Hydropower station cluster cooperative control method, device and equipment and storage medium

Abstract

The invention discloses a hydropower station cluster cooperative control method, device and equipment and a storage medium, and is applied to the field of cooperative control. Inputting the water level, the flow and the geographic position of the hydropower station data into the dynamic correlation degree model to obtain the correlation degree between the hydropower stations; inputting the power grid load of the hydropower station data, the power generation cost and the correlation degree between the hydropower stations into the space-time optimization target model to obtain a power generation capacity distribution strategy; and based on the safety constraint softening model, analyzing the generating capacity distribution strategy, and determining the final generating capacity of the hydropower station. According to the dynamic collaborative allocation algorithm based on the dynamic correlation degree model and the space-time optimization target model, the overall power generation efficiency of the hydropower station group is improved, and the power generation stability of the hydropower station group is enhanced; the safety of unit equipment of the hydropower station cluster is ensured based on the safety constraint softening model, so that the unit equipment runs in a safety range, and non-planned shutdown is avoided.

Description

Technical Field

[0001] The present invention relates to the field of collaborative control, and particularly to a collaborative control method, device, equipment and storage medium for a hydropower station cluster. Background Art

[0002] With the continuous development of smart grids and green energy, the management and control of hydropower station clusters have become an important issue. Most of the existing hydropower station management systems conduct scheduling and management based on a single hydropower station or a single unit, lacking unified scheduling and optimal control of the entire hydropower station cluster, resulting in uneven cross-station load distribution. Since a hydropower station cluster includes multiple hydropower stations and multiple units, factors such as water flow, load demand, and unit status are highly dynamic and complex. Traditional control methods are difficult to meet the collaborative control requirements of multi-unit and large-scale clusters, often suffering from problems such as low efficiency, slow response, and uneven load distribution.

[0003] Therefore, there is an urgent need for a method for unified collaborative control of hydropower station clusters. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a collaborative control method, device, equipment and storage medium for a hydropower station cluster, which solves the problems of low power generation efficiency, slow response, and uneven load distribution in the existing technology for hydropower station clusters.

[0005] To solve the above technical problems, the present invention provides a collaborative control method for a hydropower station cluster, including:

[0006] Real-time collection of hydropower station data, where the hydropower station data at least includes water level, flow rate, grid load, geographical location, and power generation cost;

[0007] Input the water level, flow rate, and geographical location of the hydropower station data into a dynamic correlation model to obtain the correlation between hydropower stations;

[0008] Input the grid load, power generation cost of the hydropower station data, and the correlation between hydropower stations into a spatio-temporal optimization target model to obtain a power generation allocation strategy;

[0009] Based on a safety constraint softening model, analyze the power generation allocation strategy to determine the final power generation of the hydropower station.

[0010] Optionally, before inputting the water level, flow rate, and geographical location of the hydropower station data into a dynamic correlation model to obtain the correlation between hydropower stations, it further includes:

[0011] Construct the dynamic correlation model, and the dynamic correlation model is:

[0012] ;

[0013] ;

[0014] Among them, represents the correlation degree between hydropower station i and hydropower station j; Qi represents the flow rate of hydropower station i before time τ; τ is calibrated according to the measured water flow velocity; Qmax represents the maximum allowable flow rate of the hydropower station; represents the geographical distance between hydropower station i and hydropower station j; σ is the distance attenuation coefficient, which is related to the terrain; α is the head gain factor; is the water level difference between hydropower station i and hydropower station j; Hnom is the reference head of the hydropower station.

[0015] Optionally, before inputting the grid load, generation cost of the hydropower station data and the correlation degree between the hydropower stations into the spatio-temporal optimization objective model, it further includes:

[0016] Construct the spatio-temporal optimization objective model, and the spatio-temporal optimization objective model is:

[0017] ;

[0018] ;

[0019] Among them, F represents the numerical objective to be optimized; represents the power generation power of hydropower station i at time t; represents the power generation power of hydropower station j at time t; is the comprehensive unit generation cost of hydropower station i; λ is the collaborative regulation factor; is the correlation degree between hydropower station i and hydropower station j; Vmax represents the maximum capacity of the reservoir or storage tank; Vt represents the actual capacity of the reservoir or storage tank at time t; qi represents the basic generation cost of hydropower station i; w1, w2, w3 are weight coefficients determined by regression according to historical data.

[0020] Optionally, before analyzing the power generation allocation strategy based on the security constraint softening model to determine the final power generation of the hydropower station, it further includes:

[0021] Construct the security constraint softening model, and the security constraint softening model is:

[0022] ;

[0023] Among them, is the security value of the power generation power of; is the security threshold; k is the softening coefficient; represents the power generation power of hydropower station i.

[0024] Optionally, the setting of the safety threshold and the softening threshold includes:

[0025] The first safety threshold and the first softening coefficient corresponding to the early warning area, the first safety threshold is 90% of the rated power, and the first softening coefficient is 1;

[0026] The second safety threshold and the second softening coefficient corresponding to the buffer zone, the second safety threshold is 95% of the rated power, and the second softening coefficient is 5;

[0027] The third safety threshold and the third softening coefficient corresponding to the hard boundary, the third safety threshold is 100% of the rated power, and the third softening coefficient is 20.

