A cross-basin multi-station hydropower station centralized control optimization platform

Abstract

The present invention relates to the technical field of water resource dispatching and management in hydropower stations, and more specifically, to a cross-basin multi-station hydropower station centralized control optimization platform. The platform comprises: a data acquisition unit that uses a variable acquisition frequency mechanism to collect hydrological data, power grid load data, and environmental meteorological data; a hydrological monitoring unit that analyzes and monitors cross-basin hydrological dynamics in real time based on hydrological data, and uses a hydrological prediction model to predict cross-basin hydrological changes; a hydrological dispatching simulation unit that simulates hydrological dispatching based on the results of cross-basin hydrological changes predicted by the hydrological prediction model, and constructs a dispatching strategy; a water resource dispatching unit that constructs an adaptive global optimization dispatching framework to generate an optimal dispatching strategy; and an emergency dispatching unit that is used to handle water resource dispatching in emergency situations. The cross-basin multi-station hydropower station centralized control optimization platform uses an adaptive global optimization dispatching framework combined with a hydrological prediction model to dynamically adjust the dispatching strategy of the hydropower station in real time, and centrally control and optimize the smooth operation of cross-basin water resources.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource dispatching and management of hydropower stations, and in particular to a cross-basin multi-station hydropower station centralized control optimization platform. Background Art

[0002] A cross-basin, multi-station hydropower station centralized control optimization platform aims to achieve efficient use of water resources and optimize power generation efficiency. Through an adaptive global optimization scheduling framework combined with a hydrological prediction model, it controls the water resource scheduling and power generation strategies of multiple hydropower stations across the basin, achieving a reasonable allocation of water resources and a dynamic balance between power generation plans and actual water demand.

[0003] The existing cross-basin multi-station hydropower station centralized control optimization platform is usually difficult to dynamically adjust the water resource scheduling strategy in real time to cope with complex hydrological changes and meteorological conditions. In addition, due to the large fluctuations in water resources and the high difficulty of coordinating cross-basin hydropower stations, it will lead to water resource waste and be unable to effectively deal with the water level imbalance caused by extreme weather. Therefore, a cross-basin multi-station hydropower station centralized control optimization platform is designed. Summary of the Invention

[0004] The purpose of the present invention is to provide a cross-basin multi-station hydropower station centralized control optimization platform to solve the problems raised in the above background technology, such as large fluctuations in water resources and high difficulty in coordinating cross-basin hydropower stations, which will lead to waste of water resources and inability to effectively deal with water level imbalances caused by extreme weather.

[0005] To achieve the above objectives, the present invention provides a cross-basin multi-station hydropower station centralized control optimization platform, comprising:

[0006] A data acquisition unit, wherein the data acquisition unit uses a variable acquisition frequency mechanism to collect hydrological data, grid load data, and environmental meteorological data from the hydropower station system;

[0007] The system further comprises a hydrological monitoring unit, which monitors cross-basin hydrological dynamics based on real-time analysis of hydrological data and uses a hydrological prediction model in combination with historical hydrological data to predict cross-basin hydrological changes;

[0008] The invention also includes a hydrological operation simulation unit, which simulates hydrological operation based on the hydrological prediction model to predict the cross-basin hydrological changes and constructs the operation strategy of each hydropower station;

[0009] The system also includes a water resource scheduling unit, which constructs an adaptive global optimization scheduling framework and dynamically adjusts the scheduling strategy of each hydropower station in combination with environmental meteorological data to obtain the optimal scheduling strategy for each hydropower station;

[0010] It also includes an emergency dispatching unit, which is used to handle water resource dispatching in emergencies and emergency situations.

[0011] As a further improvement of this technical solution, the data acquisition unit deploys sensors in hydropower stations and rivers to build an Internet of Things sensor network, and uses a variable acquisition frequency mechanism to collect hydrological data, power grid load data, and environmental meteorological data in the hydropower station system in real time;

[0012] The variable acquisition frequency mechanism is a mechanism that dynamically adjusts the data acquisition frequency according to the needs of the hydropower station and environmental conditions;

[0013] Among them, the hydrological data includes river water level, flow rate, flow and precipitation, and is stored in the edge node server through 5G transmission technology; the power grid load data includes power demand and load changes in each time period, and is transmitted to the central server through 5G transmission technology; the environmental meteorological data includes the meteorological temperature, humidity and rainfall of each power station, and is transmitted to the central server through 5G transmission technology.

[0014] As a further improvement of this technical solution, the hydrological monitoring unit includes a hydrological dynamic analysis module and a hydrological change prediction module;

[0015] Wherein, the hydrological dynamics analysis module analyzes and monitors cross-basin hydrological dynamics in real time based on real-time hydrological data;

[0016] The hydrological change prediction module uses a hydrological prediction model to predict cross-basin hydrological changes based on hydrological dynamics and historical hydrological data.

