Method for measuring competitive resource dual multiplier in cascade reservoir scheduling model
By introducing competing resource dual multipliers into the cascade reservoir scheduling model, the problem of resource allocation optimization in cascade reservoir scheduling is solved, and coordination and efficiency between resources are maximized.
Patent Information
- Application Number
- CN202411799834.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-09
- Publication Date
- 2025-04-04
AI Technical Summary
In cascade reservoir scheduling management, how to reasonably optimize and allocate different resources with competitive relationships, such as water resources, power plant capacity allocation and power resources, to ensure coordination and efficiency between resources.
By introducing dual multipliers of competing resources into the cascade reservoir scheduling model, the objective function and constraints are determined, and the weights of each sub-objective are adjusted according to priority level until the optimal solution of the objective function changes, and then the dual multipliers of each resource are obtained to assist in the optimal configuration of resources.
While distinguishing between molecular targets, it is achieved to ensure that the weight adjustment does not affect the objective function value, thereby obtaining dual multipliers representing the optimal state of the cascade reservoir scheduling model, helping dispatchers to reasonably allocate competitive resources and improve resource utilization efficiency.
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Abstract
Description
Technical Field
[0001] The present application belongs to the technical field of reservoir regulation, and more specifically, relates to a method for measuring dual multipliers of competing resources in a cascade reservoir regulation model. Background Art
[0002] As an important water resource regulation and utilization facility, cascade reservoirs need to face competition and allocation among multiple resources in scheduling and management. These resources include but are not limited to water resources, power station capacity configuration, and power resources. These resources are managed by different entities, and there is a competitive relationship between different resources. How to reasonably optimize the allocation of different resources in a competitive relationship is a technical problem that needs to be solved in this field. Summary of the invention
[0003] In view of the defects of the prior art, the purpose of this application is to reasonably optimize the allocation of different resources that are in a competitive relationship.
[0004] To achieve the above objectives, in a first aspect, the present application provides a method for measuring dual multipliers of competing resources in a cascade reservoir operation model, comprising: Determine the objective function for optimizing the operation of cascade reservoirs and the constraints corresponding to various resources associated with the operation of cascade reservoirs. The objective function is a linear function determined based on multiple cascade reservoir operation sub-objectives and the initial weights corresponding to each cascade reservoir operation sub-objective. The larger the initial weight, the higher the priority of the corresponding cascade reservoir operation sub-objective. The weight of each cascade reservoir operation sub-objective is adjusted in descending order of priority. A cascade reservoir operation sub-objective is used as a reference object, and the weight of the reference object remains fixed. The weight adjustment includes controlling the weight of the corresponding cascade reservoir operation sub-objective to shrink toward the weight of the reference object until the optimal solution of the objective function is determined to change. Determine whether the weight of each cascade reservoir scheduling sub-objective converges; In the case of non-convergence, continue to adjust the weight of each cascade reservoir scheduling sub-objective in order of priority from high to low; or, in the case of convergence, solve the dual problem of the original problem to obtain the dual multiplier corresponding to the resources of each cascade reservoir. The dual multiplier is used to characterize the increase in the optimal value of the objective function brought about by each unit increase in the corresponding resources.
[0005] In a possible implementation, when weight adjustment is performed on a cascade reservoir scheduling sub-target having a higher priority than a reference target, the weight adjustment specifically includes: According to the specified step size, the weights of the corresponding cascade reservoir scheduling sub-objectives are gradually reduced until the optimal solution of the objective function is determined to change.
[0006] In a possible implementation, when weight adjustment is performed on the cascade reservoir scheduling sub-targets with lower priority than the reference target, the weight adjustment specifically includes: According to the specified step size, the weights of the corresponding cascade reservoir scheduling sub-objectives are gradually increased until the optimal solution of the objective function is determined to change.
[0007] In a possible implementation, before determining that the optimal solution of the objective function changes, the method further includes: Based on the current weight value, solve the objective function and obtain the latest optimal solution; Determine whether the gap between the latest optimal solution and the initial optimal solution exceeds a preset solution gap threshold. The initial optimal solution is obtained by solving the objective function based on the initial weights. Among them, if the gap is greater than the preset solution gap threshold, the optimal solution of the objective function changes, and if the gap is less than or equal to the preset solution gap threshold, the optimal solution of the objective function does not change.
