Energy dynamic prediction and allocation method and system for railway power supply security

Abstract

The present invention discloses a method and system for dynamic energy prediction and distribution for railway power supply safety; real-time monitoring of regenerative braking energy, traction load status, remaining power status of energy storage devices, and grid electricity price status in the railway traction power supply system; constructing a cost function with power supply safety as the core; establishing a dynamic coupling prediction data model including traction load, remaining power status of energy storage devices, and grid electricity price, and designing a dynamic prediction algorithm that takes energy distribution priority into consideration; rolling correction of the distribution coefficient between the bidirectional energy fusion device, energy storage device and grid-connected energy feeding device based on the dynamic prediction algorithm; selecting an energy distribution path based on the distribution coefficient, and completing the dynamic prediction and distribution of regenerative braking energy in the railway traction power supply system; the method and system provided by the present invention can make optimal use of regenerative braking energy and significantly improve the energy utilization efficiency of the power supply system.

Description

Technical Field

[0001] The present invention belongs to the field of traction power supply, and in particular relates to a method and system for dynamic energy prediction and distribution for railway power supply safety. Background Art

[0002] With the rapid development of my country's high-speed rail network and the continued growth of electrified railway operating mileage, railway traction power supply systems face increasingly severe energy management challenges. In particular, under frequent train starts, stops, and braking conditions, the system must efficiently handle the dynamic distribution of regenerative braking energy. Traditional energy management methods, which primarily rely on fixed threshold control strategies, are difficult to adapt to complex and changing operating conditions and can easily lead to safety hazards such as voltage fluctuations and equipment overloads.

[0003] The current energy management of railway traction power supply systems has the following main technical bottlenecks: First, the existing system lacks multi-dimensional comprehensive consideration of power supply safety, especially the lack of a safety risk assessment mechanism, making it difficult to achieve a balance between key safety indicators such as voltage stability, current limitation and fault recovery; second, traditional modeling methods cannot accurately characterize the dynamic characteristics of multi-state coupling such as traction load, energy storage status and grid electricity price, and it is also difficult to achieve multi-step advance prediction; more importantly, the existing energy allocation strategy lacks active regulation capabilities based on prediction, and cannot formulate predictive optimal coefficient strategies. It is difficult to provide forward-looking control instructions for various energy conversion devices, which restricts the system response speed and energy utilization efficiency.

[0004] In terms of safety control, existing technologies often use static protection thresholds, which are unable to adapt to real-time changes in system operating conditions. This is especially true when responding to sudden load fluctuations or grid failures. Traditional methods struggle to adjust energy allocation strategies in a timely manner, potentially causing protective devices to malfunction or fail to operate, seriously impacting power supply reliability. Furthermore, with the increasing proportion of renewable energy grid integration and the deepening of power market reforms, power supply systems must also address the need for economical operation, placing higher demands on dynamic energy allocation algorithms.

[0005] To address the above issues, there is an urgent need to develop a dynamic energy prediction and allocation method for railway power supply security. This method establishes a multi-objective optimized safety cost function, constructs a multi-state dynamic prediction model that takes into account traction load, energy storage status and grid electricity price, and designs a dynamic prediction algorithm with energy allocation priority, thereby achieving the optimal allocation of regenerative braking energy and ensuring the safe, stable and economical operation of the power supply system. Summary of the Invention

[0006] In order to solve the problems existing in the background technology, the purpose of the present invention is to provide a method for dynamic energy prediction and allocation for railway power supply security. The device for dynamic energy prediction and allocation includes a bidirectional energy integration device, an energy storage device, and a grid-connected energy feeding device. The energy storage device uses the energy obtained to power an auxiliary monitoring system, an intelligent overhead line monitoring system, and a station energy management and control system. The method includes the following steps:

[0007] Step (1): continuously collecting and monitoring the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and power price status of the power grid in the railway traction power supply system;

[0008] Step (2): Constructing a cost function with power supply security as the core, as a constraint condition for determining the energy distribution coefficient between the bidirectional energy financing device, the energy storage device and the grid-connected energy feeding device, wherein the cost function includes factors such as voltage stability, current limit and fault recovery capability;

[0009] Step (3): Establish a dynamic coupling prediction data model based on multiple state variables, wherein the state variables include the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state, and the dynamic coupling prediction data model includes a time-varying gradient matrix; based on the dynamic coupling prediction data model, determine a dynamic prediction algorithm that considers the energy allocation priority to obtain the energy allocation coefficient between the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device:

[0010] ;

