A real-time energy optimization management method for heavy-duty electrified railway stations

Abstract

The present invention discloses a method for real-time energy optimization management of heavy-duty electrified railway stations, including: collecting real-time parameters of each part of the grid, storage, source, and vehicle in the coordinated power supply system of the heavy-duty electrified railway; establishing a multi-objective optimization function with the goal of minimizing the real-time operating cost of the system and the real-time three-phase voltage imbalance on the public power grid side of the traction substation; constructing the constraints of each part and the flow constraints of the system according to the real-time parameters of each part of the grid, storage, source, and vehicle in the system, and combining them to obtain multi-constraint optimization conditions; constructing a global optimization model of the coordinated power supply system, and simplifying it through a mode pre-positioning method; generating an energy optimization data packet according to the solution result of the optimization model, generating multiple power control instructions, and sending them to the corresponding execution device to complete energy optimization management. The present invention realizes the purpose of real-time and efficient management of energy in the coordinated power supply system of the heavy-duty electrified railway, and efficient utilization of new energy and regenerative braking energy.

Description

Technical Field

[0001] The invention relates to the technical field of electrified railways, and in particular to a real-time energy optimization management method for heavy-load electrified railway stations. Background Art

[0002] By the end of 2022, the total operating mileage of my country's electrified railways has exceeded 155,000 kilometers. However, the rapid development of electrified railways has promoted the growth of the national economy, while its energy consumption and energy conservation and emission reduction pressures are increasing day by day. At present, there are abundant new energy sources along the railway that need to be developed, and a large amount of regenerative braking energy generated by trains needs to be recovered. Introducing new energy power generation and energy storage devices on-site in electrified railways and building a "grid-source-storage-vehicle" coordinated power supply system have become important measures for electrified railway transportation to help achieve the "dual carbon" goal.

[0003] However, in the "grid-source-storage-vehicle" collaborative power supply system, the train traction load has large power and strong volatility, and the source-load temporal and spatial matching is difficult. In order to ensure reliable power supply to the train and the safe operation of the system as a whole, there is a great demand for real-time control of site energy. In addition, the "grid-source-storage-vehicle" collaborative power supply system contains many elements, and the energy transmission of each part has a counterproductive effect on the overall operation of the system. In order to give full play to the advantages of multi-source collaborative control of the system, the global optimization control technology of site energy needs to be broken through.

[0004] To achieve real-time and efficient energy management, it is necessary to have a reasonable energy control strategy. At present, the energy control strategies for electrified railway traction power supply systems mainly include rule control and optimization control. Although rule control is easy to implement and has strong real-time control capabilities, it does not take into account the differences in losses caused by energy transmission in each part and the costs of operation and maintenance, and cannot give full play to the flexibility of multi-source power supply in the collaborative power supply system. The overall optimization operation effect is weaker than optimization control. However, most of the existing optimization control strategies are day-ahead energy control based on train operation diagrams, that is, energy control methods based on "rules", which are difficult to apply to heavy-load electrified railways without day-ahead train load information.

[0005] Therefore, how to achieve real-time and efficient station energy management and control on heavy-duty electrified railways remains an urgent problem that needs to be solved. Summary of the invention

[0006] Based on this, an embodiment of the present invention provides a real-time energy optimization management method for heavy-duty electrified railway stations to solve the problems existing in the prior art such as the difficulty in real-time energy management of heavy-duty electrified railway stations and the low utilization rate of new energy and regenerative braking energy.

[0007] To achieve the above object, an embodiment of the present invention provides a method for real-time energy optimization management of a heavy-duty electrified railway station, comprising:

[0008] Collect real-time parameters of the grid, storage, source and vehicle of the coordinated power supply system of heavy-duty electrified railways;

[0009] A multi-objective optimization function is established with the goal of minimizing the real-time operating cost of the system and minimizing the real-time three-phase voltage imbalance on the public grid side of the traction substation.

[0010] According to the real-time parameters of the grid, storage, source and vehicle of the heavy-duty electrified railway coordinated power supply system, the constraints of the grid, storage, source and vehicle and the power flow constraints of the entire system are constructed, and the multi-constraint optimization conditions are obtained by combining them;

[0011] According to the multi-objective optimization function and the multi-constraint optimization conditions, a global optimization model of the collaborative power supply system is constructed, and simplified by a mode pre-positioning method;

[0012] Solving the simplified global optimization model of the collaborative power supply system, and generating an energy optimization data packet according to the solution result;

[0013] A plurality of power control instructions are generated according to the energy optimization data packet and sent to corresponding execution devices in the heavy-load electrified railway collaborative power supply system to complete energy optimization management.