[0008] In a possible implementation, judging whether the weight of each cascade reservoir scheduling sub-objective has converged includes: For the weights of the cascade reservoir scheduling sub-goals, determine whether the difference between the weights determined by the current round of weight adjustment and the weights determined by the previous round of weight adjustment is less than a preset weight difference threshold; Among them, if the gap is less than the preset weight gap threshold, the weight of the cascade reservoir scheduling sub-objective has converged; if the gap is greater than or equal to the preset weight gap threshold, the weight of the cascade reservoir scheduling sub-objective has not converged.
[0009] In a possible implementation, before adjusting the weight of each cascade reservoir scheduling sub-objective in order of priority from high to low, it also includes: Evaluate the change of the optimal solution of the objective function caused by a unit change in the weight of each cascade reservoir operation sub-objective, and use the change in the optimal solution of the objective function as the weight elasticity of the corresponding cascade reservoir operation sub-objective; A sub-goal with the largest weight elasticity is determined as a reference goal, or a sub-goal with a weight elasticity exceeding a preset weight elasticity threshold is determined as a reference goal.
[0010] In a second aspect, the present application provides a device for measuring dual multipliers of competing resources in a cascade reservoir operation model, comprising: The scheduling model determination module is used to determine the objective function for optimizing the scheduling of cascade reservoirs and the constraints corresponding to various resources associated with the scheduling of cascade reservoirs. The objective function is a linear function determined based on multiple cascade reservoir scheduling sub-objectives and the initial weights corresponding to each cascade reservoir scheduling sub-objective. The larger the initial weight, the higher the priority of the corresponding cascade reservoir scheduling sub-objective. The weight adjustment module is used to adjust the weight of each cascade reservoir scheduling sub-goal in order of priority from high to low. A cascade reservoir scheduling sub-goal is used as a reference target, and the weight of the reference target is fixed. The weight adjustment includes controlling the weight of the corresponding cascade reservoir scheduling sub-goal to shrink toward the weight of the reference target until the optimal solution of the objective function is determined to change; The convergence judgment module is used to judge whether the weight of each cascade reservoir scheduling sub-objective has converged; The weight adjustment module is also used to continue to adjust the weight of each cascade reservoir scheduling sub-objective in order of priority from high to low when convergence has not occurred; The dual multiplier acquisition module is used to solve the dual problem of the original problem when convergence has occurred, so as to obtain the dual multipliers corresponding to the resources of each cascade reservoir. The dual multipliers are used to characterize the increase in the optimal value of the objective function brought about by each unit increase in the corresponding resources.
[0011] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.
[0012] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.
[0013] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.
[0014] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art: While distinguishing the priorities among the sub-goals, ensure that the weight adjustment does not affect the objective function value of the original problem, so as to ensure that the obtained dual multiplier can still represent the information under the optimal state of the cascade reservoir operation model. At the same time, by controlling the weight of the corresponding cascade reservoir operation sub-goal to shrink toward the weight of the reference target, the magnitude of the dual multiplier can be unified, and the obtained dual multiplier can represent the increase in the optimal value of the objective function brought about by each unit increase in the corresponding cascade reservoir resources, and then the unified magnitude of each dual multiplier can assist the dispatcher to more reasonably optimize the allocation of different resources that are in a competitive relationship. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a schematic diagram of the trade-off preference relationship between sub-goals provided in the embodiment of the present application; Figure 2 is a schematic diagram of weight redundancy between sub-goals provided in an embodiment of the present application; Figure 3 is a schematic diagram of multiple redundancy provided in an embodiment of the present application; Figure 4 It is one of the flow diagrams of the method for measuring the dual multiplier of competitive resources in the cascade reservoir scheduling model provided in the embodiment of the present application; Figure 5 This is the second flow chart of the method for measuring the dual multiplier of competitive resources in the cascade reservoir scheduling model provided in the embodiment of the present application; Figure 6 Schematic diagram of a dual multiplier for ensuring the amount of power delivered in different time periods provided in an embodiment of the present application; Figure 7 is a schematic diagram of a dual multiplier of the spare capacity in different time periods provided by an embodiment of the present application; Figure 8 is a schematic diagram of a dual multiplier of the water storage upper limit in different time periods provided by an embodiment of the present application; Fig. 9 It is a schematic diagram of the dual multiplier of the interval water supply in different time periods provided by the embodiment of the present application; Fig.10 It is a schematic diagram of the dual multiplier of the installed capacity in different time periods provided by the embodiment of the present application; Fig.11 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0016] In order to facilitate a clearer understanding of the embodiments of the present application, some relevant background knowledge is first introduced as follows.