[0028] Optionally, based on the safety constraint softening model, analyze the power generation allocation strategy to determine the final power generation of the hydropower station, including:

[0029] When the power generation allocation strategy meets the safety constraint softening model, control each generating unit of the power station cluster to generate electricity according to the power generation allocation strategy;

[0030] When the power generation allocation strategy does not meet the safety constraint softening model, adjust the power generation allocation strategy.

[0031] Optionally, when the power generation allocation strategy does not meet the safety constraint softening model, adjust the power generation allocation strategy, including:

[0032] When the power generation allocation strategy reaches the first safety threshold of the safety constraint softening model, adopt a power withdrawal measure strategy;

[0033] When the power generation allocation strategy reaches the second safety threshold of the safety constraint softening model, implement a gradient limit power change rate strategy;

[0034] When the power generation allocation strategy reaches the third safety threshold of the safety constraint softening model, immediately trigger a shutdown mechanism;

[0035] The first safety threshold is less than the second safety threshold, and the second safety threshold is less than the third safety threshold.

[0036] The present invention also provides a coordinated control device for a hydropower station cluster, including:

[0037] A data acquisition module for real-time acquisition of hydropower station data, the hydropower station data at least includes water level, flow rate, grid load, geographical location, and power generation cost;

[0038] An association analysis module, configured to input the water level, flow rate, and geographical location of the hydropower station data into a dynamic association degree model to obtain the association degree between hydropower stations;

[0039] An allocation optimization module, configured to input the grid load, power generation cost of the hydropower station data, and the association degree between hydropower stations into a spatio-temporal optimization target model to obtain a power generation allocation strategy;

[0040] A safety analysis module, configured to analyze the power generation allocation strategy based on a safety constraint softening model to determine the final power generation of the hydropower station.

[0041] The present invention also provides a hydropower station cluster collaborative control device, including:

[0042] A memory, configured to store a computer program;

[0043] A processor, configured to implement the steps of the hydropower station cluster collaborative control method as described above when executing the computer program.

[0044] The present invention also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are loaded and executed by a processor, the steps of the hydropower station cluster collaborative control method as described above are implemented.

[0045] It can be seen that the present invention collects hydropower station data in real time. The hydropower station data at least includes water level, flow rate, grid load, geographical location, and power generation cost; inputs the water level, flow rate, and geographical location of the hydropower station data into a dynamic association degree model to obtain the association degree between hydropower stations; inputs the grid load, power generation cost of the hydropower station data, and the association degree between hydropower stations into a spatio-temporal optimization target model to obtain a power generation allocation strategy; analyzes the power generation allocation strategy based on a safety constraint softening model to determine the final power generation of the hydropower station. The dynamic collaborative allocation algorithm of the present invention based on the dynamic association degree model and the spatio-temporal optimization target model improves the overall power generation efficiency of the hydropower station group and enhances the stability of the hydropower station cluster power generation; and based on the safety constraint softening model, ensures the safety of the unit equipment of the hydropower station cluster, enables it to operate within a safe range, and avoids unplanned outages. Through the collaborative control of multiple hydropower station units, the overall power generation efficiency of the cluster is improved, the stability of the system is enhanced, and intelligent fault recovery and load response are realized to meet the requirements of modern smart grids.

[0046] In addition, the present invention also provides a hydropower station cluster collaborative control device, equipment, and storage medium, which also have the above-mentioned beneficial effects. Description of the Drawings

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0048] Figure 1 It is a flowchart of a method for collaborative control of a hydropower station cluster provided by an embodiment of the present invention;

[0049] Figure 2 It is an example diagram of a collaborative control platform for a hydropower station cluster provided by an embodiment of the present invention;

[0050] Figure 3 It is a schematic structural diagram of a collaborative control device for a hydropower station cluster provided by an embodiment of the present invention;

[0051] Figure 4 It is a schematic structural diagram of a collaborative control device for a hydropower station cluster provided by an embodiment of the present invention. Detailed implementation manners

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0053] First, several terms involved in this application are analyzed:

[0054] Hydropower station: A hydropower station consists of a hydraulic system, a mechanical system, an electric energy generation device, etc., and is a water conservancy hub project that realizes the conversion of water energy into electric energy.

[0055] Hydropower station cluster: A hydropower station cluster refers to the collaborative operation of multiple hydropower stations (usually multiple hydropower stations in the same region or basin) in power dispatching and management.

[0056] Collaborative control: Aiming at the safe, stable, economic, and power generation operation of hydropower stations, accurately and effectively formulate unit regulation strategies.

[0057] Active power: Active power refers to the actual alternating current energy generated or consumed per unit time, which is the average power within a cycle.