[0011] in, represents the expansion vector of the forward prediction vector of the energy allocation coefficient, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction vector, ; is the prediction gain matrix; represents the expansion vector of the forward prediction error difference vector; is the priority gain coefficient; , Represents the identity matrix with 3 rows and 3 columns; is the priority function vector;

[0012] extract in , , , , They are The allocation coefficients of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time;

[0013] Based on the dynamic coupling prediction data model, designing an estimation algorithm for the time-varying gradient matrix and the prediction gain matrix;

[0014] Step (4): Based on the calculation result of the dynamic prediction algorithm, the energy distribution coefficient between the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device is revised in a rolling manner, and the energy distribution path is selected based on the distribution coefficient;

[0015] Repeat the above steps (1) to (4) to realize the dynamic prediction and distribution of regenerative braking energy in the railway traction power supply system.

[0016] Furthermore, the dynamic coupling prediction data model based on multiple state variables established in step (3) is:

[0017] ;

[0018] in, represents the sampling time; M Represents the prediction step size, which is a positive integer; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. M A vector of step-forward prediction value vectors; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time m step-forward prediction vector, ; , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time m step-forward prediction value; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; , represents the identity matrix with 3 rows and 3 columns, is the time-varying gradient matrix, , for The time-varying gradient submatrix at the sampling moment; Represented by vector , , , The vector formed, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction increment vector, The energy distribution coefficient is The sampling time Step forward prediction increment value, for ,when When , they represent the energy distribution coefficients of the energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time The step-forward prediction increment value, ; , , Respectively represent the energy distribution coefficients of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time Step-forward prediction value.

[0019] Furthermore, in step (3), the prediction gain matrix is ​​designed based on the dynamic coupling prediction data model. The estimation algorithm is:

[0020] ;

[0021] in, , is a constant, is a one-step forward differential, and ; represents one-step forward error; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. M A vector of step-forward prediction setpoint vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forecast setpoint vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction set value; The expansion vector representing the forward prediction error difference vector is composed of the vector , , , , The vector formed, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forward prediction error vector, Represents a vector The first-order backward difference of ; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price status of the grid M A vector of step-forward prediction setpoint vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forecast setpoint vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction set value.

[0022] Furthermore, in step (3), based on the dynamic coupling prediction data model, designing an estimation algorithm for the time-varying gradient matrix includes:

[0023] design Time-varying gradient submatrix at the sampling moment When the estimation algorithm is used, the optimization goal is , where the symbol Represents the minimization function About variables Take the value and minimize the cost function as a constraint:

[0024] ;

[0025] in, Represents a vector The first-order backward difference of Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The historical increment vector at the sampling moment, The energy distribution coefficient is The historical increment value at the sampling time, for ,when When , they represent the energy distribution coefficients of the energy fusion device, energy storage device, and grid-connected energy feeding device. Historical incremental value at the sampling moment; is the step size factor, is the penalty factor, is the two-norm;

[0026] Design time-varying gradient submatrix , , When the estimation algorithm is:

[0027] ;

[0028] in, , Represented by the matrix , , The vector formed, express The time-varying gradient submatrix at the sampling moment, , is a positive integer between 3 and 5;

[0029] is an intermediate variable, and its calculation formula is:

[0030] ;

[0031] , Represented by the matrix , , The vector formed, express The time-varying gradient submatrix at the sampling moment; is a constant.

[0032] Furthermore, the method for selecting the energy distribution path based on the distribution coefficient in step (4) is:

[0033] like Not 0, path 1 is executed first; if path 1 does not completely absorb energy and Not 0, execute path 2; if there is still remaining energy and If it is not 0, execute path 3;

[0034] Among them, path 1 indicates that the regenerative braking energy is preferentially transferred to the adjacent power supply arm through the bidirectional energy financing device according to the traction load status; path 2 indicates that the remaining regenerative braking energy is allocated to the energy storage device for storage according to the remaining power state SOC adaptive algorithm of the energy storage device; path 3 indicates triggering the grid-connected energy feeding device for reverse feeding.

[0035] Furthermore, in step (2), a cost function with power supply security as the core is constructed, and the cost function is:

[0036] ;

[0037] in, is the cost function coefficient, They represent the voltage stability cost item, the current limit cost item, and the fault recovery capability cost item respectively.

[0038] Furthermore, the SOC adaptive algorithm of the energy storage device in the energy distribution path 2 includes the following steps:

[0039] When it is detected that the remaining power state SOC of the energy storage device is greater than or equal to a first threshold, reducing the value of the energy storage device allocation coefficient;

[0040] When the remaining state of charge SOC of the energy storage device is less than or equal to a second threshold, energy is prohibited from being released from the energy storage device.