[0017] (1) On the continuously differentiable multi-objective non-inferior frontier: the trade-off preference relationship can be accurately estimated.
[0018] like Figure 1 As shown in the figure, taking two sub-goals as an example, when the weights of the sub-goals are given, the position of the optimal solution on the Pareto front is determined, which represents the decision maker's preference for different sub-goals.
[0019] like Figure 1 As shown, and Represents sub-goal 1 and sub-goal 2. represent The corresponding weight, represent The corresponding weight.
[0020] The derivative at the trade-off decision point represents the preference relationship between the two sub-goals, that is: ; (2) Multiple sub-goals with priority levels: there is “weight redundancy”.
[0021] like Figure 2 As shown in Figure 2, when the first sub-goal has a priority level, the derivative (or weight ratio) changes from arrive , the same (or similar) optimal solution can be obtained: ; ; Therefore, the weight ratio has a redundancy (which can be called the weight redundancy): ; When considering which weight ratio to use to determine the trade-off preference relationship between sub-goals, is more accurate. The reason is that when If the weight ratio is too high, the utility of sub-goal 2 will be seriously underestimated. The weight ratio can be adjusted (or the difference between the weights of different sub-goals) to eliminate the "weight margin" and obtain a reasonable weight ratio. If the optimal solution of the problem changes during the process of adjusting the weight ratio, it is determined that the "weight redundancy" has been eliminated. The weight after eliminating the "weight redundancy" is the "close weight" after iterative convergence. By determining the close weight of each goal, the trade-off preference relationship between different sub-goals can be reasonably evaluated.
[0022] (3) In linear constraint space: there is “multiple redundancy”.
[0023] like Figure 3 As shown, when the level decreases from sub-goal 1 to sub-goal 3, the optimal solution is at point A ( To achieve the maximum basis, let and As far as possible). At this time, if we only increase The weight of , when "exhausting" the "weight margin" in this direction (denoted as: ), the optimal solution will go to point B (sacrificing Increase ); On the other hand, if only The weight of , when "exhausting" the "weight margin" in this direction (denoted as: ), the optimal solution will go to point C (sacrificing Increase ). It can be seen that the direction of weight change is different, and the "weight margin" is also different. Then we should consider in which direction to eliminate the "weight margin" to determine the trade-off preference relationship between sub-goals.
[0024] (4) Sub-goals with better flexibility and sub-goals with less flexibility: The focus should be on sub-goals with better flexibility.
[0025] Still Figure 3 As shown, if we add a constraint: ; Regardless How to increase the weight of will not change; it can be seen that sub-goal 1 is inflexible at this time. From an engineering perspective, it does not have the utility of reflecting the decision maker's preferences (for example, sub-goal 1 is obviously subject to resource constraints); therefore, sub-goal 2 cannot eliminate the "weight redundancy" by increasing the weight.
[0026] If the change of resources in the constraint conditions corresponding to the sub-goal has a very limited impact on the optimal solution of the planning problem due to resource constraints, then the weight elasticity of the sub-goal is small and lacks elasticity; if the change of resources in the constraint conditions corresponding to the sub-goal has a significant impact on the optimal solution of the planning problem, then the weight elasticity of the sub-goal is large and has good elasticity. In actual operation, the change of the objective function value in the optimal solution caused by a unit change in the weight value of each sub-goal can be evaluated. The greater the change in the objective function value, the greater the elasticity of the corresponding sub-goal weight compared to the weights of other sub-goals.
