Cascade multi-terminal based flexible space-time coordination allocation method and system for hydropower stations

Abstract

The application discloses a cascade multi-terminal-based flexible space-time coordination distribution method and system for a hydropower station, and belongs to the technical field of cascade hydropower and power system dispatching operation. The method comprises the following steps: a power relationship based on a cascade multi-terminal external transmission mode is used to establish a power balance mathematical model of a hierarchical converter station; a water power coupling relationship is used to establish a hydropower flexible space-time distribution model, a rigid power balance equation is converted into a flexible inequality constraint, and a double-layer capacity configuration model is further established, wherein an outer layer optimizes capacity configuration with an economic index as a target, and an inner layer performs simulation according to a wind and light capacity boundary; and then the optimal capacity configuration of the wind and light access is determined under different wind and light distribution proportions. The application effectively alleviates the restriction of the cascade multi-terminal external transmission mode on the wind and light access, improves the system economy and new energy consumption capacity, and solves the problems that the space-time coupling characteristics of the hydropower flexibility are difficult to quantify, the water-electricity nonlinear constraint is difficult to solve, and the capacity configuration is restricted by rigidity.

Description

Technical Field

[0001] This invention belongs to the fields of power system technology and clean energy base technology, and relates to a flexible spatiotemporal collaborative allocation method and system for hydropower stations based on cascaded multi-terminals. Background Technology

[0002] Against the backdrop of global efforts to address climate change, countries are actively implementing carbon reduction targets, and the large-scale development and consumption of new energy sources such as wind and solar power has become a widely recognized and important path for emission reduction. However, some areas rich in high-quality wind and solar resources are generally sparsely populated and far from load centers, making the large-scale transmission and consumption of wind and solar new energy a key issue facing global carbon reduction. Currently, exploring a large-scale, clean energy base development approach for wind and solar new energy, along with flexible resource allocation within clean energy bases and the construction of new transmission channels, has become a crucial path for the large-scale consumption of renewable energy. Ultra-high voltage (UHV) transmission technology, as a typical transmission method, enables cross-regional, long-distance, and large-capacity power transmission. Traditional UHV transmission technology uses a two-terminal converter station structure to achieve point-to-point power transmission, but with the rapid development of multi-terminal power transmission and receiving configurations in transmission infrastructure, a new operating mode of cascaded multi-terminal UHV DC is emerging. Compared to two-terminal projects, the cascaded multi-terminal UHV transmission method can connect three or more independent converter stations through a single DC line, supporting multiple power sources and multiple receiving points. It offers greater advantages in system flexibility and engineering practicality, and is particularly suitable for efficient power transmission in distributed energy bases. However, the main wiring structure of this type of cascaded multi-terminal system is more complex, posing higher technical requirements for DC protection and system coordination control.

[0003] The rapid start-up and shutdown, bidirectional power regulation, and energy storage characteristics of cascade hydropower stations are considered key flexibility resources supporting the high proportion of renewable energy consumption. However, when applying the flexibility of cascade hydropower to actual dispatching, especially in complex hydro-wind-solar-storage DC transmission systems, clean energy bases employing cascaded multi-terminal (CMT) UHVDC transmission technology face extremely high requirements for the accuracy and flexibility of power allocation across multiple sending-end converter stations. Traditional, extensive regulation capacity allocation models have become a critical bottleneck restricting the overall system's operational efficiency and safety. Therefore, developing a cascade hydropower flexibility allocation method that integrates accurate assessment, spatiotemporal coordination, and safety verification has significant theoretical value and practical urgency. Existing technologies and methods face the following prominent bottlenecks: Cascaded multi-terminal power transmission, as a novel grid connection method for large-scale renewable energy power systems, has been applied to an ultra-high-voltage direct current (UHVDC) project transmitting power from an upstream hydropower base to Wuhan, Hubei Province. This project is the world's first to adopt a scheme of separately located and cascaded high- and low-voltage converter stations at the sending end. However, existing technologies lack a systematic analytical method for understanding the impact mechanism of cascaded multi-terminal power transmission on the capacity configuration of clean energy bases, resulting in the following technological gaps: (1) Lack of mathematical modeling framework for cascaded multi-terminal transmission: For the scenario of cascaded multi-terminal transmission from large-scale renewable energy bases, the existing technology has not yet established a mathematical modeling framework for cascaded multi-terminal transmission in hydropower bases. Due to the lack of a technical gap in the mathematical expression of capacity configuration of cascaded multi-terminal transmission in clean energy bases, the capacity configuration method for the flexibility of hydropower transmission between multiple converter stations has not been established.

[0004] (2) The flexibility of hydropower is complex and difficult to quantify due to its spatiotemporal coupling characteristics: The flexibility of hydropower has significant directionality, state dependence, probability and spatiotemporal coupling. The hydropower regulation capacity is constrained by water flow lag time, hydrological conditions, installed capacity and reservoir operation characteristics. There is a lack of precise characterization and quantification technology for the relationship between multiple cascaded converter stations and hydropower flexibility.

[0005] (3) Difficulty in solving high-dimensional nonlinear coupling constraints of hydropower: The new constraints introduced by the cascaded multi-terminal transmission technology are not yet clear, making it difficult to construct a capacity configuration model that integrates complex constraints of multi-terminal hydropower coupling. The efficient solution method for its high-dimensional nonlinear optimization problem has not yet achieved a substantial breakthrough.

[0006] (4) Lack of technology to coordinate the goals of flexible utilization of hydropower and comprehensive utilization of reservoirs in cascaded multi-terminal hydropower: cascade hydropower needs to take into account multiple comprehensive utilization goals such as power generation, flood control, ecology, water supply and navigation. Existing technologies lack a dynamic balance mechanism to coordinate with the above multiple goals when the air conditioning is distributed to multiple converter stations in the flexible hydropower operation.

[0007] (5) Capacity configuration improvement technology of cascaded multi-terminal transmission method: Due to the technological gap in the application of cascaded multi-terminal in clean energy base, there is a path to improve the capacity configuration of cascaded multi-terminal. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides a capacity configuration method and system for the flexible allocation of hydropower resources among multiple converter stations based on cascaded multi-terminal power transmission. It is applicable to integrated energy systems with cascaded multi-terminal power transmission and is used to solve problems such as the flexible allocation of hydropower resources, power equivalence among converter stations, and coordinated protection of grid safety and stability in clean energy base planning.

[0009] This invention is achieved through the following technical solution: This invention provides a method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals, including: Based on the power relationship of the cascaded multi-terminal power transmission mode, a mathematical model for power balance of the graded converter station is established. Based on the power balance mathematical model of the tiered converter station, and according to the hydraulic-electric coupling relationship, a flexible hydropower time-and-space distribution model is established to transfer the flexibility of cascade hydropower between multiple converter stations in time and space. Based on the aforementioned flexible and timely hydropower allocation model, a two-layer capacity allocation model is established. The two-layer model includes an outer optimization layer and an inner simulation layer. The outer optimization layer takes minimizing the economic index as the objective function, and the inner simulation layer performs step-by-step simulation based on the maximum wind and solar access capacity transmitted from the outer layer, gradually increasing the photovoltaic and wind power installed capacity according to a preset step size. Based on the aforementioned two-layer capacity configuration model, different wind and solar resource allocation ratios are set among different converter stations. The objective function value under each ratio is solved. By fitting the response curve between the objective function value and the allocation ratio, the optimal wind and solar resource allocation ratio and the corresponding optimal capacity configuration are determined.

[0010] Preferably, the specific process of establishing the power balance mathematical model for the tiered converter station includes: Based on the voltage or capacity level of each converter station in the cascaded multi-terminal power transmission system, the converter stations are divided into low-voltage converter stations, high-voltage converter stations, and receiving-end converter stations. Based on the zoning of power grid converter stations, each hydropower station and pumped storage power station is assigned to its corresponding electrical zone; Based on the zoning ratio of wind and solar power station installed capacity, the installed capacity of wind and solar power stations in each electrical zone is determined according to the preset step size. Based on the installed capacity of wind and solar power stations in each electrical zone, power balance equations within each electrical zone and power transmission constraints of tie lines between different electrical zones are established. By setting upper and lower power limits for each converter station, transmission limit constraints for tie lines, and minimum and maximum transmission demand constraints for DC transmission projects, a mathematical model for power balance of graded converter stations is established. The specific expression for the power balance mathematical model of the tiered converter station is as follows:

[0011]

[0012]

[0013] in, This represents the power of the k-th converter station at time t. , These are the minimum and maximum allowable power for the k-th level converter station, respectively. , These are the minimum and maximum transmission limits for the tie line L of the k-th converter station, respectively. , Let be the minimum and maximum power transmission demands of the DC transmission project at time t, respectively. K send and Kreceive These represent the sets of sending-end and receiving-end converter stations, respectively.

