Cascade hydropower station inter-station load optimal distribution method

Abstract

The invention discloses a cascade hydropower station inter-station load optimal distribution method, which belongs to the technical field of hydropower dispatching, and comprises the following steps: solving a cascade hydropower station inter-station load distribution model which takes the maximum energy storage increment of a cascade hydropower station as a target and meets multiple constraints by utilizing an A * algorithm; next moment state nodes of the cascade hydropower station are generated according to unit output constraints and added into a candidate set, adjacent state nodes meeting all constraints are screened out, heuristic function values of the adjacent state nodes are calculated, and the adjacent state node with the minimum heuristic function value is selected as a new current moment state node of the cascade hydropower station for repeated iteration; and obtaining a path from the initial state node to the latest state node of the cascade hydropower station at the current moment through a reverse traversal method until an iteration termination condition is met, and taking the path as an optimal inter-station load distribution scheme of the cascade hydropower station. According to the method, the decision-making efficiency can be improved while the decision-making precision is guaranteed.

Description

Technical Field

[0001] This invention relates to a method for optimizing load allocation between cascade hydropower stations, belonging to the field of hydropower dispatching technology. Background Technology

[0002] With the acceleration of energy transition, the proportion of new energy sources such as wind power and photovoltaics is rapidly increasing. Their inherent uncertainties severely impact the economic efficiency and stable operation of the power system, leading to increasingly frequent load commands and placing higher demands on the load allocation response speed of cascade hydropower stations. Furthermore, the scale of cascade power stations is increasing year by year, as is the number of power stations in each river basin. The connection between upstream and downstream hydraulic and power systems is becoming closer, and the characteristics of each power station's generating units are complex and diverse, easily causing a series of problems such as significant fluctuations in water levels and deterioration of unit operating conditions. This further increases the difficulty of solving the model.

[0003] To address the difficulty of solving load allocation problems between cascade hydropower stations, scholars have introduced numerous optimization techniques. Among traditional algorithms, linear programming offers high efficiency, but its linearization introduces significant errors. Dynamic programming, on the other hand, can be limited by issues such as the curse of dimensionality and low efficiency. With the development of bionics, heuristic algorithms based on biological evolution mechanisms have emerged. These algorithms possess strong global search capabilities, but their results are unstable, exhibiting poor reproducibility, high dependence on parameter settings, and a tendency to get trapped in local optima. In the context of the gradual integration of water and power dispatching and the increasing demands for refined dispatching, river basins face higher intensity and frequency requirements for peak and frequency regulation. These algorithms still struggle to meet the scientific and timely demands of river basin dispatching operations. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing load allocation between cascade hydropower stations, which can efficiently solve the load allocation model between cascade hydropower stations, improving decision-making efficiency while ensuring decision accuracy.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for optimizing load allocation between cascade hydropower stations, comprising: With the goal of maximizing the energy storage increment of cascade hydropower stations, a load distribution model between cascade hydropower stations is constructed by combining constraints such as water balance, power balance, unit output, power generation flow, outflow, and water level. The A* algorithm is used to solve the load distribution model between cascade hydropower stations, and the optimal load distribution scheme between cascade hydropower stations is obtained: Starting from the current state node of the cascade hydropower station, the next state node of the cascade hydropower station is generated according to the unit output constraints, and added to the candidate set. Filter out neighboring state nodes that satisfy all constraints from the candidate set and calculate their heuristic function values; Select the nearest state node with the smallest heuristic function value as the current state node of the new cascade hydropower station, and repeat the iteration. Until the iteration termination condition is met, the complete path from the current state node of the initial cascade hydropower station to the current state node of the latest cascade hydropower station is obtained by reverse traversal method, which serves as the optimal load distribution scheme between cascade hydropower stations. The heuristic function value includes the actual cost and the estimated cost. The actual cost is the negative value of the actual energy storage increment of the cascade hydropower station calculated from the beginning of the scheduling period to the current time based on the actual state of the reservoirs in the cascade hydropower station. The estimated cost is the negative value of the estimated energy storage increment of the cascade hydropower station calculated from the current time to the end of the scheduling period by aggregating all the reservoirs in the cascade hydropower station into an equivalent reservoir and based on the power generation water consumption rate of the equivalent reservoir.