[0027] Since the change of the weight value of the inelastic sub-goal has a very limited impact on the optimal solution of the planning problem, if the weights of other sub-goals are tilted towards the weight of the inelastic sub-goal, it is not conducive to the optimization of the weights of other sub-goals. It can be seen that tilting towards the sub-goal with better elasticity is conducive to the optimization of the weights of other sub-goals.
[0028] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0029] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.
[0030] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.
[0031] First, the technical terms involved in the embodiments of the present application are introduced.
[0032] (1) The cascade reservoir operation optimization model is a model used to manage and dispatch multiple interrelated reservoir systems. The model is used to reasonably arrange the water volume dispatch of cascade reservoirs under the premise of meeting various constraints to maximize the benefits of water resource utilization. The model improves the efficiency of water resource utilization by optimizing the dispatching rules on the basis of ensuring multiple objectives such as flood control safety, water supply safety, power generation efficiency, and ecological protection.
[0033] (2) Resources associated with cascade reservoir scheduling include: water resources for hydropower generation, storage capacity designated for flood control, water resources from reservoirs or their upstream for irrigation, standby generating capacity resources to ensure the reliability of the power system, and guaranteed power transmission to designated areas (e.g. other provinces).
[0034] The above resources are competitive in the process of cascade reservoir operation. For example, hydropower generation requires a large amount of water resources, which can also be used for other purposes such as irrigation and ecological protection. In the dry season or low water period, the scarcity of water resources will intensify this competition. For example, the reservation of flood control storage capacity will limit the water storage capacity of the reservoir, thereby affecting the water used for power generation. In the rainy season or flood-prone period, more storage capacity needs to be reserved, which may reduce the amount of water available for power generation. For example, the demand for irrigation water is in direct competition with the water used for power generation. During the peak period of agricultural water use, such as the spring plowing period, the increase in irrigation water use may reduce the water used for power generation. The spare power generation capacity requires the reservoir to maintain a certain water level so that the power generation can be increased quickly when the power demand is peak, which will conflict with the need to maintain long-term stable power generation or flood control storage capacity. For example, in order to ensure the agreed power delivery, this may conflict with the storage capacity used for flood control, the water resources used for irrigation, and the spare power generation capacity resources.
[0035] (3) The dual multiplier is a variable associated with each constraint in the original problem and is used to measure the degree of “relaxation” of the corresponding constraint in the dual problem.
[0036] Dual Multiplier is a concept in mathematical optimization, especially in the duality theory of linear programming. In linear programming, every original problem (also called original problem or primal problem) corresponds to a dual problem.
[0037] Original problem: It is usually a problem of maximizing or minimizing a linear function (objective function) while satisfying a series of linear inequalities or equality constraints.
[0038] The dual problem is the counterpart of the original problem. It transforms the objective function and constraints in the original problem. The coefficients of the objective function of the original problem become the constraints of the dual problem, and the coefficients of the constraints of the original problem become the objective function of the dual problem.
[0039] In practical engineering applications, dual multipliers can represent the impact of changes in certain physical quantities on system performance.
[0040] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.
[0041] Figure 4 is one of the flow charts of the method for measuring the dual multiplier of competitive resources in the cascade reservoir scheduling model provided in the embodiment of the present application, such as Figure 4 As shown, the method includes the following steps S101 to S104.
[0042] Step S101, determine the objective function for optimizing the scheduling of cascade reservoirs and the constraints corresponding to various resources associated with the scheduling of cascade reservoirs. The objective function is a linear function determined based on multiple cascade reservoir scheduling sub-objectives (as decision variables of the objective function) and the initial weights corresponding to each cascade reservoir scheduling sub-objective. The larger the initial weight, the higher the priority of the corresponding cascade reservoir scheduling sub-objective.
[0043] The cascade reservoir operation model is composed of an objective function and various constraints. For example, the cascade reservoir operation model can be expressed by the following formula.
[0044] The objective function of the cascade reservoir operation model is: ; in, Represents maximization , , Indicates sub-goals (decision variables), with a total of sub-goals, Indicates The weights corresponding to the sub-goals; Constraints of cascade reservoir operation model: ; in, represents the constraints corresponding to resource 1 (e.g. water resources for hydroelectric power generation), Indicates the constraints corresponding to resource 2, Indicates the constraints corresponding to resource 3, and so on. Indicates the constraints corresponding to resource N. Indicates other constraints.
