Cascade hydropower optimization scheduling method and device based on regional adjustable resources

Abstract

The invention discloses a cascade hydropower optimization scheduling method and device based on regional adjustable resources, and relates to the technical field of power systems, and the method comprises the steps: collecting the operation data of cascade hydropower stations and regional combined adjustable resources; constructing a master-slave game model of the upstream and downstream regions; constructing a three-layer optimization model comprising a compensation coefficient optimization model, an upstream optimization scheduling model and a downstream optimization scheduling model; solving the three-layer optimization model, and dynamically updating a compensation coefficient and upstream and downstream scheduling strategies; and dynamically adjusting a water discharge strategy of the upstream power station and a regulation and control strategy of the combined adjustable resources based on a solving result. According to the cascade hydropower optimization scheduling method based on the regional adjustable resources, the regional adjustable resource joint income models of the upstream region and the downstream region are constructed, the income increasing compensation coefficient is introduced, reasonable allocation of benefits of the upstream region and the downstream region is achieved, and therefore the economic operation efficiency and the power supply stability of the whole drainage basin are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and in particular to a method and device for optimizing the dispatching of cascaded hydropower based on regional adjustable resources. Background Art

[0002] Hydropower stations often exist in the form of cascades in the river basin. In the cascade hydropower station system, the water release of the upper power station is directly related to the power generation efficiency of the lower power station. They are connected both hydraulically and electrically. When the wind and solar resources and electricity demand in the upstream and downstream areas of the cascade tend to be consistent in time distribution, the independent scheduling strategy of the upstream hydropower station may be perfectly matched with the downstream demand. However, once the wind and solar timing characteristics of the upstream and downstream differ, the upstream scheduling plan may no longer be applicable to the downstream demand. Specifically, the following problems are faced:

[0003] (1) Centralized scheduling has limitations:

[0004] Hydropower stations often exist in the form of cascades in series in river basins. Especially today, when my country's hydropower resources are being developed and utilized on a large scale, such clusters of cascade hydropower stations are common in areas with abundant hydropower. In a cascade hydropower station system, the water release of the upper power station is directly related to the power generation efficiency of the lower power station. They are connected both hydraulically and electrically. When the wind and solar resources and electricity demand in the upstream and downstream areas of the cascade tend to be consistent in time distribution, the independent scheduling strategy of the upstream hydropower station may be perfectly matched with the downstream demand. However, once the wind and solar timing characteristics of the upstream and downstream differ, the upstream scheduling plan may no longer be applicable to the downstream demand.

[0005] (2) Difficulties in coordinating cascade hydropower dispatching:

[0006] In a cascade hydropower station system, the water release of the upper power station directly affects the power generation efficiency of the lower power station, and they are connected both hydraulically and electrically. However, the existing technology has failed to provide an effective coordinated scheduling mechanism so that the upstream and downstream power stations can be optimized based on their respective wind and solar timing characteristics and power demand. This lack of coordination makes it difficult for the upstream and downstream power stations to achieve joint optimization, affecting the economic operation efficiency and power supply stability of the entire river basin. The present invention constructs a master-slave game model so that the upstream and downstream regions can formulate their own scheduling strategies based on the strategies of the other party, so as to maximize the utilization of resources and improve the respective benefits of the upstream and downstream regions.

[0007] (3) The existing compensation mechanism is unreasonable:

[0008] The compensation mechanism proposed in the existing literature has difficulties in determining the compensation price, which leads to uneven distribution of benefits between upstream and downstream power stations and affects the enthusiasm for coordinated scheduling. A reasonable compensation mechanism is the key to achieving coordinated scheduling of upstream and downstream power stations, but how to determine a compensation price that can both encourage upstream power stations to adjust their water release strategies to meet downstream demand and not cause downstream power stations to bear too much compensation costs is a difficult problem in the prior art. The present invention proposes a compensation mechanism based on an increased revenue compensation coefficient, which constructs a fair and reasonable benefit distribution mechanism by compensating the increased revenue to the upstream area in proportion, thereby stimulating collaboration between upstream and downstream power stations and achieving optimal allocation of resources. Summary of the invention

[0009] In view of the above-mentioned problems, the present invention is proposed.

