calculate the degree to which the particle exceeds the downstream flow constraint and output constraint in each stage, and the calculation formula is: Among them, CQ d,i , CN d,i are the discharge flow and output constraint destruction degree of the i-th hydropower station on the dth day respectively; S333. Calculate the particle constraint penalty, the calculation formula is: CQ i =(CQ 1,i ,CQ 2,i ,…CQ d,i …,CQ D,i ) CN i =(CN 1,i ,CN 2,i ,…CN d,i …,CN D,i ) C i =δ Q ·CQ i +δ N ·CN i Among them, CQ i , CN i are the discharge flow and output constraint violation degree of the ith hydropower station on the dth day; C i is the constraint penalty of the i-th hydropower station; C is the constraint penalty of the cascade hydropower station, that is, the particle constraint penalty; δ Q , δ N are the weight factors of the downstream flow and output constraint destruction degree, which are constants; S34, sorting the population according to the population particle sorting rule; specifically: According to the particle objective function value and the constraint penalty in step S33, a dual-objective sorting is performed, wherein the particle constraint penalty is the first objective and the particle objective function value is the second objective. The specific method is as follows: firstly, the particles are sorted from small to large according to the particle constraint penalty; if multiple particles have the same constraint penalty, the multiple particles are sorted from large to small according to the particle objective function value; S35. Perform individual variation according to the population particle water level variation rule; specifically: According to the particle constraint destruction degree CQ d,i , CN d,i Perform individual mutation operations, in which the mutation probability and decision variable mutation range are determined according to the degree of constraint destruction on each day. The specific method is as follows: If CQ d,i =0, and CN d,i =0, then pro=0; If CQ d,i >α1, and CN d,i =0, then pro = β1, If CQ d,i =0, and CN d,i >α2, then pro=β2, If CQ d,i >α3, and CN d,i >α4, then pro=β3, In other cases, pro = β4, Among them, pro is the mutation probability; are the maximum and minimum water levels of the ith hydropower station on the dth day; △Z d,i is the variation range of the decision variable, expressed as the water level increase of the ith hydropower station on the dth day; △1, △2, △3, △4, α1, α2, α3, α4, β1, β2, β3, β4 are all constants; S36, judging whether the evolution requirement is met, if the evolution requirement is met, proceeding to step S37, otherwise returning to step S33; S37, the solution is completed, the optimal particle is determined according to the population particle sorting rule, and the medium- and long-term water level process, hydropower generation plan, and external power transmission information are extracted.

5. An electronic device, characterized in that: comprising a memory and a processor, wherein: A memory for storing computer programs that can be run on the processor; A processor is used to execute the steps of a medium- and long-term scheduling method for water-photovoltaic complementarity based on an improved particle swarm optimization algorithm as described in any one of claims 1 to 3 when running the computer program.

6. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by at least one processor, implements the steps of a medium- and long-term scheduling method for water-photovoltaic complementarity based on an improved particle swarm optimization algorithm as described in any one of claims 1 to 3.

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