[0017] As a further improvement of this technical solution, the hydrological dynamics analysis module analyzes and monitors cross-basin hydrological dynamics in real time based on real-time hydrological data. The specific steps are as follows:

[0018] S2.1.1. Obtain real-time hydrological data from the data acquisition unit, including river water level h(t), flow velocity v(t), discharge Q(t), and precipitation P(t);

[0019] S2.1.2. Calculate the water inflow and outflow of the hydropower station as follows:

[0020] Q in (t) = v in (t)×A;

[0021] Q out (t) = v out (t)×A;

[0022] Among them, Q in (t) is the water inflow of the hydropower station at time t; Q out(t) is the water outflow of the hydropower station at time t; A is the cross-sectional area of ​​the waterway of the hydropower station; v in (t) is the water resource inlet flow rate of the hydropower station at time t; v out (t) is the outlet flow rate of water resources at the hydropower station at time t;

[0023] S2.1.3. Calculate the dynamic change of the water level of the hydropower station based on the water level change formula, as follows:

[0024]

[0025] Where Δh(t) represents the change in reservoir water level at time t;

[0026] S2.1.4. Real-time monitoring of water volume changes within the basin using the flow conservation equation is as follows:

[0027]

[0028] S2.1.5. Set the warning threshold θ of the water level change rate h , if the water level change rate exceeds the warning threshold θ h Immediately trigger the alarm and feed back to the emergency dispatch unit for emergency processing.

[0029] As a further improvement of this technical solution, the hydrological change prediction module uses a hydrological prediction model to predict cross-basin hydrological changes based on hydrological dynamics and historical hydrological data. The specific steps are as follows:

[0030] S2.2.1. Obtain historical hydrological data X from the data acquisition unit (1) hist = {h(t-1), v(t-1), Q(t-1), P(t-1), …, h(tn), v(tn), Q(tn), P(tn)}, and combined with real-time hydrological data as the input data set X of the hydrological prediction model t ={h(t),v(t),Q(t),P(t),h(t-1),v(t-1),Q(t-1),P(t-1),…};

[0031] Among them, h(t-1) is the water level of the hydropower station at time t-1; v(t-1) is the water resource flow rate of the hydropower station at time t-1; Q(t-1) is the water resource flow rate of the hydropower station at time t-1; P(t-1) is the precipitation of the hydropower station at time t-1; h(tn) is the water level of the hydropower station at time tn; v(tn) is the water resource flow rate of the hydropower station at time tn; Q(tn) is the water resource flow rate of the hydropower station at time tn; P(tn) is the precipitation of the hydropower station at time tn;

[0032] S2.2.2. Obtain meteorological data Y from the meteorological forecast systemt ={P forecast (t), T(t), RH(t)}, and with the dataset X t Combined with the complete input data set {X t ,Y t};

[0033] S2.2.3. Use long short-term memory network to build a hydrological prediction model.

[0034] As a further improvement of this technical solution, the above S2.2.3, using a long short-term memory network to construct a hydrological prediction model, is as follows:

[0035] Input layer: The input is the dataset {X t ,Y t};

[0036] Hidden layer:

[0037] i t =σ(W i ·[h t-1 ,X t ]+b i );

[0038] f t =σ(W f ·[h t-1 ,X t ]+b f );

[0039] o t =σ(W o ·[h t-1 ,X t ]+b o );

[0040] Among them, W i is the input gate weight matrix; W f is the forget gate weight matrix; W o is the output gate weight matrix; b i is the input gate bias term; b f is the forget gate bias; b o is the output gate bias term; h t-1 is the hidden layer output at time t-1; σ is the activation function;

[0041] Memory unit update:

[0042]

[0043] in, is the candidate memory unit state; tanh(·) is the hyperbolic tangent function; Wc To change the hidden state h of the previous time step t-1 and X t Mapping to candidate memory states The weight matrix of b c is the bias term; C t is the state of the memory unit at the current moment t; f t is the output value of the forget gate; i t is the output value of the input gate;

[0044] Final output:

[0045] h t =o t *tanh(C t );

[0046] Among them, h t is the hidden layer output at time t; o t is the output gate activation value at time t;

[0047] Output layer:

[0048]

[0049] in, is the hydrological data predicted at time t+1; W o is the weight matrix of the output layer; h t is the hidden layer output at time t.

[0050] As a further improvement of this technical solution, the hydrological scheduling simulation unit simulates hydrological scheduling based on the hydrological prediction model to predict cross-basin hydrological changes and constructs a scheduling strategy for each hydropower station. The specific steps are as follows:

[0051] S3.1. Results of cross-basin hydrological changes based on predictions Calculate the total amount of available water resources W in the basin at time t+1 total (t+1), as follows:

[0052]

[0053] in, is the predicted inflow at time t; Forecast precipitation at time t; Forecast the traffic flow at time t;

[0054] S3.2. Calculate the water volume W required by the i-th hydropower station at time t+1 based on the power generation demand and historical water consumption data of each hydropower station. demand (i, t+1), as follows

[0055]

[0056] Among them, P gen (i, t+1) is the power generation of the i-th hydropower station at time t+1; η is the power generation efficiency of the i-th hydropower station; ρ is the density of water; g is the acceleration due to gravity; is the water level of the i-th hydropower station at time t+1;