[0014] The above-mentioned real-time energy optimization management method for heavy-duty electrified railway stations is based on the architecture of the heavy-duty electrified railway collaborative power supply system, and constructs a global optimization model of the collaborative power supply system with the goal of minimizing the real-time operating cost of the system and the real-time three-phase voltage imbalance on the public power grid side of the traction substation, with the constraints of the network, storage, source, and vehicle parts and the flow constraints of the entire system as combined constraints, and is simplified by mode pre-positioning, thereby improving the model solution speed; finally, an energy optimization data packet is generated according to the solution result of the optimization model to generate multiple power control instructions, which are sent to the corresponding execution device for energy optimization management. The present invention achieves the purpose of real-time and efficient energy management of the collaborative power supply system of heavy-duty electrified railways, efficient utilization of new energy and regenerative braking energy, reduction of operating costs of the collaborative power supply system, and high-quality control of three-phase voltage imbalance. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of a coordinated power supply system for a heavy-load electrified railway station in one embodiment of the present invention;

[0017] Figure 2 It is a flow chart of a method for real-time energy optimization management of a heavy-load electrified railway station in one embodiment of the present invention;

[0018] Figure 3 Schematic diagram of the simplification process of the global optimization model of the collaborative power supply system in one embodiment of the present invention Figure 1 ;

[0019] Figure 4 Schematic diagram of the simplification process of the global optimization model of the collaborative power supply system in one embodiment of the present invention Figure 2 ;

[0020] Figure 5 A curve diagram of the traction load power of a heavy-load train and the power generation power of new energy in one embodiment of the present invention;

[0021] Fig. 6A A comparison diagram of the total energy consumed by new energy and regenerative braking energy in one embodiment of the present invention;

[0022] Figure 6B is a comparison chart of operating costs in one embodiment of the present invention;

[0023] Figure 7 A comparison diagram of the three-phase voltage imbalance degree of the transformer grid side in one embodiment of the present invention;

[0024] FIG8 is a comparison diagram of voltage fluctuations on the low-voltage side of a transformer in an embodiment of the present invention;

[0025] Fig. 9 It is a schematic diagram of investment economic analysis in one embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] In one embodiment, the real-time energy optimization management method for heavy-duty electrified railway stations provided by the present invention can be applied in Figure 1The heavy-duty electrified railway collaborative power supply system adopts a fusion power supply architecture, including: a traction substation, an energy management system, a power grid connected to the energy management system, new energy power generation equipment, energy storage equipment, a heavy-duty train and an RPC (Railway Power Conditioner); the power grid consists of a public power grid and a traction network; the new energy power generation equipment and the energy storage equipment are connected to the public DC terminal of the RPC; two converters are arranged on both sides of the RPC, which are respectively connected to the left power supply arm and the right power supply arm of the traction substation.

[0028] Furthermore, the traction substation is provided with a transformer, and the transformer is a Scott traction transformer.

[0029] Understandably, the heavy-duty electrified railway coordinated power supply system adopts a fusion power supply architecture, which can avoid excessive transformation of existing lines, and the project implementation is simpler and more economical. Among them, RPC realizes the bidirectional transmission of active power between the left and right power supply arms of the traction substation by coordinating and controlling the operating status of the converters on both sides. The new energy power generation equipment and energy storage equipment are connected to the heavy-duty electrified railway coordinated power supply system through the common DC terminal of RPC, which can reduce the power supply pressure of the public power grid, and at the same time make full use of the peak-filling effect of energy storage equipment, and effectively absorb the regenerative braking energy of new energy and heavy-duty trains.

[0030] like Figure 2 As shown, in one embodiment of the present invention, a real-time energy optimization management method for a heavy-duty electrified railway station is provided, and the method is applied to Figure 1 The heavy-load electrified railway coordinated power supply system shown in the figure is described, and the method specifically includes the following steps:

[0031] Step S10, collecting real-time parameters of the grid, storage, source and vehicle in the heavy-duty electrified railway coordinated power supply system.

[0032] In step S10, the real-time parameters of the grid and train parts include the heavy-load train traction load power on both sides of the traction substation (i.e., the public grid side and the traction grid side), and the heavy-load train traction load power includes the heavy-load train traction active load power P ti (i=1,2) and reactive load power Q ti (i=1,2).

[0033] The real-time parameter of the storage part is the energy storage charge state, that is, the charge state S of the energy storage device. oc

[0034] The real-time parameter of the source part is the new energy power generation, that is, the predicted power generation P of the new energy power generation equipment. pv Optionally, the new energy power generation equipment is a photovoltaic power generation equipment.

[0035] Preferably, the step S10 comprises the following steps:

[0036] Step S101, start multiple collection threads;

[0037] Step S102, collecting the traction load power of heavy-load trains, the new energy generation power and the energy storage charge state on both sides of the traction substation in real time through the multiple collection threads.

[0038] In this embodiment, in order to simultaneously collect the real-time parameters of the network, storage, source and vehicle parts of the heavy-duty electrified railway collaborative power supply system, four collection threads can be started, namely the first collection thread, the second collection thread, the third collection thread and the fourth collection thread.

[0039] Then, the heavy-load train traction load power P on the public power grid side of the traction substation is collected through the first collection thread. t1 , Q t1 , collect the traction load power P of the heavy-load train on the traction side of the traction substation through the second collection thread t2 , Q t2 , collect the predicted power generation power P of the new energy power generation equipment through the third collection thread pv , and collect the state of charge S of the energy storage device through the fourth collection thread oc , thereby completing the simultaneous acquisition of real-time parameters.