[0006] Building upon the first aspect, the objective function of the load distribution model between cascade hydropower stations is further defined as follows: ; in, , , , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first Average inflow, average discharge, unit output, and average head for each time period This indicates the flow rate used to describe power generation consumption and , The relationship between them is expressed as a power generation and consumption flow function. Indicates the first The length of each time period Represents gravitational acceleration. Indicates the total number of time periods. This indicates the total number of reservoirs in a cascade hydropower station. The water balance constraint is: ; in, , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first , Water storage capacity during a given period Indicating the first in a cascade hydropower station The reservoir in the first Average outbound flow rate for each time period; The power balance constraint is: ; in, Indicates that the dispatch department is in The total power generation instructions issued to the cascade hydropower stations in each time period; The unit output constraint is: ; in, , They represent The lower limit and the upper limit; The power generation flow constraint is: ; in, Indicating the first in a cascade hydropower station The reservoir in the first Average power generation flow over a given period , They represent The lower limit and the upper limit; Outbound flow constraints are: ; in, , They represent The lower limit and the upper limit; Water level constraints are: ; in, Indicating the first in a cascade hydropower station The reservoir in the first Water levels at different times , They represent The lower limit and the upper limit.

[0007] In conjunction with the first aspect, the further formula for calculating the actual cost is as follows: ; in, Indicates the actual cost, , , , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first Average inflow, average discharge, unit output, and average head for each time period This indicates the flow rate used to describe power generation consumption and , The relationship between them is expressed as a power generation and consumption flow function. Indicates the first The length of each time period Represents gravitational acceleration. This indicates the time period currently searched by the A* algorithm. This indicates the total number of reservoirs in a cascade hydropower station. The formula for calculating the estimated cost is: ; in, Indicates the estimated cost. , These represent the equivalent reservoirs at the th... Average inflow and average discharge over a given period Indicates the equivalent reservoir in the first Water consumption rate for power generation in each time period Indicates that the dispatch department is in The total power generation instructions issued to the cascade hydropower stations in each time period. Indicates the total number of time periods; The formula for calculating the heuristic function value is: ; in, This represents the value of the heuristic function.

[0008] In conjunction with the first aspect, the formula for calculating the power generation water consumption rate of the equivalent reservoir is as follows: ; in, Indicates the equivalent reservoir in the first The water consumption rate for power generation during a given period, Substituting into the formula for calculating the power generation water consumption rate of the equivalent reservoir, the result is obtained. , Indicates the equivalent reservoir in the first Average outbound flow rate over a given period , These represent the equivalent reservoirs at the th... , Water storage capacity during a given period , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first , The water storage volume during each period.

[0009] In conjunction with the first aspect, further methods for maintaining the candidate set include: At the initial moment, the candidate set contains the current state nodes of the initial cascade hydropower stations; During the iteration process, the neighboring state node with the smallest selected heuristic function value is removed from the candidate set.

[0010] In conjunction with the first aspect, the further iteration termination conditions include: the current time reaches the end of the scheduling period, and the total power generation of the cascade hydropower stations at the current time meets the total power generation instruction issued by the power dispatch department.

[0011] In conjunction with the first aspect, further, by using the reverse traversal method, the complete path from the initial current state node of the cascade hydropower station to the latest current state node of the cascade hydropower station includes: starting from the latest current state node of the cascade hydropower station, sequentially visiting the new current state nodes of the cascade hydropower station obtained in the previous iteration, and recording the unit output of each reservoir in the corresponding cascade hydropower station, until the initial current state node of the cascade hydropower station is reached, thus forming a complete load allocation scheme time series.

[0012] Secondly, the present invention provides a load optimization and allocation system between cascade hydropower stations, comprising: The model building module is used to construct a load distribution model between cascade hydropower stations with the goal of maximizing the energy storage increment of cascade hydropower stations, and in combination with water balance constraints, power balance constraints, unit output constraints, power generation flow constraints, outflow constraints, and water level constraints. The model solving module is used to solve the load distribution model between cascade hydropower stations using the A* algorithm, and obtain the optimal load distribution scheme between cascade hydropower stations. Starting from the current state node of the cascade hydropower station, the next state node of the cascade hydropower station is generated according to the unit output constraints, and added to the candidate set. Filter out neighboring state nodes that satisfy all constraints from the candidate set and calculate their heuristic function values; Select the nearest state node with the smallest heuristic function value as the current state node of the new cascade hydropower station, and repeat the iteration. Until the iteration termination condition is met, the complete path from the current state node of the initial cascade hydropower station to the current state node of the latest cascade hydropower station is obtained by reverse traversal method, which serves as the optimal load distribution scheme between cascade hydropower stations. The heuristic function value includes the actual cost and the estimated cost. The actual cost is the negative value of the actual energy storage increment of the cascade hydropower station calculated from the beginning of the scheduling period to the current time based on the actual state of the reservoirs in the cascade hydropower station. The estimated cost is the negative value of the estimated energy storage increment of the cascade hydropower station calculated from the current time to the end of the scheduling period by aggregating all the reservoirs in the cascade hydropower station into an equivalent reservoir and based on the power generation water consumption rate of the equivalent reservoir.