[0057] S3.3, according to the water volume W required by the i-th hydropower station at time t+1 demand (i, t+1) and the total amount of available water resources W in the basin at time t+1 total (t+1), preliminary allocation of water resources is as follows:

[0058]

[0059] Among them, W alloc (i,t+1) is the water resource allocation of the i-th hydropower station at time t+1; μ i is the priority weight of the i-th hydropower station; N is the total number of hydropower stations in the basin;

[0060] S3.4. According to the water resources allocation results W alloc (i, t+1) and the current water storage capacity of the hydropower station H(i, t), simulate the water release strategy and water storage strategy to form a scheduling strategy, which is as follows:

[0061] Water release strategy:

[0062] Q out (i, t+1)=W alloc (i,t+1) / Δt;

[0063] Among them, Q out (i, t+1) is the water release strategy of the i-th hydropower station at time t+1; Δt is the scheduling time step;

[0064] Water storage strategy:

[0065] H(i,t+1)=H(i,t)+W alloc (i,t+1)-Q out (i,t+1);

[0066] Among them, H(i,t+1) is the water storage strategy of the i-th hydropower station at time t+1; Δt is the scheduling time step.

[0067] As a further improvement of this technical solution, the water resource scheduling unit constructs an adaptive global optimization scheduling framework and dynamically adjusts the scheduling strategy of each hydropower station in combination with environmental meteorological data to obtain the optimal scheduling strategy for each hydropower station. The specific steps are as follows:

[0068] S4.1. Establish global optimization scheduling objective function F:

[0069] S4.2. Calculate and predict the change in water resources W at time t+1 based on future environmental meteorological data. forecast (t+1):

[0070] W forecast (t+1)=f(P forecast (t+1),T(t+1),RH(t+1));

[0071] Among them, P forecast (t+1) is the predicted precipitation at time t+1; T(t+1) is the predicted temperature at time t+1; RH(t+1) is the predicted relative humidity at time t+1;

[0072] S4.3, based on the predicted water resource change W at time t+1 forecast (t+1), dynamically adjust the water resource allocation W of the i-th hydropower station at time t+1 alloc (i,t+1):

[0073] W alloc (i, t+1)=W alloc (i,t)+W forecast (t+1);

[0074] Among them, W alloc (i,t) is the water resource allocation of the i-th hydropower station at time t;

[0075] S4.4. Optimize water release strategy:

[0076]

[0077] Among them, Q' out (i, t+1) is the optimal water release strategy of the i-th hydropower station at time t+1; Q out (i, t+1) is the original water release strategy of the i-th hydropower station at time t+1;

[0078] S4.5. Optimize water storage strategy:

[0079] H'(i,t+1)=H(i,t+1)+ΔH adjust (i,t+1);

[0080] Among them, H'(i,t+1) is the optimized water storage strategy of the i-th hydropower station at time t+1; H(i,t+1) is the original water storage strategy of the i-th hydropower station at time t+1; ΔH adjust (i, t+1) is the water storage strategy adjustment value of the i-th hydropower station at time t+1;

[0081] ΔH adjust (i, t+1)=k1·ΔW forecast (t+1)+k2·(H max (i)-H(i,t))+k3·P forecast (t+1);

[0082] Among them, H max (i) is the maximum water storage capacity of the reservoir of the i-th hydropower station; k1 is the weight coefficient of the water resources change prediction value; k2 is the weight coefficient of the reservoir water storage capacity difference; k3 is the weight coefficient of the precipitation prediction value.

[0083] As a further improvement of this technical solution, the establishment of the global optimization scheduling objective function F is as follows:

[0084]

[0085] Among them, α i is the weight coefficient of the power generation of the i-th hydropower station; P gen (i) is the power generation of the i-th hydropower station; β i is the weight coefficient of water resource allocation error; W alloc (i, t) is the water resource allocation of the i-th hydropower station at time t; W demand (i,t) is the water required by the i-th hydropower station at time t; N is the total number of hydropower stations in the basin;

[0086] Constructing water resource balance constraints:

[0087] W alloc (i,t)≤W total (t);

[0088] Among them, W alloc (i,t)≤W total (t) indicates that the water resources allocated to each hydropower station must not exceed the total water resources in the basin.

[0089] As a further improvement of the present technical solution, the emergency dispatch unit is used to handle water resource dispatch in emergencies and emergency situations, and includes an alarm unit and an emergency processing unit;

[0090] The alarm unit monitors the data of the hydrological monitoring unit and the hydrological scheduling simulation unit in real time. If abnormal parameters are detected that exceed the safety range, the system will trigger the alarm mechanism;

[0091] The emergency processing unit is used to process water resource scheduling according to emergency situations, specifically as follows:

[0092]

[0093] in, is the emergency water discharge of the i-th hydropower station at time t+1; H safe It is the safety water level threshold of the hydropower station.

