A new energy power station power generation control method and device based on traffic flow prediction

Abstract

The present invention discloses a method and device for controlling power generation of a new energy power station based on traffic flow prediction, including: predicting future traffic flow based on collected traffic flow data through a VMD-LSTM prediction model, and optimizing a power generation control scheme based on the predicted traffic flow and the power generation operation strategy of the new energy power station using an EVO algorithm to ensure safe operation of the system. The present invention regulates the power generation of the new energy power station from the perspective of traffic flow, realizes the effective absorption of new energy and the effective supply of electric energy required by electric vehicles, and can ensure the reliability of the system.

Description

Technical Field

[0001] The present invention relates to a power station control strategy, and in particular to a new energy power station power generation control method and device based on traffic flow prediction. Background Art

[0002] Accurate charging load forecasting results are of great significance for the operation planning of distribution networks, the determination of charging station construction capacity, and the construction of transportation energy Internet. In order to respond to this trend, power stations need to improve their own operating efficiency and flexibility while reducing their impact on the environment. Compared with traditional cars, electric vehicles are cleaner and more environmentally friendly, and countries around the world are vigorously promoting the development of electric vehicles. With the increase in the number of electric vehicles and the development of battery technology, the demand for electric vehicle travel continues to increase, and the energy supply demand for electric vehicles has also increased, gradually becoming the main component of charging load, bringing challenges to the stable operation of distribution networks and the construction of charging stations. Summary of the invention

[0003] Purpose of the invention: In view of the problems existing in the prior art, the present invention provides a new energy power station power generation control method and device based on traffic flow prediction, which can build a traffic flow prediction model based on the collected traffic flow data, and use the EVO algorithm to optimize the decision variables of the objective function, so as to obtain the optimal power station operation strategy and reduce costs. In addition, the present invention uses wind and solar power generation, which is conducive to saving resources and promoting green development.

[0004] Technical solution: The invention discloses a method for controlling power generation of a new energy power station based on traffic flow prediction, comprising:

[0005] The VMD-LSTM prediction model is used to predict future traffic flow based on the collected traffic flow data. According to the predicted traffic flow and the power generation operation strategy of the new energy power station, the EVO algorithm is used to optimize the power generation control plan; the traffic flow data includes time characteristics and spatial characteristics.

[0006] The traffic flow data includes time features and space features, which are expressed as a time-space feature matrix as follows:

[0007]

[0008] Where H is the spatiotemporal characteristic matrix of traffic flow, which is composed of the characteristic vector h of each node in the space k (t) composition; k is the number of nodes; [x i (1), x i (2), …x i (m)] is the traffic flow of node i at the mth time point.

[0009] The process of building the VMD-LSTM prediction model includes the following steps:

[0010] 1) Using VMD algorithm to decompose traffic flow signal into modal components;

[0011] 2) Use the LSTM algorithm to predict each modal component of the traffic flow signal after decomposition, and add the prediction results.

[0012] The VMD algorithm is used to decompose the traffic flow signal into modal components, including constructing a variational problem and solving the variational problem. The constructed variational problem is:

[0013]

[0014] In the formula, {u k} is the set of modal components of all traffic flow signals; {ω k} is the set of central frequencies of all traffic flow signal modal components; f(t) is the traffic flow signal, δ(t) is the impulse function, and * is the convolution operator;

[0015] The solution of variational problems includes:

[0016] The variational problem is transformed into an unconstrained variational problem for solution, and a quadratic penalty factor and a Lagrangian multiplication operator are introduced. The extended Lagrangian expression is:

[0017]

[0018] Where λ is the Lagrange multiplier; α is the quadratic penalty coefficient, L({u k},{ω k}, λ) is the Lagrangian expression. The power generation operation strategy of the new energy power station is expressed as:

[0019] minC=C1+C2

[0020] C1=N PV ·p PV +N WT ·p WT +N bat ·p bat

[0021]