[0041] The energy dynamic prediction and distribution system for railway power supply security includes:

[0042] The data monitoring module is used to continuously collect and monitor the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and grid electricity price status in the railway traction power supply system;

[0043] A safety optimization decision module is used to construct a cost function centered on power supply safety as a constraint condition for determining the energy distribution coefficient between the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device. The cost function includes factors such as voltage stability, current limit, and fault recovery capability.

[0044] A dynamic prediction calculation module is used to establish a dynamic coupling prediction data model based on multiple state variables, wherein the state variables include the traction load state, the remaining charge state (SOC) of the energy storage device, and the grid electricity price state. The dynamic coupling prediction data model includes a time-varying gradient matrix. Based on the dynamic coupling prediction data model, a dynamic prediction algorithm that considers energy allocation priority is determined to obtain the energy allocation coefficient between the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device:

[0045] ;

[0046] in, represents the expansion vector of the forward prediction vector of the energy allocation coefficient, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction vector, ; is the prediction gain matrix; represents the expansion vector of the forward prediction error difference vector; is the priority gain coefficient; , Represents the identity matrix with 3 rows and 3 columns; is the priority function vector;

[0047] extract in , , , , They are The allocation coefficients of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time;

[0048] Based on the dynamic coupling prediction data model, designing an estimation algorithm for the time-varying gradient matrix and the prediction gain matrix;

[0049] A coefficient allocation strategy module, configured to, based on the calculation results of the dynamic prediction algorithm, roll-over correct the energy allocation coefficients among the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device;

[0050] The coefficient allocation execution module is used to select an energy allocation path based on the allocation coefficient.

[0051] Furthermore, the present invention adopts the following technical solutions:

[0052] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for dynamic energy prediction and allocation for railway power supply security.

[0053] Furthermore, the present invention adopts the following technical solutions:

[0054] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for dynamic energy prediction and allocation for railway power supply security as described above is implemented.

[0055] The beneficial technical effects of the present invention are:

[0056] (1) Based on a dynamic coupling prediction data model that is updated in real time, the present invention designs a dynamic prediction algorithm that takes into account multi-step prediction errors, calculates the energy distribution coefficients of the bidirectional energy fusion device, the energy storage device, and the grid-connected energy feeding device, and accurately predicts the energy supply and demand relationship under different working conditions. In this process, by establishing a cost function for railway power supply safety, voltage stability, current protection, and fault recovery capability are incorporated into a unified optimization framework, thereby achieving dynamic prevention and control of power supply system safety risks and significantly improving the reliability and safety of the traction power supply system.

[0057] (2) The present invention establishes a dynamic adjustment mechanism with energy allocation priority, intelligently coordinates multiple energy conversion modes such as bidirectional energy integration, energy storage and grid-connected energy feeding, so as to optimize the utilization of regenerative braking energy and significantly improve the energy utilization efficiency of the power supply system;

[0058] (3) The dynamic prediction algorithm considering the energy allocation priority proposed in the present invention does not rely on an accurate mathematical model. This method has obvious advantages in dealing with traction power supply systems with strong time-varying, strong nonlinearity and strong coupling, and is easy to apply in practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic flow chart of a method for dynamic energy prediction and allocation for railway power supply security provided in Example 1 of the present invention;

[0060] Figure 2 A schematic diagram showing the connection modules of a dynamic energy prediction and distribution system for railway power supply security provided in Example 2 of the present invention;

[0061] Figure 3 A power supply system for the energy storage device provided in Example 1 of the present invention;

[0062] Figure 4 Schematic diagram of a multi-purpose power supply method for an energy storage device provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0063] The present invention discloses a method and system for dynamic energy prediction and distribution for railway power supply safety; real-time monitoring of regenerative braking energy, traction load status, remaining charge state SOC of energy storage device, and grid electricity price status in railway traction power supply system; constructing a cost function with power supply safety as the core; establishing a dynamic coupling prediction data model including traction load, remaining charge state SOC of energy storage device, and grid electricity price, and designing a dynamic prediction algorithm considering energy distribution priority; rolling correction of the distribution coefficient between the bidirectional energy fusion device, energy storage device and grid-connected energy feeding device based on the dynamic prediction algorithm; selecting the energy distribution path based on the distribution coefficient, and completing the dynamic prediction and distribution of regenerative braking energy in the railway traction power supply system; the method and system provided by the present invention can predict the energy supply and demand relationship under different working conditions, make optimal use of regenerative braking energy, and significantly improve the energy utilization efficiency of the power supply system.