[0094] Compared with the prior art, the present invention has the following beneficial effects:

[0095] 1. This cross-basin, multi-station hydropower station centralized control optimization platform, based on an adaptive global optimization scheduling framework combined with real-time hydrological and meteorological data, can dynamically adjust the water resource allocation and power generation strategies of each hydropower station to ensure efficient use of water resources and stable power generation.

[0096] 2. This cross-basin, multi-station hydropower station centralized control optimization platform uses a hydrological prediction model to accurately predict future hydrological changes, enabling early prediction of water resource trends and optimizing the water release and storage strategies of hydropower stations, thereby avoiding scheduling errors and resource waste caused by climate change or emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is the overall flow chart of the present invention;

[0098] The meaning of each number in the figure is:

[0099] 1. Data acquisition unit; 2. Hydrological monitoring unit; 3. Hydrological scheduling simulation unit; 4. Water resources scheduling unit; 5. Emergency scheduling unit. DETAILED DESCRIPTION

[0100] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0101] See also Figure 1 As shown in the figure, a cross-basin multi-station hydropower station centralized control optimization platform is provided, including:

[0102] A data acquisition unit 1, wherein the data acquisition unit 1 uses a variable acquisition frequency mechanism to collect hydrological data, grid load data, and environmental meteorological data in the hydropower station system;

[0103] In this embodiment, the data acquisition unit 1 deploys sensors in the hydropower station and the river to build an Internet of Things sensor network, and uses a variable acquisition frequency mechanism to collect hydrological data, grid load data, and environmental meteorological data in the hydropower station system in real time;

[0104] The variable acquisition frequency mechanism is a mechanism that dynamically adjusts the data acquisition frequency according to the needs of the hydropower station and environmental conditions;

[0105] In this embodiment, the variable acquisition frequency mechanism automatically switches the frequency of data acquisition based on the current operating conditions of the hydropower station. The variable acquisition frequency mechanism automatically determines whether the acquisition frequency needs to be increased by monitoring environmental changes, emergencies, and system requirements in real time, as follows:

[0106] When extreme weather conditions are detected, the system will increase the frequency of hydrological data collection to every minute or even every second to track water level changes and flow rates in real time;

[0107] When equipment anomalies are detected, the frequency of collecting power generation equipment status data will be increased from 5 minutes to every second to ensure accurate monitoring of equipment status and timely response;

[0108] During peak periods of electricity consumption, the grid load data will automatically increase its collection frequency to ensure accurate real-time monitoring of grid load changes.

[0109] Among them, the hydrological data includes river water level, flow rate, flow and precipitation, and is stored in the edge node server through 5G transmission technology; the power grid load data includes power demand and load changes in each time period, and is transmitted to the central server through 5G transmission technology; the environmental meteorological data includes the meteorological temperature, humidity and rainfall of each power station, and is transmitted to the central server through 5G transmission technology.

[0110] In this embodiment, the IoT sensor network is a network system composed of a large number of distributed sensor devices interconnected through other communication protocols 5G, in which each sensor is responsible for monitoring and collecting data, and transmitting this data in real time to the central processing unit or edge node server for processing and analysis; the purpose of building the IoT sensor network is to monitor, intelligently manage and automatically control the water resources of the hydropower station in real time, and realize automatic regulation and optimization operation.

[0111] The system further comprises a hydrological monitoring unit 2, which monitors cross-basin hydrological dynamics based on real-time analysis of hydrological data and uses a hydrological prediction model in combination with historical hydrological data to predict cross-basin hydrological changes;

[0112] In this embodiment, the hydrological monitoring unit 2 includes a hydrological dynamic analysis module and a hydrological change prediction module;

[0113] Wherein, the hydrological dynamics analysis module analyzes and monitors cross-basin hydrological dynamics in real time based on real-time hydrological data;

[0114] The hydrological change prediction module uses a hydrological prediction model to predict cross-basin hydrological changes based on hydrological dynamics and historical hydrological data.

[0115] The hydrological dynamics analysis module analyzes and monitors cross-basin hydrological dynamics in real time based on real-time hydrological data. The specific steps are as follows:

[0116] S2.1.1. Obtain real-time hydrological data from data acquisition unit 1, including river water level h(t), flow velocity v(t), flow Q(t), and precipitation P(t);

[0117] S2.1.2. Calculate the water inflow and outflow of the hydropower station as follows:

[0118] Q in (t) = v in (t)×A;

[0119] Q out (t) = v out (t)×A;

[0120] Among them, Q in (t) is the water inflow of the hydropower station at time t; Q out (t) is the water outflow of the hydropower station at time t; A is the cross-sectional area of ​​the waterway of the hydropower station; v in (t) is the water resource inlet flow rate of the hydropower station at time t; v out (t) is the outlet flow rate of water resources at the hydropower station at time t;

[0121] S2.1.3. Calculate the dynamic change of the water level of the hydropower station based on the water level change formula, as follows:

[0122]

[0123] Where Δh(t) represents the change in reservoir water level at time t;

[0124] S2.1.4. Real-time monitoring of water volume changes within the basin using the flow conservation equation is as follows:

[0125]

[0126] S2.1.5. Set the warning threshold θ of the water level change rate h , if the water level change rate exceeds the warning threshold θ h An alarm is triggered immediately and fed back to the emergency dispatch unit 5 for emergency processing.