[0040] It can be understood that this embodiment can improve the efficiency of real-time parameter collection by simultaneously collecting real-time parameters of the network, storage, source, and vehicle parts of the heavy-duty electrified railway collaborative power supply system through different collection threads.

[0041] Step S20, establishing a multi-objective optimization function with the goal of minimizing the real-time operating cost of the system and minimizing the real-time three-phase voltage imbalance on the public grid side of the traction substation.

[0042] In this embodiment, the real-time operating cost of the heavy-duty electrified railway coordinated power supply system is composed of the site power purchase cost, new energy power generation operation and maintenance cost, energy storage charging and discharging operation and maintenance cost, transformer and RPC and other key equipment operation loss costs and new energy and regenerative braking energy abandonment costs, etc. The real-time three-phase voltage imbalance on the public grid side of the traction substation is caused by negative sequence current.

[0043] Preferably, step S20 may include the following steps:

[0044] Step S201, constructing a real-time operation cost function based on the site power purchase cost, new energy power generation operation and maintenance cost, energy storage charging and discharging operation and maintenance cost, RPC power transmission operation loss cost, transformer power transmission operation loss cost and new energy and regenerative braking energy abandonment cost;

[0045] Wherein, the real-time operation cost function is:

[0046] Formula (1) C=C grid +C pv +C ess +C rpc +C tra +C aband ,

[0047] In formula (1), C is the real-time operation cost function; C grid The cost of purchasing electricity for the site; C pv The operation and maintenance costs of renewable energy power generation; C ess C is the operation and maintenance cost of energy storage charging and discharging; rpc C is the RPC power transmission operation loss cost; tra C is the transformer power transmission operation loss cost; aband It is the energy abandonment cost of new energy and regenerative braking energy.

[0048] Furthermore, the site power purchase cost C is obtained based on the power purchase cost coefficient, the control instruction interval and the active power output of the two power supply arms of the transformer. grid , which can be expressed as:

[0049] Formula (1-1)

[0050] In formula (1-1), c g is the power purchase cost coefficient; T is the control instruction interval, which is determined according to the control requirements in the actual project; P bi The active power is output for the two power supply arms of the transformer (i.e. the left power supply arm and the right power supply arm), and i=1,2.

[0051] According to the new energy operation and maintenance coefficient, control instruction interval and actual power generation of new energy, the operation and maintenance cost C of new energy power generation is obtained. pv , which can be expressed as:

[0052] Formula (1-2) C pv =c p TP pvc ,

[0053] In formula (1-2), c p is the new energy operation and maintenance coefficient, P pvc It is the actual power generated by renewable energy.

[0054] According to the energy storage operation and maintenance coefficient, control instruction interval and energy storage charging and discharging power, the energy storage charging and discharging operation and maintenance cost C is obtained. ess , which can be expressed as:

[0055] Formula (1-3) C ess=c e T|P e |,

[0056] In formula (1-3), c e is the energy storage operation and maintenance coefficient; P e It is the energy storage charging and discharging power.

[0057] According to the RPC loss cost coefficient, control instruction interval, RPC power transmission efficiency and RPC two-arm output power, the RPC power transmission operation loss cost C is obtained. rpc , which can be expressed as:

[0058] Formula (1-4)

[0059] In formula (1-4), c r is the RPC loss cost coefficient; η is the RPC power transmission efficiency; S rm is the output power of the two arms of RPC.

[0060] According to the transformer loss cost coefficient, control instruction interval, transformer load rate, transformer no-load loss and short-circuit loss, the transformer power transmission operation loss cost C is obtained. tra , which can be expressed as:

[0061] Formula (1-5)

[0062] In formula (1-5), c t is the transformer loss cost coefficient; P oz , P kz They are the no-load loss and short-circuit loss of the transformer respectively; n is the symbol of the two power supply arms of the transformer, and n=1,2; β is the transformer load rate.

[0063] And according to the new energy abandonment cost coefficient, regenerative braking energy abandonment cost coefficient, control instruction interval, new energy prediction and actual power generation, regenerative braking power generated by heavy-load trains on both sides of the traction substation and actual recovered regenerative braking power, the new energy and regenerative braking energy abandonment cost C is obtained. aband , which can be expressed as:

[0064] Formula (1-6)

[0065] In formula (1-6), c ap 、c ar are the new energy abandonment cost coefficient and the regenerative braking energy abandonment cost coefficient respectively; P pv , P pvc are the predicted power generation and actual power generation of new energy power generation equipment; P re , P recThey are the regenerative braking power generated by heavy-load trains on both sides of the traction substation and the actual recovered regenerative braking power respectively; s is the sign of both sides of the traction substation, and s=1,2.