[0010] Therefore, the technical problem solved by the present invention is: how to determine a reasonable compensation mechanism so that the upstream and downstream power stations can reach an agreement during the game process. Game theory models are usually complex, involving a large number of variables and constraints, which makes the calculation difficult and the solution efficiency low. In actual hydropower scheduling, it is necessary to respond quickly to changes in hydrology and the market, but existing game theory models often cannot meet the real-time requirements.

[0011] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing the scheduling of cascade hydropower based on regional adjustable resources, comprising:

[0012] Collecting operation data of cascade hydropower stations and regional joint adjustable resources;

[0013] Based on the operation data, a master-slave game model of the upstream and downstream regions is constructed;

[0014] Based on the master-slave game model, a three-layer optimization model including a compensation coefficient optimization model, an upstream optimization scheduling model and a downstream optimization scheduling model is constructed;

[0015] Solving the three-layer optimization model, dynamically updating the compensation coefficient and upstream and downstream scheduling strategies, until the optimal solution that meets the constraints is solved;

[0016] The water release strategy of the upstream power station and the control strategy of the combined adjustable resources are dynamically adjusted based on the solution results, and an optimized cascade hydropower scheduling plan is output.

[0017] As a preferred solution of the cascade hydropower optimization scheduling method based on regional adjustable resources described in the present invention, the operating data includes the power generation capacity, water discharge volume, time distribution characteristics of wind and solar resources and power load demand of upstream and downstream power stations.

[0018] As a preferred solution of the cascade hydropower optimization scheduling method based on regional adjustable resources described in the present invention, wherein: the master-slave game model of the upstream and downstream regions includes the downstream region as the master game party, with the goal of maximizing the downstream revenue;

[0019] As a secondary player, the upstream region aims to maximize upstream profits and achieves coordination of upstream and downstream interests through compensation coefficients.

[0020] As a preferred solution of the cascade hydropower optimization scheduling method based on regional adjustable resources described in the present invention, the compensation coefficient optimization model is expressed as:

[0021]

[0022] in, represents the total downstream cost; γ represents the compensation coefficient.

[0023] As a preferred solution of the cascade hydropower optimization scheduling method based on regional adjustable resources described in the present invention, the upstream optimization scheduling model is expressed as:

[0024]

[0025] Among them, C U represents the upstream operating cost; the downstream operating cost C D Also determined by the next lower optimization model; X U Represents the decision variables for upstream optimal scheduling.

[0026] As a preferred solution of the cascade hydropower optimization scheduling method based on regional adjustable resources described in the present invention, the downstream optimization scheduling model is expressed as:

[0027]

[0028] As a preferred solution of the cascade hydropower optimization scheduling method based on regional adjustable resources described in the present invention, the solution includes short-term scheduling solution and medium- and long-term scheduling solution.

[0029] Another object of the present invention is to provide a cascade hydropower optimization dispatching device based on regional adjustable resources, which solves the problem that the centralized dispatching method is difficult to adapt to the rapidly changing hydrological conditions and power market demand. The centralized dispatching method fails to fully consider the different interests and operating goals of upstream and downstream power stations, resulting in uneven resource allocation and low economic benefits. The centralized dispatching method is difficult to make rapid and effective adjustments in the face of emergencies and system changes.

[0030] To solve the above technical problems, the present invention provides the following technical solutions: a cascade hydropower optimization scheduling device based on regional adjustable resources, comprising: a data acquisition module for collecting operating data of cascade hydropower stations and regional joint adjustable resources; a model construction module for constructing a master-slave game model of upstream and downstream areas based on the operating data; a model optimization module for constructing a three-layer optimization model including a compensation coefficient optimization model, an upstream optimization scheduling model and a downstream optimization scheduling model based on the master-slave game model; a model solving module for solving the three-layer optimization model using a particle swarm algorithm, dynamically updating the compensation coefficient and the upstream and downstream scheduling strategies until the optimal solution that meets the constraints is solved; a hydropower scheduling module for dynamically adjusting the water release strategy of the upstream power station and the control strategy of the joint adjustable resources based on the solution results, and outputting an optimized cascade hydropower scheduling solution.