[0127] In this embodiment, the hydrological change prediction module uses a hydrological prediction model to predict cross-basin hydrological changes based on hydrological dynamics and historical hydrological data. The specific steps are as follows:

[0128] S2.2.1. Obtain historical hydrological data X from data acquisition unit 1 hist = {g(t-1), v(t-1), Q(t-1), P(t-1), …, h(tn), v(tn), Q(tn), P(tn)}, and combined with real-time hydrological data as the input data set X of the hydrological prediction model t ={h(t),v(t),Q(t),P(t),h(t-1),v(t-1),Q(t-1),P(t-1),…};

[0129] Among them, h(t-1) is the water level of the hydropower station at time t-1; v(t-1) is the water resource flow rate of the hydropower station at time t-1; Q(t-1) is the water resource flow rate of the hydropower station at time t-1; P(t-1) is the precipitation of the hydropower station at time t-1; h(tn) is the water level of the hydropower station at time tn; v(tn) is the water resource flow rate of the hydropower station at time tn; Q(tn) is the water resource flow rate of the hydropower station at time tn; P(tn) is the precipitation of the hydropower station at time tn;

[0130] S2.2.2. Obtain meteorological data Y from the meteorological forecast system t ={P forecast (t), T(t), RH(t)}, and with the dataset X t Combined with the complete input data set {X t ,Y t};

[0131] S2.2.3. Use long short-term memory network to build a hydrological prediction model.

[0132] S2.2.3, using long short-term memory network to build a hydrological prediction model, is as follows:

[0133] Input layer: The input is the dataset {X t ,Y t};

[0134] Hidden layer:

[0135] ut=σ(W i ·[h t-1 ,X t ]+b i );

[0136] f t =σ(W f ·[h t-1 ,X t ]+b f );

[0137] ot =σ(W o ·[h t-1 ,X t ]+b o );

[0138] Among them, W i is the input gate weight matrix; W f is the forget gate weight matrix; W o is the output gate weight matrix; b i is the input gate bias term; b f is the forget gate bias term; b o is the output gate bias term; h t-1 is the hidden layer output at time t-1; σ is the activation function;

[0139] Memory unit update:

[0140]

[0141] in, is the candidate memory unit state; tanh(·) is the hyperbolic tangent function; W c To change the hidden state h of the previous time step t-1 and X t Mapping to candidate memory states The weight matrix of b c is the bias term; C t is the state of the memory unit at the current moment t; f t is the output value of the forget gate; i t is the output value of the input gate;

[0142] Final output:

[0143] h t =o t *tanh(C t );

[0144] Among them, h t is the hidden layer output at time t; o t is the output gate activation value at time t;

[0145] Output layer:

[0146]

[0147] in, is the hydrological data predicted at time t+1; W o is the weight matrix of the output layer; h t is the hidden layer output at time t.

[0148] The invention also includes a hydrological dispatch simulation unit 3, which simulates hydrological dispatch based on the hydrological prediction model to predict the cross-basin hydrological changes and constructs a dispatch strategy for each hydropower station;

[0149] In this embodiment, the hydrological operation simulation unit 3 simulates hydrological operation based on the hydrological prediction model to predict the cross-basin hydrological changes and constructs the operation strategy for each hydropower station. The specific steps are as follows:

[0150] S3.1. Results of cross-basin hydrological changes based on predictions Calculate the total amount of available water resources W in the basin at time t+1 total (t+1), as follows:

[0151]

[0152] in, is the predicted inflow at time t; Forecast precipitation at time t; Forecast the traffic flow at time t;

[0153] S3.2. Calculate the water volume W required by the i-th hydropower station at time t+1 based on the power generation demand and historical water consumption data of each hydropower station. demand (i, t+1), as follows

[0154]

[0155] Among them, P gen (i, t+1) is the power generation of the i-th hydropower station at time t+1; η is the power generation efficiency of the i-th hydropower station; ρ is the density of water; g is the acceleration due to gravity; is the water level of the i-th hydropower station at time t+1;

[0156] S3.3, according to the water volume W required by the i-th hydropower station at time t+1 demand (i, t+1) and the total amount of available water resources W in the basin at time t+1 total (t+1), preliminary allocation of water resources is as follows:

[0157]

[0158] Among them, W alloc (i,t+1) is the water resource allocation of the i-th hydropower station at time t+1; μ i is the priority weight of the i-th hydropower station; N is the total number of hydropower stations in the basin;

[0159] S3.4. According to the water resources allocation results W alloc(i, t+1) and the current water storage capacity of the hydropower station H(i, t), simulate the water release strategy and water storage strategy to form a scheduling strategy, which is as follows:

[0160] Water release strategy:

[0161] Q out (i, t+1)=W alloc (i,t+1) / Δt;

[0162] Among them, Q out (i, t+1) is the water release strategy of the i-th hydropower station at time t+1; Δt is the scheduling time step;

[0163] Water storage strategy:

[0164] H(i,t+1)=H(i,t)+W alloc (i,t+1)-Q out (i,t+1);

[0165] Among them, H(i,t+1) is the water storage strategy of the i-th hydropower station at time t+1; Δt is the scheduling time step.