[0014] Preferably, the specific process of establishing the air conditioning distribution model for hydropower flexibility includes: Based on the power balance mathematical model of the graded converter stations, the hydraulic-electric coupling relationship of the electrical area where each converter station is located is established. The hydraulic-electric coupling relationship includes the influence of the outflow of the upstream hydropower station on the inflow of the downstream hydropower station, as well as the functional relationship between the power generation output of the hydropower station and the power generation flow and head. Based on the aforementioned hydraulic-electric coupling relationship, the rigid equality constraint for power balance between different converter stations is transformed into a flexible inequality constraint with the overall flexibility of the cascade hydropower as the boundary. Through the aforementioned flexible inequality constraints, a flexible hydropower time-space allocation model is obtained, enabling the spatiotemporal transfer and coordinated allocation of the upward or downward adjustment flexibility of cascade hydropower between different converter stations.

[0015] Preferably, the rigid equality constraint for power balance between different converter stations is transformed into a flexible inequality constraint with the overall flexibility of the cascade hydropower system as the boundary, specifically: Treating the cascade hydropower station group as a whole and a collaborative resource pool, the flexibility of adjusting the cascade hydropower stations is defined. and reduced flexibility ; The power balance equality constraint between different converter stations is transformed into the following flexible inequality constraint:

[0016]

[0017] in, For partitioning All hydroelectric power stations in the country Total power generation at any given time partition The flexibility of adjusting the internal cascade hydropower station group. For partitioning The flexibility of adjusting the internal cascade hydropower station group; For partitioning All hydroelectric power stations in the country Total power generation at any given time; Electrical partitions The total number of hydroelectric power stations in the area Electrical partitions and Total number of inland hydroelectric power stations; Electrical partitions Inner Hydropower station Power generation at any given moment; and The electrical zones correspond to different converter station aggregation areas. The rigid equality constraint for power balance among different converter stations is expressed as follows:

[0018] In the formula, For the first Level converter station The power transmitted at any given time; For the first Level converter station The power transmitted at any time, , Electrical zones or exist Net output power at any given time; , Electrical zones or Internal photovoltaic power station Actual power generation at any given time; , Electrical zones or The actual power generation of the internal wind farm at time t. , Electrical zones or The sum of the generating capacity of all power sources other than hydropower, photovoltaic power, wind power and pumped storage. , Electrical zones or Pumping power of an internal pumped storage power station; , Electrical zones or Domestic renewable energy curtailment power denoted by z, which represents the number of hydropower stations; z represents the number of electrical zones; and k represents the converter station level.

[0019] Preferably, based on the aforementioned flexible hydropower allocation model, a two-layer capacity allocation model is established, specifically including: The outer optimization layer takes the minimum comprehensive economic index of the capacity scheme at the end of the scheduling period as the objective function. The economic index includes investment cost, electricity sales revenue, curtailment penalty and load shortage penalty. The maximum wind and solar access capacity of each converter station is used as the decision variable, and upper limit constraints are set on the photovoltaic and wind power installed capacity in each zone. The inner simulation layer uses the aforementioned flexible hydropower distribution model as the basis for operation simulation and receives the wind and solar capacity boundaries transmitted from the outer layer as input. Starting from the preset minimum grid-connected capacity, the installed capacity of photovoltaic and wind power is gradually increased according to the preset step size. Time-series operation simulation is performed for each capacity combination to calculate the corresponding curtailment rate, power shortage rate and operating cost. The inner simulation layer feeds back the curtailment rate, power shortage rate, and operating cost results obtained from each step simulation to the outer optimization layer; the outer optimization layer updates the objective function value based on the feedback results and adjusts the search direction of the wind and solar capacity boundary; the inner simulation layer re-executes the step simulation under the updated boundary of the outer layer until the outer objective function converges or reaches the preset iteration termination condition; Specifically, the outer optimization layer has the lowest overall economic performance index. The objective function is expressed as:

[0020] in, , , and These represent the investment cost, electricity sales revenue, power curtailment penalty, and load shortage penalty for connecting to the k-th level converter station, respectively.

[0021] Preferably, in the preset step size, the simulation step size for the grid-connected photovoltaic installed capacity is: The simulation step size for grid-connected wind power capacity is ; The photovoltaic installed capacity used in the nth step simulation is: ; The installed wind power capacity used in the nth step simulation is: .

[0022] Preferably, the wind and solar resource allocation ratio is the ratio of photovoltaic capacity or wind power capacity connected to different converter stations; by setting multiple different allocation ratios, the objective function value under each ratio is solved by mixed integer linear programming, and the response curve of the objective function value changing with the allocation ratio is obtained by curve fitting method, and the allocation ratio corresponding to the minimum point of the response curve is taken as the optimal wind and solar resource allocation ratio.

[0023] Preferably, it further includes: nesting and coupling the reservoir comprehensive utilization target into the hydropower flexibility time-sharing allocation model, wherein the reservoir comprehensive utilization target includes at least one of flood control target, water supply target, ecological target and navigation target, and achieving synergistic optimization with hydropower flexibility utilization through water balance constraints, water level boundary constraints and head and tail water level constraints.

[0024] A cascaded multi-terminal hydropower station flexible spatiotemporal collaborative allocation system includes: The power balancing modeling module for tiered converter stations establishes a mathematical model for power balancing of tiered converter stations based on the power relationship of the cascaded multi-terminal transmission mode. The hydropower flexibility air conditioning configuration modeling module, based on the power balance mathematical model of the graded converter station, establishes a hydropower flexibility air conditioning configuration model according to the hydraulic-electric coupling relationship; The dual-layer capacity configuration module establishes a dual-layer capacity configuration model based on the aforementioned hydropower flexible time-and-time air-conditioning model. The dual-layer model includes an outer optimization layer and an inner simulation layer. The outer optimization layer takes minimizing the economic index as the objective function, and the inner simulation layer performs step-by-step simulation based on the maximum wind and solar access capacity transmitted from the outer layer, gradually increasing the photovoltaic and wind power installed capacity according to a preset step size. The wind and solar resource allocation ratio optimization module, based on the dual-layer capacity configuration model, sets different wind and solar resource allocation ratios among different converter stations, solves the objective function value under each ratio, and determines the optimal wind and solar resource allocation ratio and the corresponding optimal capacity configuration by fitting the response curve of the objective function value and the allocation ratio.

[0025] Compared with the prior art, the present invention has the following beneficial technical effects: This invention discloses a spatiotemporal coordinated allocation method for the flexibility of cascaded multi-terminal hydropower stations. Based on the power relationship of the cascaded multi-terminal power transmission mode, a mathematical model for power balance of tiered converter stations is established. Then, according to the hydraulic-electric coupling relationship, a spatiotemporal allocation model for hydropower flexibility is established, transforming rigid power balance equations into flexible inequality constraints to realize the spatiotemporal transfer of cascaded hydropower flexibility among multiple converter stations. Furthermore, a two-layer capacity configuration model is established: the outer layer optimizes capacity configuration with economic indicators as the objective, while the inner layer performs stepwise simulation and bidirectional feedback based on wind and solar capacity boundaries. Finally, by scanning different wind and solar allocation ratios and fitting response curves, the optimal capacity configuration is determined. This invention effectively alleviates the constraints of cascaded multi-terminal power transmission on wind and solar access, improves system economy and new energy consumption capacity, enables spatiotemporal coordinated allocation of the flexibility adjustment capacity of cascaded hydropower stations, and improves the capacity configuration efficiency of cascaded multi-terminal power transmission systems.

[0026] Furthermore, this invention establishes a power balance mathematical model for graded converter stations based on the power relationship of the cascaded multi-terminal transmission mode. For the first time, it systematically describes the power balance relationship and grid operation constraints among low-voltage, high-voltage, and receiving-end converter stations, filling the technical gap in the mathematical expression of capacity configuration in the cascaded multi-terminal transmission scenario and providing a reliable model foundation for subsequent flexible allocation and capacity optimization.

[0027] Furthermore, the present invention is based on hydraulics When establishing a hydropower flexibility allocation model based on power coupling relationships, the cascade hydropower system is treated as a unified collaborative resource pool. Upward and downward adjustment flexibility are defined, and this is achieved through hydraulic... Electric coupling transforms the rigid equality constraint of power balance between different converter stations into a flexible inequality constraint, thereby realizing the spatiotemporal transfer and coordinated allocation of the flexibility of cascade hydropower among multiple converter stations. This overcomes the technical bottleneck of traditional methods in accurately characterizing the directionality, state dependence and spatiotemporal coupling of hydropower flexibility.

[0028] Furthermore, the two-layer capacity configuration model established in this invention adopts a bidirectional feedback mechanism combining outer-layer economic optimization and inner-layer step simulation, and introduces mixed-integer linear programming (MILP) and successive approximation method (SA) to optimize water level. Reservoir capacity curve, tailwater level The discharge curve and the power output function of the hydropower station are linearized, which realizes the efficient and accurate solution of high-dimensional nonlinear optimization problems and breaks through the technical obstacles of traditional solution methods that are difficult to converge or have excessive computational load.

[0029] Furthermore, this invention effectively transforms rigid power balance constraints into flexible inequality constraints by setting different wind and solar resource allocation ratios among different converter stations, solving for the objective function value under each ratio, fitting the response curve, and determining the optimal allocation ratio. This significantly weakens the limitation on wind and solar access capacity imposed by cascaded multi-terminal transmission topologies. This invention transforms the original rigid power balance condition into a flexible allocation mechanism based on hydropower flexibility, effectively mitigating the constraints of cascaded multi-terminal transmission topologies on the access capacity of renewable energy sources such as wind and solar. Compared to traditional non-layered and non-regional equal allocation methods, this invention can significantly improve wind and solar grid-connected capacity and reduce the overall cost per unit of power generation while ensuring the safe and stable operation of the system, achieving optimized capacity configuration under cascaded multi-terminal transmission.