[0022]

[0023] Where C is the objective function of the power generation operation strategy of the new energy power station; C1 is the equipment operation cost of the new energy power station; C2 is the power purchase cost of the new energy power station; N PV is the number of PV panels in operation; p PVis the operating cost of each set of PV panels; N WT is the number of wind turbines in operation; p WT is the operating cost of each wind turbine generator set; N bat is the number of battery operations; p bat is the operating cost of each battery pack; u E and u S They are the unit electricity purchase price and the unit electricity sales price respectively; is a variable, indicating whether to purchase electricity from the grid; P t E , P t S are the purchased power and sold power at time t respectively; P t PV is the output power of the unit photovoltaic module at time t; P t WT is the output power of the unit wind turbine at time t; P t di , P t ch are the charging and discharging power of the battery at time t; D t is the required electrical energy, D t =h t q t ,h t is the predicted traffic flow data; q t The power demand at time t; are the maximum number of photovoltaic modules, wind turbines, and batteries installed; P max is the maximum power traded with the grid.

[0024] The number of PV panels in operation N PV 、N number of wind turbines in operation WT 、N number of running batteries bat To optimize the variables, the EVO algorithm is used, which includes the following steps:

[0025] 11) Initialize the position and parameters of particles;

[0026] 12) The randomly generated particle positions are brought into the power generation operation strategy of the new energy power station for evaluation;

[0027] 13) Determine the enrichment boundary of the particle, the formula is as follows:

[0028]

[0029] Where EB is the enrichment boundary of particles in the universe; NEL i is the neutron enrichment level of the ith particle, and n is the number of particles;

[0030] 14) Determine the stability level of the particles, the formula is as follows:

[0031]

[0032] In the formula, SL i is the stability level of the ith particle; BS and WS are the particles with the best and worst stability in the universe;

[0033] 15) Obtain a better power generation control solution by updating the location of the number of equipment in operation:

[0034] When the neutron enrichment level is higher than the enrichment limit and the particle stability level is higher than the stability limit, α rays are emitted to improve the stability of the product in the physical reaction. The position update formula is as follows:

[0035]

[0036] In the formula, is the position of the newly generated particles in the universe; X i is the current position vector of the i-th particle in the universe; X BS is the position vector of the particle with the best stability; is the jth decision variable of the ith particle; AlphaIndexII is a random integer in the range [0, d], where d is the number of decision variables;

[0037] When the stability level of a particle is lower than the stability limit, the particle releases β rays to improve the stability level of the particle. The position update formula is as follows:

[0038]

[0039]

[0040] Where, X CP is the position vector of the center of all particles; r1 and r2 are random numbers in [0, 1];

[0041] When the neutron enrichment level is lower than the enrichment boundary, the particle moves to the stable band through electron capture or positron emission, and the position update formula is as follows:

[0042]

[0043] Where r is a random number in [0, 1];

[0044] Preferably, an adaptive weight factor is introduced to improve the position of the newly generated particles and improve the local optimization ability of the EVO algorithm. The calculation formula is as follows:

[0045]

[0046] Where ω is the adaptive weight factor.

[0047] 16) Determine whether the termination condition is met. If not, return to step 12); if met, output the current position of the decision variable.

[0048] The present invention proposes a computer program product, including a computer program and / or instructions, which, when executed by a processor, implements the power generation control method of a new energy power station based on traffic flow prediction.

[0049] The present invention proposes a new energy power station power generation control device based on traffic flow prediction, comprising a data acquisition module, a data processing module and a power generation control module;

[0050] Data collection module, used to detect traffic flow;

[0051] A data processing module, used to predict future traffic flow based on the collected traffic flow data by using a VMD-LSTM prediction model; the traffic flow data includes time features and spatial features;

[0052] The power generation control module is used to optimize the power generation control scheme using the EVO algorithm according to the predicted traffic flow and the power generation operation strategy of the new energy power station.