[0166] The system further comprises a water resource scheduling unit 4, which constructs an adaptive global optimization scheduling framework and dynamically adjusts the scheduling strategy of each hydropower station in combination with environmental meteorological data to obtain the optimal scheduling strategy for each hydropower station;

[0167] In this embodiment, the water resource scheduling unit 4 constructs an adaptive global optimization scheduling framework and dynamically adjusts the scheduling strategy of each hydropower station in combination with environmental meteorological data to obtain the optimal scheduling strategy for each hydropower station. The specific steps are as follows:

[0168] S4.1. Establish global optimization scheduling objective function F:

[0169] S4.2. Calculate and predict the change in water resources W at time t+1 based on future environmental meteorological data. forecast (t+1):

[0170] W forecast (t+1)=f(P forecast (t+1),T(t+1),RH(t+1));

[0171] Among them, P forecast (t+1) is the predicted precipitation at time t+1; T(t+1) is the predicted temperature at time t+1; RH(t+1) is the predicted relative humidity at time t+1;

[0172] S4.3, based on the predicted water resource change W at time t+1 forecast(t+1), dynamically adjust the water resource allocation W of the i-th hydropower station at time t+1 alloc (i,t+1):

[0173] W alloc (i, t+1)=W alloc (i,t)+W forecast (t+1);

[0174] Among them, W alloc (i,t) is the water resource allocation of the i-th hydropower station at time t;

[0175] S4.4. Optimize water release strategy:

[0176]

[0177] Among them, Q' out (i, t+1) is the optimal water release strategy of the i-th hydropower station at time t+1; Q out (i, t+1) is the original water release strategy of the i-th hydropower station at time t+1;

[0178] S4.5. Optimize water storage strategy:

[0179] H'(i,t+1)=H(i,t+1)+ΔH adjust (i,t+1);

[0180] Among them, H'(i,t+1) is the optimized water storage strategy of the i-th hydropower station at time t+1; H(i,t+1) is the original water storage strategy of the i-th hydropower station at time t+1; ΔH adjust (i, t+1) is the water storage strategy adjustment value of the i-th hydropower station at time t+1;

[0181] ΔH adjust (i, t+1)=k1·ΔW forecast (t+1)+k2·(H max (i)-H(i,t))+k3·P forecast (t+1);

[0182] Among them, H max (i) is the maximum water storage capacity of the reservoir of the i-th hydropower station; k1 is the weight coefficient of the water resources change prediction value; k2 is the weight coefficient of the reservoir water storage capacity difference; k3 is the weight coefficient of the precipitation prediction value.

[0183] In this embodiment, k1·ΔW forecast(t+1) is the impact of the hydrological prediction model on future water resource changes. If water resources are predicted to increase at time t+1, this term is positive, and water storage should be increased accordingly. If water resources decrease, this term is negative, and water storage should be reduced to release water resources to the downstream.

[0184] k2·(H max (i)-H(i,t)) is the impact of the current reservoir water storage state on the water storage adjustment. If the current reservoir water storage H(i,t) is close to the maximum water storage H max (i), then the adjustment value should be reduced to prevent the reservoir from overflowing;

[0185] k3·P forecast (t+1) is the impact of future rainfall forecast on water storage strategy; if there is a large amount of rainfall in the future, P forecast (t+1), water storage should be reduced to prevent the reservoir from overflowing.

[0186] In this embodiment, a global optimization scheduling objective function F is established, specifically as follows:

[0187]

[0188] Among them, α i is the weight coefficient of the power generation of the i-th hydropower station; P gen (i) is the power generation of the i-th hydropower station; β i is the weight coefficient of water resource allocation error; W alloc (i, t) is the water resource allocation of the i-th hydropower station at time t; W demand (i,t) is the water required by the i-th hydropower station at time t; N is the total number of hydropower stations in the basin;

[0189] Constructing water resource balance constraints:

[0190] W alloc (i,t)≤W total (t);

[0191] Among them, W alloc (i,t)≤W total (t) indicates that the water resources allocated to each hydropower station must not exceed the total water resources in the basin.

[0192] It also includes an emergency dispatch unit 5, which is used to handle water resource dispatch in emergencies and emergency situations;

[0193] In this embodiment, the emergency dispatch unit 5 is used to handle water resource dispatch in emergencies and emergency situations, and includes an alarm unit and an emergency processing unit;

[0194] The alarm unit monitors the data of the hydrological monitoring unit 2 and the hydrological scheduling simulation unit 3 in real time. If abnormal parameters are detected that exceed the safety range, the system will trigger the alarm mechanism;

[0195] The emergency processing unit is used to process water resource scheduling according to emergency situations, specifically as follows:

[0196]

[0197] in, is the emergency water discharge of the i-th hydropower station at time t+1; H safe It is the safety water level threshold of the hydropower station.