[0058] Unit control unit: It is a key device and system in a hydropower station used to control and monitor the operating status of generating units (including generators, steam turbines, or water turbines, etc.). It is usually an integrated automation system responsible for regulating the operating parameters of the units, monitoring the unit status, and protecting the safe operation of the units to ensure the efficient, safe, and stable operation of the units.

[0059] Head: The difference in water levels between the upstream and downstream of a hydropower station, also known as the head drop.

[0060] With the continuous development of smart grids and green energy, the management and control of hydropower station clusters have become an important issue. Most of the existing hydropower station monitoring systems are scheduled and managed based on a single hydropower station or a single unit, lacking unified scheduling and optimal control for the entire hydropower station cluster. Since a hydropower station cluster includes multiple hydropower stations and multiple units, factors such as water flow, load demand, and unit status have strong dynamics and complexity. Traditional control methods are difficult to meet the collaborative control requirements of multi-unit and large-scale clusters, and often have problems such as low efficiency, slow response, and uneven load distribution. Therefore, there is an urgent need for an intelligent platform that can perform unified collaborative control on hydropower station clusters.

[0061] Most of the existing hydropower station monitoring systems are scheduled and managed based on a single hydropower station or a single unit, lacking unified scheduling and optimal control for the entire hydropower station cluster. Control algorithms generally use algorithms such as genetic algorithm optimized scheduling, dynamic regulation of water flow, and maximum power point tracking (MPPT). Although these technologies can optimize the operation of a single unit to a certain extent, there are still deficiencies in the coordination and resource sharing between hydropower stations, and they cannot fully consider the mutual influence and real-time dynamic changes between units.

[0062] Therefore, there are the following defects in the current stage of hydropower station cluster control:

[0063] (1) Data islands and collaborative lag: Each hydropower station operates independently, lacking a real-time data sharing mechanism, resulting in uneven cross-station load distribution (such as some units being overloaded during peak load periods while other stations have redundant output). Traditional scheduling relies on manual experience, and it takes more than 5 minutes to respond to grid commands, making it difficult to meet the minute-level frequency modulation requirements.

[0064] (2) Insufficient equipment health management: The monitoring of unit status is limited to single parameters (such as temperature, vibration), without comprehensive health assessment by combining multiple indicators (seepage flow, lubricating oil pressure), and the false alarm rate of faults reaches more than 15%.

[0065] (3) Weak emergency fault tolerance ability: The start-up of standby units during sudden failures depends on manual decision-making, and the cross-station collaborative compensation delay exceeds 10 minutes, resulting in a power grid power fluctuation of more than 5%.

[0066] To solve the above problems, the present application proposes a method that improves the overall power generation efficiency of the cluster, enhances the stability of the system, and realizes intelligent fault recovery and load response through the coordinated control of multiple hydropower station units to meet the requirements of modern smart grids. For details, please refer to Figure 1 , Figure 1 which is a flowchart of a coordinated control method for a hydropower station cluster provided by an embodiment of the present invention. The method may include:

[0067] S101: Collect hydropower station data in real time.

[0068] The execution subject of this embodiment is a terminal / control platform. The type of the terminal is not limited in this embodiment, as long as it can complete the operations of the coordinated control method for the hydropower station cluster. The hydropower station data in this embodiment may at least include water level, flow rate, grid load, geographical location, and power generation cost.

[0069] S102: Input the water level, flow rate, and geographical location of the hydropower station data into a dynamic correlation model to obtain the correlation between hydropower stations.

[0070] In this embodiment, the collected data of each hydropower station is input into the correlation model, and the correlation output by the correlation model is obtained. This correlation can reflect the close relationship between each hydropower station.

[0071] It should be noted that before inputting the water level, flow rate, and geographical location of the hydropower station data into the dynamic correlation model to obtain the correlation between hydropower stations, the following steps may also be included:

[0072] Construct a dynamic correlation model, and the dynamic correlation model is:

[0073] ;

[0074] where represents the correlation between hydropower station i and hydropower station j. τ needs to be calibrated according to the measured water flow velocity (for example, when the flow velocity is 2 m / s, τ≈7 hours for 50 km); the value of σ is related to the terrain (σ² = 50 km² in mountainous areas and σ² = 150 km² in plains).

[0075] Table 1 Explanation table of each parameter in the dynamic correlation model

[0076]

[0077] It can be seen from the above dynamic correlation model that the correlation strength between hydropower stations is determined by three factors:

[0078] (1) The greater the upstream water inflow, the stronger the impact on the downstream (water flow factor).

[0079] (2) The closer the power stations are, the higher the correlation (distance decay factor).

[0080] (3) The greater the water level drop, the greater the collaborative regulation space (drop gain term).

[0081] S103: Input the grid load, generation cost, and correlation degree between hydropower stations of hydropower station data into the spatio-temporal optimization target model to obtain the power generation allocation strategy.

[0082] In this embodiment, input the data and correlation degree of each hydropower station into the spatio-temporal optimization target model. Based on the principle of maximizing profit (power generation) with the least cost, the optimal allocation strategy is required. The power generation allocation strategy clarifies the power generation / power generation power of each hydropower station. Generating electricity according to this power generation allocation strategy can reduce the generation cost.