[0031] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for optimizing the scheduling of cascade hydropower based on regional adjustable resources are implemented.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for optimizing the scheduling of cascade hydropower based on regional adjustable resources.

[0033] Beneficial effects of the present invention: The cascade hydropower optimization scheduling method based on regional adjustable resources provided by the present invention realizes the reasonable distribution of the interests of the upstream and downstream regions by constructing the joint revenue model of the regional adjustable resources of the upstream and downstream regions, and introduces the revenue increase compensation coefficient, thereby improving the economic operation efficiency and power supply stability of the entire river basin. The present invention uses a master-slave game model to enable the upstream and downstream regions to formulate their own scheduling strategies on the basis of considering each other's strategies, so as to maximize the utilization of resources, improve the respective benefits of the upstream and downstream regions, and achieve a win-win situation among the regions. The results of the example show that the model can simultaneously improve the respective benefits of the upstream and downstream regions, maximize the utilization of resources, and has significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0035] Figure 1 An overall flow chart of a method for optimizing the scheduling of cascaded hydropower based on regional adjustable resources provided by one embodiment of the present invention.

[0036] Figure 2 A flowchart of a master-slave game relationship of a cascade hydropower optimization scheduling method based on regional adjustable resources is provided for one embodiment of the present invention.

[0037] Figure 3 An overall structural diagram of a cascade hydropower optimization scheduling system based on regional adjustable resources provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] Example 1

[0041] Reference Figure 1 , which is an embodiment of the present invention, provides a cascade hydropower optimization scheduling method based on regional adjustable resources, comprising:

[0042] Collecting operation data of cascade hydropower stations and regional joint adjustable resources;

[0043] Based on the operation data, a master-slave game model of the upstream and downstream regions is constructed;

[0044] Based on the master-slave game model, a three-layer optimization model including a compensation coefficient optimization model, an upstream optimization scheduling model and a downstream optimization scheduling model is constructed;

[0045] Solving the three-layer optimization model, dynamically updating the compensation coefficient and upstream and downstream scheduling strategies, until the optimal solution that meets the constraints is solved;

[0046] The water release strategy of the upstream power station and the control strategy of the combined adjustable resources are dynamically adjusted based on the solution results, and an optimized cascade hydropower scheduling plan is output.

[0047] The operating data includes the power generation capacity of upstream and downstream power stations, water discharge, time distribution characteristics of wind and solar resources, and power load demand.

[0048] The master-slave game model of the upstream and downstream regions includes the downstream region as the master game party, with the goal of maximizing the downstream benefits;

[0049] As a secondary player, the upstream region aims to maximize upstream profits and achieves coordination of upstream and downstream interests through compensation coefficients.

[0050] The compensation coefficient optimization model is expressed as:

[0051]

[0052] in, represents the total downstream cost; γ represents the compensation coefficient.

[0053] The upstream optimization scheduling model is expressed as:

[0054]

[0055] Among them, C U represents the upstream operating cost; the downstream operating cost C D Also determined by the next lower optimization model; X U Represents the decision variables for upstream optimal scheduling.

[0056] The downstream optimization scheduling model is expressed as:

[0057]

[0058] The solving includes short-term scheduling solution and medium- and long-term scheduling solution.

[0059] It should be noted that in exploring the issue of promoting coordinated dispatching and cooperation of cascade hydropower stations among different stakeholders, the existing methods mainly propose two major strategies. The first strategy is that the upstream and downstream parties determine the unit compensation price of water volume through negotiation in advance. The downstream pays the upstream according to the difference between the actual water release of the upstream and the initial water release during independent dispatching. Another widely studied strategy is to compensate the upstream according to a certain proportion of the increased income within the cycle. The present invention innovatively constructs a compensation coefficient and dispatch strategy optimization model based on non-cooperative game theory. In this model, the wind, solar, water and fire energy alliance in the downstream area (referred to as "downstream") agrees to provide a certain proportion of income growth to the wind, solar, water and fire energy alliance in the upstream area (referred to as "upstream") as compensation, so as to encourage the upstream to adjust its dispatch strategy, thereby further improving the downstream's income. The upstream and downstream alliances play a game on the compensation coefficient and the upstream's dispatch strategy, and each formulates a strategy based on the principle of maximizing benefits.