[0030] Furthermore, this invention, through water balance constraints, water level boundary constraints, and first and last water level constraints in the two-layer capacity configuration model, nests and couples multiple comprehensive utilization objectives such as flood control, water supply, ecology, and navigation to the inner layer hydropower flexibility time-sharing allocation model, forming a dynamic coordination mechanism between power generation, peak shaving, and comprehensive utilization objectives, thus solving the technical defects of existing technologies in the conflict between hydropower flexibility allocation and multiple objectives of reservoirs. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1This is a flowchart of the flexible spatiotemporal collaborative allocation method for hydropower stations based on cascaded multi-terminals, as per the present invention. Figure 2 This invention relates to the main circuit of a cascaded multi-terminal ultra-high voltage direct current transmission system (CMT). Figure 3 This invention relates to a cascaded multi-terminal power transmission method for a hydropower base in the upper reaches of a river. Figure 4 This is a structural diagram of the two-layer capacity configuration model based on cascaded multi-terminal external transmission of the present invention; Figure 5 This is a schematic diagram of the cascaded multi-terminal power transmission hydraulic-electric coupling system of the present invention; Figure 6 This invention addresses the power transmission requirements of the receiving-end converter station. Figure 7 These are the capacity configuration results under different allocation ratios of the present invention; Figure 8 This is the curve for obtaining the optimal partition ratio of the present invention; Figure 9 This refers to the spatiotemporal differences in the hydropower flexibility regulation of hydropower station 1 under different zoning ratios in Embodiment 1 of the present invention.

[0033] Figure 10 This refers to the spatiotemporal differences in the flexible hydropower regulation of hydropower station 2 under different zoning ratios in Embodiment 1 of the present invention. Detailed Implementation To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0034] To facilitate understanding of this invention, the key technical terms of this invention are defined and explained below: Cascaded multi-terminal transmission mode: This is an advanced DC transmission technology that connects three or more independent converter stations through a single DC line, supporting multiple power sources and multiple receiving points. This mode enables cross-regional, long-distance, and high-capacity power transmission, and is particularly suitable for power transmission from clean energy bases. It utilizes cascaded multi-terminal ultra-high-voltage DC transmission technology to transmit power from clean energy bases (such as hydropower, wind power, and photovoltaic power) to load centers over long distances. Unlike traditional two-terminal (point-to-point) DC transmission, cascaded multi-terminal systems connect three or more independent converter stations through a single DC line, supporting multiple power sources and multiple receiving points, offering greater system flexibility and engineering practicality. A typical example is the hybrid cascaded ultra-high-voltage DC transmission project from the upper reaches of the Yangtze River to Wuhan.

[0035] Converter station: In a DC transmission system, a substation that converts AC to DC power. The sending-end converter station converts AC to DC, and the receiving-end converter station converts DC to AC. This invention involves low-voltage converter stations, high-voltage converter stations, and receiving-end converter stations, with converter stations of different voltage levels cascaded together via DC lines.

[0036] The power balance mathematical model for tiered converter stations is a mathematical model used to describe the power distribution and balance relationship among converter stations of different voltage levels in a cascaded multi-terminal power transmission system. This model divides power stations into different electrical zones based on grid zoning, establishes power balance equations within each zone, power transmission constraints for inter-zone tie lines, and boundary constraints such as upper and lower limits of converter station power and DC transmission demand. It forms the basis for subsequent flexible allocation and capacity configuration of hydropower.

[0037] The hydraulic-electric coupling relationship refers to the interaction between the hydraulic connections between cascade hydropower stations and the power generation (electricity) of the hydropower stations. Specifically, it includes the impact of hydrological parameters such as reservoir water level, flow rate, and head on the power output and efficiency of the hydropower stations, as well as the mutual influence of water flow between the cascade hydropower stations. This invention utilizes this coupling relationship to transform the power balance constraints of different converter stations from rigid to flexible, realizing the flexible transfer of hydropower in time and space.

[0038] The power balance mathematical model for tiered converter stations is a mathematical model used to describe the power distribution and balance relationship among converter stations of different voltage levels in a cascaded multi-terminal power transmission system. This model divides power stations into different electrical zones based on grid zoning, establishes power balance equations within each zone, power transmission constraints for inter-zone tie lines, and boundary constraints such as upper and lower limits of converter station power and DC transmission demand. It forms the basis for subsequent flexible allocation and capacity configuration of hydropower.

[0039] Hydraulic-electric coupling relationship: refers to the interaction between the hydraulic connections between cascade hydropower stations (the impact of upstream outflow on downstream inflow, flow lag time, head changes, etc.) and the power generation (electricity) of the hydropower stations. This invention utilizes this coupling relationship to transform the power balance constraints of different converter stations from rigid to flexible, realizing the transfer of hydropower flexibility in time and space.

[0040] The Hydropower Flexibility Scheduling Model is a mathematical model based on the hydraulic-electric coupling relationship. It describes the coordinated allocation and support of the upward and downward adjustment flexibility of a cascade hydropower station group across different converter stations and time periods. The core of this model is to transform the originally rigid power balance equation constraint into a flexible inequality constraint with the overall hydropower flexibility as the boundary, thereby allowing for flexible adjustment of hydropower resources across regions and time periods.

[0041] Increased flexibility: The ability of a cascade hydropower station group to increase its output based on its original output is limited by available reservoir capacity, head, and rated capacity of the generating units.

[0042] Reduced flexibility: The ability of a cascade hydropower station group to reduce its output from its original level is limited by factors such as ecological flow, irrigation water use, navigation demand, and minimum stable output of the generating units.

[0043] The two-layer capacity configuration model is a nested optimization model consisting of an outer optimization layer and an inner simulation layer. The outer layer, with economic indicators (investment, electricity sales, curtailment, and power shortage penalties) as objectives, determines the upper limit of wind and solar power access capacity for each converter station. The inner layer, based on a flexible and timely hydropower allocation model, performs step-by-step simulations on the capacity boundaries given by the outer layer to evaluate operating costs. Through bidirectional feedback iteration between the two layers, the optimal capacity configuration scheme is ultimately found.

[0044] Outer Optimization Layer: The upper layer of the two-layer model, focusing on the macro level. The objective function is to minimize the comprehensive economic index of the capacity scheme at the end of the scheduling period, with the decision variable being the maximum wind and solar access capacity of each converter station. The outer layer adjusts the capacity boundary based on the operating costs fed back from the inner layer, converging towards the optimal solution.

[0045] Inner simulation layer: The lower layer of the two-layer model, focusing on operational simulation. Using the flexible time-of-use (TOU) power grid model as its core, it receives wind and solar capacity boundaries from the outer layer. Starting from a preset minimum grid-connected capacity, it gradually increases the installed capacity of photovoltaic and wind power at fixed step sizes, performing 8760 hours of time-series operational simulation for each capacity combination. It calculates the curtailment rate, power shortage rate, and operating costs, and feeds the results back to the outer layer.

[0046] The wind-solar resource allocation ratio refers to the ratio of photovoltaic installed capacity or wind power installed capacity between different converter stations (or different electrical zones). By scanning different allocation ratios, the optimal ratio that minimizes the total economic cost of the system can be found.

[0047] Optimal capacity configuration: refers to the scheme that achieves the best combination of installed capacity of wind power, photovoltaic power and other power sources under the premise of meeting system operation constraints and optimization objectives.

[0048] Response curve: A function curve fitted with the wind and solar resource allocation ratio on the x-axis and the objective function value (such as total cost per unit of power generation) on the y-axis. The minimum point of the curve corresponds to the optimal wind and solar resource allocation ratio and the optimal objective function value, which are used to determine the optimal capacity configuration of the system.

[0049] Mixed Integer Linear Programming (MILP): A mathematical optimization method used to solve optimization problems where both the objective function and constraints are linear, and some variables are required to be integers (such as unit start-up and shutdown states). This invention uses MILP to solve a two-layer capacity configuration model and performs piecewise linearization on nonlinear factors (such as water level-reservoir capacity curves, tailrace water level-discharge curves, and hydropower station output functions).

[0050] Successive approximation method (SA): A numerical method for iteratively solving nonlinear problems. In this invention, for high-dimensional nonlinear coupled constraints that cannot be fully linearized (such as the influence of head changes on output), the successive approximation method is adopted: first, given the rated head, the linearized model is solved, and then the head is updated according to the solution results. This process is repeated iteratively until convergence, thereby approximating the optimal solution of the original nonlinear problem.

