CN117937487B - Distributed energy management method for traction power supply system of heavy haul railway - Google Patents
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Abstract
A distributed energy management method for a heavy haul railway traction power supply system, comprising: acquiring traction load process data of a traction station in a traction power supply system of a heavy haul railway; establishing an objective function oriented to a traction station, and constructing intelligent soft switch operation characteristic constraint; taking an intelligent soft switch of the subarea as a decoupling point, introducing a power flow decoupling operator, and carrying out power flow solution of full-line traction to obtain a decoupled objective function; establishing a Lagrangian function of the decoupling points, and concatenating the decoupled objective functions to form a new optimized objective function, and solving the new optimized objective function by using an alternate direction multiplier method to obtain a distributed energy scheduling model; and solving the distributed energy scheduling model to obtain an intelligent soft switch of the traction station and an intelligent soft switch optimal power flow control scheme of the partition station. According to the invention, a plurality of SOPs along the heavy-load railway actively and orderly participate in the flow distribution regulation of the traction network by a distributed energy management method, and the energy requirement of the heavy-load locomotive is responded.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a distributed energy management method of a traction power supply system of a heavy haul railway.
Background
The heavy haul railway is an important component of railway freight, and has the characteristics of high efficiency, low cost and large running capacity. The traction power supply system is the only power source of the heavy-duty railway and is an important guarantee for meeting the continuous increase of the heavy-duty transportation demand, but the electric split phase divides the traction power supply system into a plurality of independent power supply units, so that how to improve the power supply capacity and the energy utilization efficiency, increase the driving density and realize the green high-efficiency operation becomes the focus of research and development of the technicians in the field.
Power electronic converter control technology represented by Soft Open Point (SOP) becomes a key to solve this problem. SOP is a kind of power flow control device formed by inverter, rectifier and DC bus, it can realize the electric communication with traction network of different phases on both sides of traction station or partition station electricity split phase, make the locomotive energy structure change from single uncontrollable power source based on transformer to the flexible power source of multisource, will help to cut peaks and fill valleys, raise the safe power supply margin, save the power supply resource, reduce the basic electric charge. However, how to let a plurality of SOPs along a heavy haul railway actively and orderly participate in the adjustment of the power flow distribution of a traction network, and respond to the energy requirement of the heavy haul locomotive is a key technical problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a distributed energy management method for a traction power supply system of a heavy-load railway, which enables a plurality of SOPs along the heavy-load railway to actively and orderly participate in the flow distribution regulation of a traction network and respond to the energy requirement of the heavy-load locomotive.
A distributed energy management method for a heavy haul railway traction power supply system, comprising:
step S1, acquiring traction load process data of a traction station in a traction power supply system of a heavy haul railway;
step S2, based on the electric quantity relation of the traction station, taking the minimum electricity rate and the minimum electricity rate of the required quantity under the two electricity price making mechanisms as optimization targets, taking the intelligent soft switch as an optimization target, establishing an objective function facing the traction station, and constructing intelligent soft switch operation characteristic constraint according to the position relation of the traction station and the partition station;
step S3, based on traction load process data of adjacent traction stations, intelligent soft switches of the partition stations are used as decoupling points, a power flow decoupling operator is introduced, and power flow solving of full-line traction stations is carried out, so that a decoupled objective function is obtained;
Step S4, a Lagrangian function of a decoupling point is established, the decoupled objective functions in the step S4 are connected in series to form a new optimized objective function, and the new optimized objective function is solved by using an alternate direction multiplier method to obtain a distributed energy scheduling model;
and S5, taking the traction load process data obtained in the step S1 as input, solving the distributed energy scheduling model obtained in the step S4 to obtain an intelligent soft switch of a traction station and an intelligent soft switch optimal power flow control scheme of a partition station, and finishing energy management optimization of a traction power supply system of the heavy haul railway.