[0060] Existing centralized dispatching methods have obvious deficiencies in dealing with the coordination of interests between upstream and downstream power stations in cascade hydropower systems. Centralized dispatching often ignores the conflicts of interest between upstream and downstream power stations due to differences in geographical location and operational objectives, and is unable to meet the needs of multiple stakeholders at the same time. This leads to uneven resource allocation between power stations, low economic efficiency, and possible waste of water resources. The present invention solves the above problems by introducing a game optimization dispatching method for regional joint adjustable resources, thereby achieving maximum utilization of resources and a balance of interests among multiple parties. By constructing a joint revenue model for regional adjustable resources of each upstream and downstream, and introducing a revenue increase compensation coefficient, the present invention can stimulate collaboration between upstream and downstream power stations and improve the economic operation efficiency and power supply stability of the entire river basin.

[0061] In a cascade hydropower station system, the water release of the upper power station directly affects the power generation efficiency of the lower power station, but the existing technology fails to provide an effective coordinated scheduling mechanism so that the upstream and downstream power stations can be optimized and scheduled based on their respective wind and solar timing characteristics and power demand. This lack of coordination makes it difficult for the upstream and downstream power stations to achieve joint optimization, affecting the economic operation efficiency and power supply stability of the entire river basin. The present invention constructs a master-slave game model so that the upstream and downstream regions can formulate their own scheduling strategies based on the strategies of the other party, so as to maximize the utilization of resources and improve the respective benefits of the upstream and downstream regions. This method not only improves the economic benefits of hydropower scheduling, but also enhances the synergy between upstream and downstream power stations, thereby improving the economic operation efficiency and power supply stability of the entire river basin.

[0062] The compensation mechanism proposed in the existing literature has difficulties in determining the compensation price, which leads to uneven distribution of benefits between upstream and downstream power stations and affects the enthusiasm for coordinated scheduling. A reasonable compensation mechanism is the key to achieving coordinated scheduling of upstream and downstream power stations, but how to determine a compensation price that can both encourage upstream power stations to adjust their water release strategies to meet downstream demand and not cause downstream power stations to bear too much compensation costs is a difficult problem in the prior art. The present invention proposes a compensation mechanism based on an increased revenue compensation coefficient, which constructs a fair and reasonable benefit distribution mechanism by compensating the increased revenue to the upstream area in proportion, thereby stimulating collaboration between upstream and downstream power stations and achieving optimal allocation of resources. This method not only solves the problem of benefit distribution between upstream and downstream power stations, but also improves the scheduling efficiency and economic benefits of the entire hydropower system.

[0063] The above comparison shows that the present invention can better adapt to the uncertainty and dynamic changes in actual hydropower scheduling when dealing with cascade hydropower scheduling problems, and has higher practicality and effectiveness.

[0064] The method of this embodiment realizes the reasonable distribution of the interests of the upstream and downstream regions by constructing a joint revenue model of regional adjustable resources for each of the upstream and downstream regions and introducing a revenue increase compensation coefficient, thereby improving the economic operation efficiency and power supply stability of the entire river basin. The present invention uses a master-slave game model to enable the upstream and downstream regions to formulate their own scheduling strategies based on the strategies of the other party, so as to maximize the utilization of resources, improve the respective benefits of the upstream and downstream regions, and achieve a win-win situation among the regions. The results of the example show that the model can simultaneously improve the respective benefits of the upstream and downstream regions, maximize the utilization of resources, and has significant economic and social benefits.