[0045] Step S102, adjusting the weight of each cascade reservoir scheduling sub-goal in order of priority from high to low, with one cascade reservoir scheduling sub-goal serving as a reference target, the weight of the reference target being fixed, and the weight adjustment comprising controlling the weight of the corresponding cascade reservoir scheduling sub-goal to shrink toward the weight of the reference target until it is determined that the optimal solution of the objective function has changed.
[0046] Step S103, determining whether the weight of each cascade reservoir scheduling sub-objective has converged.
[0047] Step S104, if convergence has not occurred, continue to adjust the weight of each cascade reservoir scheduling sub-objective in order of priority from high to low; or, if convergence has occurred, solve the dual problem of the original problem to obtain the dual multiplier corresponding to each cascade reservoir resource, and the dual multiplier is used to characterize the increase in the optimal value of the objective function brought about by each unit increase in the corresponding resource.
[0048] It is understandable that the multiple resources associated with the cascade reservoir operation can be different resources in a competitive relationship. By adjusting the weight of each cascade reservoir operation sub-goal in order of priority from high to low, and each weight adjustment is terminated by the change in the optimal solution of the objective function, it is possible to distinguish the priorities between the sub-goals while ensuring that the weight adjustment does not affect the objective function value of the original problem, thereby ensuring that the obtained dual multiplier can still represent the information in the optimal state of the cascade reservoir operation model. At the same time, by controlling the weight of the corresponding cascade reservoir operation sub-goal to shrink to the weight of the reference target, the magnitude of the dual multiplier can be unified, and the obtained dual multiplier can represent the increase in the optimal value of the objective function brought about by each increase in the corresponding cascade reservoir resources. The dual multipliers of the unified magnitude can assist the dispatcher to more reasonably optimize the configuration of different resources in a competitive relationship.
[0049] The method for measuring the dual multiplier of competing resources in the cascade reservoir scheduling model provided in the present application is illustrated below with an example.
[0050] Figure 5 FIG. 2 is a flow chart of the method for measuring the dual multiplier of competitive resources in the cascade reservoir scheduling model provided in the embodiment of the present application, such as Figure 5 As shown, the process includes the following steps S201 to S207.
[0051] Step S201, set the initial weight values of sub-goals at all levels in the multi-objective optimization problem. The initial weights of all sub-goals constitute an initial weight vector to ensure that the initial weight vector can reflect the priority levels between different sub-goals. The higher the weight value, the higher the priority. Figure 5 middle Indicates The initial weight corresponding to the sub-goal is , the first-level sub-goal has the highest priority, The highest-level subgoal has the lowest priority.
[0052] Step S202: select a sub-goal of a certain level as a reference goal, fix its weight value as the reference goal weight, and the remaining sub-goals should shrink towards the reference goal. Figure 5 As shown, The sub-goal is the reference goal. Represents the weight of the reference target.
[0053] Step S203, setting an appropriate step length , by reducing the weights of sub-goals with higher priority than the reference goal ( ), or increase the weight of sub-goals with lower priority than the reference goal ( ), and solve the problems in turn.
[0054] Step S204, record the successive optimization results. If the optimal solution to the problem changes, determine that the corresponding sub-goal weight at this time is a close weight (inflection point). Once the inflection point is detected, the process goes to step S205; if the optimal solution does not change, the process continues to execute step S203.
[0055] Step S205, fix the close weight of the sub-goal, and continue to search for the close weight of the adjacent sub-goals until all sub-goals are traversed.
[0056] Step S206, repeating steps S202 to S205, iteratively updating the close weight of each sub-goal until all sub-goal weights reach a convergence state. At this point, the close weights of all sub-goals form a close weight vector , the obtained close fitting weight vector is what we are looking for.
[0057] Step S207, bringing the obtained close-fitting weight vector into the cascade reservoir optimization operation model, solving its dual problem, and obtaining the dual multipliers of different competing resources.