[0066] It can be understood that this embodiment comprehensively considers the costs of purchasing electricity, operation and maintenance of new energy power generation, operation and maintenance of energy storage, operating loss costs of key equipment such as transformers and RPCs, and the energy abandonment cost of new energy and regenerative braking energy, so as to further improve the real-time operation cost function of the collaborative power supply system and enhance the global optimization capability of the entire system.

[0067] Step S202, constructing a real-time three-phase voltage imbalance function according to the rated line voltage on the three-phase grid side, the grid short-circuit capacity and the negative sequence current of the traction transformer.

[0068] Wherein, the real-time three-phase voltage imbalance function is:

[0069] Formula (2)

[0070] In formula (2), ε u is the real-time three-phase voltage unbalance function; U N , S N are the rated line voltage and short-circuit capacity of the three-phase grid side respectively; I (-1) is the negative sequence current of the traction transformer.

[0071] Step S203, introducing weight coefficients to construct a multi-objective optimization function according to the real-time operation cost function and the real-time three-phase voltage imbalance function.

[0072] Wherein, the multi-objective optimization function is:

[0073] Formula (3) minF = ω1C + ω2ε u ,

[0074] In formula (3), ω1 and ω2 are multi-objective weights; C is the real-time operation cost function; ε u It is the real-time three-phase voltage imbalance function.

[0075] It is understandable that the present embodiment introduces weight coefficients to construct a multi-objective optimization function, which can comprehensively consider multiple objective problems, avoid the generation of local optimal solutions, and at the same time simplify the complexity of multi-objective optimization and improve the solution efficiency.

[0076] Step S30, based on the real-time parameters of the grid, storage, source, and vehicle in the heavy-duty electrified railway coordinated power supply system, construct the constraints of the grid, storage, source, and vehicle and the flow constraints of the entire system, and combine them to obtain multi-constraint optimization conditions.

[0077] In this embodiment, the constraints of the grid part include capacity transmission constraints of key equipment such as transformers and RPCs, constraints on active power not being fed into the grid, and the like.

[0078] The constraints of the storage part include the charging and discharging power constraints and the state of charge constraints of the energy storage equipment.

[0079] The constraints of the source part include the power generation constraints of new energy power generation equipment.

[0080] The constraints of the train part include the regenerative braking energy constraints of the heavy-load train.

[0081] The power flow constraints of the entire system include active power constraints, reactive power constraints and energy constraints.

[0082] Preferably, the step S30 comprises the following steps:

[0083] Step S301, constructing the capacity transmission constraint and active power non-feedback constraint of the grid part according to the output power parameters and capacity parameters of the transformer and the RPC.

[0084] Among them, the capacity transmission constraint of the transformer is:

[0085] Formula (4)

[0086] In formula (4), P bi , Q bi are the transformer output active power and reactive power respectively; S bN is the transformer capacity.

[0087] The capacity transmission constraint of the RPC is:

[0088] Formula (5)

[0089] In formula (5), P rm , Q rm are the active power and reactive power output by RPC respectively; S rN RPC port capacity.

[0090] The active power non-feedback constraint can be expressed as:

[0091] Formula (6) P bi ≥0,i=1,2.

[0092] Step S302: constructing power generation constraints of the source part according to the power parameters of the new energy power generation equipment.

[0093] Wherein, the power generation constraint is:

[0094] Formula (7) 0≤P pvc ≤P pv,

[0095] In formula (7), P pvc P is the actual power generation of new energy; pv Predict power generation for renewable energy sources.

[0096] Step S303: constructing a charge and discharge power constraint and a state of charge constraint of the storage part according to the state of charge of the energy storage device and the charge and discharge parameters of the energy storage device.

[0097] Wherein, the charge and discharge power constraint is:

[0098] Formula (8)

[0099] In formula (8), P e It is the energy storage charging and discharging power; They are the energy storage charging power and energy storage discharging power respectively; α is the charging and discharging sign, 0 means energy storage discharging, and 1 means energy storage charging.

[0100] The state of charge constraint is:

[0101] Formula (9)

[0102] In formula (9), S oc is the energy storage charge state; T is the control instruction interval; η ch , η dch are energy storage charging efficiency and energy storage discharging efficiency respectively; E ess For the energy storage capacity.

[0103] Step S304, constructing the regenerative braking energy constraint of the train part according to the power parameters of the regenerative braking of the heavy-load train.

[0104] Wherein, the regenerative braking energy constraint is:

[0105] Formula (10) 0≤P recs ≤P res ,s=1,2,

[0106] In formula (10), P res P is the regenerative braking power generated by heavy-load trains on both sides of the traction substation; recs It is the actual amount of regenerative braking power recovered.

[0107] Step S305, constructing the power flow constraint of the entire system according to the power parameter of the transformer, the power parameter of the RPC, the power parameter of the energy storage device and the traction load power of the heavy-load train;

[0108] The power flow constraint of the entire system is:

[0109] Formula (11)

[0110] In formula (11), P bi , Q bi are the transformer output active power and reactive power respectively; P rm , Q rm are the active power and reactive power output by RPC respectively; P ti , Q ti are the active load and reactive load of the heavy-load traction train respectively; P pvc P is the actual power generation of new energy; e It is the energy storage charging and discharging power.