[0013] Thirdly, the present invention provides a computer device, comprising: Storage medium: used to store computer programs; Processor: Used to execute the computer program to implement the load optimization allocation method between cascade hydropower stations as described in the first aspect.

[0014] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the load optimization allocation method between cascade hydropower stations described in the first aspect.

[0015] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method for optimizing load allocation between cascade hydropower stations as described in the first aspect.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a method for optimizing load allocation between cascade hydropower stations, aiming to address the difficulties in coordinating water and power dispatching, and the challenge of balancing decision-making accuracy and efficiency in load allocation for cascade hydropower stations. Based on the heuristic search principle of the A* algorithm, and considering the actual characteristics of load allocation between cascade hydropower stations, a cost function that takes into account power generation benefits is constructed. Furthermore, addressing the differences in continuous state variables, complex hydraulic connections, and path search problems in reservoir scheduling, a nearest-node expansion method and an A* algorithm solution process suitable for the load allocation problem between cascade hydropower stations are proposed. This achieves joint optimization of load allocation among power stations under complex operational constraints, with the goal of maximizing the incremental energy storage of the cascade hydropower system. This method possesses powerful global optimization capabilities and efficient model solution speed, and has significant engineering implications for improving the intelligence level of new power systems, ensuring the safe and stable operation of the system, and enhancing the overall economic benefits of the system. Attached Figure Description

[0017] Figure 1 This is a flowchart of the load optimization allocation method between cascade hydropower stations provided in an embodiment of the present invention; Figure 2 This is a flowchart of the neighbor node expansion process provided in an embodiment of the present invention; Figure 3 This is a flowchart of the A* algorithm provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a cluster of hydropower stations provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. Unless otherwise specified, embodiments of the present invention and the technical features thereof can be combined with each other.

[0020] This invention provides a method for optimizing load allocation between cascade hydropower stations, comprising: With the goal of maximizing the energy storage increment of cascade hydropower stations, a load distribution model between cascade hydropower stations is constructed by combining constraints such as water balance, power balance, unit output, power generation flow, outflow, and water level. The A* algorithm is used to solve the load distribution model between cascade hydropower stations, and the optimal load distribution scheme between cascade hydropower stations is obtained, including: Starting from the current state node of the cascade hydropower station, the next state node of the cascade hydropower station is generated according to the unit output constraints, and added to the candidate set. Filter out neighboring state nodes that satisfy all constraints from the candidate set and calculate their heuristic function values; Select the nearest state node with the smallest heuristic function value as the current state node of the new cascade hydropower station, and repeat the iteration. Until the iteration termination condition is met, the complete path from the initial current state node of the cascade hydropower station to the latest current state node of the cascade hydropower station is obtained by reverse traversal method, which serves as the optimal load distribution scheme between the cascade hydropower stations.

[0021] In this embodiment, the heuristic function value includes the actual cost and the estimated cost. The actual cost is the negative value of the actual energy storage increment of the cascade hydropower station calculated from the beginning of the scheduling period to the current time based on the actual state of the reservoirs in the cascade hydropower station. The estimated cost is the negative value of the estimated energy storage increment of the cascade hydropower station calculated from the current time to the end of the scheduling period by aggregating all the reservoirs in the cascade hydropower station into an equivalent reservoir and based on the power generation water consumption rate of the equivalent reservoir.

[0022] The load optimization allocation method between cascade hydropower stations provided in this invention addresses the difficulty of solving the load optimization allocation problem between cascade hydropower station groups under high-intensity peak-shaving and frequency regulation. With maximizing energy storage increment as the main objective, it comprehensively considers water balance constraints, power balance constraints, unit output constraints, power generation flow constraints, reservoir outflow constraints, and water level constraints to construct a load allocation model between cascade hydropower stations. Based on the A* algorithm principle, it proposes a heuristic function value suitable for this model and a corresponding solution process. Under the premise of satisfying grid load commands, it selects the optimal load allocation scheme, achieving efficient solution of the load allocation model between cascade hydropower stations and optimization of the load allocation scheme between stations.