[0058] It should be noted that the reference target is a sub-target with good elasticity and is sensitive to the objective function. The remaining sub-targets should be inclined towards the sub-target with good elasticity. Due to resource constraints, the weight elasticity of some sub-targets is small and lacks elasticity. The change of weight value has a very limited impact on the optimal solution of the planning problem, resulting in a serious disconnection between the weight of the sub-target and the remaining sub-targets, which is not conducive to the optimization of the weights of other sub-targets. Therefore, it is not suitable to select the sub-targets that lack elasticity as reference targets.
[0059] Exemplarily, the change in the optimal solution of the objective function caused by a unit change in the weight of each cascade reservoir scheduling sub-objective can be evaluated, and the change in the optimal solution of the objective function can be used as the weight elasticity of the corresponding cascade reservoir scheduling sub-objective; a sub-objective with the largest weight elasticity is determined as a reference target, or a sub-objective with a weight elasticity exceeding a preset weight elasticity threshold is determined as a reference target.
[0060] The following is an example of an engineering case to illustrate the method of measuring the dual multiplier of competing resources in the cascade reservoir scheduling model provided in this application.
[0061] The proposed method is applied to the allocation of resources involved in the optimal dispatching of hydropower stations. The proposed method is used to fairly and uniformly allocate water resources, power resources, reserved storage capacity resources, spare capacity resources, etc. This method can achieve the reasonable allocation of five different resources involved in the dispatching process, namely: water resources for hydropower generation, storage capacity designated for flood control, water resources for irrigation from reservoirs or their upstream, spare generation capacity resources to ensure the reliability of the power system, and guaranteed power transmission to designated areas (such as other provinces). These resources are managed by different entities and are highly competitive. The allocation of one resource usually requires a trade-off or compromise between one or more other resources.
[0062] Taking a hydropower station as an example, the six sub-goals in the dispatching model are recorded as . They represent the total amount of abandoned water, the guaranteed amount of electricity delivered, the total amount of power generated, the system's guaranteed idle capacity, the total amount of water stored, and the total idle capacity. Since the third-level sub-goal energy generation (total power generation) is significantly sensitive to changes in the objective function (as a sub-goal with good elasticity), this sub-goal is selected as the reference goal. Among them, the system's guaranteed idle capacity is the minimum idle capacity of the reservoir during the entire period, that is, the vacant and unused capacity. The total idle capacity is the total idle capacity of the reservoir during the entire period.
[0063] Table 1 summarizes the initial values and optimization results of the weights of each sub-objective. As shown in Table 1, sub-objectives of smaller magnitude require higher weights to ensure their priority, while sub-objectives of larger magnitude only require lower weights. For this cascade reservoir multi-objective optimization scheduling model, the optimal close-fitting weight vector is .
[0064] Table 1. Results of close fitting weights
[0065] Table 2 summarizes the optimal solutions of the scheduling model in two scenarios. It can be seen that the replacement of the original weights (initial weights) with the close weights does not change the optimal solution of the original problem. Therefore, it can be guaranteed that the solution of the dual multiplier in the close weight scenario can still represent the resource configuration information contained in the original problem.
[0066] Table 2 Optimal solutions for cascade reservoir operation under different weights
[0067] Based on the close weight vector and the initial weight vector, the dual multipliers of different resources are unified. Taking water resources as the benchmark, the utility of each resource in different periods of a year is quantified. The results are as follows: Figure 6-10 As shown. Among them, spare capacity: the capacity of equipment that needs to be added to the hydropower station to ensure power demand in the event of equipment maintenance, accidents, frequency regulation, etc. Water storage limit: the maximum capacity of water that a reservoir can store. Interval water inflow: the runoff between the upper reservoir and the lower reservoir. Installed capacity: the sum of the rated powers of all hydropower generating units installed in the hydropower station. It can be seen that in the optimization results based on close weights, the dual multipliers of the same resource in the same period are unified to the same order of magnitude. Even if the sub-goals affected by the unit increment of resources in different periods may be inconsistent, they can be reflected in the results of different sizes of the same order of magnitude. In the optimization results based on the original weights, the dual multipliers of the same resource in different periods also have differences in order of magnitude, such as spare capacity resources, installed capacity resources, etc., so it is impossible to commensurate different resources, and it is impossible to measure the utility of different resources based on this result. It is worth noting that the dual multipliers of reserve capacity and installed capacity show complete complementarity, because these two resources have the opposite effects on the power system: adding one unit of capacity and reducing one unit of capacity from the system; ensuring that the dual multiplier of the power transmission in the middle of the year is greater than that at the beginning and end of the year; the dual multiplier of the upper limit of water storage resources reaches its maximum value in the 44th week; and the utility of interval water resources in the dry season is higher than that in the flood season. By quantifying the dual multipliers of different resources managed by the reservoir at different times, the reservoir can clearly know how to allocate resources in different periods to obtain the maximum utilization of resources, thereby achieving the purpose of optimal resource allocation.