[0064] The following is a further clear and complete description of the method and system for dynamic energy prediction and allocation for railway power supply security provided by the present invention in conjunction with the accompanying drawings: Example 1

[0065] Figure 1 A flow chart of a method for dynamic energy prediction and allocation for railway power supply security provided by this embodiment is provided. The method for dynamic energy prediction and allocation for railway power supply security provided by this embodiment, wherein the device for dynamic energy prediction and allocation includes a bidirectional energy financing device, an energy storage device, and a grid-connected energy feeding device, and the method includes the following steps:

[0066] Step (1): continuously collecting and monitoring the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and power price status of the power grid in the railway traction power supply system;

[0067] Step (2): Constructing a cost function with power supply security as the core, as a constraint condition for determining the energy distribution coefficient between the bidirectional energy financing device, the energy storage device and the grid-connected energy feeding device, wherein the cost function includes factors such as voltage stability, current limit and fault recovery capability;

[0068] Step (3): Establish a dynamic coupling prediction data model based on multiple state variables, wherein the state variables include the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state, and the dynamic coupling prediction data model includes a time-varying gradient matrix; based on the dynamic coupling prediction data model, design a dynamic prediction algorithm that considers the energy allocation priority to obtain the energy allocation coefficient between the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device:

[0069] ;

[0070] in, The expansion vector representing the forward prediction vector of the energy allocation coefficient is composed of the vector , , The vector formed, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction vector, ; is the prediction gain matrix; The expansion vector representing the forward prediction error difference vector is composed of the vector , , , , The vector formed, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forward prediction error vector, Represents a vector The first-order backward difference of , is an adjustable time window parameter; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price status of the grid M A vector of step-forward prediction setpoint vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forecast setpoint vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction set value; is the priority gain coefficient; , Represents the identity matrix with 3 rows and 3 columns; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; is the priority function vector, represents the priority vector, , , Respectively represent the priorities of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device;

[0071] extract in , , , , They are The allocation coefficients of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time;

[0072] Based on the dynamic coupling prediction data model, designing an estimation algorithm for the time-varying gradient matrix and the prediction gain matrix;

[0073] Step (4): Based on the calculation results of the dynamic prediction algorithm, the energy distribution coefficient between the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device is revised in a rolling manner, and the energy distribution path is selected based on the distribution coefficient; if Not 0, path 1 is executed first; if path 1 does not completely absorb energy and Not 0, execute path 2; if there is still remaining energy and If it is not 0, execute path 3;

[0074] Repeat the above steps (1) to (4) to realize the dynamic prediction and distribution of regenerative braking energy in the railway traction power supply system.

[0075] In detail, the method for selecting the energy distribution path based on the distribution coefficient is as follows: the energy distribution coefficient between the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device refers to the vector obtained by dynamic prediction algorithm. ,in , , They are The distribution coefficients of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device at the sampling time are as follows: Not 0, path 1 is executed first; if path 1 does not completely absorb energy and Not 0, execute path 2; if there is still remaining energy and If it is not 0, execute path 3; path 1 indicates that the regenerative braking energy is preferentially transferred to the adjacent power supply arm through the bidirectional energy financing device according to the traction load status; path 2 indicates that the remaining regenerative braking energy is allocated to the energy storage device for storage according to the remaining power state SOC adaptive algorithm of the energy storage device; path 3 indicates that the grid-connected energy feeding device is triggered to perform reverse feeding.

[0076] In step (2), a cost function with power supply security as the core is constructed, and the cost function is:

[0077] ;

[0078] in, is the cost function coefficient, Respectively represent the voltage stability cost item, the current limit cost item, and the fault recovery capability cost item;

[0079] The voltage stability cost term is used to penalize the traction network voltage from deviating from the target voltage value:

[0080] ;

[0081] in, Indicates the traction network i The actual voltage of each node, Indicates the target voltage value of the traction network. Indicates the number of traction network nodes;

[0082] The current limit penalty is used to penalize the traction network current exceeding the rated current value:

[0083] ;

[0084] in, Indicates the traction network i The actual current of each node, Indicates the rated current value;

[0085] The fault recovery capability cost item is:

[0086] ;

[0087] in, Indicates the traction power supply system l The recovery time at each fault, Indicates the l The influence coefficient of each fault location, Indicates the number of faults in the traction power supply system.