[0111] Step S306, the capacity transmission constraint and active power non-feedback constraint of the grid part, the power generation constraint of the source part, the charging and discharging power constraint and charge state constraint of the storage part, the regenerative braking energy constraint of the vehicle part, and the power flow constraint of the entire system are combined to form a multi-constraint optimization condition.

[0112] It can be understood that this embodiment combines the constraints of each part and the power flow constraints of the overall system to obtain multi-constraint optimization conditions, which is conducive to improving the global optimization capability of the site.

[0113] Step S40: construct a global optimization model of the collaborative power supply system according to the multi-objective optimization function and the multi-constraint optimization conditions, and simplify it by means of mode pre-positioning.

[0114] In this embodiment, after constructing the global optimization model of the collaborative power supply system, some unknown quantities in the global optimization model of the collaborative power supply system are determined in advance through pre-mode judgment, so as to simplify the model and shorten the model solution time.

[0115] As a preference, refer to Figure 3 to Figure 4 In step S40, the global optimization model of the collaborative power supply system is simplified, which may include the following steps:

[0116] Step S401, dividing the working state of the entire system according to the traction load power of the heavy-load train within the two power supply arms of the traction substation.

[0117] In step S401, the working state includes a two-side traction state, a right-side traction and left-side braking state, a right-side braking and left-side traction state, and a two-side braking state.

[0118] Specifically, by judging the active load power P of heavy-load train traction on both sides of the traction substation ti (i=1,2) to judge the working status of the whole system. t1 and P t2are both greater than or equal to 0, then the entire system is judged to be in a state of two-sided traction; if P t1 and P t2 If both are less than 0, the entire system is judged to be in a state of braking on both sides; if P t1 ≥0, P t2 <0, the whole system is judged to be in the state of right-side traction and left-side braking; if P t1 <0, P t2 ≥0, it is determined that the entire system is in the right-side braking and left-side traction state.

[0119] Step S402, by comparing the new energy power generation power with the load demand of the traction heavy-load train, the output power state of the traction substation and the charging and discharging state of the energy storage device in each working state are determined.

[0120] In step S402, the load demand of the heavy-load train is determined by the active load power P of the heavy-load train. ti OK, it can be expressed as: P demand =P t1 +P t2 .

[0121] Specifically, in each working state, the new energy power generation power P pv and the load demand P of heavy-load train demand A comparison is made, and based on the comparison result, it is determined whether the traction substation needs to output electric energy, thereby obtaining the transformer output power state of the traction substation, and based on the comparison result, it is determined whether the energy storage device needs to be supplemented and discharged, thereby obtaining the charge and discharge state of the energy storage device.

[0122] Step S403: determining the system mode in the working state according to the output power state of the traction substation and the charge and discharge state of the energy storage device in each working state.

[0123] Specifically, if P pv ≥P t1 +P t2 When the energy storage devices on both sides are in the traction state, it can be determined that they are in the charging state (i.e., P e ≥0), the output power of the traction substation is 0 (i.e. P b1 =0, P b2 =0), at this time, the whole system is in mode 1; it can also be determined that the energy storage device in the state of one side traction and one side braking is in the charging state (i.e. P e ≥0), the braking end output power of the traction substation is 0 (i.e. P b1 =0, or P b2 =0), at this time, if P e ≥0, P b2 = 0, the whole system is in mode 4. If Pe ≥0, P b1 = 0, the whole system is in mode 7. If P pv <P t1 +P t2 , it can be determined that the energy storage device in the state of one side pulling and the other side braking is in the discharge state (i.e. P e ≤0), the braking end output power of the traction substation is 0 (i.e. P b1 =0, or P b2 =0), at this time, if P e ≤0, P b2 = 0, the whole system is in mode 5. If P e ≥0, P b1 =0, the whole system is in mode 8.

[0124] If the entire system is in braking state, that is, P t1 +P t2 ≤0, it can be determined that the energy storage devices under braking on both sides are in a charging state (i.e., P e ≥0), the output power of the traction substation is 0 (i.e. P b1 =0, P b2 =0), at this time, the whole system is in mode 1; it can also be determined that the energy storage device in the state of one side traction and one side braking is in the charging state (i.e. P e ≥0), the braking end output power of the traction substation is 0 (i.e. P b1 =0, or P b2 =0), at this time, if P e ≥0, P b2 = 0, the whole system is in mode 6. If P e ≥0, P b1 =0, the whole system is in mode 9.

[0125] Step S404: Under each of the system modes, a simplified global optimization model of the cooperative power supply system is obtained by reducing variables.

[0126] After completing the pre-mode judgment through the above steps S401 to S403, under various system modes, variable reduction is performed based on the zeroed parameters to obtain a simplified global optimization model of the collaborative power supply system. It can be understood that for different system modes, the simplified global optimization model of the collaborative power supply system is different, but compared with the original model, they are simplified to different degrees, and the solution can be obtained for different system modes.