The distributed energy management method for the heavy-duty railway traction power supply system provided by the invention has the following beneficial effects:
(1) According to the invention, a plurality of SOPs are controlled to actively and orderly participate in the power flow distribution of the traction network, and the energy demand of the heavy-load locomotive is responded, so that peak clipping and valley filling are realized, the safety power supply margin is improved, the power supply resources are saved, and the basic electric charge is reduced;
(2) Based on the coupling relation between different traction stations in the traction power supply system, the invention adopts the tide decoupling operator to realize the electrical decoupling between adjacent traction stations, distributes the communication pressure faced by the original centralized scheduling to each traction station for autonomous decision, and reduces the requirement on the real-time performance of energy management communication;
(3) The distributed energy scheduling model provided by the invention can realize the optimal scheduling convergence of the whole system only through interaction of adjacent traction, thereby realizing the distributed optimal scheduling of the heavy haul railway traction.
Drawings
FIG. 1 is a schematic flow chart of a distributed energy management method of a heavy haul railway traction power supply system of the present invention;
Fig. 2 is a schematic structural diagram of a heavy-duty railway traction power supply system in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The distributed energy management method for the heavy haul railway traction power supply system provided by the embodiment of the invention can be realized by the following steps: (1) enabling the flow of power between the independent power supply units to be controllable and adjustable; (2) The load peak-valley difference is reduced by improving the utilization rate of the regenerated energy, and the electricity cost is reduced; (3) improving the power supply capacity of the power supply unit.
Referring to fig. 1, the distributed energy management method for the traction power supply system of the heavy haul railway specifically includes steps S1 to S5.
Step S1, traction load process data of a traction station in a traction power supply system of a heavy haul railway are obtained.
In this embodiment, a schematic structural diagram of a traction power supply system for a heavy haul railway is shown in fig. 2, in whichThe power supply interval is from the 1 st traction station to the 1 st partition station power supply interval,/>The power supply interval is from the 1 st partition to the 2 nd traction partition, and the upper corner mark/>, in the variablesRepresentation/>-Electrical quantity in power supply section, upper corner mark in variable/>Correspondence/>-An electrical quantity of the power supply section.
Taking the example of FIG. 2 as an illustration, the traction load process data includes the traction stations-Power supply interval and/>Active power data per minute over the power supply interval, which data can be acquired by the integrated automation system for traction.
And S2, based on the electric quantity relation of the traction station, taking the minimum electricity rate and the minimum electricity rate of the required quantity under the two electricity price making mechanisms as optimization targets, taking the intelligent soft switch as an optimization target, establishing an objective function facing the traction station, and constructing the operation characteristic constraint of the intelligent soft switch according to the position relation of the traction station and the partition station.
In step S2, an objective function is established for the traction stationThe expression of (2) is:
；
；
Wherein, Is the unit price of electricity charge,/>Is the unit price of electricity selling,/>In order to obtain the unit price of the electricity fee,Power purchased from grid for traction,/>Power fed back to the grid for traction,/>The power is the required quantity of the traction substation; /(I)For the whole day time length, 1440 is taken in this embodiment; /(I)Time granularity, in minutes. The upper corner mark grid in the formula represents electricity purchasing to the power grid of the traction substation, fed represents electricity selling to the power grid of the traction substation, and dem is the required quantity.
In step S2, the intelligent soft switch operation characteristic constraint of the traction station comprises equality constraint, inequality constraint and coupling constraint;
The expression of the equality constraint is:
；
Wherein, Intelligent soft switch in/>, which is connected by the nth seat partition and the mth seat tractionActive power flow in the power supply section,/>Intelligent soft switch connected for n-th partition and m+1-th tractionActive power flow in the power supply section,/>Intelligent soft switch for mth seat traction station/>Power in the power supply section,/>Intelligent soft switch for mth seat traction station/>Power in the power supply section,/>For traction load at/>Traction power over the power supply interval,/>For traction load at/>Braking power over the power supply interval,/>For traction load at/>Traction power over the power supply interval,/>For traction load at/>Braking power over the supply interval.
The above equation constraint describes the power balance relationship between the traction power supply section and the traction power supply section.
The expression of the inequality constraint is:
；
Wherein, Rated capacity of intelligent soft switch for partition,/>The rated capacity of the intelligent soft switch for traction.