[0088] In addition, the method of the dynamic coupling prediction data model based on multiple state variables established in step (3) is:

[0089] ;

[0090] in, represents the sampling time; M Represents the prediction step size, which is a positive integer; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. M A vector of step-forward prediction value vectors; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time m step-forward prediction vector, ; , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time m step-forward prediction value; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; , represents the identity matrix with 3 rows and 3 columns, is the time-varying gradient matrix, , for The time-varying gradient submatrix at the sampling moment; Represented by vector , , , The vector formed, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction increment vector, The energy distribution coefficient is The sampling time Step forward prediction increment value, for ,when When , they represent the energy distribution coefficients of the energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time The step-forward prediction increment value, ; , , Respectively represent the energy distribution coefficients of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction value;

[0091] In step (3), based on the dynamic coupling prediction data model, a method for designing a dynamic prediction algorithm considering energy allocation priority is as follows:

[0092] ;

[0093] in, The expansion vector representing the forward prediction vector of the energy allocation coefficient is composed of the vector , , The vector formed, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction vector, ; is the prediction gain matrix; The expansion vector representing the forward prediction error difference vector is composed of the vector , , , , The vector formed, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forward prediction error vector, Represents a vector The first-order backward difference of , It is an adjustable time window parameter, which is generally a positive integer within 5; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price status of the grid M A vector of step-forward prediction setpoint vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forecast setpoint vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction set value; is the priority gain coefficient; , Represents the identity matrix with 3 rows and 3 columns; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; is the priority function vector, represents the priority vector, , , Respectively represent the priorities of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device;

[0094] extract in , , , , They are The distribution coefficients of the bidirectional energy integration device, energy storage device, and grid-connected energy feedback device at the sampling time.

[0095] Furthermore, in step (3), based on the dynamic coupling prediction data model, the estimation algorithm for designing the time-varying gradient matrix and the prediction gain matrix includes:

[0096] design Time-varying gradient submatrix at the sampling moment When the estimation algorithm is used, the optimization goal is , where the symbol Represents the minimization function About variables In this process, the cost function Minimization as a constraint:

[0097] ;

[0098] in, Represents a vector The first-order backward difference of Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The historical increment vector at the sampling moment, The energy distribution coefficient is The historical increment value at the sampling time, for ,when When , they represent the energy distribution coefficients of the energy fusion device, energy storage device, and grid-connected energy feeding device. Historical incremental value at the sampling moment; is the step size factor, is the penalty factor, is the two-norm;

[0099] Design time-varying gradient submatrix , , When the estimation algorithm

[0100] ;

[0101] in, , Represented by the matrix , , The vector formed, express The time-varying gradient submatrix at the sampling moment, , A positive integer between 3 and 5;

[0102] is an intermediate variable, and its calculation formula is:

[0103] ;

[0104] , Represented by the matrix , , The vector formed, express The time-varying gradient submatrix at the sampling moment; is a constant;

[0105] Based on the dynamic coupling prediction data model, the prediction gain matrix is ​​designed When the estimation algorithm

[0106] ;

[0107] in, , is a constant, is a one-step forward differential, and ; represents one-step forward error; Represents the dynamic coupling prediction data model in step (3); Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. M A vector of step-forward prediction setpoint vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forecast setpoint vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction set value; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. M A vector of step-forward prediction value vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-wise prediction value vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction value; Represents the prediction gain matrix in The value at the sampling moment; The expansion vector representing the forward prediction error difference vector is composed of the vector , , , , The vector formed, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forward prediction error vector, Represents a vector The first-order backward difference of ; , Represents the identity matrix with 3 rows and 3 columns; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price status of the grid M A vector of step-forward prediction setpoint vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forecast setpoint vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction set value; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; , represents the identity matrix with 3 rows and 3 columns, is the time-varying gradient matrix, , for The time-varying gradient submatrix at the sampling moment;

[0108] Represented by vector , , , The vector formed, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction increment vector, The energy distribution coefficient is The sampling time Step forward prediction increment value, for ,when When , they represent the energy distribution coefficients of the energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time The step-forward prediction increment value, ; , , Respectively represent the energy distribution coefficients of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time Step-forward prediction value.

[0109] The SOC adaptive algorithm of the energy storage device in the energy distribution path 2 includes the following steps:

[0110] When it is detected that the remaining power state SOC of the energy storage device is greater than or equal to the first threshold, the energy storage device allocation coefficient is reduced. The first threshold is used to prevent energy storage overcharging, and its value is based on the safety window parameter calibrated by the energy storage device;

[0111] When the remaining charge state SOC of the energy storage device is less than or equal to a second threshold, energy is prohibited from being released from the energy storage device; the second threshold is used to avoid over-discharge, and its value is based on a safety window parameter calibrated by the energy storage device.