[0068] The following is a description of the device for measuring the dual multiplier of competing resources in the cascade reservoir scheduling model provided in the present application. The device for measuring the dual multiplier of competing resources in the cascade reservoir scheduling model described below and the method for measuring the dual multiplier of competing resources in the cascade reservoir scheduling model described above can be referenced to each other.
[0069] The present application provides a device for measuring dual multipliers of competitive resources in a cascade reservoir scheduling model, comprising: a scheduling model determination module, a weight adjustment module, a convergence judgment module and a dual multiplier acquisition module. Among them: The scheduling model determination module is used to determine the objective function for optimizing the scheduling of cascade reservoirs and the constraints corresponding to various resources associated with the scheduling of cascade reservoirs. The objective function is a linear function determined based on multiple cascade reservoir scheduling sub-objectives and the initial weights corresponding to each cascade reservoir scheduling sub-objective. The larger the initial weight, the higher the priority of the corresponding cascade reservoir scheduling sub-objective. The weight adjustment module is used to adjust the weight of each cascade reservoir scheduling sub-goal in order of priority from high to low. A cascade reservoir scheduling sub-goal is used as a reference target, and the weight of the reference target is fixed. The weight adjustment includes controlling the weight of the corresponding cascade reservoir scheduling sub-goal to shrink toward the weight of the reference target until the optimal solution of the objective function is determined to change; The convergence judgment module is used to judge whether the weight of each cascade reservoir scheduling sub-objective has converged; The weight adjustment module is also used to continue to adjust the weight of each cascade reservoir scheduling sub-objective in order of priority from high to low when convergence has not occurred; The dual multiplier acquisition module is used to solve the dual problem of the original problem when convergence has occurred, so as to obtain the dual multipliers corresponding to the resources of each cascade reservoir. The dual multipliers are used to characterize the increase in the optimal value of the objective function brought about by each unit increase in the corresponding resources.
[0070] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.
[0071] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.
[0072] Based on the method in the above embodiment, an embodiment of the present application provides an electronic device, Fig.11 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Fig.11As shown, the electronic device may include: a processor (Processor) 810, a communication interface (Communications Interface) 820, a memory (Memory) 830 and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call the logic instructions in the memory 830 to execute the method in the above embodiment.
[0073] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.
[0074] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.
[0075] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.
[0076] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.
[0077] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.
[0078] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.
[0079] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.
[0080] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.
Claims
1. A method for measuring dual multipliers of competing resources in a cascade reservoir operation model, characterized in that: include: Determine the objective function for optimizing the operation of cascade reservoirs and the constraints corresponding to various resources associated with the operation of cascade reservoirs. The objective function is a linear function determined based on multiple cascade reservoir operation sub-objectives and the initial weights corresponding to each cascade reservoir operation sub-objective. The larger the initial weight, the higher the priority of the corresponding cascade reservoir operation sub-objective. The weight of each cascade reservoir operation sub-objective is adjusted in descending order of priority. A cascade reservoir operation sub-objective is used as a reference object, and the weight of the reference object remains fixed. The weight adjustment includes controlling the weight of the corresponding cascade reservoir operation sub-objective to shrink toward the weight of the reference object until the optimal solution of the objective function is determined to change. Determine whether the weight of each cascade reservoir scheduling sub-objective converges; In the case of non-convergence, continue to adjust the weight of each cascade reservoir scheduling sub-objective in order of priority from high to low; or, in the case of convergence, solve the dual problem of the original problem to obtain the dual multiplier corresponding to the resources of each cascade reservoir. The dual multiplier is used to characterize the increase in the optimal value of the objective function brought about by each unit increase in the corresponding resources.