[0053] Preferably, the data acquisition module uses a loop coil detector to detect traffic flow.

[0054] Beneficial effects: Compared with the prior art, the present invention has the following advantages: The present invention regulates the capacity of the power station according to the traffic flow, greatly improves the energy utilization rate, and aims to achieve efficient, intelligent and sustainable development of the power station. The present invention proposes a VMD-LSTM prediction model, which can better handle the uncertainty and noise in traffic flow data by introducing the VMD algorithm. The EVO algorithm is used to optimize the decision variables of the objective function to obtain the optimal power station operation strategy and reduce costs. In addition, the position of the newly generated particles is improved by introducing an adaptive weight factor, which improves the local optimization ability of the EVO algorithm. At the same time, in addition, the present invention uses photovoltaic and wind power generation, equipped with a power grid, to achieve complementarity and mutual backup between different types of power sources, reduce environmental pollution, and promote the development of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of the power generation control method of a new energy power station based on traffic flow prediction according to the present invention;

[0056] Figure 2 This is a schematic diagram of the structure of the power generation control device of the new energy power station based on traffic flow prediction described in the present invention. DETAILED DESCRIPTION

[0057] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0058] The method for controlling power generation of a new energy power station based on traffic flow prediction described in the present invention comprises: predicting future traffic flow based on collected traffic flow data by using a VMD-LSTM prediction model, and optimizing a power generation control scheme using an EVO algorithm based on the predicted traffic flow and the power generation operation strategy of the new energy power station; the traffic flow data includes temporal features and spatial features. Figure 1 As shown, it is a specific execution flow chart of the algorithm, which includes the following steps:

[0059] The VMD-LSTM prediction model establishment process of the present invention is as follows:

[0060] 1) The VMD algorithm is used to decompose the traffic flow time-amplitude signal into several modal components. The process is as follows:

[0061] VMD decomposes the original signal into k modal components u k (t), the formula is as follows:

[0062] u k (t) = A k (t)coS[φ k (t)]

[0063] In the formula, u k (t) is the modal component; A k (t) is the instantaneous amplitude; φ k (t) is the phase function; k is the number of modal components; t is the time variable.

[0064] The VMD algorithm can be divided into two parts: the construction and solution of the variational problem.

[0065] Construction of variational problem:

[0066] Apply the Hibert transform to each modal component obtained by decomposition to obtain the analytical signal and the single-sided spectrum:

[0067]

[0068] Where δ(t) is the pulse function and * is the convolution operator.

[0069] Add index item Modulate the spectrum of each modal component to the corresponding baseband:

[0070]

[0071] In the formula, ω k is the center frequency.

[0072] The bandwidth of each modal component is obtained by solving the signal gradient L 2 The constrained variational problem is estimated by the norm of

[0073]

[0074] In the formula, {u k} is the set of all modal components; {ω k} is the set of all center frequencies; f(t) is the original signal.

[0075] Solution of variational problem:

[0076] The above constrained variational problem is transformed into an unconstrained variational problem, and the quadratic penalty factor and Lagrangian multiplication operator are introduced. The extended Lagrangian expression is:

[0077]

[0078] Where λ is the Lagrange multiplier and α is the quadratic penalty coefficient.

[0079] 2) Use LSTM to model each modal component separately.

[0080] The LSTM model is mainly composed of a forget gate, an input gate, a memory gate, and an output gate. The calculation formula is as follows:

[0081] f t =σ(W f ·[x t ,h t-1 ]+b f )

[0082] i t =σ(W i ·[x t ,h t-1 ]+b i )

[0083]

[0084]

[0085] O t =σ(W o ·[x t ,h t-1 ]+b o )

[0086] h t =O t tanh(C t )

[0087] In the formula, f t 、i t , C t , O t is the information update process of the forget gate, input gate, memory gate, and output gate, W f , W i , W c , W o Its corresponding weight matrix, b f 、b i 、b c 、b o is the corresponding bias constant; C t is a temporary state; σ is the sigmoid function; tanh is the hyperbolic tangent function; x t 、h t-1 is the input and hidden layer state of the internal LSTM neural network.