[0112] In detail, the priority function vector The definition includes the following steps:

[0113] First, assign priorities to bidirectional energy transfer devices, energy storage devices, and grid-connected energy feeding devices. , , 1 is the highest priority, 2 is the medium priority, and 3 is the lowest priority;

[0114] Secondly, define the priority functions for the bidirectional energy transfer device, energy storage device, and grid-connected energy feeding device respectively. ,in , used to indicate that higher priorities obtain larger priority function values;

[0115] Finally, the priority function vector is formed ;

[0116] In step (4), the energy distribution coefficient between the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device is revised based on the dynamic prediction algorithm. The rolling revision refers to the real-time update of the dynamic prediction algorithm. The prediction gain matrix in ,vector and the priority function vector .

[0117] It should be noted that after the energy storage device obtains energy, its power supply uses are multifaceted. Figure 3 A power supply system for the energy storage device provided in this embodiment; Figure 4 This is a schematic diagram of a multi-purpose power supply method for an energy storage device provided in this embodiment. The specific multi-purpose power supply method includes the following steps:

[0118] (a) Setting a power supply start threshold ESS_start and a power supply switching threshold ESS_switch of the energy storage device, where ESS_switch>ESS_start; and monitoring the remaining charge state SOC of the energy storage device;

[0119] (b) When the remaining charge state of the energy storage device SOC ≥ ESS_start, the power supply is started in the following priority order:

[0120] Highest priority: auxiliary monitoring system, including environmental monitoring subsystem and security subsystem;

[0121] Secondary priority: catenary intelligent monitoring system, including the electrical connection clamp monitoring unit, insulator monitoring unit and positioning device monitoring unit;

[0122] The third priority: station energy management and control systems, including electronic systems for heating and air conditioning and commercial use;

[0123] Among them, for power supply objects of secondary priority and third priority, dynamic coefficient allocation is implemented according to the power supply distance, and power-consuming equipment with a distance from the energy storage device ≤ the set radius will be given priority in obtaining power supply;

[0124] If the remaining state of charge SOC of the energy storage device is less than ESS_start, return to step (a) and continue to monitor the remaining state of charge SOC of the energy storage device;

[0125] (c) When the remaining power state SOC of the energy storage device is greater than or equal to ESS_switch, the system automatically switches back to the original power supply mode. If the remaining power state SOC of the energy storage device is less than ESS_switch, the system returns to step (b) to determine the relationship between the remaining power state SOC of the energy storage device and ESS_start. Example 2

[0126] Figure 2 This is a schematic diagram of the module connections of the energy dynamic prediction and distribution system for railway power supply security provided by this embodiment. This embodiment provides an energy dynamic prediction and distribution system for railway power supply security, including:

[0127] The data monitoring module is used to continuously collect and monitor the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and grid electricity price status in the railway traction power supply system;

[0128] A safety optimization decision module is used to construct a cost function centered on power supply safety as a constraint condition for determining the energy distribution coefficient between the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device. The cost function includes factors such as voltage stability, current limit, and fault recovery capability.

[0129] A dynamic prediction calculation module is used to establish a dynamic coupling prediction data model based on multiple state variables, wherein the state variables include the traction load state, the remaining charge state (SOC) of the energy storage device, and the grid electricity price state. The dynamic coupling prediction data model includes a time-varying gradient matrix. Based on the dynamic coupling prediction data model, a dynamic prediction algorithm that considers energy allocation priority is determined to obtain the energy allocation coefficient between the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device:

[0130] ;

[0131] in, represents the expansion vector of the forward prediction vector of the energy allocation coefficient, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction vector, ; is the prediction gain matrix; represents the expansion vector of the forward prediction error difference vector; is the priority gain coefficient; , Represents the identity matrix with 3 rows and 3 columns; is the priority function vector;

[0132] extract in , , , , They are The allocation coefficients of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time;

[0133] Based on the dynamic coupling prediction data model, designing an estimation algorithm for the time-varying gradient matrix and the prediction gain matrix;

[0134] A coefficient allocation strategy module, configured to, based on the calculation results of the dynamic prediction algorithm, roll-over correct the energy allocation coefficients among the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device;

[0135] A coefficient allocation execution module, configured to select an energy allocation path based on the allocation coefficient;

[0136] Furthermore, the present invention adopts the following technical solutions:

[0137] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for dynamic energy prediction and allocation for railway power supply security.

[0138] Furthermore, the present invention adopts the following technical solutions:

[0139] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for dynamic energy prediction and allocation for railway power supply security as described above is implemented.