[0083] It should be noted that before inputting the grid load, generation cost, and correlation degree between hydropower stations of hydropower station data into the spatio-temporal optimization target model, the following steps may also be included:

[0084] Construct a spatio-temporal optimization target model, and the spatio-temporal optimization target model is:

[0085] ;

[0086] ;

[0087] Among them, F represents the numerical objective to be optimized; Vmax−Vt reflects the available capacity of the reservoir or storage tank. The weight coefficients w1, w2, w3 in are determined by historical data regression (for example, for a certain hydropower station, w1 = 0.6, w2 = 0.3, w3 = 0.1).

[0088] Table 2 Explanation table of each parameter in the spatio-temporal optimization target model

[0089]

[0090] Through the above spatio-temporal optimization target model, two core objectives can be balanced:

[0091] (1) Maximize economic benefits: Prioritize allowing high-head and low-cost power stations to generate more electricity.

[0092] (2) System stability: The smaller the power difference between power stations with high correlation, the better, to avoid the oscillation of "one rising while the other falling".

[0093] S104: Based on the security constraint softening model, analyze the power generation allocation strategy to determine the final power generation of hydropower stations.

[0094] In this embodiment, a safety constraint softening model is also provided. This model is used to perform safety verification on the power generation allocation strategy obtained through the above optimization, and to maximize the power generation while minimizing the cost under the premise of ensuring safety.

[0095] It should be noted that before analyzing the power generation allocation strategy based on the safety constraint softening model to determine the final power generation of the hydropower station, the following steps may also be included:

[0096] Construct a safety constraint softening model, and the safety constraint softening model is:

[0097] ;

[0098] Among them, is the safety value of the power generation power , and the hard boundary needs to be lower than the mechanical limit of the equipment (such as taking 105% of the rated power). The above parameter values need to be calibrated through measured data in combination with the specific characteristics of the power station (installed capacity, head range, grid requirements, etc.).

[0099] Table 3 Explanation table of each parameter in the safety constraint softening model

[0100]

[0101] Among them, the setting process of the safety threshold and the softening threshold may include:

[0102] The first safety threshold and the first softening coefficient corresponding to the early warning area, the first safety threshold is 90% of the rated power, and the first softening coefficient is 1; the second safety threshold and the second softening coefficient corresponding to the buffer zone, the second safety threshold is 95% of the rated power, and the second softening coefficient is 5; the third safety threshold and the third softening coefficient corresponding to the hard boundary, the third safety threshold is 100% of the rated power, and the third softening coefficient is 20.

[0103] Specifically, early warning area: 90% rated power (k = 1); buffer zone: 95% rated power (k = 5); hard boundary: 100% rated power (k = 20).

[0104] Furthermore, the above analysis of the power generation allocation strategy based on the safety constraint softening model to determine the final power generation of the hydropower station may include the following steps:

[0105] Step 21: When the power generation allocation strategy meets the safety constraint softening model, control each generator set of the power station cluster to generate electricity according to the power generation allocation strategy;

[0106] Step 22: When the power generation allocation strategy does not meet the safety constraint softening model, adjust the power generation allocation strategy.

[0107] Specifically, the security constraint softening model is used to perform security detection on the power generation allocation strategy calculated above. If it meets the security requirements, each generator set in the power station cluster is controlled to generate electricity according to the power generation allocation strategy; if it does not meet the security requirements, the power generation allocation strategy needs to be adjusted to meet the security requirements.

[0108] Furthermore, when the power generation allocation strategy does not meet the security constraint softening model, adjusting the power generation allocation strategy may include the following steps:

[0109] Step 221: When the power generation allocation strategy reaches the first security threshold of the security constraint softening model, a power withdrawal measure strategy is adopted;

[0110] Step 222: When the power generation allocation strategy reaches the second security threshold of the security constraint softening model, a gradient-limited power change rate strategy is implemented;

[0111] Step 223: When the power generation allocation strategy reaches the third security threshold of the security constraint softening model, the shutdown mechanism is immediately triggered; where the first security threshold is less than the second security threshold, and the second security threshold is less than the third security threshold.

[0112] Specifically, the security constraint softening model in this embodiment adopts a three-level flexible constraint, and the closer the power is to the danger zone, the stronger the adjustment force:

[0113] (1) Early warning area: When the power generation allocation approaches the early warning area, a slight power withdrawal measure is taken, similar to the light braking of a car, to avoid further increase.

[0114] (2) Buffer zone: When the power generation allocation enters the buffer zone, a gradient-limited power change rate is implemented to gradually slow down the power growth.

[0115] (3) Hard boundary: When the power generation allocation reaches the hard boundary, the protection shutdown mechanism is immediately triggered to ensure the safety of the equipment.