[0023] A heuristic function suitable for the load allocation model between cascade hydropower stations is proposed. Specifically, based on the principles of the A* algorithm and the actual needs of load allocation between cascade hydropower stations, a method for calculating the actual cost and a method for calculating the estimated cost are proposed. The concept of aggregated reservoirs is introduced to integrate all reservoirs into an equivalent reservoir, thereby reducing the computational load of the estimation process and improving the search efficiency. The prediction of power generation water consumption rate is introduced to guide the algorithm to prioritize the search for high-efficiency paths, thereby achieving high efficiency in the solution.

[0024] To address the differences between continuous state variables, complex hydraulic connections, and path search problems in reservoir scheduling, this paper proposes a nearest-node expansion method and an A* algorithm solution process suitable for load allocation between cascade hydropower stations. By balancing solution efficiency and power deviation, an appropriate discretization precision is selected. Taking into account various constraints of cascade hydropower stations, and using the maximum increase in total cascade energy storage as the criterion, the A* algorithm is employed to solve the load allocation model between cascade hydropower stations, thereby enabling the formulation of an optimized load allocation scheme for the power stations.

[0025] Figure 1 This is a flowchart illustrating the load optimization allocation method between cascade hydropower stations provided in this embodiment. This flowchart only shows the logical sequence of the method in this embodiment; however, it can be implemented in different ways, provided there are no conflicts. Figure 1 Complete the steps shown or described in the order indicated.

[0026] The load optimization allocation method between cascade hydropower stations provided in this embodiment can be applied to a terminal and can be executed by a load optimization allocation system between cascade hydropower stations. This system can be implemented by software and / or hardware and can be integrated into the terminal, such as any tablet computer or computer device with communication function.

[0027] This invention provides a method for optimizing load allocation between cascade hydropower stations, specifically including the following steps: Step 1: With the goal of maximizing the energy storage increment of cascade hydropower stations, and combining constraints such as water balance, power balance, unit output, power generation flow, outflow, and water level, construct a load distribution model between cascade hydropower stations. In this embodiment, the objective function of the load distribution model between cascade hydropower stations is: ; in, , , , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first Average inflow, average discharge, unit output, and average head for each time period This indicates the flow rate used to describe power generation consumption and , The relationship between them is expressed as a power generation and consumption flow function. Indicates the first The length of each time period Represents gravitational acceleration. Indicates the total number of time periods. This indicates the total number of reservoirs in a cascade hydropower station. , The units are all in m 3 / s, The unit is kW. The unit is m.

[0028] The water balance constraint is: ; in, , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first , Water storage capacity during a given period Indicating the first in a cascade hydropower station The reservoir in the first Average outbound flow rate over a given period. , The units are all in m 3 , The unit is m 3 / s.

[0029] The power balance constraint is: ; in, Indicates that the dispatch department is in The total power generation instructions issued to the cascade hydropower stations during each time period. The unit is kW·h.

[0030] The unit output constraint is: ; in, , They represent The lower limit and the upper limit. , The unit for all values ​​is kW.

[0031] The power generation flow constraint is: ; in, Indicating the first in a cascade hydropower station The reservoir in the first Average power generation flow over a given period , They represent The lower limit and the upper limit. , , The units are all in m 3 / s.

[0032] Outbound flow constraints are: ; in, , They represent The lower limit and the upper limit. , The units are all in m 3 / s.

[0033] Water level constraints are: ; in, Indicating the first in a cascade hydropower station The reservoir in the first Water levels at different times , They represent The lower limit and the upper limit. , , The unit for all values ​​is m.

[0034] Step 2: Use the A* algorithm to solve the load distribution model between cascade hydropower stations and obtain the optimal load distribution scheme between cascade hydropower stations.

[0035] In this embodiment, the A* algorithm is used to solve the load distribution model between cascade hydropower stations to obtain the optimal load distribution scheme between cascade hydropower stations. The specific steps include: Step 1: Starting from the current state node of the cascade hydropower station, generate the next state node of the cascade hydropower station according to the unit output constraints, and add it to the candidate set; In this embodiment, the method for maintaining the candidate set specifically includes: At the initial moment, the candidate set contains the current state nodes of the initial cascade hydropower stations; During the iteration process, the neighboring state node with the smallest selected heuristic function value is removed from the candidate set.