[0088] Using the EVO algorithm to optimize the decision variables in the objective function: the number of photovoltaic panels in operation N PV 、N number of wind turbines in operation WT 、N number of running batteries bat The process is as follows:

[0089] 1) Initialize the particle position and parameters: N PV 、N WT and N bat , the particle position formula is as follows:

[0090]

[0091] In the formula, is the jth decision variable used to determine the initial position of the i-th candidate; rand is an arbitrary vector in the range of [0, 1]; are the upper and lower boundaries of the problem to be solved; n is the total number of particles (candidate solutions) in the universe (search space); d is the dimension of the problem to be solved.

[0092] 2) The randomly generated particle positions are brought into the objective function for evaluation, so that each particle generates a set of corresponding device operation numbers N PV 、N WT 、N bat , the objective function formula is as follows:

[0093] min C=C1+C2

[0094] C1=N PV ·p PV +N WT ·p WT +N bat ·pbat

[0095]

[0096]

[0097] Constraint (1) ensures the stability of the energy supply of the smart power station; constraints (2)-(4) limit the maximum number of installations; and constraint (5) limits the maximum power traded with the grid.

[0098] In the formula, C is the objective function value, representing the lowest cost; C1 is the equipment operating cost; C2 is the electricity purchase cost; N PV is the number of PV panels in operation; p PV is the operating cost of each set of PV panels; N WT is the number of wind turbines in operation; p WT is the operating cost of each wind turbine generator set; N bat is the number of battery operations; p bat is the operating cost of each battery pack; u E and u S They are the unit electricity purchase price and the unit electricity sales price respectively; It is a 0-1 variable. When it is 0, it buys electricity from the power grid, and when it is 1, it sells electricity to the power grid; P t E , P t S are the purchased power and sold power at time t respectively; P t PV is the output power of the unit photovoltaic module at time t; P t WT is the output power of the unit wind turbine at time t; P t di , P t ch are the charging and discharging power of the battery at time t; D t is the required electrical energy, D t =h t q t ,h t is the traffic flow data predicted by the VMD-LSTM model above; q t The power demand at time t; are the maximum number of photovoltaic modules, wind turbines, and batteries installed; P max is the maximum power traded with the grid.

[0099] 3) Determine the enrichment boundary of the particles, the formula is as follows:

[0100]

[0101] Where EB is the enrichment boundary of particles in the universe; NEL i is the neutron enrichment level of the i-th particle, representing the density of possible solutions. The larger the density, the more likely it is that the optimal solution, i.e., the lowest cost, will appear.

[0102] 4) According to the objective function evaluation, determine the stability level of the particles, the formula is as follows:

[0103]

[0104] In the formula, SL i is the stability level of the i-th particle; BS and WS are the particles with the best and worst stability in the universe.

[0105] 5) By updating the position of the number of operating devices, an operation plan more suitable for the smart power station of the present invention is obtained, thereby obtaining the lowest cost.

[0106] When the neutron enrichment level is higher than the enrichment boundary,

[0107] When the stability level of the particle is higher than the stability limit, α rays are emitted to improve the stability of the product in physical reactions. The position update formula is as follows:

[0108]

[0109] In the formula, is the position of the newly generated particles in the universe; X i is the current position vector of the i-th particle in the universe; X BS is the position vector of the particle with the best stability; is the j-th decision variable; AlphaIndexII is a random integer in the range of [0,d].

[0110] When the stability level of a particle is lower than the stability limit, the particle releases β rays to improve the stability level of the particle. The position update formula is as follows:

[0111]

[0112]

[0113] Where, X CP is the position vector of the particle center; r1 and r2 are random numbers in the range of [0,1].