[0116] Applying the collaborative control method for a hydropower station cluster provided by the embodiments of the present invention, by collecting hydropower station data in real time, the hydropower station data at least includes water level, flow rate, grid load, geographical location, and power generation cost; inputting the water level, flow rate, and geographical location of the hydropower station data into the dynamic correlation model to obtain the correlation between hydropower stations; inputting the grid load, power generation cost, and the correlation between hydropower stations of the hydropower station data into the spatio-temporal optimization target model to obtain the power generation allocation strategy; based on the safety constraint softening model, analyzing the power generation allocation strategy to determine the final hydropower station power generation. The dynamic collaborative allocation algorithm of the present invention based on the dynamic correlation model and the spatio-temporal optimization target model improves the overall power generation efficiency of the hydropower station group and enhances the stability of the hydropower station cluster power generation; and based on the safety constraint softening model, it ensures the safety of the unit equipment of the hydropower station cluster, enables it to operate within a safe range, and avoids unplanned shutdowns. Through the collaborative control of multiple hydropower station units, the overall power generation efficiency of the cluster is improved, the stability of the system is enhanced, and intelligent fault recovery and load response are realized to meet the requirements of modern smart grids.

[0117] Moreover, the real-time acquisition, synchronization, and fusion processing technology of multi-source heterogeneous data provides accurate and timely data support for collaborative control; constructing a dynamic correlation model, comprehensively considering water flow factors, distance attenuation factors, and head gain terms, calculating the correlation strength between power stations, and providing a basis for collaborative control; balancing the maximization of economic benefits and system stability through the spatio-temporal optimization target model, and realizing the collaborative optimization of power distribution through a dynamic optimization algorithm; through the safety constraint softening model with three-level flexible constraints, according to the degree of the generated power approaching the danger zone, the adjustment intensity gradually increases to ensure equipment safety, ensure that the equipment operates within a safe range, and avoid unplanned shutdowns; the dynamic collaborative allocation algorithm based on the dynamic correlation model and the spatio-temporal optimization target model ensures the improvement of the overall power generation efficiency of the hydropower station group and the enhancement of system stability.

[0118] To make the present invention easier to understand, please specifically refer to the following examples:

[0119] Step 1: Data input and processing includes: Outlier filtering: eliminating sensor noise data (such as an instantaneous flow rate mutation exceeding 20%); Spatio-temporal alignment: correcting the data time delay between upstream and downstream according to the water flow propagation time; Normalization processing: mapping parameters such as water level and flow rate to the interval [0,1].

[0120] ‌Table 4 Example data table

[0121]

[0122] Step 2: Optimize the algorithm process: (1) Model construction: Construct a dynamic correlation model, a spatio-temporal optimization target model, and a security constraint softening model; (2) Determine the power generation allocation strategy: Calculate the correlation degree among hydropower stations based on real-time data; Based on the correlation degree and the spatio-temporal optimization target model, adopt parallel search to determine the optimal solution, that is, the power generation allocation strategy. Specifically, the particle swarm algorithm can be used to explore high-benefit areas, and the genetic algorithm can ensure the diversity of the solution set; (3) Safety verification: Perform three-level constraint detection on the power generation allocation strategy obtained by the above solution; (4) Dynamic output: Output the final strategy.

[0123] Specifically, the application scenarios of dynamic adjustment are as follows:

[0124] Table 5 Response table during sudden rainfall in a certain basin

[0125]

[0126] Table 6 Comparison table of implementation effects

[0127]

[0128] It can be seen that the present invention breaks through the data barriers of hydropower station groups, realizes the second-level synchronization and fusion processing of multi-source heterogeneous data; constructs a dynamic optimization model, balances power generation efficiency, equipment life and grid stability, and reduces the unplanned downtime by more than 40%; establishes an intelligent emergency response mechanism to ensure that the cross-station power compensation rate exceeds 90% after a fault, and the grid power fluctuation is controlled within 2%. The fusion algorithm based on the dynamic correlation model of the present invention can accurately predict the output fluctuation of hydropower stations and optimize the power distribution in real time, effectively solve the problem of grid power transmission safety hazards caused by unstable hydropower generation output, and improve the dispatching response speed by more than 40%; Secondly, through the collaborative mechanism of the spatio-temporal optimization target model, while ensuring voltage stability, the network loss is reduced by 12%-15%, realizing the dual optimization of economy and security; In addition, the adopted security constraint softening model can accurately control the water turbine units, meeting the policy requirements of the new power system for multi-energy complementary collaborative control.

