Hybrid pumped storage power station intraday optimization scheduling method, system and equipment based on cascade collaboration and medium

Abstract

The invention belongs to the technical field of power systems, and relates to a cascade collaboration-based intraday optimal scheduling method, system and equipment for a hybrid pumped storage power station, and a medium. The method comprises the following steps: collecting input data; calculating cascade output of each power station in a peak regulation period and cascade power station output in a valley filling period according to the collected data; calculating the sum of the output of the cascade power stations in the peak shaving period and the sum of the output of the cascade power stations in the valley filling period; taking the maximum sum of the output of the cascade power stations in the peak regulation period and the minimum sum of the output of the cascade power stations in the valley filling period as targets to construct an optimization target function; setting constraint conditions; according to the constructed optimization objective function and a set constraint condition, with benefit optimization as a target, constructing a cascade power station intra-day collaborative peak regulation optimization model; input data are obtained, calculation is carried out through the cascade power station intra-day collaborative peak regulation optimization model, and a cascade power station intra-day collaborative peak regulation optimization strategy is obtained. The problem of intra-day scheduling after hybrid pumped storage is built is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power systems, and in particular relates to a method, system, equipment and medium for optimizing the daily scheduling of a hybrid pumped-storage power station based on cascade coordination. Background Art

[0002] As the most important component of the new energy system, the new power system has accelerated the construction of energy storage and optimized the system regulation capacity, which has become an important part of building the new power system. Pumped storage power stations are currently the most mature, widely used, and safest large-scale energy storage technology. They have multiple functions such as peak regulation, valley filling, energy storage, frequency regulation, phase regulation, emergency accidents, and black start. They are not only an important regulating power source for the new power system, but also an important new energy basic supporting facility for the new energy system.

[0003] As a new type of pumped storage technology, hybrid pumped storage relies on conventional hydropower stations to build pumped storage power stations, with a shorter construction period and more diversified application scenarios. However, hybrid pumped storage power stations have close hydraulic connections with conventional hydropower stations. In the daily dispatching process, hybrid pumped storage power stations coexist with conventional hydropower stations. Hybrid pumped storage power stations are generally daily regulation power stations. The short-term dispatching within the day restricts the benefits of conventional hydropower stations and hybrid pumped storage power stations. At the same time, the characteristics of new energy generation also affect the dispatching method of pumped storage. Therefore, how to give full play to the functions of hybrid pumped storage power stations while meeting the operation needs of the power system and enabling conventional hydropower stations to obtain the best benefits is a difficulty that needs to be faced in the daily dispatching of large-scale hybrid pumped storage power stations after they are built. Summary of the invention

[0004] The purpose of the present invention is to provide a method, system, equipment and medium for daily optimal scheduling of a hybrid pumped-storage power station based on cascade coordination, so as to solve the problem of daily scheduling after the construction of the hybrid pumped-storage power station, and provide a theoretical basis for the actual daily optimal operation of a large-scale cascade coordinated hybrid pumped-storage power station after its construction.

[0005] The present invention is achieved through the following technical solutions:

[0006] The present invention discloses a method for optimizing the daily dispatching of a hybrid pumped storage power station based on cascade coordination, comprising the following steps:

[0007] Step 1: Collect input data, including storage capacity curves of each power station, water level-flow relationship curves, inflow runoff, expected output, and dispatching operation mode;

[0008] Step 2: Calculate the cascade output of each power station during the peak load period and the cascade power station output during the valley load period according to the data collected in step 1;

[0009] Step 3, calculating the sum of the outputs of the cascade power stations during the peak-shaving period according to the cascade outputs of each power station during the peak-shaving period;

[0010] The sum of the outputs of the cascade power stations during the valley filling period is calculated based on the outputs of the cascade power stations during the valley filling period;

[0011] Step 4: Taking the sum of the output of the cascade power stations during the peak load period as the maximum and the sum of the output of the cascade power stations during the valley load period as the minimum as the goal, construct an optimization objective function;

[0012] Step 5: Set constraints;

[0013] Step 6: Based on the optimization objective function constructed in step 4 and the constraints set in step 5, a daily coordinated peak-shaving optimization model for cascade power stations is constructed with the goal of achieving optimal benefits;

[0014] Step 7: Obtain input data, calculate through the cascade power station intra-day collaborative peak-shaving optimization model, and obtain the cascade power station intra-day collaborative peak-shaving optimization strategy.