[0051] In traditional, existing cascaded multi-terminal UHVDC transmission systems, the lack of a mathematical modeling framework for cascaded multi-terminal systems makes it difficult to quantify the spatiotemporal coupling characteristics of hydropower flexibility, hinders the solution of high-dimensional nonlinear coupling constraints between hydraulics and electricity, and results in insufficient coordination between hydropower flexibility allocation and reservoir comprehensive utilization objectives. This leads to deviations in power allocation among multiple converter stations, limited system flexibility, an imperfect safety verification mechanism, and impacts system stability and operational efficiency. Specifically, the directional, state-dependent, and probabilistic characteristics of hydropower flexibility are not fully incorporated into the model. Furthermore, the constraints of water flow lag and hydrological conditions on flexibility are not accurately characterized, making it difficult to maintain power balance among converter stations and compressing the system's safety boundary.

[0052] For example, in a certain ultra-high voltage direct current (UHVDC) project, the project adopted a cascaded scheme with separate high- and low-voltage converter stations at the sending end, connecting multiple cascade hydropower stations, wind farms, and photovoltaic power stations. During periods of fluctuating wind and solar power output, due to the lack of flexible air conditioning and distribution mechanisms for hydropower, the power balance between the high-voltage and low-voltage converter stations was disrupted, triggering over-limit power on the tie lines, initiating protection actions, causing DC system shutdown, and reducing the power supply reliability of the receiving-end grid. Specifically, when the output of the photovoltaic power station suddenly drops, the regulation capacity of the cascade hydropower stations fails to promptly transfer power between the high-voltage and low-voltage converter stations, exacerbating the power imbalance and amplifying system frequency fluctuations.

[0053] If the above problems are not resolved, the system will face the risk of continuous power imbalance, the probability of DC blocking will increase, the curtailment of new energy power will rise, the conflict between reservoir operation and comprehensive utilization goals such as flood control, water supply, ecology and shipping will be exacerbated, the safe and stable operation of the power grid will be threatened, the large-scale consumption of renewable energy will be hindered, and the implementation effect of clean energy base development will be weakened.

[0054] To address these issues, this invention provides a spatiotemporal coordinated allocation method for the flexibility regulation capacity of cascade hydropower stations. This method aims to solve technical problems such as the lack of a mathematical modeling framework for cascaded multi-terminal systems, the difficulty in quantifying the complex spatiotemporal coupling characteristics of hydropower flexibility, the difficulty in solving the high-dimensional nonlinear coupling constraints between hydraulic and electric power, the need for synergy between the goals of hydropower flexibility utilization and reservoir comprehensive utilization in cascaded multi-terminal systems, and the constraints imposed by cascaded multi-terminal power transmission methods on capacity configuration improvement. The method includes: establishing a power balance mathematical model for tiered converter stations based on the cascaded multi-terminal power transmission mode; constructing a spatiotemporal allocation model for hydropower flexibility based on the hydraulic-electric coupling relationship; proposing a capacity configuration method for spatiotemporal allocation of hydropower flexibility among multiple converter stations in the cascaded multi-terminal power transmission mode; setting different wind and solar resource allocation ratios between two converter stations; solving for objective function values ​​under different ratios by adjusting the wind and solar resource allocation ratios; fitting function curves based on the correspondence between the objective function values ​​and the allocation ratios; and obtaining the optimal capacity configuration of the system based on the optimal wind and solar resource allocation ratio and the optimal objective function value. Through the above optimization steps, the limitations on capacity configuration imposed by cascaded multi-terminal power transmission methods are effectively mitigated. This invention enables the spatiotemporal coordinated allocation of the flexible adjustment capabilities of cascaded hydropower stations, improving the capacity configuration efficiency of cascaded multi-terminal power transmission systems.

[0055] The flexible spatiotemporal collaborative allocation method for this cascaded multi-terminal hydropower station is as follows: Figure 1 As shown, it specifically includes: S1. Based on the power relationship of the cascaded multi-terminal power transmission mode, a mathematical model for power balance of the graded converter station is established. S2, Based on the power balance mathematical model of the tiered converter station, and according to the hydraulic-electric coupling relationship, a flexible hydropower time-and-space distribution model is established to transfer the flexibility of cascade hydropower between multiple converter stations in time and space. S3. Based on the aforementioned flexible and timely hydropower allocation model, a two-layer capacity allocation model is established. The two-layer model includes an outer optimization layer and an inner simulation layer. The outer optimization layer takes minimizing the economic index as the objective function. The inner simulation layer performs step-by-step simulation based on the maximum wind and solar access capacity transmitted from the outer layer, and gradually increases the photovoltaic and wind power installed capacity according to a preset step size. S4. Based on the dual-layer capacity configuration model, different wind and solar resource allocation ratios are set among different converter stations. The objective function value under each ratio is solved. By fitting the response curve between the objective function value and the allocation ratio, the optimal wind and solar resource allocation ratio and the corresponding optimal capacity configuration are determined.

[0056] S1, specifically, the process of establishing the power balance mathematical model for the tiered converter station includes: Based on the voltage or capacity level of each converter station in the cascaded multi-terminal power transmission system, the converter stations are divided into low-voltage converter stations, high-voltage converter stations, and receiving-end converter stations. Based on the zoning of power grid converter stations, each hydropower station and pumped storage power station is assigned to its corresponding electrical zone; Based on the zoning ratio of wind and solar power station installed capacity, the installed capacity of wind and solar power stations in each electrical zone is determined according to the preset step size. Based on the installed capacity of wind and solar power stations in each electrical zone, power balance equations within each electrical zone and power transmission constraints of tie lines between different electrical zones are established. By setting upper and lower power limits for each converter station, transmission limit constraints for tie lines, and minimum and maximum transmission demand constraints for DC transmission projects, a mathematical model for power balance of graded converter stations is established. The specific expression for the power balance mathematical model of the tiered converter station is as follows:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] in, k 1.k 2. k 3 indicates the level of each converter station, for example... k 1. Low-voltage converter station k 2 is the high-voltage converter station. k 3 receiving-end converter stations; This represents the power of the k-th converter station at time t. , These are the minimum and maximum allowable power for the k-th level converter station, respectively. , These are the minimum and maximum transmission limits for the tie line L of the k-th converter station, respectively. , Let be the minimum and maximum power transmission demands of the DC transmission project at time t, respectively. K send and K receive These represent the sets of sending-end and receiving-end converter stations, respectively.

[0063] like Figure 2 As shown, the cascaded multi-terminal transmission technology of this invention changes the basic mode of capacity allocation in hydropower bases. The configuration system pre-sets the voltage or capacity levels of the cascaded hydropower and pumped storage power plants connected to the converter stations based on the installed capacity and geographical location of the cascaded hydropower plants, thereby constructing a transmission system with a simplified power grid structure and a clear hierarchical architecture. Based on this, a mathematical model is established to ensure the safe and stable operation of each converter station, and this model is integrated into the overall operational framework of the hydropower base, forming a complete optimized configuration system.

[0064] S2, specifically, refers to the process of establishing the air conditioning configuration model for hydropower flexibility, which includes: Based on the power balance mathematical model of the graded converter stations, the hydraulic-electric coupling relationship of the electrical area where each converter station is located is established. The hydraulic-electric coupling relationship includes the influence of the outflow of the upstream hydropower station on the inflow of the downstream hydropower station, as well as the functional relationship between the power generation output of the hydropower station and the power generation flow and head. Based on the aforementioned hydraulic-electric coupling relationship, the rigid equality constraint for power balance between different converter stations is transformed into a flexible inequality constraint with the overall flexibility of the cascade hydropower as the boundary. Through the aforementioned flexible inequality constraints, a flexible hydropower time-space allocation model is obtained, enabling the spatiotemporal transfer and coordinated allocation of the upward or downward adjustment flexibility of cascade hydropower between different converter stations.

[0065] The rigid equality constraint for power balance among different converter stations is expressed as follows:

[0066] In the formula, For the first Level converter station The power transmitted at any given time; For the first Level converter station The power transmitted at any time, , Electrical zones or exist Net output power at any given time; , Electrical zones or Internal photovoltaic power station Actual power generation at any given time; , Electrical zones or The actual power generation of the internal wind farm at time t. , Electrical zones or The sum of the generating capacity of all power sources other than hydropower, photovoltaic power, wind power and pumped storage. , Electrical zones or Pumping power of an internal pumped storage power station; , Electrical zones or Domestic renewable energy curtailment power denoted by z, which represents the number of hydropower stations; z represents the number of electrical zones; and k represents the converter station level.

[0067] Since the wind and solar power output and pumped storage operation status in the two zones are independent of each other, the rigid constraints of the above equations can be transformed into flexible inequality constraints. Therefore, the power balance equation between the two converter stations can realize the flexible transfer of cascade hydropower between the two converter stations in time and space through the hydraulic and electric coupling.

[0068] Specifically, the rigid equality constraint for power balance between different converter stations is transformed into a flexible inequality constraint with the overall flexibility of the cascade hydropower system as the boundary, as follows: Treating the cascade hydropower station group as a whole and a collaborative resource pool, the flexibility of adjusting the cascade hydropower stations is defined. and reduced flexibility ; The power balance equality constraint between different converter stations is transformed into the following flexible inequality constraint:

[0069]

[0070] in, For partitioning All hydroelectric power stations in the country Total power generation at any given time partition The flexibility of adjusting the internal cascade hydropower station group. For partitioning The flexibility of adjusting the internal cascade hydropower station group; For partitioning All hydroelectric power stations in the country Total power generation at any given time; Electrical partitions The total number of hydroelectric power stations in the area Electrical partitions and Total number of inland hydroelectric power stations; Electrical partitions Inner Hydropower station Power generation at any given moment; and The two converter stations are divided into electrical zones, corresponding to different converter station collection areas. Since the wind and solar power output and pumped storage operation status in the two zones are independent of each other, the rigid constraints of the above equations can be transformed into flexible inequality constraints. Therefore, the power balance equation between the two converter stations can be realized through the coupling of hydraulic and electric power to achieve the flexible transfer of cascade hydropower between the two converter stations in time and space.