[0140] From the above description of the embodiments, it will be clear to those skilled in the art that the facilities of the present invention can be implemented using software plus the necessary general-purpose hardware platforms. Embodiments of the present invention can be implemented using existing processors, or by dedicated processors used in appropriate systems for this or other purposes, or by hardwired systems. Embodiments of the present invention also include non-transitory computer-readable storage media, including machine-readable media for carrying or having stored thereon machine-executable instructions or data structures. Such machine-readable media can be any available medium that can be accessed by a general-purpose or special-purpose computer or other machine with a processor. For example, such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store the required program code in the form of machine-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communications connection (hardwired, wireless, or a combination of hardwired and wireless), such connection is also considered a machine-readable medium.

[0141] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for dynamic energy prediction and allocation for railway power supply security, characterized in that: The device for dynamic energy prediction and allocation includes a bidirectional energy integration device, an energy storage device, and a grid-connected energy feeding device. The energy storage device uses the energy obtained to power the auxiliary monitoring system, the contact network intelligent monitoring system, and the station energy management and control system. The method comprises the following steps: Step (1): continuously collecting and monitoring the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and power price status of the power grid in the railway traction power supply system; Step (2): Constructing a cost function with power supply security as the core, as a constraint condition for determining the energy distribution coefficient between the bidirectional energy financing device, the energy storage device and the grid-connected energy feeding device, wherein the cost function includes factors such as voltage stability, current limit and fault recovery capability; Step (3): establishing a dynamic coupling prediction data model based on multiple state variables, wherein the state variables include the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state, and the dynamic coupling prediction data model includes a time-varying gradient matrix; based on the dynamic coupling prediction data model, determining a dynamic prediction algorithm that considers the energy allocation priority; based on the dynamic coupling prediction data model, designing an estimation algorithm for the time-varying gradient matrix and the prediction gain matrix; Among them, the dynamic prediction algorithm is: ； in, represents the expansion vector of the forward prediction vector of the energy allocation coefficient, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction vector, ; is the prediction gain matrix; represents the expansion vector of the forward prediction error difference vector; is the priority gain coefficient; , Represents the identity matrix with 3 rows and 3 columns; is the priority function vector; extract in , , , , They are The allocation coefficients of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; Step (4): Based on the calculation result of the dynamic prediction algorithm, the energy distribution coefficient between the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device is revised in a rolling manner, and the energy distribution path is selected based on the distribution coefficient; The dynamic coupling prediction data model based on multiple state variables established in step (3) is: ； in, represents the sampling time; M Represents the prediction step size, which is a positive integer; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. M A vector of step-forward prediction value vectors; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time m step-forward prediction value vector, ; , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time m step-forward prediction value; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; , represents the identity matrix with 3 rows and 3 columns, is the time-varying gradient matrix, , for The time-varying gradient submatrix at the sampling moment; Represented by vector , , , The vector formed, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction increment vector, The energy distribution coefficient is The sampling time Step forward prediction increment value, for ,when When , they represent the energy distribution coefficients of the energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time The step-forward prediction increment value, ; , , Respectively represent the energy distribution coefficients of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time Step-forward prediction value.

2. The method for dynamic energy prediction and allocation for railway power supply security according to claim 1, characterized in that: In step (3), the prediction gain matrix is ​​designed based on the dynamic coupling prediction data model. The estimation algorithm is: ； in, , is a constant, is a one-step forward differential, and ; represents one-step forward error; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. M A vector of step-forward prediction setpoint vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forecast setpoint vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction set value; The expansion vector representing the forward prediction error difference vector is composed of the vector , , , , The vector formed, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forward prediction error vector, Represents a vector The first-order backward difference of ; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price status of the grid M A vector of step-forward prediction setpoint vectors, Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time step-forecast setpoint vector, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time Step prediction set value.

3. The method for dynamic energy prediction and allocation for railway power supply security according to claim 1, characterized in that: In step (3), based on the dynamic coupling prediction data model, the estimation algorithm of the time-varying gradient matrix is ​​designed, which includes: design Time-varying gradient submatrix at the sampling moment When the estimation algorithm is used, the optimization goal is , where the symbol Represents the minimization function About variables Take the value and minimize the cost function as a constraint: ； in, Represents a vector The first-order backward difference of Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The historical increment vector at the sampling moment, The energy distribution coefficient is The historical increment value at the sampling time, for ,when When , they represent the energy distribution coefficients of the energy fusion device, energy storage device, and grid-connected energy feeding device. Historical incremental value at the sampling moment; is the step size factor, is the penalty factor, is the two-norm; Design time-varying gradient submatrix , , When the estimation algorithm is: ； in, , Represented by the matrix , , The vector formed, express The time-varying gradient submatrix at the sampling moment, , is a positive integer between 3 and 5; is an intermediate variable, and its calculation formula is: ； , Represented by the matrix , , The vector formed, express The time-varying gradient submatrix at the sampling moment; is a constant.