[0015] Furthermore, in step 3, the sum of the outputs of the cascade power plants during the peak-shaving period is equivalent to the minimum difference between the expected output and the decision output of the cascade power plants during the peak-shaving period;

[0016] The sum of the outputs of the cascade power stations during the valley-filling period is calculated based on the outputs of the cascade power stations during the valley-filling period. Specifically, the outputs of all cascade power stations during the valley-filling period are added together while the pumping input of the energy storage power station is deducted.

[0017] Furthermore, in step 4, the optimization objective function is as follows:

[0018]

[0019] In the formula, T' represents the total peak load period, T'' represents the total valley load period, t' represents a certain moment in the peak load period, and t" represents a certain moment in the valley load period.

[0020] P SY,max,t′ represents the expected output of the upstream hydropower station, P XY,max,t′ It indicates the expected output of the downstream hydropower station;

[0021] P SY,t′ represents the output of the upstream hydropower station during the peak load period, P XY,t′ It indicates the output of downstream hydropower station during peak load period;

[0022] P SY,t″ represents the output of the upstream hydropower station during the valley filling period, P XY,t″ It indicates the output of downstream hydropower station during valley filling period;

[0023] P pump,max,t′ Indicates the expected output of the hybrid storage power station; Indicates the output of the hybrid power station during the peak load regulation phase; Indicates the input of the hybrid storage power station in the valley filling stage.

[0024] Furthermore, the constraints include: hydropower station output constraints, hybrid storage power station output constraints, grid demand constraints, water balance constraints, flow balance constraints, reservoir capacity constraints, water level constraints, downstream flow constraints, head constraints, and water abandonment constraints.

[0025] Furthermore, the output constraint of the hydropower station is:

[0026] P SY,min ≤P SY,t ≤P SY,max ;

[0027] P XY,min ≤P XY,t ≤P XY,max ;

[0028] Where: P SY,t is the output constraint of the upstream hydropower station at time t, P SY,min is the minimum output constraint of the upstream hydropower station, P SY,max It is the maximum output constraint of the upstream hydropower station;

[0029] P XY,t is the output constraint of the downstream hydropower station at time t, P XY,min is the minimum output constraint of the downstream hydropower station, P XY,max It is the maximum output constraint of the downstream hydropower station;

[0030] The output constraint of hybrid pumped storage power station is:

[0031]

[0032] Where: is the pumping power of the hybrid pumped storage power station in period t, is the power generation of the hybrid pumped storage power station in period t; P pump,min is the minimum expected output of the hybrid pumped storage power station, P pumpe,max The maximum expected output of a hybrid pumped storage power station.

[0033] Furthermore, the grid demand constraint is:

[0034]

[0035] Where: P fhxq,t is the power grid demand output constraint at time t, P SY,t is the output constraint of the upstream hydropower station at time t, is the power generation of the hybrid pumped storage power station in period t;

[0036] The water balance constraint is:

[0037]

[0038] Where: V SY,t+1 、V SY,t are the storage capacities of the upstream hydropower station at time periods t+1 and t respectively; QI SY,t , Q SY,t are the inflow and outflow of the upstream hydropower station in period t respectively; V XY,t+1 、V XY,t are the storage capacities of the downstream hydropower station at time periods t+1 and t respectively; QI XY,t , Q XY,t are the inflow and outflow of the downstream hydropower station in period t respectively; is the pumping flow of the hybrid pumped storage power station in period t; is the power generation flow of the hybrid pumped storage power station in period t;

[0039] The flow balance constraint is:

[0040] QI XY,t =Qo SY,t +q t ;

[0041] In the formula, QI XY,t is the inflow of the downstream hydropower station; Qo SY,t The outflow of the upstream hydropower station; q t It is the inflow flow between the upstream and downstream power stations.