The inequality constraint described above describes that the operation of the SOP is limited by its rated capacity.
The expression of the coupling constraint is:
；
Wherein, Takes the maximum value as/>；/>Representing the binary variable of the mth seat traction station.
The coupling constraints described above describe the electricity purchase/sale strategy between the traction house and the grid.
The expression of the intelligent soft switch operation characteristic constraint of the partition is as follows:
。
and step S3, based on traction load process data of adjacent traction stations, taking the intelligent soft switch of the partition station as a decoupling point, introducing a power flow decoupling operator, and carrying out power flow solving of full-line traction stations to obtain a decoupled objective function.
In step S3, the expression of the power flow decoupling operator is:
；
Wherein, For the mth seat traction house/>-A power flow decoupling operator of the intelligent soft switch where the nth partition is located on the power supply interval;
in step S3, the performing the solution of the power flow for full-line traction specifically includes:
And (3) transmitting opposite side power flow information to two adjacent traction stations through an intelligent soft switch of the partition station, updating a power flow decoupling operator, and obtaining a global optimal operation plan when iteratively solving until convergence.
And S4, establishing a Lagrangian function of the decoupling point, and concatenating the decoupled objective functions in the step S4 to form a new optimized objective function, and solving the new optimized objective function by using an alternate direction multiplier method to obtain the distributed energy scheduling model.
In step S4, the solving the new optimization objective function by using the alternate direction multiplier method specifically includes:
Step S4.1, constructing an extended Lagrangian function of the flow decoupling operator drawn by the mth seat in the kth iteration based on the expression of the flow decoupling operator ；
；
Wherein W is the total number of towns; Lagrangian multipliers for the nth bin at the kth iteration; /(I) For/>, at the kth iteration；/>For/>, at the kth iteration；Penalty factor at the kth iteration; /(I)Performing L-2 norm operation;
step S4.2, a distributed energy management model of a heavy-duty railway traction power supply system during the kth iteration is established, and the distributed energy management model is used for controlling a plurality of intelligent soft switches to actively and orderly participate in traction network power flow distribution, responding to the energy requirement of a heavy-duty locomotive, realizing peak clipping and valley filling, improving the safety power supply margin, saving power supply resources and reducing basic electric charge, wherein the standard form of the distributed energy management model is as follows:
；
the limiting conditions are:
；
Wherein, For/>, at the kth iteration；/>As a continuous decision variable at the kth iteration,，/>For/>, at the kth iteration，/>For/>, at the kth iteration，/>At the kth iteration，/>For/>, at the kth iteration，/>At the kth iteration；/>Is a binary decision variable at the kth iteration,/>；/>Representing distributed energy management model concerns/>And/>Is a feasible region of optimization; matrix D, B, E, H,/>、/>、/>Is a constrained real array; /(I)Corresponding to the equality constraint; /(I)Corresponding to the inequality constraint; Corresponding to the coupling constraint;
Step S4.3, calculating an original residual and a dual residual after each iteration of the distributed energy management model, wherein the original residual is the original residual in the (k+1) th iteration Dual residual with k+1st iteration/>The expression of (2) is:
；
；
Wherein, />, At the k+1st iteration；/>For the k+1st iteration/>；
Step S4.4, updating、/>、/>、/>，/>Representing the continuous decision variable at the k+1st iteration,/>Represents the lagrangian multiplier for the nth bin at the k +1 iteration,Representing penalty factors at the k+1th iteration; the update rule is: /(I)Is updated according to the first formula of the method,Update according to the second formula,/>Update according to the third formula,/>Updating according to a fourth formula;
the first formula is:
；
Wherein, Representing a continuous decision variable;
the second formula is:
；
Wherein, Represents/>, at the k+1st iteration；
The third formula is:
；
the fourth formula is:
；
Step S4.4, when And/>Stopping iterating the output result when the following formula is satisfied, otherwise, returning to the step S4.1:
；
Wherein, Indicating convergence accuracy.