[0071] This invention treats cascaded hydropower as a unified, collaborative resource pool, enabling flexible cross-regional support and significantly enhancing the system's ability to cope with localized power imbalances. This invention requires spatial coordination of power allocation between different converter stations. The hydropower capacity connected to two terminal converter stations must match the capacity of the corresponding converters. Simultaneously, by connecting wind and solar resources to converter stations with different voltage levels or capacities, an initial allocation of transmission power is achieved.

[0072] S3, specifically: Based on the aforementioned flexible hydropower allocation model, establish a two-layer capacity allocation model, including: The outer optimization layer takes the minimum comprehensive economic index of the capacity scheme at the end of the scheduling period as the objective function. The economic index includes investment cost, electricity sales revenue, curtailment penalty and load shortage penalty. The maximum wind and solar access capacity of each converter station is used as the decision variable, and upper limit constraints are set on the photovoltaic and wind power installed capacity in each zone. The inner simulation layer uses the aforementioned flexible hydropower distribution model as the basis for operation simulation and receives the wind and solar capacity boundaries transmitted from the outer layer as input. Starting from the preset minimum grid-connected capacity, the installed capacity of photovoltaic and wind power is gradually increased according to the preset step size. Time-series operation simulation is performed for each capacity combination to calculate the corresponding curtailment rate, power shortage rate and operating cost. The inner simulation layer feeds back the curtailment rate, power shortage rate, and operating cost results obtained from each step simulation to the outer optimization layer. The outer optimization layer updates the objective function value based on the feedback results and adjusts the search direction of the wind and solar capacity boundary. The inner simulation layer re-executes the step simulation under the updated boundary of the outer layer until the outer objective function converges or reaches the preset iteration termination condition.

[0073] The core objective of capacity configuration is to achieve flexible time-sharing allocation of power between embedded cascade hydropower stations and pumped storage power stations. This involves conducting power operation stability control at each level of converter stations and simulating flexible operation of hydropower bases. Specifically, the first layer embeds a mathematical model of power balance for cascade converter stations and a time-sharing model of hydropower flexibility based on the power relationship of the aforementioned systems in a cascaded multi-terminal transmission mode. The second layer is used to implement bidirectional interactive feedback between the two layers. The outer layer optimization focuses on achieving the optimal comprehensive indicators of the capacity scheme at the end of the scheduling period at the macro level, minimizing economic indicators. As the outer objective function, it includes the total cost per unit of power generation of the hydropower base system. The total economic cost includes electricity sales revenue, curtailment penalty, load shortage penalty, and investment cost, which includes the following:

[0074] , , and These represent the investment cost, electricity sales revenue, power curtailment penalty, and load shortage penalty for connecting to the k-th level converter station, respectively. The unit installed cost of each type of power source connected to the k-th level converter station is multiplied by its installed capacity. It is obtained by multiplying the power generation of various power sources by their on-grid electricity price. This is derived by multiplying the amount of abandoned hydropower and variable renewable energy (VRE) by their respective penalty prices. It is mainly calculated by multiplying the load deficit by the penalty rate compared to a given external transmission curve.

[0075] The decision variable passed from the outer optimization layer to the inner simulation layer is the maximum wind and solar access capacity, specifically: The starting point for the simulation calculation of the inner simulation layer is the preset minimum grid-connected capacity; During the simulation calculation, the installed capacity of photovoltaic power generation and wind power generation gradually increases according to a preset step size. Each execution of the lower-level simulation calculation corresponds to a defined variable renewable energy grid-connected capacity boundary. Based on the above progressively increasing simulation process, the decision variables used in the nth step simulation calculation are determined through corresponding preset rules or formulas, and the expressions are as follows: The photovoltaic installed capacity used in the nth step simulation is: ; The installed wind power capacity used in the nth step simulation is: .

[0076] The simulation step size for the grid-connected photovoltaic installed capacity is... The simulation step size for grid-connected wind power capacity is ; Different wind and solar resource allocation ratios are set between the two converter stations, specifically:

[0077]

[0078]

[0079]

[0080] express, These represent the maximum photovoltaic (PV) capacity and the maximum wind power (WH) capacity connected to the k-th level converter station, respectively. Different wind and solar resource allocation ratios are set. τ is the proportionality coefficient, which is used to obtain decision variables under different access ratios.

[0081]

[0082]

[0083] The simulation terminates when the decision variable reaches its maximum value.

[0084] like Figure 3 As shown, this method employs a two-layer nested optimization structure. The outer layer partitions the capacity, and based on the wind and solar access capacity input from the outer layer, drives the inner layer to execute the time-sharing allocation process for hydropower flexibility, thereby achieving equal power distribution among converter stations. Simultaneously, multiple objectives for comprehensive reservoir utilization are nested and coupled into the inner-layer hydropower flexibility time-sharing allocation model, enabling cascaded multi-terminal coordination of hydropower flexibility utilization and comprehensive reservoir utilization objectives.

[0085] In S4, the wind and solar resource allocation ratio is the ratio of photovoltaic capacity or wind power capacity connected to different converter stations. By setting multiple different allocation ratios, the objective function value under each ratio is solved by mixed integer linear programming. The response curve of the objective function value changing with the allocation ratio is obtained by curve fitting method. The allocation ratio corresponding to the minimum point of the response curve is taken as the optimal wind and solar resource allocation ratio.

[0086] The specific process for obtaining the optimal allocation ratio of wind and solar resources includes: The decision variables determined by the inner simulation layer, including curtailment rate, power shortage rate, and operating costs under different wind and solar installed capacity combinations, are fed back to the objective function and constraints of the outer optimization layer. Based on this, the outer optimization layer updates the comprehensive economic indicators of the current capacity scheme, forming a complete two-way closed-loop feedback.

[0087] Under the premise of keeping the total wind and solar resources constant, the allocation ratio of wind and solar resources between different converter stations (or between different electrical zones) is systematically adjusted. The allocation ratio can be defined as the ratio of photovoltaic capacity or the ratio of wind power capacity. For each set allocation ratio, a two-layer optimization process is repeatedly executed to obtain the minimum economic index of the outer objective function under that ratio; A discrete data point set is constructed using the wind and solar resource allocation ratio as the x-axis and the corresponding optimal objective function value as the y-axis. Curve fitting methods (such as polynomial fitting, cubic spline interpolation, or locally weighted regression) are used to fit these data points, resulting in a continuous and smooth response curve. This curve quantitatively describes the variation of the system's economic indicators with the wind and solar resource allocation ratio.

[0088] Extreme value analysis was performed on the obtained response curve to determine the minimum point on the curve. The horizontal axis corresponding to this minimum point represents the optimal wind and solar resource allocation ratio, and the vertical axis represents the optimal objective function value (minimum economic index). This ratio indicates the optimal allocation method of wind and solar resources among different converter stations under the constraint of cascaded multi-terminal transmission.

[0089] Based on the optimal wind and solar resource allocation ratio and the optimal objective function value, the optimal capacity configuration scheme of the system is derived, specifically including: the optimal installed capacity of photovoltaic power and wind power in each converter station (each electrical zone), as well as the capacity configuration of flexible resources such as hydropower and pumped storage that match them. This capacity configuration scheme simultaneously meets the requirements of optimal system economy and operational feasibility.

[0090] Through the above optimization steps, the power balance condition under rigid constraints is transformed into a flexible allocation mechanism based on the flexibility of hydropower, effectively mitigating the constraints of cascaded multi-terminal transmission topologies on the capacity access of renewable energy sources such as wind and solar power. Compared with traditional non-layered and non-zoning equal allocation methods, this invention can significantly improve the grid-connected capacity of wind and solar power while ensuring the safe and stable operation of the system, reduce the comprehensive cost per unit of power generation, and achieve optimized capacity allocation under the cascaded multi-terminal transmission method. Characteristic curves representing the relationship between zoning ratios and wind and solar access capacity are plotted; by fitting and analyzing the trend lines of these characteristic curves, the optimal capacity allocation ratio for wind and solar resources is determined. This technical solution can significantly weaken the constraints of cascaded multi-terminal transmission topologies on capacity access schemes, improve the rationality and accuracy of capacity allocation, and enhance capacity allocation through the cascaded multi-terminal transmission method.

[0091] This invention also provides a cascaded multi-terminal hydropower station flexibility spatiotemporal collaborative allocation system, including a tiered converter station power balancing modeling module, a hydropower flexibility spatiotemporal allocation modeling module, a two-layer capacity configuration module, and a wind-solar allocation ratio optimization module. These modules are connected sequentially to form a data transmission and feedback closed loop.