[0065] Specifically, through the regional joint adjustable resource joint benefit model, the model takes into account the power generation efficiency and water release strategy of upstream and downstream power stations in the cascade hydropower system, as well as the power generation characteristics of renewable energy such as wind and solar energy. Through this model, the power load in the upstream and downstream regions is mainly balanced by the wind, solar, water and thermal power generation resources in their respective regions, without taking into account the purchase and sale of electricity between regions through transmission channels. The innovation of this model is that it can more effectively coordinate hydropower with wind, solar and thermal power generation in the entire region, significantly improving the economic operation efficiency and power supply stability of the entire basin.

[0066] Specifically, an increase in revenue compensation coefficient is introduced to stimulate the willingness of upstream regions to cooperate. In the master-slave game model, the downstream region, as the leader, formulates the increase in revenue compensation coefficient, while the upstream region, as the follower, formulates the optimization scheduling strategy. The innovation of this mechanism is that it can use economic incentives to prompt the upstream region to adjust its scheduling strategy to increase the benefits of the downstream region. The introduction of this compensation mechanism not only solves the problem of interest distribution between upstream and downstream regions, but also improves the scheduling efficiency of the entire hydropower system.

[0067] Specifically, the present invention constructs a game optimization scheduling model including three layers of optimization. In this model, the top layer is responsible for optimizing the compensation coefficient, the second layer is responsible for the upstream optimization scheduling, and the third layer is responsible for the downstream optimization scheduling. The innovation of this model is that it jointly seeks the Nash equilibrium solution that maximizes the comprehensive benefits of the upstream and downstream regions through information transmission and feedback mechanisms. The construction of this multi-layer optimization model not only improves the solution efficiency of the model, but also ensures the practicality and effectiveness of the model.

[0068] Example 2

[0069] Reference Figure 2 , which is an embodiment of the present invention, provides a cascade hydropower optimization scheduling method based on regional adjustable resources.

[0070] Traditional methods propose hydropower dispatching methods based on optimization algorithms, such as using genetic algorithms, particle swarm algorithms, etc. to solve hydropower dispatching problems. These patents usually focus on algorithm optimization and computational efficiency improvement, but are still insufficient in dealing with the interest coordination and compensation mechanism between upstream and downstream power stations. In addition, these patents rarely consider the uncertainty and dynamic changes in actual hydropower dispatching, such as the randomness of hydrological conditions and the volatility of the power market.

[0071] In traditional hydropower dispatching, centralized optimization dispatching methods are widely used. This method is usually based on a single objective function, such as minimizing power generation costs or maximizing economic benefits, while ignoring the complex interest relationship between upstream and downstream power stations in the cascade hydropower system.

[0072] Based on the existing technology, the present invention proposes a new method for optimizing the scheduling of cascade hydropower based on regional adjustable resources. This method not only takes into account the coordination of interests between upstream and downstream power stations, but also introduces a revenue increase compensation coefficient to balance the interests of all parties, and improves the efficiency of model solution through particle swarm algorithm. When dealing with cascade hydropower scheduling problems, this method can better adapt to the uncertainty and dynamic changes in actual hydropower scheduling, and has higher practicality and effectiveness.

[0073] The first step of the method is to construct a solution model for the compensation coefficient to obtain the compensation coefficient from the downstream to the upstream. The second step is to construct an upstream optimization scheduling strategy model based on the first step to obtain the upstream water release strategy result. Finally, based on the result of the second step, a downstream optimization scheduling strategy model is constructed to achieve a reasonable distribution of benefits in the upstream and downstream regions. The following are the main steps of the present invention and its technical implementation method:

[0074] First, a regional adjustable resource joint benefit model is established for each upstream and downstream region, which takes into account the power generation efficiency and water release strategy of hydropower stations, as well as the power generation characteristics of renewable energy such as wind and solar energy. Through mathematical programming methods, a multi-energy alliance covering wind, solar, hydropower and traditional thermal power is constructed, and a complementary scheduling strategy is adopted to meet electricity demand in a low-carbon and efficient manner.