4. The method for dynamic energy prediction and allocation for railway power supply security according to claim 1, characterized in that: The method for selecting the energy distribution path based on the distribution coefficient in step (4) is: like Not 0, path 1 is executed first; if path 1 does not completely absorb energy and If it is not 0, execute path 2; If there is still energy remaining and If it is not 0, execute path 3; Among them, path 1 indicates that the regenerative braking energy is preferentially transferred to the adjacent power supply arm through the bidirectional energy financing device according to the traction load status; path 2 indicates that the remaining regenerative braking energy is allocated to the energy storage device for storage according to the remaining power state SOC adaptive algorithm of the energy storage device; path 3 indicates triggering the grid-connected energy feeding device for reverse feeding.

5. The method for dynamic energy prediction and allocation for railway power supply security according to claim 1, characterized in that: In step (2), a cost function with power supply security as the core is constructed, and the cost function is: ； in, is the cost function coefficient, They represent the voltage stability cost, current limit cost, and fault recovery capability cost respectively.

6. The method for dynamic energy prediction and allocation for railway power supply security according to claim 4, characterized in that: The SOC adaptive algorithm of the energy storage device in the energy distribution path 2 includes the following steps: When it is detected that the remaining power state SOC of the energy storage device is greater than or equal to a first threshold, reducing the value of the energy storage device allocation coefficient; When the remaining state of charge SOC of the energy storage device is less than or equal to a second threshold, energy is prohibited from being released from the energy storage device.

7. The energy dynamic prediction and distribution system for railway power supply security is characterized by: include: The data monitoring module is used to continuously collect and monitor the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and grid electricity price status in the railway traction power supply system; A safety optimization decision module is used to construct a cost function centered on power supply safety as a constraint to determine the energy allocation coefficient between the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device. The cost function includes factors such as voltage stability, current limit, and fault recovery capability. A dynamic prediction calculation module is used to establish a dynamic coupling prediction data model based on multiple state variables, wherein the state variables include the traction load state, the remaining charge state (SOC) of the energy storage device, and the grid electricity price state. The dynamic coupling prediction data model includes a time-varying gradient matrix. Based on the dynamic coupling prediction data model, a dynamic prediction algorithm that considers energy allocation priority is determined to obtain the energy allocation coefficient between the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device: ； in, represents the expansion vector of the forward prediction vector of the energy allocation coefficient, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction vector, ; is the prediction gain matrix; represents the expansion vector of the forward prediction error difference vector; is the priority gain coefficient; , Represents the identity matrix with 3 rows and 3 columns; is the priority function vector; extract in , , , , They are The allocation coefficients of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; Based on the dynamic coupling prediction data model, designing an estimation algorithm for the time-varying gradient matrix and the prediction gain matrix; A coefficient allocation strategy module, configured to, based on the calculation results of the dynamic prediction algorithm, roll-over correct the energy allocation coefficients among the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device; A coefficient allocation execution module, configured to select an energy allocation path based on the allocation coefficient; The dynamic coupling prediction data model based on multiple state variables is established as follows: ； in, represents the sampling time; M Represents the prediction step size, which is a positive integer; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. M A vector of step-forward prediction value vectors; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status. The sampling time m step-forward prediction vector, ; , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The sampling time m step-forward prediction value; Indicates the traction load status, the remaining power state SOC of the energy storage device, and the power price of the grid. A vector of values ​​at the sampling time, , , They represent the traction load status, the remaining power state SOC of the energy storage device, and the grid electricity price status respectively. The value at the sampling moment; , represents the identity matrix with 3 rows and 3 columns, is the time-varying gradient matrix, , for The time-varying gradient submatrix at the sampling moment; Represented by vector , , , The vector formed, Indicates the energy distribution coefficient of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time step-forward prediction increment vector, The energy distribution coefficient is The sampling time Step forward prediction increment value, for ,when When , they represent the energy distribution coefficients of the energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time The step-forward prediction increment value, ; , , Respectively represent the energy distribution coefficients of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device. The sampling time Step-forward prediction value.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for dynamic energy prediction and allocation for railway power supply security as described in any one of claims 1 to 6 is implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for dynamic energy prediction and allocation for railway power supply security as described in any one of claims 1 to 6 is implemented.

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