[0092] In practical implementation, firstly, a mathematical model describing the power balance of each converter station and grid constraints is established through the power balancing modeling module of the tiered converter station; then, the hydraulic system is introduced through the hydropower flexibility and air conditioning distribution modeling module. The power coupling relationship transforms the rigid power balance equation into a flexible inequality constraint, enabling the spatiotemporal transfer of the flexibility of cascade hydropower among multiple converter stations. Based on this, the outer optimization layer of the dual-layer capacity configuration module optimizes capacity configuration with the goal of minimizing economic indicators, while the inner simulation layer performs step simulation based on the wind and solar capacity boundaries transferred from the outer layer, forming a two-way feedback. Finally, the wind and solar allocation ratio optimization module determines the optimal wind and solar resource allocation ratio and the corresponding optimal capacity configuration by scanning different wind and solar allocation ratios and fitting response curves.

[0093] The technical solution of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0094] Example 1 This embodiment takes a hydropower base in the upper reaches of a river as the research object, and elaborates on the implementation process and effect verification of the method of the present invention.

[0095] I. Overview of the Research Subjects This embodiment takes a hydropower base on the upper reaches of a certain river as the research object. The main stream section of the base starts from the upper reaches of a tributary and ends at the lower reaches of another section, with a total length of approximately 772 kilometers. Seven cascade hydropower stations are planned, such as... Figure 5 As shown, this embodiment selects seven conventional cascade hydropower stations (hydropower station 1, hydropower station 2, hydropower station 3, hydropower station 4, hydropower station 5, hydropower station 6, and hydropower station 7), with a total installed capacity of 5,516 MW. Two pumped storage power stations (total installed capacity 2000MW) are also configured as flexible regulating power sources.

[0096] The cascaded multi-terminal transmission project is a hybrid cascaded ultra-high voltage direct current (UHVDC) transmission project from an upstream area of ​​a river to a receiving area. The sending end consists of a ±400kV converter station A and a ±800kV converter station B, which are connected by a 114.251-kilometer ±400kV DC line (e.g., Figure 2 As shown), the receiving end is converter station C. According to water... wind Light The power storage grid connection point divides the system into two electrical zones: Electrical Zone 1 (corresponding to Converter Station A, low-voltage end): gathers four hydropower stations: Hydropower Station 2, Hydropower Station 3, Hydropower Station 4, and Hydropower Station 5, supporting the grid connection of wind power and photovoltaic power in this area; Electrical Zone 2 (corresponding to Converter Station B, high-voltage end): This area's wind and solar power grid connection is supported by Hydropower Station 1 (e.g., Figure 3 (as shown) Considering the hydraulic connections between the cascade hydropower stations, the two zones are coupled not only through electrical connections at the converter stations but also through hydraulic connections between the cascade hydropower stations. Ultimately, the power is jointly transmitted via a DC transmission system. A typical annual power transmission curve for the DC transmission system is shown below. Figure 6 As shown, the curve contains 288 points. The curve consists of 12 monthly curves, each containing 24 points.

[0097] The specific implementation steps are as follows: Step 1: Establish a mathematical model for power balancing of tiered converter stations The power balancing modeling module for tiered converter stations performs the following operations: (1) Based on the voltage level of each converter station in the cascaded multi-terminal power transmission system, the converter stations are divided into low-voltage converter station (k1, corresponding to converter station A), high-voltage converter station (k2, corresponding to converter station B) and receiving-end converter station (converter station C).

[0098] (2) Based on the grid converter station zoning, each hydropower station, wind farm, photovoltaic power station and pumped storage power station is assigned to the corresponding electrical area: Hydropower station 2, hydropower station 3, hydropower station 4, hydropower station 5, hydropower station 6, hydropower station 7 and wind, solar and pumped storage in zone 1 are assigned to electrical area z1; Hydropower station 1 and wind, solar and pumped storage in zone 2 are assigned to electrical area z2.

[0099] (3) Establish the power balance equations within each electrical zone and the power transmission constraints of the tie lines between different electrical zones. The specific power balance equations are as follows: For the low-voltage converter station (k1) and its corresponding electrical zone z1: For the high-voltage converter station (k2) and its corresponding electrical zone z2: In the formula: , These represent the power transmitted by the k1 and k2 level converter stations at time t, respectively. , These are the net output power of electrical zones z1 and z2 at time t, respectively. , I1 and I2 are the total power generation of all hydropower stations in partitions z1 and z2 at time t, respectively (where I1 is the total number of hydropower stations in partition z1 and I2 is the sum of the total number of hydropower stations in partitions z1 and z2). , These represent the power generation of the photovoltaic power station and the wind farm within partition z at time t, respectively. It is the sum of the generating power of other power sources (such as conventional thermal power, biomass, etc., if any) within zone z, excluding hydropower, photovoltaic, wind power and pumped storage. The pumping power of the pumped storage power station within zone z; This represents the amount of renewable energy power curtailed within zone z.

[0100] (4) Set upper and lower power limits for each converter station, transmission limit limits for tie lines, and minimum and maximum transmission demand constraints for DC transmission projects:

[0101]

[0102]

[0103] in, , These are the minimum and maximum allowable power for the k-th level converter station, respectively. , These are the minimum and maximum transmission limits for the tie line L of the k-th converter station, respectively. , These represent the minimum and maximum power transmission demands of the DC transmission project at time t, respectively. and These represent the sets of sending-end and receiving-end converter stations, respectively.

[0104] Through the above steps, the power balance modeling module of the graded converter station outputs a complete power balance mathematical model.

[0105] Step 2: Establish a model for air conditioning configuration when water and electricity are flexible. The hydropower flexibility modeling module, based on the power balance mathematical model obtained in step 1, performs the following operations: (1) Establish the hydraulic-electric coupling relationship of the electrical zones where each converter station is located. Specifically, this includes: Water balance relationship: The outflow from the upstream hydropower station (including power generation flow and water discharge flow) becomes part of the inflow to the downstream hydropower station after the water flow lag time.

[0106] Hydropower station output function: The relationship between the power generation of a hydropower station and its flow rate and head can usually be expressed as follows: ,in The overall efficiency coefficient, For power generation flow, For water head.

[0107] (2) The rigid equality constraint of power balance between different converter stations is transformed into a flexible inequality constraint with the overall flexibility of cascade hydropower as the boundary.

[0108] Specifically, the original rigid equality constraint originates from the power balance equation in step 1, requiring that the power transmitted by the two converter stations be equal to the net power of their respective zones, and that the power allocation ratio of the DC transmission system be satisfied between them. This rigid constraint limits the flexibility of wind and solar power integration.

[0109] This embodiment treats the cascade hydropower station group as a whole collaborative resource pool, defined as follows: Reduce flexibility The minimum output limit of the cascade hydropower station group within zone z is determined by ecological flow, irrigation water use, and minimum stable output of the generating units.

[0110] Increase flexibility The maximum output limit of the cascade hydropower station group within zone z is determined by available reservoir capacity, head, and rated capacity of the generating units.

[0111] The power balance equality constraint between different converter stations is transformed into the following flexible inequality constraint:

[0112]

[0113] in, For partitioning All hydroelectric power stations in the country Total power generation at any given time partition The flexibility of adjusting the internal cascade hydropower station group. For partitioning The flexibility of adjusting the internal cascade hydropower station group; For partitioning All hydroelectric power stations in the country Total power generation at any given time; Electrical partitions The total number of hydroelectric power stations in the area Electrical partitions and Total number of inland hydroelectric power stations; Electrical partitions Inner Hydropower station Power generation at any given moment; and The two converter stations are divided into electrical zones, corresponding to different converter station collection areas. Since the wind and solar power output and pumped storage operation status in the two zones are independent of each other, the rigid constraints of the above equations can be transformed into flexible inequality constraints. Therefore, the power balance equation between the two converter stations can be realized through the coupling of hydraulic and electric power to achieve the flexible transfer of cascade hydropower between the two converter stations in time and space.

[0114] The aforementioned flexible constraints allow the total hydropower generation capacity within each zone to fluctuate within a certain range, rather than being strictly equal to a fixed value. This is achieved through hydraulic... Electric coupling allows the hydropower flexibility of the two zones to support each other: when zone 1 needs to increase its power output, it can increase the power generation flow of its hydropower station (provided that there is sufficient water from upstream), while reducing the output of the hydropower station in zone 2 (by reducing the power generation flow and increasing water storage), thereby realizing the spatiotemporal transfer of flexibility between converter stations.

[0115] (3) Through the above flexible inequality constraints, a complete flexible time-space allocation model for hydropower is obtained. This model can describe the spatiotemporal transfer and coordinated allocation of the upward or downward adjustment flexibility of cascade hydropower between different converter stations.

[0116] Step 3: Establish a two-layer capacity configuration model like Figure 4 As shown, the dual-layer capacity configuration module constructs an outer optimization layer and an inner simulation layer based on the hydropower flexibility time-of-use configuration model obtained in step 2, and realizes bidirectional feedback.