And S5, taking the traction load process data obtained in the step S1 as input, solving the distributed energy scheduling model obtained in the step S4 to obtain an intelligent soft switch of a traction station and an intelligent soft switch optimal power flow control scheme of a partition station, and finishing energy management optimization of a traction power supply system of the heavy haul railway.
In this embodiment, the specific process of solving the distributed energy scheduling model obtained in step S4 is:
Step S5.1, initializing system parameters, initializing k=1 for iteration times, and setting a penalty factor in the 1 st iteration Lagrangian multiplier/>And convergence accuracy/>Let the tide decoupling operator at the 1 st iteration；
Step S5.2, iterative calculation, solving the distributed energy management model of the 1 st traction station and the 2 nd traction stationObtain/>And/>Updating a power flow decoupling operatorThe number of iterations k=k+1;
step S5.3, convergence judgment, calculation of original residual error And dual residual/>Judging whether convergence accuracy is met, and stopping calculation if the convergence accuracy is met; otherwise, continuing the solving process;
Step S5.4, parameter updating, and penalty factor updating And Lagrangian multiplier/>And jumping to the step S5.2 iteration;
and S5.5, outputting a result, and outputting an SOP (SOP-source-of-traction) and SOP (SOP-source-of-partition) optimal power flow control scheme if the residual error meets convergence accuracy, thereby completing energy management optimization of the traction power supply system of the heavy haul railway.
In summary, the distributed energy management method for the traction power supply system of the heavy haul railway has the following beneficial effects:
(1) According to the invention, a plurality of SOPs are controlled to actively and orderly participate in the power flow distribution of the traction network, and the energy demand of the heavy-load locomotive is responded, so that peak clipping and valley filling are realized, the safety power supply margin is improved, the power supply resources are saved, and the basic electric charge is reduced;
(2) Based on the coupling relation between different traction stations in the traction power supply system, the invention adopts the tide decoupling operator to realize the electrical decoupling between adjacent traction stations, distributes the communication pressure faced by the original centralized scheduling to each traction station for autonomous decision, and reduces the requirement on the real-time performance of energy management communication;
(3) The distributed energy scheduling model provided by the invention can realize the optimal scheduling convergence of the whole system only through interaction of adjacent traction, thereby realizing the distributed optimal scheduling of the heavy haul railway traction.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (4)
1. A method for distributed energy management of a heavy haul railway traction power supply system, comprising:
step S1, acquiring traction load process data of a traction station in a traction power supply system of a heavy haul railway;
step S2, based on the electric quantity relation of the traction station, taking the minimum electricity rate and the minimum electricity rate of the required quantity under the two electricity price making mechanisms as optimization targets, taking the intelligent soft switch as an optimization target, establishing an objective function facing the traction station, and constructing intelligent soft switch operation characteristic constraint according to the position relation of the traction station and the partition station;
step S3, based on traction load process data of adjacent traction stations, intelligent soft switches of the partition stations are used as decoupling points, a power flow decoupling operator is introduced, and power flow solving of full-line traction stations is carried out, so that a decoupled objective function is obtained;
Step S4, a Lagrangian function of a decoupling point is established, the decoupled objective functions in the step S3 are connected in series to form a new optimized objective function, and the new optimized objective function is solved by using an alternate direction multiplier method to obtain a distributed energy scheduling model;
S5, taking the traction load process data obtained in the step S1 as input, solving the distributed energy scheduling model obtained in the step S4 to obtain an intelligent soft switch of a traction station and an intelligent soft switch optimal power flow control scheme of a partition station, and finishing energy management optimization of a traction power supply system of the heavy haul railway;
in step S2, the established traction-oriented objective function The expression of (2) is:
；
；
Wherein, Is the unit price of electricity charge,/>Is the unit price of electricity selling,/>Is the unit price of electricity chargePower purchased from grid for traction,/>Power fed back to the grid for traction,/>For the power required by traction substation,/>For the whole day length,/>Is the granularity of time.