[0129] For the convenience of understanding the present invention, specifically refer to Figure 2 , Figure 2 which is an example diagram of a collaborative control platform for a hydropower station cluster provided by an embodiment of the present invention, and specifically may include:

[0130] This platform can achieve collaborative allocation through three stages: "dynamic perception → spatio-temporal optimization → safe execution": Data acquisition layer: Real-time acquisition of hydropower station cluster data, such as water level, flow rate, grid load and other data. Model analysis layer: Construct a dynamic correlation model, a spatio-temporal optimization target model and a safety constraint softening model, and calculate the optimal and safest power allocation scheme. Decision execution layer: Output smooth adjustment instructions to avoid the safety no-go zone of equipment. Through the design of the hierarchical control architecture and the collaborative workflow between the data acquisition layer, the model analysis layer and the decision execution layer, this platform ensures the realization of collaborative control. By real-time collecting multi-source heterogeneous data such as water level, flow rate, grid load of the hydropower station group through the data acquisition layer, second-level synchronization and fusion processing are achieved, breaking through the data barrier; and an intelligent emergency response mechanism can also be added, which can specifically include key links such as fault detection, cross-station power compensation, and grid power fluctuation control, improving the emergency fault tolerance ability of the system. By establishing an intelligent emergency response mechanism, it is ensured that the cross-station power compensation rate exceeds 90% after a fault, and the grid power fluctuation is controlled within 2%, improving the system stability.

[0131] The following introduces the hydropower station cluster collaborative control device provided by the embodiments of the present invention. The hydropower station cluster collaborative control device described below can be correspondingly referred to the hydropower station cluster collaborative control method described above.

[0132] Specifically, please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a hydropower station cluster collaborative control device provided by the embodiments of the present invention, and can include:

[0133] A data acquisition module 100, configured to collect hydropower station data in real time, and the hydropower station data at least includes water level, flow rate, grid load, geographical location and power generation cost;

[0134] An association analysis module 200, configured to input the water level, flow rate and geographical location of the hydropower station data into a dynamic correlation model to obtain the correlation degree between hydropower stations;

[0135] An allocation optimization module 300, configured to input the grid load, power generation cost of the hydropower station data and the correlation degree between hydropower stations into a spatio-temporal optimization target model to obtain a power generation allocation strategy;

[0136] A safety analysis module 400, configured to analyze the power generation allocation strategy based on a safety constraint softening model to determine the final hydropower station power generation.

[0137] Based on the above embodiments, the hydropower station cluster collaborative control device may further include:

[0138] A dynamic correlation model construction module, configured to construct the dynamic correlation model, and the dynamic correlation model is:

[0139] ;

[0140] ;

[0141] Among them, represents the correlation degree between hydropower station i and hydropower station j; Qi represents the flow rate of hydropower station i before time τ; τ is calibrated based on the measured water flow velocity; Qmax represents the maximum allowable flow rate of the hydropower station; represents the geographical distance between hydropower station i and hydropower station j; σ is the distance attenuation coefficient, which is related to the terrain; α is the head gain factor; is the water level difference between hydropower station i and hydropower station j; Hnom is the reference head of the hydropower station.

[0142] Based on the above embodiments, the hydropower station cluster cooperative control device may further include:

[0143] a spatio-temporal optimization target model construction module for constructing the spatio-temporal optimization target model, and the spatio-temporal optimization target model is:

[0144] ;

[0145] ;

[0146] Among them, F represents the numerical target to be optimized; represents the power generation power of hydropower station i at time t; represents the power generation power of hydropower station j at time t; is the comprehensive unit power generation cost of hydropower station i; λ is the cooperative regulation factor; is the correlation degree between hydropower station i and hydropower station j; Vmax represents the maximum capacity of the reservoir or storage tank; Vt represents the actual capacity of the reservoir or storage tank at time t; qi represents the basic power generation cost of hydropower station i; w1, w2, w3 are weight coefficients determined by regression based on historical data.

[0147] Based on the above embodiments, the hydropower station cluster cooperative control device may further include:

[0148] a safety constraint softening model construction module for constructing the safety constraint softening model, and the safety constraint softening model is:

[0149] ;

[0150] Among them, is the safety value of the power generation power ; is the safety threshold; k is the softening coefficient; represents the power generation power of hydropower station i.

[0151] Based on the above embodiments, the safety constraint softening model construction module may include:

[0152] An early warning area setting unit for the first safety threshold and the first softening coefficient corresponding to the early warning area, where the first safety threshold is 90% of the rated power and the first softening coefficient is 1;

[0153] A buffer area setting unit for the second safety threshold and the second softening coefficient corresponding to the buffer area, where the second safety threshold is 95% of the rated power and the second softening coefficient is 5;

[0154] A hard boundary setting unit for the third safety threshold and the third softening coefficient corresponding to the hard boundary, where the third safety threshold is 100% of the rated power and the third softening coefficient is 20.

[0155] Based on the above embodiments, the safety analysis module 400 includes:

[0156] A first unit for controlling each generating unit of the power station cluster to generate electricity according to the power generation allocation strategy when the power generation allocation strategy meets the safety constraint softening model;

[0157] A second unit for adjusting the power generation allocation strategy when the power generation allocation strategy does not meet the safety constraint softening model.

[0158] Based on the above embodiments, the second unit includes:

[0159] A first sub-unit for taking a power withdrawal measure strategy when the power generation allocation strategy reaches the first safety threshold of the safety constraint softening model;

[0160] A second sub-unit for implementing a gradient-limited power change rate strategy when the power generation allocation strategy reaches the second safety threshold of the safety constraint softening model;

[0161] A third sub-unit for immediately triggering a shutdown mechanism when the power generation allocation strategy reaches the third safety threshold of the safety constraint softening model;

[0162] The first safety threshold is less than the second safety threshold, and the second safety threshold is less than the third safety threshold.