[0036] Step 2: Select neighboring state nodes that satisfy all constraints from the candidate set and calculate their heuristic function values; The expansion of neighboring nodes is a core step in the A* algorithm's search process and directly determines the algorithm's search efficiency and path quality. In the reservoir scheduling problem, if the state... for The adjacent nodes, in addition to satisfying the constraints when the power plant state changes, also need to satisfy... During the expansion process near the node, it is necessary to utilize... , The set consists of two parts: the first part records nodes that have been expanded but not yet searched, along with their cost function values; the second part records nodes that have been searched, along with their cost function values.

[0037] Specifically, such as Figure 2 As shown, the process for expanding adjacent nodes is as follows: Step 1: Set the current status of the cascade hydropower stations Not here The neighboring states in a set form a set. ; Step 2: In the set Select one of the cascade power stations in the state And calculate its current cost. ; Step 3: Determine the status of the cascade hydropower stations Is it in In the set, if the state of the cascade power station already exists... In the set, and Less than The collection stores Then an update is required. From the initial state to the final state of the cascade hydropower stations in the set All scheduling process information, that is, the status of cascade power stations Set to cascade power station status Retrieve the status of the cascade hydropower stations in the previous time period and update the status of the cascade hydropower stations. The cost function value; if Greater than The collection stores Then maintain The collection reaches the state of cascade hydropower stations. Dispatch process information; if the status of cascade power stations Not here In the set, then in the calculation The status of the cascade power stations was then added. Collect and record the status of the cascade hydropower stations. Set to cascade power station status Status of the cascade hydropower stations in the previous period; Step 4: Change the status of the cascade power stations from Delete, determine the set Is it empty? If it is empty, the expansion ends; if it is not empty, proceed to Step 3.

[0038] In this embodiment, the heuristic function value includes the actual cost and the estimated cost.

[0039] Specifically, the formula for calculating the heuristic function value is as follows: ; in, Represents the heuristic function value. Indicates the actual cost, This indicates the estimated cost.

[0040] In this embodiment, the actual cost is the negative value of the actual energy storage increment of the cascade hydropower station, calculated from the beginning of the scheduling period to the current time based on the actual state of the reservoirs in the cascade hydropower station.

[0041] Specifically, the formula for calculating the actual cost is as follows: ; in, This indicates the time period currently searched by the A* algorithm.

[0042] In this embodiment, the estimated cost is the negative value of the estimated energy storage increment of the cascade hydropower station, calculated from the current time to the end of the scheduling period by aggregating all reservoirs in the cascade hydropower station into an equivalent reservoir, based on the power generation water consumption rate of the equivalent reservoir.

[0043] like Figure 4 As shown, after combining all the reservoirs in the cascade hydropower station into an equivalent reservoir, the average inflow and storage capacity of the equivalent reservoir are the sum of the values ​​of each individual reservoir, i.e.: ; in, , These represent the equivalent reservoirs at the th... Average inflow and water storage volume over a given period. The unit is m 3 / s, The unit is m 3 .

[0044] The average outflow of the equivalent reservoir can be determined based on the average inflow and initial and final storage volumes of the equivalent reservoir, i.e.: ; in, Indicates the equivalent reservoir in the first Average outbound flow rate over a given period Indicates the equivalent reservoir in the first Water storage capacity during a given period , Indicating the first in a cascade hydropower station The reservoir in the first The water storage volume during each period. The unit is m 3 / s, , The units are all in m 3 .

[0045] Daily regulating hydropower stations have relatively small reservoir capacities, and their water levels often fluctuate significantly within a day to complete power generation tasks, resulting in substantial changes in their condition. However, by treating multiple daily or even annual regulating hydropower stations as a single aggregated hydropower station, on the one hand, the regulating reservoir capacity becomes larger, leading to stronger regulating capabilities, and the total daily energy storage remains within a relatively stable range with minor fluctuations; on the other hand, the outflow from upstream power stations becomes the inflow from downstream power stations, ensuring that the total downstream flow of the aggregated hydropower station does not change drastically due to the operating modes of a few power stations. When calculating the estimated costs, the cascade hydropower stations in the basin are treated as aggregated hydropower stations to calculate their power generation water consumption rates.

[0046] The formula for calculating the power generation water consumption rate of an equivalent reservoir is: ; in, Indicates the equivalent reservoir in the first Water consumption rate for power generation during each time period. The unit is m³ / kW·h.

[0047] According to the formula for calculating the power generation water consumption rate of an equivalent reservoir, let The equivalent reservoir was calculated in the first... Water consumption rate for power generation in each time period ,according to Calculate the estimated cost.