[0117] 3.1 Outer Optimization Layer The outer optimization layer takes minimizing the comprehensive economic index of the end-of-scheduling capacity plan as its objective function. In this embodiment, the objective function is expressed as:

[0118] in, , , and These represent the investment cost, electricity sales revenue, power curtailment penalty, and load shortage penalty for connecting to the k-th level converter station, respectively. The unit installed cost of each type of power source connected to the k-th level converter station is multiplied by its installed capacity. It is obtained by multiplying the power generation of various power sources by their on-grid electricity price. This is derived by multiplying the amount of abandoned hydropower and variable renewable energy (VRE) by their respective penalty prices. It is mainly calculated by multiplying the load deficit by the penalty rate compared to a given external transmission curve.

[0119] The decision variable for the outer optimization layer is the maximum photovoltaic access capacity of each converter station. Maximum wind power access capacity At the same time, upper limits are set for the installed capacity of photovoltaic and wind power in each zone, such as not exceeding the land resources or grid capacity of that zone.

[0120] The inner simulation layer uses the hydropower flexibility time-of-use air conditioning model from step 2 as the basis for simulation, and receives the wind and solar capacity boundaries transmitted from the outer layer as input. The specific simulation process is as follows: (1) Initialization: Set the initial grid-connected capacity of photovoltaic and wind power to the preset minimum value (e.g., 100 MW each).

[0121] (2) Step-by-step simulation: The installed capacity of photovoltaic and wind power is gradually increased according to the preset step size. Among them, the simulation step size of the grid-connected photovoltaic installed capacity is: (e.g., 50 MW), the simulation step size for grid-connected wind power capacity is... (e.g., 50 MW); The photovoltaic installed capacity used in the nth step simulation is: ; The installed wind power capacity used in the nth step simulation is: .

[0122] For each capacity combination, an 8760-hour time-series simulation is used to optimize the scheduling of cascade hydropower, pumped storage, and wind and solar power output under the conditions of meeting the flexibility of hydropower, power balance constraints, and grid security constraints. The curtailment rate, power shortage rate, and operating costs (including curtailment penalties and power shortage penalties) under this capacity combination are calculated.

[0123] Different wind and solar resource allocation ratios are set between the two converter stations, specifically:

[0124]

[0125]

[0126]

[0127] express, These represent the maximum photovoltaic (PV) capacity and the maximum wind power (WH) capacity connected to the k-th level converter station, respectively. Different wind and solar resource allocation ratios are set. τ is the proportionality coefficient, which is used to obtain decision variables under different access ratios.

[0128]

[0129]

[0130] When the installed capacity of wind and solar power reaches the maximum value of the outer layer transmission. and At that point, the inner layer simulation terminates.

[0131] 3.3 Two-layer bidirectional feedback mechanism The inner simulation layer feeds back the curtailment rate, power shortage rate, and operating cost results obtained from each step of the simulation to the outer optimization layer. The outer optimization layer updates the objective function value based on the feedback and adjusts the search direction of the wind and solar capacity boundary (e.g., updating decision variables using a genetic algorithm or particle swarm optimization algorithm). The inner simulation layer re-executes the step-by-step simulation under the updated boundary of the outer layer until the outer objective function converges (e.g., the change is less than a preset threshold for three consecutive generations) or reaches a preset iteration termination condition (e.g., a maximum of 100 iterations).

[0132] Through the aforementioned two-layer bidirectional feedback, the outer optimization layer can obtain a true economic evaluation of each candidate capacity scheme, while the inner simulation layer can gradually approach the optimal capacity configuration under the guidance of the outer layer.

[0133] Step 4: Optimizing the wind-solar allocation ratio and solving for the optimal capacity configuration The wind-solar allocation ratio optimization module performs the following operations based on the two-layer capacity configuration model from step 3: (1) Set different wind and solar resource allocation ratios. In this embodiment, the wind and solar resource allocation ratio is defined as the ratio of photovoltaic capacity or wind power capacity between two converter stations, for example... Set a series of ratio values, such as 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8.

[0134] (2) For each allocation ratio, the two-level capacity allocation model is called for optimization. The solution uses the mixed integer linear programming (MILP) method. To improve the solution efficiency, nonlinear factors are linearized: Water level-reservoir capacity curve: piecewise linearization; Tailwater level-discharge curve: piecewise linearization; The power output function of the hydropower station is solved iteratively using the successive approximation method (SA).

[0135] (3) Record the optimal objective function value (minimum economic index) for each allocation ratio.

[0136] (4) Using the distribution ratio as the horizontal axis and the objective function value as the vertical axis, the response curve is obtained by curve fitting method (such as cubic spline interpolation or polynomial fitting).

[0137] (5) Determine the minimum point of the response curve. The allocation ratio corresponding to this minimum point is the optimal wind and solar resource allocation ratio, and the corresponding objective function value is the optimal objective function value.

[0138] (6) Based on the optimal wind and solar resource allocation ratio and the optimal objective function value, the optimal capacity configuration of the system (i.e., the photovoltaic and wind power installed capacity of each converter station) is derived.

[0139] This embodiment uses the power transmission demand of the receiving-end converter station in the ultra-high voltage transmission system as the input boundary condition for the configuration method optimization. Figure 6 This invention constructs a general mixed-integer linear programming (MILP) model. By linearizing the water level-storage capacity curve, tailrace level-discharge curve, and hydropower station output function, and introducing the successive approximation method (SA) to iteratively solve the nonlinear problem, the model achieves accurate solution and optimized configuration. By setting different wind and solar resource allocation ratios, the capacity configuration is optimized, obtaining the capacity configuration results corresponding to each allocation ratio. A spatial interpolation method is used to construct a quantitative response relationship between wind and solar access capacity and the system cost per unit of power generation, generating the corresponding response curve. Based on the minimum point of this response curve, the economically optimal capacity configuration scheme is determined.

[0140] In this embodiment, the optimal wind and solar access capacity obtained through the above steps is approximately 12,000 MW, and the optimal wind and solar resource allocation ratio is between 0.5 and 0.55. The results show that the method of this invention can effectively alleviate the limitations imposed by cascaded multi-terminal transmission on wind and solar capacity configuration, significantly improving the rationality and economy of capacity configuration.

[0141] In scenarios where the comprehensive utilization of the reservoir needs to be considered, this embodiment also nests and couples objectives such as flood control, water supply, ecology, and navigation into the flexible and flexible hydropower allocation model. This is specifically achieved through the following constraints: Water balance constraints: taking into account upstream outflow, inter-regional runoff, power generation flow, water wastage, ecological flow, etc. Water level boundary constraints: During the scheduling period, the water level shall not be lower than the dead water level or higher than the flood control limit water level or the normal storage water level at any time. Initial and final water level constraints: The initial water level during the scheduling period is a given value, and the final water level must be controlled within a specified range (e.g., to meet downstream water demand).

[0142] By introducing corresponding penalty terms or hard constraints into the above constraints, it is possible to ensure the comprehensive utilization requirements of the reservoir while carrying out flexible allocation of hydropower, thereby achieving multi-objective collaborative optimization.

[0143] Specifically, the above refers to the construction of a flexible regulation model for cascade hydropower stations; Step 1: Establish a hydraulic coupling model of the cascade hydropower station and construct water balance constraints:

[0144]

[0145]

[0146] in, for Time period The reservoir capacity corresponding to the power station; for Time period The inflow of the power station into the reservoir; for Time period Ecological flow of the power station; for Time period The power generation flow of the power plant; for Time period The power station's natural inflow runoff; for Time period The natural runoff within the power station; for Time period The discharge flow of the power plant; for Time period The amount of water discarded by the power station.

[0147] Step 2: Construct water level boundaries and initial and final water level constraints:

[0148]

[0149] in, for Time period The water level in front of the power station dam, for The water level at the power station at time 0; for The water level at the power station at time T; for Initial water level during the power plant's dispatch period; for The power station controls the water level.

[0150] Step 3: Design a flexible allocation mechanism in the spatiotemporal dimensions to distribute the cascade hydropower regulation capacity to different converter stations.

[0151] This embodiment uses 2022 runoff data and historical wind and solar power output data from the upper reaches of a certain river to perform 8760 hours of time-series simulation optimization scheduling of an integrated hydropower-wind power-photovoltaic-pumped storage system using the method of this invention. Key model parameters are set as follows: the optimization objective is to minimize the total system curtailment while ensuring that the DC transmission curve deviation does not exceed ±5%; mixed integer linear programming (MILP) is used for the solution. The obtained solution results are as follows. Figure 7 , Figure 8 , Figure 9 and Figure 10 As shown, the optimal wind and solar access capacity is 12,000 MW, and the wind and solar resource allocation ratio is between 0.5 and 0.55, which can effectively alleviate the constraints of cascaded multi-terminal transmission on the wind and solar capacity configuration of the Jinshang Hydropower Base.

[0152] In summary, this embodiment, using the Jinshang Hydropower Base as an example, fully demonstrates the implementation process and application effects of the method of this invention. The results show that this invention, by constructing a cascaded multi-terminal mathematical modeling framework, quantifying the complex spatiotemporal coupling characteristics of hydropower flexibility, solving the high-dimensional nonlinear coupling constraints of hydraulic-electricity systems, achieving the goals of hydropower flexibility utilization and reservoir comprehensive utilization under a collaborative cascaded multi-terminal mode, and optimizing the capacity configuration improvement effect of the cascaded multi-terminal transmission method, forms a complete scheduling solution for DC transmission systems containing large-scale renewable energy.