[0042] Furthermore, the storage capacity constraint is:

[0043] V SY,min ≤V SY,t ≤V SY,max ;

[0044] V XY,min ≤V XY,t ≤V XY,max ;

[0045] Where V SY,t is the reservoir capacity of the upstream hydropower station at time t, V XY,t is the reservoir capacity of the downstream hydropower station at time t; V SY,min is the minimum storage capacity constraint of the upstream hydropower station, V SY,max is the maximum storage capacity constraint of the upstream hydropower station; V XY,min is the minimum storage capacity constraint of the downstream hydropower station, V XY,max is the maximum storage capacity constraint of the downstream hydropower station;

[0046] The water level constraint is;

[0047] Z SY,min≤Z SY,t ≤Z SY,max ;

[0048] Z XY,min ≤Z XY,t ≤Z XY,max ;

[0049] In the formula, Z SY,t is the water level of the upstream hydropower station at time t, Z XY,t is the water level of the downstream hydropower station at time t; Z SY,min is the minimum water level constraint of the upstream hydropower station, Z SY,max is the maximum water level constraint of the upstream hydropower station; Z XY,min is the minimum water level constraint of the downstream hydropower station, Z XY,max It is the maximum water level constraint for the downstream hydropower station;

[0050] The downstream flow constraint is:

[0051] Q hi,min ≤Q hi,t ≤Q hi,max ;

[0052] In the formula, Q hi,t is the flow of hi power station at time t, Q hi,min , Q hi,max are the minimum and maximum flow constraints of hi power station;

[0053] The head constraint is:

[0054] H hi,min ≤H hi,t ≤Hhi ,max ;

[0055] In the formula, H hi,t H is the generating head of hi power station during period t; hi,min , H hi,max The minimum and maximum generating head constraints of hi power station;

[0056] The water abandonment constraint is:

[0057]

[0058] In the formula, Q hi,t is the discharge flow of hi power station in time period t, is the power generation flow of the power station, It is the abandoned water flow of the power station.

[0059] The present invention also discloses a hybrid pumped storage power station intraday optimization dispatching system based on cascade coordination, comprising:

[0060] The data acquisition module is used to collect the storage capacity curve, water level-flow relationship curve, inflow runoff, expected output, and dispatching operation mode of each power station;

[0061] The output calculation module is used to calculate the cascade output of each power station during the peak-shaving period and the cascade power station output during the valley-filling period according to the data collected by the data acquisition module;

[0062] The output sum calculation module is used to calculate the sum of the outputs of the cascade power stations during the peak-shaving period according to the cascade outputs of each power station during the peak-shaving period;

[0063] The sum of the outputs of the cascade power stations during the valley filling period is calculated based on the outputs of the cascade power stations during the valley filling period;

[0064] A model building module is used to build an optimization objective function with the goal of maximizing the sum of the output of cascade power stations during the peak-shaving period and minimizing the sum of the output of cascade power stations during the valley-filling period;

[0065] Set constraints;

[0066] According to the constructed optimization objective function and set constraints, with the goal of optimal efficiency, a daily coordinated peak-shaving optimization model for cascade power stations is constructed;

[0067] The result solving module is used to obtain input data, input the intra-day coordinated peak-shaving optimization model of the cascade power stations, and obtain the intra-day coordinated peak-shaving optimization strategy of the cascade power stations.

[0068] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intraday optimization scheduling method of a hybrid pumped-storage power station based on cascade coordination are implemented.

[0069] The present invention also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the intra-day optimization scheduling method of the hybrid pumped-storage power station based on cascade coordination are implemented.

[0070] Compared with the prior art, the present invention has the following beneficial technical effects:

[0071] The invention discloses a hybrid pumped storage power station intraday optimization scheduling method based on cascade coordination. By collecting data such as the storage capacity curve and the water level flow relationship curve of each power station, the cascade output in different time periods is accurately calculated, so as to formulate a more reasonable power generation plan, so that the power station can give full play to its power generation capacity during the peak-shaving period, reasonably adjust the output during the valley-filling period, and improve the overall energy utilization efficiency; considering factors such as the reservoir runoff, it can better coordinate the water use between the power stations, realize the optimal allocation of water resources, avoid the waste of water resources, improve the utilization efficiency of water resources, and ensure the stable operation and efficient power generation of the power station; with the maximum sum of the output of the cascade power station during the peak-shaving period and the minimum sum of the output of the cascade power station during the valley-filling period as the goal, the optimization function is constructed, so that the power station can provide more power support during the peak load of the power grid, reduce the output during the low load, effectively balance the load of the power grid, alleviate the peak-valley difference problem of the power grid, and enhance the stability and reliability of the power grid. Through the intraday collaborative peak-shaving optimization model, the output of the power station can be quickly adjusted according to the real-time load demand of the power grid, the response speed of the power station to the load change can be improved, the dynamic demand of the power grid can be better met, and the safe and stable operation of the power system can be guaranteed. The present invention fully considers the hydraulic connection between the hybrid pumped-storage power station and the conventional hydropower station, and combines the new energy operation demand under the new power system, providing a theoretical basis for the daily operation of the cascade coordinated hybrid pumped-storage power station after its completion. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of a method for optimizing the daily dispatch of a hybrid pumped-storage power station based on cascade coordination according to the present invention;