[0114] When the neutron enrichment level is lower than the enrichment boundary, the particle moves to the stable band through electron capture or positron emission. The position update formula is as follows:

[0115]

[0116] Where r is a random number in the range of [0, 1].

[0117] 6) Determine whether the termination condition is met. If the termination condition is not met, return to step 2); if it is met, go to step 7).

[0118] 7) Output the optimal smart power station operation plan.

[0119] Furthermore, the algorithm is improved by improving 5), introducing an adaptive weight factor to improve the position of the newly generated particles and enhance the local optimization ability of the EVO algorithm. The improved formula is as follows:

[0120]

[0121] Example 2

[0122] In one embodiment, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the method for controlling power generation of a new energy power station based on traffic flow prediction.

[0123] Example 3

[0124] The new energy power station power generation control device based on traffic flow prediction described in the present invention includes: a data acquisition module, a data processing module and a power generation control module.

[0125] The device is applied to a common renewable energy power generation system, and its structural diagram is as follows: Figure 2 As shown in , it also includes a power supply module and power-consuming equipment (such as a smart power station).

[0126] The data acquisition module includes a loop coil detector, which is used to detect traffic flow, that is, the number of vehicles passing through a certain section of road (node) per unit time;

[0127] The data processing module includes a prediction model and an algorithm. The prediction model predicts the traffic flow in the future based on the traffic flow data measured by the loop coil detector; the algorithm is used to optimize the model parameters;

[0128] The power supply module includes a solar array panel, a wind turbine, a battery and a power grid. The solar array panel is used to realize photovoltaic power generation, the wind turbine is used to realize wind power generation, the battery is used to store energy, and the power grid is used to realize the complementarity and mutual backup between different types of power sources.

[0129] The control system in the power generation control module makes timely decisions by collecting and analyzing traffic flow data and photovoltaic and wind power generation data to ensure safe and stable operation of the system; the energy storage inverter receives instructions from the control system to control charging and discharging; the smart power station is used to provide charging services for electric vehicles.

[0130] The control module regulates the capacitance of the smart power station by controlling the number of photovoltaic modules, wind turbines and batteries in operation. When the traffic flow is heavy during a period of time, the power generation control module controls the power station to operate multiple photovoltaic modules, wind turbines and batteries to increase the capacity of the power station; if green energy cannot meet the power supply demand, it will purchase electricity from the power grid. When the traffic flow is light, the power generation control module controls the power station to operate a small number of photovoltaic modules, wind turbines and batteries in the power station to reduce the capacity of the power station; if green energy can meet the power supply demand, it will sell electricity to the power grid.