[0075] Considering the differences in the timing characteristics of wind and solar loads in the upstream and downstream regions, the optimal scheduling of the upstream and downstream forms a two-layer optimization model with nested upper and lower layers, in which the upper layer is the optimal scheduling of the upstream, taking into account the downstream benefits and compensation benefits to maximize the comprehensive benefits; the lower layer is the optimal scheduling of the downstream, taking the upstream water release strategy as one of the constraints, aiming to maximize its own benefits. Furthermore, the downstream, as the provider of compensation, becomes the upper-level leader, while the upstream, as the party that accepts compensation and adjusts the scheduling strategy, becomes the lower-level follower, which constitutes a three-layer optimization model that includes compensation coefficient optimization, upstream optimal scheduling, and downstream optimal scheduling. In this model, the top layer is responsible for optimizing the compensation coefficient, the second layer is responsible for the upstream optimal scheduling, and the third layer is responsible for the downstream optimal scheduling. The layers interact with each other through information transmission and feedback mechanisms to jointly seek the Nash equilibrium solution that maximizes the comprehensive benefits of the upstream and downstream regions, such as Figure 3 shown.

[0076] Constructing a game optimization scheduling model based on bilateral compensation contracts

[0077] (1) First layer: Optimizing compensation coefficient

[0078] 1) In order to minimize the total downstream cost after paying the compensation fee, the objective function is constructed as follows:

[0079]

[0080] Where: represents the total downstream cost; γ represents the compensation coefficient.

[0081] 2) The feasible region A1 contains the following two types of constraints:

[0082] Compensation coefficient range

[0083] 0≤γ≤1

[0084] Total downstream cost

[0085]

[0086] Where: and λ They represent the initial operating costs of the downstream and upstream when the upstream operates independently; C D represents the actual operating cost of the downstream, and its cost value is determined by the lower-level optimization model; Ω2 represents the minimum value of the upstream total cost calculated by the lower-level optimization model after downstream compensation; λ is a proportional coefficient, which represents the expected proportion of the upstream total cost to the initial cost that the upstream expects to reduce through downstream compensation.

[0087] (2) Second layer: upstream optimization scheduling

[0088] 1) The total construction cost is as follows:

[0089]

[0090] Where: C U represents the upstream operating cost; the downstream operating cost C D Also determined by the next lower optimization model; X U represents the decision variable for upstream optimization scheduling, as shown in Eq.

[0091] 2) The upstream optimization scheduling decision variables are defined as follows:

[0092]

[0093] Where: i represents the serial number of the thermal power unit, Φ U represents the upstream thermal power unit set; j represents the hydropower station number, Ψ U represents the set of upstream hydropower stations; t represents the time period number, T represents the number of time periods in the scheduling cycle; r represents the scenario number; They represent the output of upstream thermal power unit i, the output of hydropower station j, the reservoir capacity of hydropower station j, the abandoned water flow of hydropower station j, and the generated water flow of hydropower station j during period t respectively; and They respectively represent the amount of wind and solar power abandonment and load loss in the upstream under scenario r during period t.

[0094] 3) Upstream operating cost constraints are:

[0095]

[0096] Where: a U,i represents the unit power generation cost of upstream thermal power unit i; b u Indicates the unit electricity price of users for upstream load loss compensation; represents the probability that the upstream is in scenario r.

[0097] 4) The upstream power balance constraint is:

[0098]

[0099] Where: and They represent the wind power output and photovoltaic power output of the upstream in period t under scenario r respectively; Indicates the upstream load size in period t.

[0100] 5) Upstream cascade hydropower constraints

[0101]

[0102] In the formula: Δt represents the number of hours per unit time period; represents the interval flow of upstream reservoir j in time period t; Respectively represent the initial storage capacity and expected final storage capacity of the upstream reservoir j; V U,j,min , V U,j,max Respectively represent the minimum and maximum storage capacity of the upstream reservoir j; P U,j,min , P U,j,max Respectively represent the minimum and maximum output of the upstream hydropower station j; A U,j represents the output coefficient of upstream reservoir j; represents the net head of upstream reservoir j at time period t; represents the total inflow of upstream reservoir j in period t.