[0073] Figure 2 It is a block diagram of a hybrid pumped storage power station intraday optimization dispatching system based on cascade coordination of the present invention;

[0074] Figure 3 The water level variation process output by the daily coordinated peak-shaving optimization model of cascade power stations is the peak-shaving optimization strategy. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present invention more clear, the following is further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, not all embodiments.

[0076] The detailed description of the embodiments of the present invention provided in the following drawings is not intended to limit the scope of the invention claimed for protection, but merely represents a selected embodiment of the present invention. Based on the drawings and embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0077] The features and performances of the present invention are further described in detail below in conjunction with the embodiments.

[0078] Example 1

[0079] like Figure 1 As shown, the present invention discloses a method for optimizing the daily scheduling of a hybrid pumped storage power station based on cascade coordination, comprising the following steps:

[0080] Step 1: Collect input data, including storage capacity curves of each power station, water level-flow relationship curves, inflow runoff, expected output, and dispatching operation mode;

[0081] Step 2: Calculate the cascade output of each power station during the peak load period and the cascade power station output during the valley load period according to the data collected in step 1;

[0082] Step 3, calculating the sum of the outputs of the cascade power stations during the peak-shaving period according to the cascade outputs of each power station during the peak-shaving period;

[0083] The sum of the outputs of the cascade power stations during the valley filling period is calculated based on the outputs of the cascade power stations during the valley filling period;

[0084] Step 4: Taking the sum of the output of the cascade power stations during the peak load period as the maximum and the sum of the output of the cascade power stations during the valley load period as the minimum as the goal, construct an optimization objective function;

[0085] Step 5: Set constraints;

[0086] Step 6: Based on the optimization objective function constructed in step 4 and the constraints set in step 5, a daily coordinated peak-shaving optimization model for cascade power stations is constructed with the goal of achieving optimal benefits;

[0087] Step 7: Obtain input data, calculate through the cascade power station intra-day collaborative peak-shaving optimization model, and obtain the cascade power station intra-day collaborative peak-shaving optimization strategy.

[0088] By collecting data such as the reservoir capacity curve and water level-flow relationship curve of each power station, the cascade output in different time periods can be accurately calculated, so as to formulate a more reasonable power generation plan, so that the power station can fully utilize its power generation capacity during the peak period, reasonably adjust the output during the valley period, and improve the overall energy utilization efficiency.

[0089] Taking into account factors such as reservoir runoff can better coordinate water use among power stations, achieve optimal allocation of water resources, avoid waste of water resources, improve water resource utilization efficiency, and ensure stable operation and efficient power generation of power stations.

[0090] An optimization function is constructed with the goal of maximizing the sum of the output of cascade power stations during the peak load period and minimizing the sum of the output of cascade power stations during the valley load period, so that the power stations can provide more power support during the peak load of the power grid and reduce output during the low load period, effectively balancing the load of the power grid, alleviating the peak-valley difference problem of the power grid, and enhancing the stability and reliability of the power grid.

[0091] Through the intraday collaborative peak-shaving optimization model, the output of the power station can be quickly adjusted according to the real-time load demand of the power grid, the response speed of the power station to load changes can be improved, the dynamic needs of the power grid can be better met, and the safe and stable operation of the power system can be ensured.

[0092] Under the premise of meeting the needs of the power grid, the power station can be optimized and dispatched, and the output and operation mode of each power station can be reasonably arranged to reduce the operating costs of the power station, such as reducing unnecessary start and stop times and reducing equipment wear, thereby improving the economic benefits of the power station. By improving energy utilization efficiency and enhancing the stability of the power grid, the power station can better participate in power market transactions and obtain more power generation income, which also helps to improve the competitiveness of the power station in the power market.

[0093] Hybrid pumped-storage power stations can be coordinated with new energy power generation such as wind power and photovoltaics. When there is excess power generation from new energy, the excess electricity can be converted into water potential energy and stored. When there is insufficient power generation from new energy, the electricity supply can be supplemented by releasing water to generate electricity, thereby improving the absorption capacity of new energy, promoting the large-scale development of new energy, and promoting the optimization and upgrading of the energy structure.