[0163] It should be noted that the order of the modules and units in the above hydroelectric power station cluster collaborative control device can be changed before and after without affecting the logic.

[0164] Applying the hydroelectric power station cluster collaborative control device provided by the embodiments of the present invention, a data acquisition module 100 is configured to collect hydroelectric power station data in real time, and the hydroelectric power station data at least includes water level, flow rate, grid load, geographical location, and power generation cost; a correlation analysis module 200 is configured to input the water level, flow rate, and geographical location of the hydroelectric power station data into a dynamic correlation degree model to obtain the correlation degree between hydroelectric power stations; an allocation optimization module 300 is configured to input the grid load, power generation cost of the hydroelectric power station data, and the correlation degree between hydroelectric power stations into a spatio-temporal optimization target model to obtain a power generation allocation strategy; a safety analysis module 400 is configured to analyze the power generation allocation strategy based on a safety constraint softening model to determine the final power generation of the hydroelectric power station. Based on the dynamic collaborative allocation algorithm of the dynamic correlation degree model and the spatio-temporal optimization target model, this device improves the overall power generation efficiency of the hydroelectric power station group and enhances the stability of the power generation of the hydroelectric power station cluster; and based on the safety constraint softening model, it ensures the safety of the unit equipment of the hydroelectric power station cluster, enables it to operate within a safe range, and avoids unplanned shutdowns. Through the collaborative control of multiple hydroelectric power station units, the overall power generation efficiency of the cluster is improved, the stability of the system is enhanced, and intelligent fault recovery and load response are realized to meet the requirements of modern smart grids.

[0165] The following introduces the hydroelectric power station cluster collaborative control device provided by the embodiments of the present invention. The hydroelectric power station cluster collaborative control device described below can be correspondingly referred to the hydroelectric power station cluster collaborative control method described above.

[0166] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of a hydroelectric power station cluster collaborative control device provided by an embodiment of the present invention, and may include:

[0167] A memory 10 is configured to store computer programs;

[0168] A processor 20 is configured to execute the computer program to implement the above-mentioned hydroelectric power station cluster collaborative control method.

[0169] The memory 10, the processor 20, and the communication interface 31 all complete communication with each other through a communication bus 32.

[0170] In the embodiments of the present invention, the memory 10 is used to store one or more programs, and the programs may include program codes, and the program codes include computer operation instructions. In the embodiments of the present invention, the memory 10 may store programs for implementing the following functions:

[0171] Collect hydroelectric power station data in real time, and the hydroelectric power station data at least includes water level, flow rate, grid load, geographical location, and power generation cost;

[0172] Input the water level, flow rate, and geographical location of the hydropower station data into the dynamic correlation model to obtain the correlation between hydropower stations.

[0173] Input the grid load, generation cost, and the correlation between hydropower stations of the hydropower station data into the spatio-temporal optimization objective model to obtain the power generation allocation strategy.

[0174] Based on the security constraint softening model, analyze the power generation allocation strategy to determine the final hydropower station power generation.

[0175] In a possible implementation, the memory 10 may include a program storage area and a data storage area. Among them, the program storage area may store the operating system and application programs required for at least one function, etc.; the data storage area may store the data created during use.

[0176] In addition, the memory 10 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include NVRAM. The memory stores the operating system and operation instructions, executable modules or data structures, or subsets thereof, or extended sets thereof. Among them, the operation instructions may include various operation instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0177] The processor 20 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices. The processor 20 may be a microprocessor or any conventional processor, etc. The processor 20 may call the program stored in the memory 10.

[0178] The communication interface 31 may be an interface of a communication module for connecting to other devices or systems.

[0179] Of course, it should be noted that Figure 4 The structure shown does not constitute a limitation on the hydropower station cluster collaborative control device in the embodiments of the present invention. In practical applications, the hydropower station cluster collaborative control device may include more or fewer components than Figure 4 shown, or combine some components.

[0180] Next, the computer-readable storage medium provided by the embodiments of the present invention will be introduced. The computer-readable storage medium described below can be mutually corresponded and referred to the hydropower station cluster collaborative control method described above.

[0181] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned collaborative control method for a hydropower station cluster are implemented.

[0182] The computer-readable storage medium may include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0183] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0184] Those skilled in the art can further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to their functions in the above description. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0185] Finally, it should also be noted that in this article, relationships such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including", or any other variant is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device.