[0127] Step S50, solving the simplified global optimization model of the collaborative power supply system, and generating an energy optimization data packet according to the solution result.

[0128] In this embodiment, the energy optimization data packet may include the transmission power of RPC, the actual power generation power of the new energy power generation equipment and the charging and discharging power of the energy storage equipment; the transmission power of RPC includes the transmission active power and reactive power of RPC.

[0129] Preferably, the step S50 comprises the following steps:

[0130] Step S501, calling a preset solver to solve the simplified global optimization model of the collaborative power supply system to obtain the RPC transmission power, the actual output power of new energy power generation, and the energy storage charging and discharging power;

[0131] Step S502: Generate an energy optimization data packet according to the RPC transmission power, the actual power generation power of the new energy, and the energy storage charging and discharging power.

[0132] In this embodiment, the preset solver is the CPLEX business planning solver. At this time, in the Matlab operating environment, the CPLEX business planning solver is called to solve the simplified collaborative power supply system global optimization model to obtain the RPC transmission active power P ri and reactive power Q ri 、 Actual power generation of new energy P pvc And the energy storage charging and discharging power P e , and combine to obtain an energy-optimized data packet containing multiple power parameters.

[0133] Step S60, generating multiple power control instructions according to the energy optimization data packet, and sending them to corresponding execution devices in the heavy-load electrified railway collaborative power supply system to complete energy optimization management.

[0134] Preferably, for the energy optimization data packet including RPC transmission power, actual power generation power of new energy and energy storage charging and discharging power, three power control instructions are generated, namely RPC power control instruction, new energy power generation equipment power control instruction and energy storage equipment power control instruction.

[0135] Then, the RPC power control instructions, the new energy power generation equipment power control instructions and the energy storage equipment power control instructions are respectively sent to the RPC, new energy power generation equipment and energy storage equipment in the heavy-duty electrified railway collaborative power supply system to control each device to complete energy optimization management.

[0136] In summary, the real-time energy optimization management method for heavy-duty electrified railway stations provided in this embodiment is based on the architecture of the heavy-duty electrified railway collaborative power supply system, and constructs a system global optimization model with the goal of minimizing the real-time operating cost of the system and the real-time three-phase voltage imbalance on the public power grid side of the traction substation, with the constraints of the network, storage, source, and vehicle parts and the flow constraints of the entire system as combined constraints, and simplifies it through the mode pre-position method, thereby improving the model solution speed; finally, an energy optimization data packet is generated according to the solution result of the optimization model to generate multiple power control instructions, which are sent to the corresponding execution device for energy optimization management. The present invention achieves the purpose of real-time and efficient energy management of the collaborative power supply system of heavy-duty electrified railways, efficient utilization of new energy and regenerative braking energy, reduction of operating costs of the collaborative power supply system, and high-quality control of three-phase voltage imbalance.

[0137] Table 1 Simulation parameters

[0138]

[0139] In one embodiment, in order to verify the effectiveness and superiority of the real-time energy optimization management method for heavy-duty electrified railway stations of the present invention, verification analysis is performed based on the heavy-duty train traction load data measured at a traction substation and the new energy power generation data obtained by simulation calculation based on the light intensity strategy data. Figure 5 A curve chart of heavy-duty train traction load data and new energy power generation data is given. In this embodiment, the RPC and transformer loss cost coefficients, new energy and regenerative braking energy abandonment cost coefficients are the same as the grid electricity price, all of which are taken as 0.557 yuan / kWh; the operation and maintenance cost coefficients of new energy power generation equipment and energy storage equipment are both taken as 0.05 yuan / kWh. The remaining simulation parameters are shown in Table 1.

[0140] The simulation results are analyzed as follows:

[0141] (1) Comparative analysis of operating effects under different energy management modes

[0142] In 24 hours, the total power generation of new energy power generation equipment is 49.34MWh, and the total energy generated by heavy-duty train regenerative braking is 7.08MWh. Figure 6 shows a comparison of the total energy consumption and operating cost of new energy and regenerative braking energy under the real-time energy optimization management method for heavy-duty electrified railway stations of the present invention (hereinafter referred to as "this method") and the traditional "rule-based" energy management method (hereinafter referred to as "traditional method").

[0143] In terms of new energy and regenerative braking energy consumption, the traditional method consumed 47.53MWh of photovoltaic energy and 6.48MWh of regenerative braking energy, and the corresponding photovoltaic and regenerative braking consumption rates were 96.37% and 92.96% respectively; this method consumed 47.76MWh of photovoltaic energy and 7.02MWh of regenerative braking energy, and the corresponding photovoltaic and regenerative braking consumption rates were 96.82% and 99.09% respectively. In comparison, the total energy consumption of this method is 0.77MWh higher than that of the traditional method.