[0094] This dispatching method can enhance the flexibility and regulatory capacity of the power system, enabling it to better adapt to the intermittent nature and uncertainty of renewable energy generation, provide strong support for the access and consumption of renewable energy, and reduce the occurrence of wind and solar power abandonment.

[0095] Taking into account factors such as the expected output and dispatching operation mode of each power station, the complementary advantages and coordinated operation of different power stations can be achieved, the overall benefits of the cascade power station group can be brought into play, and the comprehensive performance and operation efficiency of the power station group can be improved.

[0096] By constructing a daily coordinated peak-shaving optimization model for cascade power stations, the load can be reasonably distributed according to the actual situation of each power station, avoiding excessive load on some power stations that affects their life and reliability. At the same time, it also ensures that the entire power station group can operate efficiently and stably, extending the service life of power station equipment and reducing equipment maintenance and update costs.

[0097] Example 3

[0098] In step 4, the optimization objective function is as follows:

[0099]

[0100] In the formula, T' represents the total peak load period, T'' represents the total valley load period, t' represents a certain moment in the peak load period, and t" represents a certain moment in the valley load period.

[0101] P SY,max,t′ represents the expected output of the upstream hydropower station, P XY,max,t′ It indicates the expected output of the downstream hydropower station;

[0102] P SY,t′ represents the output of the upstream hydropower station during the peak load period, P XY,t′ It indicates the output of downstream hydropower station during peak load period;

[0103] P SY,t″ represents the output of the upstream hydropower station during the valley filling period, P XY,t″ It indicates the output of downstream hydropower station during valley filling period;

[0104] P pump,max,t′ Indicates the expected output of the hybrid storage power station; Indicates the output of the hybrid power station during the peak load regulation phase; Indicates the input of the hybrid storage power station in the valley filling stage.

[0105] Example 4

[0106] In step 5, the constraints include: hydropower station output constraints, hybrid storage power station output constraints, grid demand constraints, water balance constraints, flow balance constraints, reservoir capacity constraints, water level constraints, downstream flow constraints, head constraints, water abandonment constraints and other constraints. The various constraints are as follows:

[0107] The output constraints of the hydropower station are:

[0108] P SY,min ≤P SY,t ≤P SY,max

[0109] P XY,mib ≤P XY,t ≤P XY,max

[0110] Where: P SY,t is the output constraint of the upstream hydropower station at time t, P SY,min is the minimum output constraint of the upstream hydropower station, P SY,max It is the maximum output constraint of the upstream hydropower station;

[0111] P XY,t is the output constraint of the downstream hydropower station at time t, P XY,min is the minimum output constraint of the downstream hydropower station, P XY,max It is the maximum output constraint of the downstream hydropower station.

[0112] The output constraint of hybrid pumped storage power station is:

[0113]

[0114] Where: is the pumping power of the hybrid pumped storage power station in period t, is the power generation of the hybrid pumped storage power station in period t; P pump,min is the minimum expected output of the hybrid pumped storage power station, P pumpe,max The maximum expected output of a hybrid pumped storage power station.

[0115] The grid demand constraint is:

[0116]

[0117] Where: P fhxq,t is the power grid demand output constraint at time t, P SY,t is the output constraint of the upstream hydropower station at time t, is the power generation capacity of the hybrid pumped storage power station in period t.

[0118] The water balance constraint is:

[0119]

[0120] Where: V SY,t+1 、V SY,t are the storage capacities of the upstream hydropower station at time periods t+1 and t respectively; QI SY,t , Q SY,t are the inflow and outflow of the upstream hydropower station in period t respectively; V XY,t+1 、V XY,t are the storage capacities of the downstream hydropower station at time periods t+1 and t respectively; QI XY,t , Q XY,t are the inflow and outflow of the downstream hydropower station in period t respectively; is the pumping flow of the hybrid pumped storage power station in period t; is the power generation flow of the hybrid pumped storage power station in period t.

[0121] The flow balance constraint is:

[0122] QI XY,t =Qo SY,t +q t

[0123] In the formula, QI XY,t is the inflow of the downstream hydropower station; Qo SY,t The outflow of the upstream hydropower station; q t It is the inflow flow between the upstream and downstream power stations.