[0048] Specifically, the formula for calculating the estimated cost is as follows: ; in, Indicates the equivalent reservoir in the first Water consumption rate for power generation during each time period. The unit is m³ / kW·h.

[0049] Step 3: Select the nearest state node with the smallest heuristic function value as the new current state node of the cascade hydropower station. If the new current state node of the cascade hydropower station satisfies the iteration termination condition, then obtain the complete path from the initial current state node of the cascade hydropower station to the latest current state node of the cascade hydropower station through the reverse traversal method, which is used as the optimal load allocation scheme between the cascade hydropower stations. Otherwise, return to step 1 and repeat the iteration starting from the new current state node of the cascade hydropower station.

[0050] In this embodiment, the iteration termination condition specifically includes: the current time reaches the end of the scheduling period, and the total power generation of the cascade hydropower stations at the current time meets the total power generation instruction issued by the power dispatch department.

[0051] If the current state node of the cascade hydropower station obtained in a certain iteration satisfies the following conditions: the current time has reached the end of the scheduling period, and the total power generation of the cascade hydropower station at the current time meets the total power generation instruction issued by the power dispatching department, then the current state node of the cascade hydropower station obtained in this iteration shall be taken as the latest current state node of the cascade hydropower station, and the iteration shall be stopped.

[0052] In this embodiment, the complete path from the initial current state node of the cascade hydropower station to the latest current state node of the cascade hydropower station is obtained by reverse traversal method. Specifically, it includes: starting from the latest current state node of the cascade hydropower station, sequentially visiting the new current state nodes of the cascade hydropower station obtained in the previous iteration, and recording the unit output of each reservoir in the corresponding cascade hydropower station, until the initial current state node of the cascade hydropower station is visited, thus forming a complete load allocation scheme time series.

[0053] In pathfinding problems, the final target is often a specific coordinate in space. However, in reservoir scheduling problems, the target of a cascade hydropower station needs to be adjusted according to scheduling requirements in addition to considering time. In the load allocation problem of cascade hydropower stations, the conditions for determining whether the state of the cascade hydropower stations reaches the target state include two aspects: first, the current time is the same as the target time; second, the sum of the power generation of each station is the same as the target power generation.

[0054] This embodiment applies the A* algorithm to solving the inter-station load distribution problem in hydropower stations, such as... Figure 3 As shown, the specific solution process is as follows: Step 1: Determine the initial state of the cascade hydropower stations Target cascade power station status ,create , Set it and initialize it; Step 2: Initialize the state of the cascade power stations join in gather; Step3: Judgment If the set is empty, the algorithm fails; otherwise, select... The state of the cascade power station with the lowest cost function value in the set is set as the current state of the cascade power station. And join gather; Step 4: Determine the status of the cascade hydropower stations Is it the status of the target cascade hydropower station? ; Step 5: If it is the target cascade power station status If the path search is successful, output the result after reverse traversal; Step 6: If it is not the target cascade power station status Then extend the status of adjacent cascade power stations and jump back to Step 3.

[0055] The load allocation method for cascade hydropower stations provided in this invention is applicable to the rapid and efficient optimization of load allocation when cascade hydropower stations face high-intensity peak-shaving and frequency regulation tasks, given the increasing proportion of new energy grid integration and the growing scale of hydropower. It enables the scientific and rational allocation of grid commands, improving the overall power generation efficiency of cascade hydropower stations and meeting the feasibility and timeliness requirements of actual production in the river basin. It solves the problem that traditional solution algorithms are insufficient to meet the scientific and timeliness requirements of load allocation model solutions for cascade hydropower stations facing high-intensity peak-shaving and frequency regulation tasks in the context of increasing proportion of new energy grid integration and the growing scale of hydropower.

[0056] This invention provides a load optimization and allocation system between cascade hydropower stations, comprising: The model building module is used to construct a load distribution model between cascade hydropower stations with the goal of maximizing the energy storage increment of cascade hydropower stations, and in combination with water balance constraints, power balance constraints, unit output constraints, power generation flow constraints, outflow constraints, and water level constraints. The model solving module is used to solve the load distribution model between cascade hydropower stations using the A* algorithm, and obtain the optimal load distribution scheme between cascade hydropower stations.