2. The method of distributed energy management for a heavy haul railway traction power supply system of claim 1, wherein in step S2, the intelligent soft switching operating characteristic constraints of the haul site include equality constraints, inequality constraints, and coupling constraints;
The expression of the equality constraint is:
；
Wherein, Intelligent soft switch in/>, which is connected by the nth seat partition and the mth seat tractionActive power flow in the power supply section,/>Intelligent soft switch connected for n-th partition and m+1-th tractionActive power flow in the power supply section,/>Intelligent soft switch for mth seat traction station/>Power in the power supply section,/>Intelligent soft switch for mth seat traction station/>Power in the power supply section,/>For traction load at/>Traction power over the power supply interval,/>For traction load at/>Braking power over the power supply interval,/>For traction load at/>Traction power over the power supply interval,/>For traction load at/>-Braking power over the power supply interval;
the expression of the inequality constraint is:
；
Wherein, Rated capacity of intelligent soft switch for partition,/>Rated capacity of the intelligent soft switch for traction;
The expression of the coupling constraint is:
；
Wherein, Takes the maximum value as/>；/>Representing a binary variable of an mth seat traction station;
the expression of the intelligent soft switch operation characteristic constraint of the partition is as follows:
。
3. The method for distributed energy management of a heavy haul railway traction power supply system according to claim 2, wherein in step S3, the expression of the power flow decoupling operator is:
；
Wherein, For the mth seat traction house/>-A power flow decoupling operator of the intelligent soft switch where the nth partition is located on the power supply interval;
in step S3, the performing the solution of the power flow for full-line traction specifically includes:
And (3) transmitting opposite side power flow information to two adjacent traction stations through an intelligent soft switch of the partition station, updating a power flow decoupling operator, and obtaining a global optimal operation plan when iteratively solving until convergence.
4. The distributed energy management method of a heavy haul railway traction power supply system according to claim 3, wherein in step S4, solving the new optimization objective function using the alternate direction multiplier method specifically comprises:
Step S4.1, constructing an extended Lagrangian function of the flow decoupling operator drawn by the mth seat in the kth iteration based on the expression of the flow decoupling operator ；
；
Wherein W is the total number of towns; lagrangian multipliers for the nth bin at the kth iteration; for/>, at the kth iteration ；/>For/>, at the kth iteration；/>Penalty factor at the kth iteration; /(I)Performing L-2 norm operation;
Step S4.2, a distributed energy management model of a heavy-duty railway traction power supply system during the kth iteration is established and is used for controlling a plurality of intelligent soft switches to actively and orderly participate in traction network power flow distribution and responding to energy requirements of a heavy-duty locomotive, wherein the distributed energy management model has the standard form that:
；
the limiting conditions are:
；
Wherein, For/>, at the kth iteration；/>As a continuous decision variable at the kth iteration,，/>For/>, at the kth iteration，/>For/>, at the kth iteration，/>At the kth iteration，/>For/>, at the kth iteration，/>At the kth iteration；/>Is a binary decision variable at the kth iteration,/>；/>Representing distributed energy management model concerns/>And/>Is a feasible region of optimization; matrix D, B, E, H,/>、/>、/>Is a constrained real array; /(I)Corresponding to the equality constraint; /(I)Corresponding to the inequality constraint; Corresponding to the coupling constraint;
Step S4.3, calculating an original residual and a dual residual after each iteration of the distributed energy management model, wherein the original residual is the original residual in the (k+1) th iteration Dual residual with k+1st iteration/>The expression of (2) is:
；
；
Wherein, />, At the k+1st iteration；/>For the k+1st iteration/>；
Step S4.4, updating、/>、/>、/>，/>Representing the continuous decision variable at the k+1st iteration,/>Represents the Lagrangian multiplier in the nth bin at the k+1st iteration,/>Representing penalty factors at the k+1th iteration; the update rule is: /(I)Update according to the first formula,/>Update according to the second formula,/>Update according to the third formula,/>Updating according to a fourth formula;
the first formula is:
；
Wherein, Representing a continuous decision variable;
the second formula is:
；
Wherein, Represents/>, at the k+1st iteration；
The third formula is:
；
the fourth formula is:
；
Step S4.4, when And/>Stopping iterating the output result when the following formula is satisfied, otherwise, returning to the step S4.1:
；
Wherein, Indicating convergence accuracy.
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