[0124] The storage capacity constraint is:

[0125] V SY,min ≤V SY,t ≤V SY,max ;

[0126] V XY,min ≤V XY,t ≤V XY,max ;

[0127] Where V SY,t is the reservoir capacity of the upstream hydropower station at time t, V XY,t is the reservoir capacity of the downstream hydropower station at time t; V SY,min is the minimum storage capacity constraint of the upstream hydropower station, V SY,max is the maximum storage capacity constraint of the upstream hydropower station; V XY,min is the minimum storage capacity constraint of the downstream hydropower station, V XY,max is the maximum storage capacity constraint of the downstream hydropower station.

[0128] The water level constraint is;

[0129] Z SY,min ≤Z SY,t ≤Z SY,max ;

[0130] Z XY,min ≤Z XY,t ≤Z XY,max ;

[0131] In the formula, Z SY,t is the water level of the upstream hydropower station at time t, Z XY,t is the water level of the downstream hydropower station at time t; Z SY,min is the minimum water level constraint of the upstream hydropower station, Z SY,max is the maximum water level constraint of the upstream hydropower station; Z XY,min is the minimum water level constraint of the downstream hydropower station, Z XY,max It is the maximum water level constraint of the downstream hydropower station.

[0132] The downstream flow constraint is:

[0133] Q hi,min ≤Q hi,t ≤Q hi,max

[0134] In the formula, Q hi,t is the flow of hi power station at time t, Q hi,min , Q hi,max are the minimum and maximum flow constraints of hi power station. hi power station represents a certain power station.

[0135] The head constraint is:

[0136] H hi,min ≤Hhi,t ≤Hhi ,max In the formula, H hi,t H is the generating head of hi power station during period t; hi,min , H hi,max are the minimum and maximum generating head constraints of hi power station.

[0137] The water abandonment constraint is:

[0138]

[0139] In the formula, Q hi,t is the discharge flow of hi power station in time period t, is the power generation flow of the power station, It is the abandoned water flow of the power station.

[0140] Example 5

[0141] In step 5, an optimization calculation is performed according to the optimization objective function and constraint conditions to obtain an optimization plan for coordinated peak load regulation within the day of the cascade power stations. The specific plan is:

[0142] Input data: storage capacity curve of each power station, water level-flow relationship curve, inflow runoff, expected output, and dispatching operation mode;

[0143] like Figure 3 As shown in the figure, the output data of the cascade power station daily coordinated peak-shaving optimization model are: the change process of the water level and flow of each reservoir during the day. Peak-shaving mainly refers to power generation, and power generation corresponds to the change of water level in the reservoir. With the change of water level, there is also the power generation process, and with the power generation process, there is naturally a peak-shaving strategy.

[0144] Example 6

[0145] like Figure 2 As shown, the present invention discloses a hybrid pumped storage power station intraday optimization scheduling system based on cascade coordination, comprising:

[0146] The data acquisition module is used to collect the storage capacity curve, water level-flow relationship curve, inflow runoff, expected output, and dispatching operation mode of each power station;

[0147] The output calculation module is used to calculate the cascade output of each power station during the peak-shaving period and the cascade power station output during the valley-filling period according to the data collected by the data acquisition module;

[0148] The output sum calculation module is used to calculate the sum of the outputs of the cascade power stations during the peak-shaving period according to the cascade outputs of each power station during the peak-shaving period;

[0149] The sum of the outputs of the cascade power stations during the valley filling period is calculated based on the outputs of the cascade power stations during the valley filling period;

[0150] A model building module is used to build an optimization objective function with the goal of maximizing the sum of the output of cascade power stations during the peak-shaving period and minimizing the sum of the output of cascade power stations during the valley-filling period;

[0151] Set constraints;

[0152] According to the constructed optimization objective function and set constraints, with the goal of optimal efficiency, a daily coordinated peak-shaving optimization model for cascade power stations is constructed;

[0153] The result solving module is used to obtain input data, input the intra-day coordinated peak-shaving optimization model of the cascade power stations, and obtain the intra-day coordinated peak-shaving optimization strategy of the cascade power stations.