[0144] In terms of daily operating costs, the operating costs under the two energy management methods are lower than the initial operating costs without management. Among them, the traditional method reduces the initial cost by 42.04%, while this method can further reduce the operating cost, and its reduction rate reaches 42.59%. This shows that compared with the traditional method, this method can further improve the energy-saving and emission-reduction capabilities and economic operation level of the traction power supply system.

[0145] In terms of the three-phase voltage imbalance on the grid side of the traction transformer, Figure 7 A comparison chart is given. Figure 7 It can be seen that when energy management is not performed, the voltage imbalance exceeds the national standard requirements many times (ε u ≤2%), and its maximum value reached 3.21%. Under the two energy management methods, the voltage imbalance was effectively improved, but compared with the traditional method, this method reduced the voltage imbalance more significantly, and the maximum value was only 1.24%, and all test times met the national standard requirements.

[0146] In terms of voltage fluctuations on the low-voltage side of the traction substation transformer, a comparison chart is shown in Figure 8. As can be seen from Figure 8, the voltage fluctuation range of transformer bridge arm 1 under the traditional method is [0.945, 1006], and the voltage fluctuation range of transformer bridge arm 2 is [0.923, 1.028]. In this method, the voltage fluctuations on both sides of the transformer are within the range of [0.996, 1]. Therefore, under this method, the voltage levels of the traction network on both sides have been improved as a whole, which helps to improve the energy transmission efficiency of the traction network.

[0147] (2) Economic investment benefit analysis of the present invention

[0148] Compared with the traditional traction power supply system, the heavy-duty electrified railway coordinated power supply system introduces new energy and energy storage, and sets up RPC in the traction substation, which increases the construction cost of the traction power supply system to a certain extent. In order to verify the feasibility of the project of the present invention, the economic analysis of the heavy-duty electrified railway coordinated power supply system under the management of the present invention is carried out. The cost parameters of each part are shown in Table 2.

[0149] Table 2 Cost parameters

[0150] category Capacity / MW Unit price / 10,000 yuan Total price / 10,000 yuan Four-quadrant converter 2×10 20 400 Isolation transformer 20 3 60 Energy storage equipment 2 108 216 New energy power generation equipment 6.3 450 2835 Total Cost — — 3511

[0151] In this embodiment, the life cycle of the coordinated heavy-load electrified railway power supply system is set to 15 years. Figure 6B The operating cost comparison chart shows that this method can save 54,300 yuan per day. If the measured data of that day is used for investment analysis, this method can save about 19.81 million yuan per year, and it is estimated that the investment can be recovered in 1.77 years. The estimated benefit over the entire life cycle is 2.64×10 4 Ten thousand yuan, such as Fig. 9 Therefore, under this method, the payback period is much shorter than the system life cycle, and the construction of the actual project has good investment prospects.

[0152] Through the above simulation analysis, it can be seen that the real-time optimization management method of energy for heavy-duty electrified railway stations of the present invention makes up for the shortcomings of the traditional "rule-based" energy management method, such as the shallow global optimization degree, low new energy and regenerative braking energy consumption rate, and weak three-phase voltage imbalance control ability. It realizes the real-time management of the energy of the heavy-duty electrified railway collaborative power supply system, the efficient utilization of new energy and regenerative braking energy, and the high-quality control of three-phase voltage imbalance, and has better economic investment benefits.

[0153] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0154] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A real-time energy optimization management method for heavy-duty electrified railway stations, characterized in that: include: Collect real-time parameters of the grid, storage, source and vehicle of the coordinated power supply system of heavy-duty electrified railways; A multi-objective optimization function is established with the goal of minimizing the real-time operating cost of the system and minimizing the real-time three-phase voltage imbalance on the public grid side of the traction substation. According to the real-time parameters of the grid, storage, source and vehicle of the heavy-duty electrified railway coordinated power supply system, the constraints of the grid, storage, source and vehicle and the power flow constraints of the entire system are constructed, and the multi-constraint optimization conditions are obtained by combining them; According to the multi-objective optimization function and the multi-constraint optimization conditions, a global optimization model of the collaborative power supply system is constructed, and simplified by a mode pre-positioning method; Solve the simplified global optimization model of the coordinated power supply system and generate an energy optimization data package based on the solution results; Generate multiple power control instructions according to the energy optimization data packet, and send them to corresponding execution devices in the heavy-load electrified railway collaborative power supply system to complete energy optimization management; The simplification of the global optimization model of the collaborative power supply system includes: The working state of the whole system is divided according to the traction load power of heavy-load trains within the two power supply arms of the traction substation; In each of the working states, the output power state of the traction substation and the charging and discharging state of the energy storage device are determined by comparing the power generated by the new energy source with the load demand of the traction heavy-load train; Determining a system mode in a working state according to the output power state of the traction substation and the charge and discharge state of the energy storage device in each working state; In each of the system modes, a simplified global optimization model of the cooperative power supply system is obtained by reducing variables.