[0186] The above has introduced in detail a method, device, equipment and computer-readable storage medium for collaborative control of a hydropower station cluster. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A collaborative control method for a hydropower station cluster, characterized in that, including: Collecting hydropower station data in real time, where the hydropower station data at least includes water level, flow rate, grid load, geographical location, and power generation cost; Inputting the water level, flow rate, and geographical location of the hydropower station data into a dynamic correlation model to obtain the correlation between hydropower stations; Inputting the grid load, power generation cost of the hydropower station data, and the correlation between hydropower stations into a spatio-temporal optimization target model to obtain a power generation allocation strategy; Analyzing the power generation allocation strategy based on a safety constraint softening model to determine the final hydropower station power generation.

2. The collaborative control method for a hydropower station cluster according to claim 1, wherein Before inputting the water level, flow rate, and geographical location of the hydropower station data into a dynamic correlation model to obtain the correlation between hydropower stations, it further includes: Constructing the dynamic correlation model, where the dynamic correlation model is: ； ； Among them, represents the correlation degree between hydropower station i and hydropower station j; Qi represents the flow rate of hydropower station i before time τ; τ is calibrated according to the measured water flow velocity; Qmax represents the maximum allowable flow rate of the hydropower station; represents the geographical distance between hydropower station i and hydropower station j; σ is the distance attenuation coefficient, which is related to the terrain; α is the head gain factor; is the water level difference between hydropower station i and hydropower station j; Hnom is the reference head of the hydropower station.

3. The collaborative control method for a hydropower station cluster according to claim 1, wherein Before inputting the grid load, power generation cost of the hydropower station data, and the correlation between hydropower stations into a spatio-temporal optimization target model, it further includes: Constructing the spatio-temporal optimization target model, where the spatio-temporal optimization target model is: ； ； Among them, F represents the numerical objective to be optimized; represents the power generation of hydropower station i at time t; represents the power generation of hydropower station j at time t; is the comprehensive cost per unit power generation of hydropower station i; λ is the collaborative regulation factor; is the correlation degree between hydropower station i and hydropower station j; Vmax represents the maximum capacity of the reservoir or storage pool; Vt represents the actual capacity of the reservoir or storage pool at time t; qi represents the basic power generation cost of hydropower station i; w1, w2, w3 are weight coefficients determined by regression based on historical data.

4. The collaborative control method for a hydropower station cluster according to claim 1, wherein, Before analyzing the power generation allocation strategy based on a safety constraint softening model to determine the final hydropower station power generation, it further includes: Constructing the safety constraint softening model, where the safety constraint softening model is: ； Among them, is the safe value of the power generation power ; is the safety threshold; k is the softening coefficient; represents the power generation power of hydropower station i.

5. The collaborative control method for a hydropower station cluster according to claim 4, wherein The setting of the safety threshold and softening threshold includes: The first safety threshold and the first softening coefficient corresponding to the early warning area, where the first safety threshold is 90% of the rated power and the first softening coefficient is 1; The second safety threshold and the second softening coefficient corresponding to the buffer area, where the second safety threshold is 95% of the rated power and the second softening coefficient is 5; The third safety threshold and the third softening coefficient corresponding to the hard boundary, where the third safety threshold is 100% of the rated power and the third softening coefficient is 20.

6. The collaborative control method for a hydropower station cluster according to claim 1, wherein Analyzing the power generation allocation strategy based on a safety constraint softening model to determine the final hydropower station power generation, including: When the power generation allocation strategy meets the safety constraint softening model, then controlling each generating unit of the power station cluster to generate electricity according to the power generation allocation strategy; When the power generation allocation strategy does not meet the safety constraint softening model, then adjusting the power generation allocation strategy.

7. The collaborative control method for a hydropower station cluster according to claim 6, wherein When the power generation allocation strategy does not meet the safety constraint softening model, then adjusting the power generation allocation strategy, including: When the power generation allocation strategy reaches the first safety threshold of the safety constraint softening model, then adopting a power withdrawal measure strategy; When the power generation allocation strategy reaches the second safety threshold of the safety constraint softening model, then implementing a gradient-limited power change rate strategy; When the power generation allocation strategy reaches the third safety threshold of the safety constraint softening model, then immediately triggering a shutdown mechanism; The first safety threshold is less than the second safety threshold, and the second safety threshold is less than the third safety threshold.

8. A collaborative control device for a hydropower station cluster, characterized in that including: A data acquisition module for collecting hydropower station data in real time, where the hydropower station data at least includes water level, flow rate, grid load, geographical location, and power generation cost; An association analysis module, configured to input the water level, flow rate, and geographical location of the hydropower station data into a dynamic association degree model to obtain the association degree between hydropower stations; A distribution optimization module, configured to input the grid load, power generation cost, and the association degree between hydropower stations of the hydropower station data into a spatio-temporal optimization target model to obtain a power generation allocation strategy; A safety analysis module, configured to analyze the power generation allocation strategy based on a safety constraint softening model to determine the final power generation of the hydropower station.

9. A hydroelectric power station cluster collaborative control device, characterized in that, Comprising: A memory, configured to store a computer program; A processor, configured to implement the steps of the hydropower station cluster collaborative control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are loaded and executed by a processor, the steps of the hydropower station cluster collaborative control method according to any one of claims 1 to 7 are implemented.

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