2. The method for measuring dual multipliers of competing resources in a cascade reservoir operation model according to claim 1 is characterized in that: When weight adjustment is performed on the cascade reservoir scheduling sub-target with a higher priority than the reference target, the weight adjustment specifically includes: According to the specified step size, the weights of the corresponding cascade reservoir scheduling sub-objectives are gradually reduced until the optimal solution of the objective function is determined to change.
3. The method for measuring dual multipliers of competing resources in a cascade reservoir operation model according to claim 1 is characterized in that: When weight adjustment is performed on the cascade reservoir scheduling sub-targets with lower priority than the reference target, the weight adjustment specifically includes: According to the specified step size, the weights of the corresponding cascade reservoir scheduling sub-objectives are gradually increased until the optimal solution of the objective function is determined to change.
4. The method for measuring dual multipliers of competing resources in a cascade reservoir operation model according to claim 1 is characterized in that: Before determining that the optimal solution of the objective function changes, it also includes: Based on the current weight value, solve the objective function and obtain the latest optimal solution; Determine whether the gap between the latest optimal solution and the initial optimal solution exceeds a preset solution gap threshold. The initial optimal solution is obtained by solving the objective function based on the initial weights. Among them, if the gap is greater than the preset solution gap threshold, the optimal solution of the objective function changes, and if the gap is less than or equal to the preset solution gap threshold, the optimal solution of the objective function does not change.
5. The method for measuring dual multipliers of competing resources in a cascade reservoir operation model according to claim 1 is characterized in that: The step of judging whether the weight of each cascade reservoir scheduling sub-objective has converged includes: For the weights of the cascade reservoir scheduling sub-goals, determine whether the difference between the weights determined by the current round of weight adjustment and the weights determined by the previous round of weight adjustment is less than a preset weight difference threshold; Among them, if the gap is less than the preset weight gap threshold, the weight of the cascade reservoir scheduling sub-objective has converged; if the gap is greater than or equal to the preset weight gap threshold, the weight of the cascade reservoir scheduling sub-objective has not converged.
6. The method for measuring dual multipliers of competing resources in a cascade reservoir operation model according to any one of claims 1 to 5, characterized in that: Before adjusting the weight of each cascade reservoir operation sub-objective in order of priority from high to low, it also includes: Evaluate the change of the optimal solution of the objective function caused by a unit change in the weight of each cascade reservoir operation sub-objective, and use the change in the optimal solution of the objective function as the weight elasticity of the corresponding cascade reservoir operation sub-objective; A sub-goal with the largest weight elasticity is determined as a reference goal, or a sub-goal with a weight elasticity exceeding a preset weight elasticity threshold is determined as a reference goal.
7. A device for measuring dual multipliers of competing resources in a cascade reservoir operation model, characterized in that: include: The scheduling model determination module is used to determine the objective function for optimizing the scheduling of cascade reservoirs and the constraints corresponding to various resources associated with the scheduling of cascade reservoirs. The objective function is a linear function determined based on multiple cascade reservoir scheduling sub-objectives and the initial weights corresponding to each cascade reservoir scheduling sub-objective. The larger the initial weight, the higher the priority of the corresponding cascade reservoir scheduling sub-objective. The weight adjustment module is used to adjust the weight of each cascade reservoir scheduling sub-goal in order of priority from high to low. A cascade reservoir scheduling sub-goal is used as a reference target, and the weight of the reference target is fixed. The weight adjustment includes controlling the weight of the corresponding cascade reservoir scheduling sub-goal to shrink toward the weight of the reference target until the optimal solution of the objective function is determined to change; The convergence judgment module is used to judge whether the weight of each cascade reservoir scheduling sub-objective has converged; The weight adjustment module is also used to continue to adjust the weight of each cascade reservoir scheduling sub-objective in order of priority from high to low when convergence has not occurred; The dual multiplier acquisition module is used to solve the dual problem of the original problem when convergence has occurred, so as to obtain the dual multipliers corresponding to the resources of each cascade reservoir. The dual multipliers are used to characterize the increase in the optimal value of the objective function brought about by each unit increase in the corresponding resources.
8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 6.
10. A computer program product, characterized in that When the computer program product runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 6.