[0103] 6) Upstream thermal power constraints

[0104] P U,i,min ≤P U,i,min ≤P U,i,max

[0105]

[0106] Where: P U,i,min , P U,i,max Respectively represent the minimum and maximum output of upstream thermal power unit i; R U,i,max Represents the maximum ramp rate of upstream thermal power unit i.

[0107] 3. The third layer: downstream optimization scheduling

[0108] (1) Objective function

[0109]

[0110] (2) Decision variables

[0111]

[0112] Where: The meaning of each variable is the same as that of the upstream, except that the subscript is changed from U to D.

[0113] (3) Constraints

[0114]

[0115] Where: They represent the abandoned water flow and the generated water flow of the upstream hydropower station J during period t respectively; J represents the serial number of the last hydropower station in the cascade.

[0116] 3. Model solution

[0117] (1) Short-term scheduling solution

[0118] In short-term scheduling, due to the short time scale, the net water head H,j can be considered constant. Therefore, both the second-level and third-level optimization models can be regarded as linear programming problems. In order to transform the two-level model into a single-level model, the KKT conditions of the third-level optimization model can be used as the constraints of the second level. The KKT conditions are necessary conditions for local optimal solutions in multivariable optimization problems, including the conditions of zero gradient, non-negative Lagrange multipliers for inequality constraints, and complementary relaxation conditions. The KKT conditions may contain non-convex constraints, which are not easy to solve directly. The big M method is a commonly used linearization technique that transforms non-convex constraints into linear constraints by introducing large numbers M and auxiliary variables.

[0119] (2) Medium- and long-term scheduling solutions

[0120] 1) Based on McCormick's envelope method, a linear approximation is made for the upstream cascade hydropower constraints as follows:

[0121]

[0122]

[0123] Where: Q U,j,min , Q U,j,max Respectively represent the minimum and maximum power generation flow; H U,j,min , H U,j,max Respectively represent the minimum and maximum net water head. That is, it also includes the following constraints:

[0124] Q U,j,min ≤Q U,j ≤Q U,j,max

[0125] H U,j,min ≤H U,j ≤H U,j,max

[0126] 2) Consider linearizing the net water head and storage capacity

[0127]

[0128] Where: s and S represent the segment number and the total number of segments respectively; β u,j,s Represents the slope of each segment, often β u,j,1 >β u,j,2 >...>β u,j,s ; δ U,j,s represents the segmented intercept; represents the storage capacity segmentation point, Represents the storage capacity variables of each segment, and their sum constitutes the total storage capacity.

[0129] (3) Medium- and long-term scheduling solution process

[0130] For the first-layer optimization model that contains logical judgment constraints and has relatively few variables and constraints, the present invention adopts a heuristic algorithm, the particle swarm algorithm, to solve it. The algorithm uses the objective function of the first layer as the fitness function, first sets an initial compensation coefficient, and substitutes this coefficient as a constant into the second and third-layer optimization models. Subsequently, using the linearization method mentioned above, the second and third-layer optimization models are converted into single-layer integer linear programming models for solving to obtain their optimal solutions. This optimal solution is then fed back to the first-layer optimization model to update the compensation coefficient. Through the continuous iterative update strategy between the first layer and the lower two layers of models, until the optimization result is stabilized within a very small range, it can be considered that the Nash equilibrium solution of the game has been found, which marks the completion of the entire optimization process.

[0131] Example 3

[0132] An embodiment of the present invention is different from the first two embodiments in that:

[0133] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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. The computer software product is stored in a storage medium, including several 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 methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0134] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0135] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0136] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0137] Example 4

[0138] Reference Figure 3 , which is an embodiment of the present invention, provides a cascade hydropower optimization scheduling device based on regional adjustable resources, comprising:

[0139] Data collection module 100, model building module 200, model optimization module 300, model solving module 400 and hydropower dispatching module 500;

[0140] The data acquisition module 100 is used to collect the operation data of cascade hydropower stations and regional joint adjustable resources;