[0198] The basic principles, main features, and advantages of the present invention are shown and described above. It should be understood by those skilled in the art that the present invention is not limited to the above-described embodiments. The above-described embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention claimed.

Claims

1. A cross-basin multi-station hydropower station centralized control optimization platform, characterized by: include: A data acquisition unit (1), wherein the data acquisition unit (1) uses a variable acquisition frequency mechanism to acquire hydrological data, grid load data, and environmental meteorological data in a hydropower station system; A hydrological monitoring unit (2), wherein the hydrological monitoring unit (2) monitors cross-basin hydrological dynamics based on real-time analysis of hydrological data, and predicts cross-basin hydrological changes using a hydrological prediction model in combination with historical hydrological data; The hydrological dispatch simulation unit (3) simulates hydrological dispatch based on the hydrological prediction model to predict the cross-basin hydrological change results and constructs the dispatch strategy for each hydropower station; the specific steps are as follows: S3.

1. Results of cross-basin hydrological changes based on predictions ,calculate Total available water resources in the basin at any given moment ; S3.

2. Calculate the power generation demand and historical water consumption data of each hydropower station. Hydropower stations in Water required at any time ; S3.3, according to Hydropower stations in Water required at any time and Total available water resources in the basin at any given moment , preliminary allocation of water resources; S3.

4. Based on the results of water resource allocation and the current water storage capacity of the hydropower station , simulate the water release strategy and water storage strategy to form a scheduling strategy; The water resource scheduling unit (4) constructs an adaptive global optimization scheduling framework and dynamically adjusts the scheduling strategy of each hydropower station in combination with environmental meteorological data to obtain the optimal scheduling strategy for each hydropower station. The specific steps are as follows: S4.

1. Establishing a global optimization scheduling objective function ; The details are as follows: ； in, For the The weight coefficient of the power generation of each hydropower station; For the Power generation of hydropower stations; is the weight coefficient of water resource allocation error; For the Hydropower stations at the moment water resource allocation; For the Hydropower stations in The amount of water required at any given time; is the total number of hydropower stations in the basin; Constructing water resource balance constraints: ； in, Indicates that the water resources allocated to each hydropower station must not exceed the total water resources in the basin S4.

2. Calculate and predict based on future environmental and meteorological data Changes in water resources at each moment ; S4.

3. Based on predictions Changes in water resources at each moment , dynamically adjust the Hydropower stations at the moment Water allocation ; S4.

4. Optimize water release strategy: ； in, For the Hydropower stations at the moment Optimized water release strategy; For the Hydropower stations at the moment The original water release strategy; S4.

5. Optimize water storage strategy: ； in, For the Hydropower stations at the moment Optimized water storage strategies; For the Hydropower stations at the moment original water storage strategy; For the Hydropower stations at the moment The water storage strategy adjustment value; ; in, For the The maximum water storage capacity of each hydropower station reservoir; is the weight coefficient of the predicted value of water resources change; is the weight coefficient of reservoir water storage difference; is the weight coefficient of the precipitation prediction value, for Predicted precipitation at the time, The impact of hydrological prediction models on future water resources changes, The impact of the current reservoir water storage status on water storage adjustment, To predict the impact of future rainfall on water storage strategies; An emergency dispatch unit (5) is used to handle water resource dispatch in emergencies and emergency situations.

2. The cross-basin multi-station hydropower station centralized control optimization platform according to claim 1 is characterized by: The data acquisition unit (1) deploys sensors in the hydropower station and the river to build an Internet of Things sensor network, and uses a variable acquisition frequency mechanism to collect hydrological data, power grid load data, and environmental meteorological data in the hydropower station system in real time; The variable acquisition frequency mechanism is a mechanism that dynamically adjusts the data acquisition frequency according to the needs of the hydropower station and environmental conditions; Among them, the hydrological data includes river water level, flow rate, flow and precipitation, and is stored in the edge node server through 5G transmission technology; the power grid load data includes power demand and load changes in each time period, and is transmitted to the central server through 5G transmission technology; the environmental meteorological data includes the meteorological temperature, humidity and rainfall of each power station, and is transmitted to the central server through 5G transmission technology.

3. The cross-basin multi-station hydropower station centralized control optimization platform according to claim 1 is characterized by: The hydrological monitoring unit (2) includes a hydrological dynamic analysis module and a hydrological change prediction module; Wherein, the hydrological dynamics analysis module analyzes and monitors cross-basin hydrological dynamics in real time based on real-time hydrological data; The hydrological change prediction module uses a hydrological prediction model to predict cross-basin hydrological changes based on hydrological dynamics and historical hydrological data.

4. The cross-basin multi-station hydropower station centralized control optimization platform according to claim 3 is characterized by: The hydrological dynamics analysis module analyzes and monitors cross-basin hydrological dynamics in real time based on real-time hydrological data. The specific steps are as follows: S2.1.