Claims

1. A new energy power station power generation control method based on traffic flow prediction, characterized in that: include: The VMD-LSTM prediction model is used to predict future traffic flow based on the collected traffic flow data. The EVO algorithm is used to optimize the power generation control scheme based on the predicted traffic flow and the power generation operation strategy of the new energy power station; the traffic flow data includes time characteristics and spatial characteristics; According to the predicted traffic flow and the power generation operation strategy of the new energy power station, the EVO algorithm is used to find the optimal power generation control solution, including: PV 、N number of wind turbines in operation WT 、N number of running batteries bat To optimize the variables, the EVO algorithm is used; the EVO algorithm optimization includes the following steps: 11) Initialize the position and parameters of particles; 12) The randomly generated particle positions are brought into the power generation operation strategy of the new energy power station for evaluation; 13) Determine the enrichment boundary of the particle, the formula is as follows: Where EB is the enrichment boundary of particles in the universe; NEL i is the neutron enrichment level of the ith particle, and n is the number of particles; 14) Determine the stability level of the particles, the formula is as follows: In the formula, SL i is the stability level of the ith particle; BS and WS are the particles with the best and worst stability in the universe; 15) Obtain a better power generation control solution by updating the location of the number of equipment in operation: When the neutron enrichment level is higher than the enrichment limit and the particle stability level is higher than the stability limit, α rays are emitted to improve the stability of the product in the physical reaction. The position update formula is as follows: In the formula, is the position of the newly generated particles in the universe; X i is the current position vector of the i-th particle in the universe; X BS is the position vector of the particle with the best stability; is the jth decision variable of the ith particle; AlphaIndexII is a random integer in the range [0, d], where d is the number of decision variables; When the stability level of a particle is lower than the stability limit, the particle releases β rays to improve the stability level of the particle. The position update formula is as follows: Where, X CP is the position vector of the center of all particles; r1 and r2 are random numbers in [0,1]; When the neutron enrichment level is lower than the enrichment boundary, the particle moves to the stable band through electron capture or positron emission, and the position update formula is as follows: Where r is a random number in [0,1]; 16) Determine whether the termination condition is met. If not, return to step 12); if satisfied, output the current position of the decision variable; The process of building the VMD-LSTM prediction model includes the following steps: 1) Using VMD algorithm to decompose traffic flow signal into modal components; 2) Use the LSTM algorithm to predict each modal component of the traffic flow signal after decomposition, and add the prediction results; The power generation operation strategy of the new energy power station is expressed as: minC=C1+C2 C1=N PV ·p PV +N WT ·p WT +N bat ·p bat Where C is the objective function of the power generation operation strategy of the new energy power station; C1 is the equipment operation cost of the new energy power station; C2 is the power purchase cost of the new energy power station; N PV is the number of PV panels in operation; p PV is the operating cost of each set of PV panels; N WT is the number of wind turbines in operation; p WT is the operating cost of each wind turbine generator set; N bat is the number of battery operations; p bat is the operating cost of each battery pack; u E and u S They are the unit electricity purchase price and the unit electricity sales price respectively; is a variable, indicating whether to purchase electricity from the grid; P t E , P t S are the purchased power and sold power at time t respectively; P t PV is the output power of the unit photovoltaic module at time t; P t WT is the output power of the unit wind turbine at time t; P t di , P t ch are the charging and discharging power of the battery at time t; D t is the required electrical energy, D t =h t q t ,h t is the predicted traffic flow data; q t The power demand at time t; are the maximum number of photovoltaic modules, wind turbines, and batteries installed; P max is the maximum power traded with the grid.

2. The method for controlling power generation of a new energy power station based on traffic flow prediction according to claim 1 is characterized in that: The traffic flow data includes time features and space features, which are expressed as a time-space feature matrix as follows: Where H is the spatiotemporal characteristic matrix of traffic flow, which is composed of the characteristic vector h of each node in the space k (t) composition; k is the number of nodes; [x i (1),x i (2),…x i (m)] is the traffic flow of node i at the mth time point.

3. The method for controlling power generation of a new energy power station based on traffic flow prediction according to claim 1 is characterized in that: The VMD algorithm is used to decompose the traffic flow signal into modal components, including constructing a variational problem and solving the variational problem. The constructed variational problem is: In the formula, {u k } is the set of modal components of all traffic flow signals; {ω k } is the set of central frequencies of all traffic flow signal modal components; f(t) is the traffic flow signal, δ(t) is the impulse function, and * is the convolution operator; The solution of variational problems includes: The variational problem is transformed into an unconstrained variational problem for solution, and a quadratic penalty factor and a Lagrangian multiplication operator are introduced. The extended Lagrangian expression is: Where λ is the Lagrange multiplier; α is the quadratic penalty coefficient, L({u k },{ω k },λ) is the Lagrangian expression.

4. The method for controlling power generation of a new energy power station based on traffic flow prediction according to claim 1 is characterized in that: In step 15), an adaptive weight factor is introduced to improve the position of the newly generated particles and enhance the local optimization capability of the EVO algorithm. The calculation formula is as follows: Where ω is the adaptive weight factor.

5. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program / instruction is executed by a processor, the power generation control method of a new energy power station based on traffic flow prediction described in any one of claims 1 to 4 is implemented.