[0141] The model building module 200 is used to build a master-slave game model of the upstream and downstream areas based on the operation data;

[0142] The model optimization module 300 is used to construct a three-layer optimization model including a compensation coefficient optimization model, an upstream optimization scheduling model and a downstream optimization scheduling model based on the master-slave game model;

[0143] The model solving module 400 is used to solve the three-layer optimization model, dynamically update the compensation coefficient and the upstream and downstream scheduling strategies, until the optimal solution that meets the constraint conditions is solved;

[0144] The hydropower dispatching module 500 is used to dynamically adjust the water release strategy of the upstream power station and the control strategy of the combined adjustable resources based on the solution results, and output an optimized cascade hydropower dispatching plan.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the dispatching of cascade hydropower based on regional adjustable resources, characterized in that: include: Collecting operation data of cascade hydropower stations and regional joint adjustable resources; Based on the operation data, a master-slave game model of the upstream and downstream regions is constructed; Based on the master-slave game model, a three-layer optimization model including a compensation coefficient optimization model, an upstream optimization scheduling model and a downstream optimization scheduling model is constructed; Solving the three-layer optimization model, dynamically updating the compensation coefficient and upstream and downstream scheduling strategies, until the optimal solution that meets the constraints is solved; The water release strategy of the upstream power station and the control strategy of the combined adjustable resources are dynamically adjusted based on the solution results, and an optimized cascade hydropower scheduling plan is output.

2. The method for optimizing the dispatching of cascade hydropower based on regional adjustable resources as claimed in claim 1, characterized in that: The operating data includes the power generation capacity of upstream and downstream power stations, water discharge, time distribution characteristics of wind and solar resources, and power load demand.

3. The method for optimizing the dispatching of cascade hydropower based on regional adjustable resources as claimed in claim 2, characterized in that: The master-slave game model of the upstream and downstream regions includes the downstream region as the master game party, with the goal of maximizing the downstream benefits; As a secondary player, the upstream region aims to maximize upstream profits and achieves coordination of upstream and downstream interests through compensation coefficients.

4. The method for optimizing the dispatching of cascade hydropower based on regional adjustable resources as claimed in claim 3 is characterized by: The compensation coefficient optimization model is expressed as: in, represents the total downstream cost; γ represents the compensation coefficient.

5. The method for optimizing the dispatching of cascade hydropower based on regional adjustable resources as claimed in claim 4, characterized in that: The upstream optimization scheduling model is expressed as: Among them, C U represents the upstream operating cost; the downstream operating cost C D Also determined by the next lower optimization model; X U Represents the decision variables for upstream optimal scheduling.

6. The method for optimizing the dispatching of cascade hydropower based on regional adjustable resources as claimed in claim 5, characterized in that: The downstream optimization scheduling model is expressed as:

7. The method for optimizing the dispatching of cascade hydropower based on regional adjustable resources as claimed in claim 6, characterized in that: The solving includes short-term scheduling solution and medium- and long-term scheduling solution.

8. A device using the method for optimizing the dispatching of cascade hydropower based on regional adjustable resources as claimed in any one of claims 1 to 7, characterized in that: include: The data collection module (100) is used to collect the operation data of the cascade hydropower stations and the regional joint adjustable resources; The model building module (200) is used to build a master-slave game model of the upstream and downstream areas based on the operation data; The model optimization module (300) is used to construct a three-layer optimization model including a compensation coefficient optimization model, an upstream optimization scheduling model and a downstream optimization scheduling model based on the master-slave game model; The model solving module (400) is used to solve the three-layer optimization model, dynamically update the compensation coefficient and the upstream and downstream scheduling strategies, until the optimal solution that meets the constraint conditions is solved; The hydropower dispatching module (500) is used to dynamically adjust the water release strategy of the upstream power station and the control strategy of the combined adjustable resources based on the solution results, and output an optimized cascade hydropower dispatching plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the cascade hydropower optimization scheduling method based on regional adjustable resources described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the scheduling of cascade hydropower based on regional adjustable resources described in any one of claims 1 to 7 are implemented.

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