1. Obtain real-time hydrological data, including river water level, from the data acquisition unit (1). , flow rate ,flow and precipitation ; S2.1.

2. Calculate the water inflow and outflow of the hydropower station as follows: ; ; in, for The inflow of water resources from the hydropower station at any moment; for The outflow of water resources from the hydropower station at any moment; is the cross-sectional area of ​​the waterway of the hydropower station; for The flow rate of water resources at the hydropower station at any given moment; for The flow rate of water resources outlet of the hydropower station at any moment; S2.1.

3. Calculate the dynamic change of the water level of the hydropower station based on the water level change formula, as follows: ； in, Indicates at time changes in reservoir water levels; S2.1.

4. Real-time monitoring of water volume changes within the basin is performed using the flow conservation equation, as follows: ； S2.1.

5. Set the warning threshold for the water level change rate If the water level change rate exceeds the warning threshold An alarm is triggered immediately and fed back to the emergency dispatch unit (5) for emergency processing.

5. The cross-basin multi-station hydropower station centralized control optimization platform according to claim 4 is characterized by: The hydrological change prediction module uses a hydrological prediction model to predict cross-basin hydrological changes based on hydrological dynamics and historical hydrological data. The specific steps are as follows: S2.2.

1. Obtain historical hydrological data from the data collection unit (1) , and combined with real-time hydrological data as the input data set of the hydrological prediction model ; in, for The water level of the hydropower station at the moment; for The water resource flow rate of the hydropower station at the moment; for The water resource flow of the hydropower station at the moment; for The amount of precipitation at the hydropower station at the moment; for The water level of the hydropower station at the moment; for The water resource flow rate of the hydropower station at the moment; for The water resource flow of the hydropower station at the moment; for The amount of precipitation at the hydropower station at the moment; S2.2.

2. Obtaining meteorological data from the meteorological forecast system , and with the dataset Combined to build a complete input data set for the hydrological prediction model ; S2.2.

3. Use long short-term memory network to build a hydrological prediction model.

6. The cross-basin multi-station hydropower station centralized control optimization platform according to claim 5 is characterized by: In S2.2.3, a long short-term memory network is used to construct a hydrological prediction model, as follows: Input layer: input is the data set ; Hidden layer: ； ； ； in, is the input gate weight matrix; is the forget gate weight matrix; is the output gate weight matrix; is the input gate bias term; is the forget gate bias term; is the output gate bias term; for The hidden layer output at time t; is the activation function; Memory unit update: ； ； in, is the candidate memory unit state; is the hyperbolic tangent function; To set the hidden state of the previous time step and Mapping to candidate memory states The weight matrix of is the bias term; For the current The state of the memory unit at that moment; is the output value of the forget gate; is the output value of the input gate; Final output: ； in, For the moment The hidden layer output of For the moment The output gate activation value of Output layer: ； in, For prediction Hydrological data at each moment; is the weight matrix of the output layer; For the moment The hidden layer output of .

7. The cross-basin multi-station hydropower station centralized control optimization platform according to claim 6 is characterized by: The calculation in S3.1 Total available water resources in the basin at any given moment , as follows: ； in, for Forecast inflow at all times; for Precipitation is always predicted; for Predict traffic flow at all times; S3.2 calculates the Hydropower stations in Water required at any time , as follows ； in, For the Hydropower stations at the moment Power generation; For the The power generation efficiency of each hydropower station; is the density of water; is the acceleration due to gravity; For the Hydropower stations at the moment the water level; The preliminary allocation of water resources in S3.3 is as follows: ； in, For the Hydropower stations at the moment water resource allocation; For the The priority weight of each hydropower station; is the total number of hydropower stations in the basin; Water release strategy in S3.4: ； in, For the Hydropower stations at the moment Water release strategy; is the scheduling time step; Water storage strategy: ； in, For the Hydropower stations at the moment water storage strategy.

8. The cross-basin multi-station hydropower station centralized control optimization platform according to claim 7 is characterized by: The predictions are calculated in S4.2 Changes in water resources at each moment : ； in, for Predicted precipitation at the time; for The predicted temperature at the time; for The predicted relative humidity at the time; Dynamically adjust the Hydropower stations at the moment Water allocation : ； in, For the Hydropower stations at the moment water resource allocation.

9. The cross-basin multi-station hydropower station centralized control optimization platform according to claim 7 is characterized by: The emergency dispatch unit (5) is used to handle water resource dispatch in emergencies and emergency situations, and includes an alarm unit and an emergency processing unit; The alarm unit is used to monitor the data of the hydrological monitoring unit (2) and the hydrological scheduling simulation unit (3) in real time. If an abnormal parameter is detected that exceeds a safe range, the system will trigger an alarm mechanism. The emergency processing unit is used to process water resource scheduling according to emergency situations, specifically as follows: ； in, For the Hydropower stations at the moment Emergency water release volume; It is the safety water level threshold of the hydropower station.

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