[0153] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A spatiotemporal collaborative allocation method for the flexibility of cascaded multi-terminal hydropower stations, characterized in that: include: Based on the power relationship of the cascaded multi-terminal power transmission mode, a mathematical model for power balance of the graded converter station is established. Based on the power balance mathematical model of the graded converter station, and according to the hydraulic-electric coupling relationship, a flexible power supply and distribution model for hydropower is established. Based on the aforementioned flexible and timely hydropower allocation model, a two-layer capacity allocation model is established. The two-layer model includes an outer optimization layer and an inner simulation layer. The outer optimization layer takes minimizing the economic index as the objective function, and the inner simulation layer performs step-by-step simulation based on the maximum wind and solar access capacity transmitted from the outer layer, gradually increasing the photovoltaic and wind power installed capacity according to a preset step size. Based on the simulation results of the dual-layer capacity configuration model, different wind and solar resource allocation ratios are set among different converter stations. The objective function value under each ratio is solved. By fitting the response curve between the objective function value and the allocation ratio, the optimal wind and solar resource allocation ratio and the corresponding optimal capacity configuration are determined.

2. The method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals as described in claim 1, characterized in that, The specific process of establishing the power balance mathematical model for the tiered converter station includes: Based on the voltage or capacity level of each converter station in the cascaded multi-terminal power transmission system, the converter stations are divided into low-voltage converter stations, high-voltage converter stations, and receiving-end converter stations. Based on the zoning of power grid converter stations, each hydropower station and pumped storage power station is assigned to its corresponding electrical zone; Based on the zoning ratio of wind and solar power station installed capacity, the installed capacity of wind and solar power stations in each electrical zone is determined according to the preset step size. Based on the installed capacity of wind and solar power stations in each electrical zone, power balance equations within each electrical zone and power transmission constraints of tie lines between different electrical zones are established. By setting upper and lower power limits for each converter station, transmission limit constraints for tie lines, and minimum and maximum transmission demand constraints for DC transmission projects, a mathematical model for power balance of graded converter stations is established. The specific expression for the power balance mathematical model of the tiered converter station is as follows: in, This represents the power of the k-th converter station at time t. , These are the minimum and maximum allowable power for the k-th level converter station, respectively. , These are the minimum and maximum transmission limits for the tie line L of the k-th converter station, respectively. , Let be the minimum and maximum power transmission demands of the DC transmission project at time t, respectively. K send and K receive These represent the sets of sending-end and receiving-end converter stations, respectively.

3. The method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals as described in claim 1, characterized in that, The specific process for establishing the air conditioning allocation model for hydropower flexibility includes: Based on the power balance mathematical model of the graded converter stations, the hydraulic-electric coupling relationship of the electrical area where each converter station is located is established. The hydraulic-electric coupling relationship includes the influence of the outflow of the upstream hydropower station on the inflow of the downstream hydropower station, as well as the functional relationship between the power generation output of the hydropower station and the power generation flow and head. Based on the aforementioned hydraulic-electric coupling relationship, the rigid equality constraint for power balance between different converter stations is transformed into a flexible inequality constraint with the overall flexibility of the cascade hydropower as the boundary. Through the aforementioned flexible inequality constraints, a flexible hydropower time-space allocation model is obtained, enabling the spatiotemporal transfer and coordinated allocation of the upward or downward adjustment flexibility of cascade hydropower between different converter stations.

4. The method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals as described in claim 3, characterized in that, The rigid equality constraint for power balance between different converter stations is transformed into a flexible inequality constraint with the overall flexibility of the cascade hydropower system as the boundary, specifically: Treating the cascade hydropower station group as a whole and a collaborative resource pool, the flexibility of adjusting the cascade hydropower stations is defined. and reduced flexibility ; The power balance equality constraint between different converter stations is transformed into the following flexible inequality constraint: in, For partitioning All hydroelectric power stations in the country Total power generation at any given time partition The flexibility of adjusting the internal cascade hydropower station group. For partitioning The flexibility of adjusting the internal cascade hydropower station group; For partitioning All hydroelectric power stations in the country Total power generation at any given time; Electrical partitions The total number of hydroelectric power stations in the area Electrical partitions and Total number of inland hydroelectric power stations; Electrical partitions Inner Hydropower station Power generation at any given moment; and The electrical zones correspond to different converter station aggregation areas. The rigid equality constraint for power balance among different converter stations is expressed as follows: In the formula, For the first Level converter station The power transmitted at any given time; For the first Level converter station The power transmitted at any time, , Electrical zones or exist Net output power at any given time; , Electrical zones or Internal photovoltaic power station Actual power generation at any given time; , Electrical zones or The actual power generation of the internal wind farm at time t. , Electrical zones or The sum of the generating capacity of all power sources other than hydropower, photovoltaic power, wind power and pumped storage. , Electrical zones or Pumping power of an internal pumped storage power station; , Electrical zones or Domestic renewable energy curtailment power denoted by z, which represents the number of hydropower stations; z represents the number of electrical zones; and k represents the converter station level.

5. The method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals as described in claim 1, characterized in that, Based on the aforementioned flexible capacity allocation model for hydropower, a two-layer capacity allocation model is established, specifically including: The outer optimization layer takes the minimum comprehensive economic index of the capacity scheme at the end of the scheduling period as the objective function. The economic index includes investment cost, electricity sales revenue, curtailment penalty and load shortage penalty. The maximum wind and solar access capacity of each converter station is used as the decision variable, and upper limit constraints are set on the photovoltaic and wind power installed capacity in each zone. The inner simulation layer uses the aforementioned flexible hydropower distribution model as the basis for operation simulation and receives the wind and solar capacity boundaries transmitted from the outer layer as input. Starting from the preset minimum grid-connected capacity, the installed capacity of photovoltaic and wind power is gradually increased according to the preset step size. Time-series operation simulation is performed for each capacity combination to calculate the corresponding curtailment rate, power shortage rate and operating cost. The inner simulation layer feeds back the curtailment rate, power shortage rate, and operating cost results obtained from each step simulation to the outer optimization layer. The outer optimization layer updates the objective function value based on the feedback results and adjusts the search direction of the wind and solar capacity boundary. The inner simulation layer re-executes the step simulation under the updated boundary of the outer layer until the outer objective function converges or reaches the preset iteration termination condition.

6. The method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals as described in claim 5, characterized in that, The outer optimization layer has the lowest overall economic performance index. f(k) The objective function is expressed as: in, , , and These represent the investment cost, electricity sales revenue, power curtailment penalty, and load shortage penalty for connecting to the k-th level converter station, respectively.

7. The method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals as described in claim 5, characterized in that, In the preset step size, the simulation step size for the grid-connected photovoltaic installed capacity is: The simulation step size for grid-connected wind power capacity is ; The photovoltaic installed capacity used in the nth step simulation is: ; The installed capacity of the electric power unit used in the nth step of the wind simulation is: .

8. The method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals as described in claim 1, characterized in that, The wind and solar resource allocation ratio is the ratio of photovoltaic capacity or wind power capacity connected to different converter stations. By setting multiple different allocation ratios, the objective function value under each ratio is solved by mixed integer linear programming. The response curve of the objective function value changing with the allocation ratio is obtained by curve fitting method. The allocation ratio corresponding to the minimum point of the response curve is taken as the optimal wind and solar resource allocation ratio.

9. The method for flexible spatiotemporal collaborative allocation of hydropower station resources based on cascaded multi-terminals as described in claim 1, characterized in that, Also includes: The comprehensive utilization objectives of the reservoir are nested and coupled into the flexible hydropower allocation model. The comprehensive utilization objectives of the reservoir include at least one of flood control objectives, water supply objectives, ecological objectives and navigation objectives. Co-optimization with the flexible hydropower utilization is achieved through water balance constraints, water level boundary constraints and head and tail water level constraints.

10. A flexible spatiotemporal collaborative allocation system for hydropower stations based on cascaded multi-terminals, characterized in that: include: The power balancing modeling module for tiered converter stations establishes a mathematical model for power balancing of tiered converter stations based on the power relationship of the cascaded multi-terminal transmission mode. The hydropower flexibility air conditioning configuration modeling module, based on the power balance mathematical model of the graded converter station, establishes a hydropower flexibility air conditioning configuration model according to the hydraulic-electric coupling relationship; The dual-layer capacity configuration module establishes a dual-layer capacity configuration model based on the aforementioned hydropower flexible time-and-time air-conditioning model. The dual-layer model includes an outer optimization layer and an inner simulation layer. The outer optimization layer takes minimizing the economic index as the objective function, and the inner simulation layer performs step-by-step simulation based on the maximum wind and solar access capacity transmitted from the outer layer, gradually increasing the photovoltaic and wind power installed capacity according to a preset step size. The wind and solar resource allocation ratio optimization module, based on the dual-layer capacity configuration model, sets different wind and solar resource allocation ratios among different converter stations, solves the objective function value under each ratio, and determines the optimal wind and solar resource allocation ratio and the corresponding optimal capacity configuration by fitting the response curve of the objective function value and the allocation ratio.

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