[0057] In this embodiment, the A* algorithm is used to solve the load distribution model between cascade hydropower stations, and the optimal load distribution scheme between cascade hydropower stations is obtained, specifically including: Starting from the current state node of the cascade hydropower station, the next state node of the cascade hydropower station is generated according to the unit output constraints, and added to the candidate set. Filter out neighboring state nodes that satisfy all constraints from the candidate set and calculate their heuristic function values; Select the nearest state node with the smallest heuristic function value as the current state node of the new cascade hydropower station, and repeat the iteration. Until the iteration termination condition is met, the complete path from the initial current state node of the cascade hydropower station to the latest current state node of the cascade hydropower station is obtained by reverse traversal method, which serves as the optimal load distribution scheme between the cascade hydropower stations.

[0058] In this embodiment, the heuristic function value includes the actual cost and the estimated cost. The actual cost is the negative value of the actual energy storage increment of the cascade hydropower station calculated from the beginning of the scheduling period to the current time based on the actual state of the reservoirs in the cascade hydropower station. The estimated cost is the negative value of the estimated energy storage increment of the cascade hydropower station calculated from the current time to the end of the scheduling period by aggregating all the reservoirs in the cascade hydropower station into an equivalent reservoir and based on the power generation water consumption rate of the equivalent reservoir.

[0059] The load optimization and allocation system between cascade hydropower stations provided in this embodiment of the invention can execute the load optimization and allocation method between cascade hydropower stations provided in this embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0060] This invention provides a computer device, comprising: Storage medium: used to store computer programs; Processor: Used to execute computer programs to implement the load optimization allocation method between cascade hydropower stations provided in the embodiments of the present invention.

[0061] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for optimizing load allocation between cascade hydropower stations provided in this invention.

[0062] This invention provides a computer program product, including a computer program that, when executed by a processor, implements the method for optimizing load allocation between cascade hydropower stations provided in this invention.

[0063] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0065] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0067] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing load allocation between cascade hydropower stations, characterized in that, include: With the goal of maximizing the energy storage increment of cascade hydropower stations, a load distribution model between cascade hydropower stations is constructed by combining constraints such as water balance, power balance, unit output, power generation flow, outflow, and water level. The A* algorithm is used to solve the load distribution model between cascade hydropower stations, and the optimal load distribution scheme between cascade hydropower stations is obtained: Starting from the current state node of the cascade hydropower station, the next state node of the cascade hydropower station is generated according to the unit output constraints, and added to the candidate set. Select neighboring state nodes that satisfy all constraints from the candidate set and calculate their heuristic function values; The nearest state node with the smallest heuristic function value is selected as the current state node of the new cascade hydropower station, and the process is repeated iteratively. Until the iteration termination condition is met, the complete path from the current state node of the initial cascade hydropower station to the current state node of the latest cascade hydropower station is obtained by reverse traversal method, which serves as the optimal load distribution scheme between cascade hydropower stations. The heuristic function value includes the actual cost and the estimated cost. The actual cost is the negative value of the actual energy storage increment of the cascade hydropower station calculated from the beginning of the scheduling period to the current time based on the actual state of the reservoirs in the cascade hydropower station. The estimated cost is the negative value of the estimated energy storage increment of the cascade hydropower station calculated from the current time to the end of the scheduling period by aggregating all the reservoirs in the cascade hydropower station into an equivalent reservoir and based on the power generation water consumption rate of the equivalent reservoir.

2. The method for optimizing load allocation between cascade hydropower stations according to claim 1, characterized in that, The objective function of the load distribution model between cascade hydropower stations is: ； in, , , , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first Average inflow, average discharge, unit output, and average head for each time period This indicates the flow rate used to describe power generation consumption and , The relationship between them is expressed as a power generation and consumption flow function. Indicates the first The length of each time period Represents gravitational acceleration. Indicates the total number of time periods. This indicates the total number of reservoirs in a cascade hydropower station. The water balance constraint is: ； in, , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first , Water storage capacity during a given period Indicating the first in a cascade hydropower station The reservoir in the first Average outbound flow rate for each time period; The power balance constraint is: ； in, Indicates that the dispatch department is in The total power generation instructions issued to the cascade hydropower stations in each time period; The unit output constraint is: ； in, , They represent The lower limit and the upper limit; The power generation flow constraint is: ； in, Indicating the first in a cascade hydropower station The reservoir in the first Average power generation flow over a given period , They represent The lower limit and the upper limit; Outbound flow constraints are: ； in, , They represent The lower limit and the upper limit; Water level constraints are: ； in, Indicating the first in a cascade hydropower station The reservoir in the first Water levels at different times , They represent The lower limit and the upper limit.