[0154] Example 7

[0155] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intraday optimization scheduling method of the hybrid pumped storage power station based on cascade collaboration are implemented, wherein the memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, the network interface, and the memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0156] Example 8

[0157] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intraday optimization scheduling method of the hybrid pumped storage power station based on cascade coordination are implemented. Specifically, the computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include a random access memory and / or a cache memory, etc. The non-volatile memory may include a read-only memory, a hard disk, a flash memory, an optical disk, a magnetic disk, etc.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the daily dispatch of a hybrid pumped storage power station based on cascade coordination, characterized in that: The following steps are involved: Step 1: Collect input data, including the storage capacity curve of each power station, the water level-flow relationship curve, the inflow runoff, the expected output and the dispatching operation mode; Step 2: Calculate the cascade output of each power station during the peak load period and the cascade power station output during the valley load period according to the data collected in step 1; Step 3, calculating the sum of the outputs of the cascade power stations during the peak-shaving period according to the cascade outputs of each power station during the peak-shaving period; The sum of the outputs of the cascade power stations during the valley filling period is calculated based on the outputs of the cascade power stations during the valley filling period; Step 4: Taking the sum of the output of the cascade power stations during the peak load period as the maximum and the sum of the output of the cascade power stations during the valley load period as the minimum as the goal, construct an optimization objective function; Step 5: Set constraints; Step 6: Based on the optimization objective function constructed in step 4 and the constraints set in step 5, a daily coordinated peak-shaving optimization model for cascade power stations is constructed with the goal of achieving optimal benefits; Step 7: Obtain input data, calculate through the cascade power station intra-day collaborative peak-shaving optimization model, and obtain the cascade power station intra-day collaborative peak-shaving optimization strategy.

2. The intraday optimization scheduling method of a hybrid pumped storage power station based on cascade coordination according to claim 1 is characterized in that: In step 3, the sum of the outputs of the cascade power plants during the peak load period is equivalent to the minimum difference between the expected output and the decision output of the cascade power plants during the peak load period; The sum of the outputs of the cascade power stations during the valley-filling period is calculated based on the outputs of the cascade power stations during the valley-filling period. Specifically, the outputs of all cascade power stations during the valley-filling period are added together while the pumping input of the energy storage power station is deducted.

3. The intraday optimization scheduling method of a hybrid pumped storage power station based on cascade coordination according to claim 1 is characterized in that: In step 4, the optimization objective function is as follows: In the formula, T' represents the total peak load period, T'' represents the total valley load period, t' represents a certain moment in the peak load period, and t" represents a certain moment in the valley load period. P SY,max,t′ represents the expected output of the upstream hydropower station, P XY,max,t′ It indicates the expected output of the downstream hydropower station; P SY,t′ represents the output of the upstream hydropower station during the peak load period, P XY,t′ It indicates the output of downstream hydropower station during peak load period; P SY,t″ represents the output of the upstream hydropower station during the valley filling period, P XY,t″ It indicates the output of downstream hydropower station during valley filling period; P pump,max,t′ Indicates the expected output of the hybrid storage power station; Indicates the output of the hybrid power station during the peak load regulation phase; Indicates the input of the hybrid storage power station in the valley filling stage.

4. The intraday optimization scheduling method of a hybrid pumped storage power station based on cascade coordination according to claim 1 is characterized in that: The constraints include: hydropower station output constraints, mixed storage power station output constraints, grid demand constraints, water balance constraints, flow balance constraints, reservoir capacity constraints, water level constraints, downstream flow constraints, head constraints, and water abandonment constraints.

5. The method for optimizing the daily dispatch of a hybrid pumped storage power station based on cascade coordination according to claim 4 is characterized in that: The output constraints of the hydropower station are: P SY,min ≤P SY,t ≤P SY,max ; P XY,min ≤P XY,t ≤P XY,max ; Where: P SY,t is the output constraint of the upstream hydropower station at time t, P SY,nin is the minimum output constraint of the upstream hydropower station, P SY,max It is the maximum output constraint of the upstream hydropower station; P XY,t is the output constraint of the downstream hydropower station at time t, P XY,min is the minimum output constraint of the downstream hydropower station, P XY,max It is the maximum output constraint of the downstream hydropower station; The output constraint of hybrid pumped storage power station is: Where: is the pumping power of the hybrid pumped storage power station in period t, is the power generation of the hybrid pumped storage power station in period t; P pump,min is the minimum expected output of the hybrid pumped storage power station, P pumpe,max The maximum expected output of a hybrid pumped storage power station.