6. A new energy power station power generation control device based on traffic flow prediction, characterized in that: It includes a data acquisition module, a data processing module and a power generation control module; Data collection module, used to detect traffic flow; A data processing module, used to predict future traffic flow based on the collected traffic flow data by using a VMD-LSTM prediction model; the traffic flow data includes time features and spatial features; The process of building the VMD-LSTM prediction model includes the following steps: 1) Using VMD algorithm to decompose traffic flow signal into modal components; 2) Use the LSTM algorithm to predict each modal component of the traffic flow signal after decomposition, and add the prediction results; The power generation control module is used to optimize the power generation control scheme using the EVO algorithm according to the predicted traffic flow and the power generation operation strategy of the new energy power station; the power generation control scheme is optimized using the EVO algorithm according to the predicted traffic flow and the power generation operation strategy of the new energy power station, including: PV 、N number of wind turbines in operation WT 、N number of running batteries bat To optimize the variables, the EVO algorithm is used; the EVO algorithm optimization includes the following steps: 11) Initialize the position and parameters of particles; 12) The randomly generated particle positions are brought into the power generation operation strategy of the new energy power station for evaluation; 13) Determine the enrichment boundary of the particle, the formula is as follows: Where EB is the enrichment boundary of particles in the universe; NEL i is the neutron enrichment level of the ith particle, and n is the number of particles; 14) Determine the stability level of the particles, the formula is as follows: In the formula, SL i is the stability level of the ith particle; BS and WS are the particles with the best and worst stability in the universe; 15) Obtain a better power generation control solution by updating the location of the number of equipment in operation: When the neutron enrichment level is higher than the enrichment limit and the particle stability level is higher than the stability limit, α rays are emitted to improve the stability of the product in the physical reaction. The position update formula is as follows: In the formula, is the position of the newly generated particles in the universe; X i is the current position vector of the i-th particle in the universe; X BS is the position vector of the particle with the best stability; is the jth decision variable of the ith particle; AlphaIndexII is a random integer in the range [0, d], where d is the number of decision variables; When the stability level of a particle is lower than the stability limit, the particle releases β rays to improve the stability level of the particle. The position update formula is as follows: Where, X CP is the position vector of the center of all particles; r1 and r2 are random numbers in [0,1]; When the neutron enrichment level is lower than the enrichment boundary, the particle moves to the stable band through electron capture or positron emission, and the position update formula is as follows: Where r is a random number in [0,1]; 16) Determine whether the termination condition is met. If not, return to step 12); if satisfied, output the current position of the decision variable; The power generation operation strategy of the new energy power station is expressed as: minC=C1+C2 C1=N PV ·p PV +N WT ·p WT +N bat ·p bat Where C is the objective function of the power generation operation strategy of the new energy power station; C1 is the equipment operation cost of the new energy power station; C2 is the power purchase cost of the new energy power station; N PV is the number of PV panels in operation; p PV is the operating cost of each set of PV panels; N WT is the number of wind turbines in operation; p WT is the operating cost of each wind turbine generator set; N bat is the number of battery operations; p bat is the operating cost of each battery pack; u E and u S They are the unit electricity purchase price and the unit electricity sales price respectively; is a variable, indicating whether to purchase electricity from the grid; P t E , P t S are the purchased power and sold power at time t respectively; P t PV is the output power of the unit photovoltaic module at time t; P t WT is the output power of the unit wind turbine at time t; P t di , P t ch are the charging and discharging power of the battery at time t; D t is the required electrical energy, D t =h t q t ,h t is the predicted traffic flow data; q t The power demand at time t; are the maximum number of photovoltaic modules, wind turbines, and batteries installed; P max is the maximum power traded with the grid.

7. The new energy power station power generation control device based on traffic flow prediction according to claim 6 is characterized in that: The data acquisition module uses a loop coil detector to detect traffic flow.

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