3. The method for optimizing load allocation between cascade hydropower stations according to claim 1, characterized in that, The formula for calculating the actual cost is: ； in, Indicates the actual cost, , , , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first Average inflow, average discharge, unit output, and average head for each time period This indicates the flow rate used to describe power generation consumption and , The relationship between them is expressed as a power generation and consumption flow function. Indicates the first The length of each time period Represents gravitational acceleration. This indicates the time period currently searched by the A* algorithm. This indicates the total number of reservoirs in a cascade hydropower station. The formula for calculating the estimated cost is: ； in, Indicates the estimated cost. , These represent the equivalent reservoirs at the th... Average inflow and average discharge over a given period Indicates the equivalent reservoir in the first Water consumption rate for power generation in each time period Indicates that the dispatch department is in The total power generation instructions issued to the cascade hydropower stations in each time period. Indicates the total number of time periods; The formula for calculating the heuristic function value is: ； in, This represents the value of the heuristic function.

4. The method for optimizing load allocation between cascade hydropower stations according to claim 3, characterized in that, The formula for calculating the power generation water consumption rate of the equivalent reservoir is: ； in, Indicates the equivalent reservoir in the first The water consumption rate for power generation during a given period, Substituting into the formula for calculating the power generation water consumption rate of the equivalent reservoir, the result is obtained. , Indicates the equivalent reservoir in the first Average outbound flow rate over a given period , These represent the equivalent reservoirs at the th... , Water storage capacity during a given period , These represent the 1st, 2nd, and 3rd tiers of the cascade hydropower stations. The reservoir in the first , The water storage volume during each period.

5. The method for optimizing load allocation between cascade hydropower stations according to claim 1, characterized in that, Methods for maintaining the candidate set include: At the initial moment, the candidate set contains the current state nodes of the initial cascade hydropower stations; During the iteration process, the neighboring state node with the smallest selected heuristic function value is removed from the candidate set.

6. The method for optimizing load allocation between cascade hydropower stations according to claim 1, characterized in that, The iteration termination conditions include: the current time reaches the end of the scheduling period, and the total power generation of the cascade hydropower stations at the current time meets the total power generation instruction issued by the power dispatch department.

7. The method for optimizing load allocation between cascade hydropower stations according to claim 1, characterized in that, By using the reverse traversal method, the complete path from the initial current state node of the cascade hydropower station to the latest current state node of the cascade hydropower station includes: starting from the latest current state node of the cascade hydropower station, sequentially visiting the new current state nodes of the cascade hydropower station obtained in the previous iteration, and recording the unit output of each reservoir in the corresponding cascade hydropower station, until the initial current state node of the cascade hydropower station is reached, thus forming a complete load allocation scheme time series.

8. A load optimization and distribution system between cascade hydropower stations, characterized in that, include: The model building module is used to construct a load distribution model between cascade hydropower stations with the goal of maximizing the energy storage increment of cascade hydropower stations, and in combination with water balance constraints, power balance constraints, unit output constraints, power generation flow constraints, outflow constraints, and water level constraints. The model solving module is used to solve the load distribution model between cascade hydropower stations using the A* algorithm, and obtain the optimal load distribution scheme between cascade hydropower stations. Starting from the current state node of the cascade hydropower station, the next state node of the cascade hydropower station is generated according to the unit output constraints, and added to the candidate set. Select neighboring state nodes that satisfy all constraints from the candidate set and calculate their heuristic function values; The nearest state node with the smallest heuristic function value is selected as the current state node of the new cascade hydropower station, and the process is repeated iteratively. Until the iteration termination condition is met, the complete path from the current state node of the initial cascade hydropower station to the current state node of the latest cascade hydropower station is obtained by reverse traversal method, which serves as the optimal load distribution scheme between cascade hydropower stations. The heuristic function value includes the actual cost and the estimated cost. The actual cost is the negative value of the actual energy storage increment of the cascade hydropower station calculated from the beginning of the scheduling period to the current time based on the actual state of the reservoirs in the cascade hydropower station. The estimated cost is the negative value of the estimated energy storage increment of the cascade hydropower station calculated from the current time to the end of the scheduling period by aggregating all the reservoirs in the cascade hydropower station into an equivalent reservoir and based on the power generation water consumption rate of the equivalent reservoir.

9. A computer device, characterized in that, include: Storage medium: used to store computer programs; Processor: Used to execute the computer program to implement the load optimization allocation method between cascade hydropower stations as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the load optimization allocation method between cascade hydropower stations as described in any one of claims 1 to 7.

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