6. The method for optimizing the daily dispatch of a hybrid pumped storage power station based on cascade coordination according to claim 4, characterized in that: The grid demand constraint is: Where: P fhxq,t is the power grid demand output constraint at time t, P SY,t is the output constraint of the upstream hydropower station at time t, is the power generation of the hybrid pumped storage power station in period t; The water balance constraint is: Where: V SY,t+1 、V SY,t are the storage capacities of the upstream hydropower station at time periods t+1 and t respectively; QI SY,t , Q SY,t are the inflow and outflow of the upstream hydropower station in period t respectively; V XY,t+1 、V XY,t are the storage capacities of the downstream hydropower station at time periods t+1 and t respectively; QI XY,t , Q XY,t are the inflow and outflow of the downstream hydropower station in period t respectively; is the pumping flow of the hybrid pumped storage power station in period t; is the power generation flow of the hybrid pumped storage power station in period t; The flow balance constraint is: QI XY,t =Qo SY,t +q t ; In the formula, QI XY,t is the inflow of the downstream hydropower station; Qo SY,t The outflow of the upstream hydropower station; q t It is the inflow flow between the upstream and downstream power stations.

7. The method for optimizing the daily dispatch of a hybrid pumped storage power station based on cascade coordination according to claim 4 is characterized in that: The storage capacity constraint is: In SY,min ≤V SY,t ≤V SY,max ; In XY,min ≤V XY,t ≤V XY,max ; Where V SY,t is the reservoir capacity of the upstream hydropower station at time t, V XY,t is the reservoir capacity of the downstream hydropower station at time t; V SY,min is the minimum storage capacity constraint of the upstream hydropower station, V SY,max is the maximum storage capacity constraint of the upstream hydropower station; V XY,min is the minimum storage capacity constraint of the downstream hydropower station, V XY,max is the maximum storage capacity constraint of the downstream hydropower station; The water level constraint is; WITH SY,min ≤Z SY,t ≤Z SY,maX ; WITH XY,min ≤Z XY,t ≤Z XY,max ; In the formula, Z SY,t is the water level of the upstream hydropower station at time t, Z XY,t is the water level of the downstream hydropower station at time t; Z SY,min is the minimum water level constraint of the upstream hydropower station, Z SY,max is the maximum water level constraint of the upstream hydropower station; Z XY,min is the minimum water level constraint of the downstream hydropower station, Z XY,max It is the maximum water level constraint for the downstream hydropower station; The downstream flow constraint is: Q hi,min ≤Q hi,t ≤Q ji,max ; In the formula, Q hi,t is the flow of hi power station at time t, Q hi,min , Q hi,max are the minimum and maximum flow constraints of hi power station; The head constraint is: H hi,min ≤H hi,t ≤Hhi, max ; In the formula, H hi,t H is the generating head of hi power station during period t; hi,min , H hi,max The minimum and maximum generating head constraints of hi power station; The water abandonment constraint is: In the formula, Q hi,t is the discharge flow of hi power station in time period t, is the power generation flow of the power station, It is the abandoned water flow of the power station.

8. A hybrid pumped storage power station intraday optimization dispatching system based on cascade coordination, characterized in that: include: The data acquisition module is used to collect the storage capacity curve, water level-flow relationship curve, inflow runoff, expected output, and dispatching operation mode of each power station; The output calculation module is used to calculate the cascade output of each power station during the peak-shaving period and the cascade power station output during the valley-filling period according to the data collected by the data acquisition module; The output sum calculation module is used to calculate the sum of the outputs of the cascade power stations during the peak-shaving period according to the cascade outputs of each power station during the peak-shaving period; The sum of the outputs of the cascade power stations during the valley filling period is calculated based on the outputs of the cascade power stations during the valley filling period; A model building module is used to build an optimization objective function with the goal of maximizing the sum of the output of cascade power stations during the peak-shaving period and minimizing the sum of the output of cascade power stations during the valley-filling period; Set constraints; According to the constructed optimization objective function and set constraints, with the goal of optimal efficiency, a daily coordinated peak-shaving optimization model for cascade power stations is constructed; The result solving module is used to obtain input data, input the intra-day coordinated peak-shaving optimization model of the cascade power stations, and obtain the intra-day coordinated peak-shaving optimization strategy of the cascade power stations.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the intra-day optimal scheduling method of a hybrid pumped-storage power station based on cascade coordination as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the intra-day optimal scheduling method of a hybrid pumped-storage power station based on cascade coordination as described in any one of claims 1 to 7 are implemented.

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