2. The real-time energy optimization management method for heavy-duty electrified railway stations according to claim 1 is characterized in that: The real-time parameters of the network, storage, source and vehicle of the heavy-load electrified railway coordinated power supply system are collected, including: Start multiple collection threads; The traction load power of heavy-load trains on both sides of the traction substation, the new energy power generation power and the energy storage charge state are collected in real time through the multiple collection threads.

3. The real-time energy optimization management method for heavy-duty electrified railway stations according to claim 1 is characterized in that: The multi-objective optimization function is established with the goal of minimizing the real-time operation cost of the system and minimizing the real-time three-phase voltage imbalance on the public power grid side of the traction substation, including: A real-time operation cost function is constructed based on the site power purchase cost, new energy power generation operation and maintenance cost, energy storage charging and discharging operation and maintenance cost, RPC power transmission operation loss cost, transformer power transmission operation loss cost, and new energy and regenerative braking energy abandonment cost. According to the rated line voltage on the three-phase grid side, the grid short-circuit capacity and the negative sequence current of the traction transformer, a real-time three-phase voltage imbalance function is constructed; According to the real-time operation cost function and the real-time three-phase voltage imbalance function, weight coefficients are introduced to construct a multi-objective optimization function.

4. The method for real-time energy optimization management of heavy-duty electrified railway stations according to claim 3 is characterized in that: The multi-objective optimization function is established with the goal of minimizing the real-time operation cost of the system and minimizing the real-time three-phase voltage imbalance on the public power grid side of the traction substation, and also includes: The site power purchase cost is obtained based on the power purchase cost coefficient, control instruction interval, and the active power output of the two power supply arms of the transformer; Obtain the operation and maintenance costs of new energy power generation based on the new energy operation and maintenance coefficient, control instruction interval and actual power generation of new energy; Obtain the energy storage charging and discharging operation and maintenance costs based on the energy storage operation and maintenance coefficient, control instruction interval, and energy storage charging and discharging power; According to the RPC loss cost coefficient, control instruction interval, RPC power transmission efficiency and RPC two-arm output power, the RPC power transmission operation loss cost is obtained; Obtain the transformer power transmission operation loss cost based on the transformer loss cost coefficient, control instruction interval, transformer load rate, transformer no-load loss and short-circuit loss; The energy abandonment cost of new energy and regenerative braking energy is obtained based on the new energy abandonment cost coefficient, the regenerative braking energy abandonment cost coefficient, the control instruction interval, the new energy prediction and actual power generation power, the regenerative braking power generated by the heavy-loaded trains on both sides of the traction substation and the actual recovered regenerative braking power.

5. The method for real-time energy optimization management of heavy-duty electrified railway stations according to claim 3 is characterized in that: The real-time operation cost function is: Formula (1) C = C grid +C pv +C ess +C rpc +C tra +C aband , In formula (1), C is the real-time operation cost function; C grid The cost of purchasing electricity for the site; C pv The operation and maintenance costs of renewable energy power generation; C ess C is the operation and maintenance cost of energy storage charging and discharging; rpc C is the RPC power transmission operation loss cost; tra C is the transformer power transmission operation loss cost; aband The energy abandonment cost for new energy and regenerative braking energy; The real-time three-phase voltage imbalance function is: Formula (2) In formula (2), ε u is the real-time three-phase voltage unbalance function; U N , S N are the rated line voltage and short-circuit capacity of the three-phase grid side respectively; I (-1) is the negative sequence current of the traction transformer; The multi-objective optimization function is: Formula (3) minF = ω1C + ω2ε u , In formula (3), ω1 and ω2 are multi-objective weights.

6. The method for real-time energy optimization management of heavy-duty electrified railway stations according to claim 2, characterized in that: According to the real-time parameters of the grid, storage, source, and vehicle of the heavy-duty electrified railway coordinated power supply system, the constraints of the grid, storage, source, and vehicle and the power flow constraints of the entire system are constructed, and the multi-constraint optimization conditions are obtained by combining them, including: According to the output power parameters and capacity parameters of the transformer and RPC, the capacity transmission constraints of the grid part and the active power non-feedback constraints are established; According to the power parameters of the new energy power generation equipment, the power generation constraints of the source part are constructed; According to the state of charge of the energy storage device and the charge and discharge parameters of the energy storage device, the charge and discharge power constraints and state of charge constraints of the storage part are constructed; According to the power parameters of regenerative braking of heavy-duty trains, the regenerative braking energy constraints of the train part are constructed; Constructing the power flow constraint of the entire system according to the power parameter of the transformer, the power parameter of the RPC, the power parameter of the energy storage device and the traction load power of the heavy-load train; The capacity transmission constraints and active power non-feedback constraints of the grid part, the power generation constraints of the source part, the charging and discharging power constraints and charge state constraints of the storage part, the regenerative braking energy constraints of the vehicle part, and the flow constraints of the entire system are combined to form multi-constraint optimization conditions.

7. The method for real-time energy optimization management of heavy-duty electrified railway stations according to claim 6, characterized in that: The capacity transfer constraint of the transformer is: Formula (4) In formula (4), P bi , Q bi are the transformer output active power and reactive power respectively; S bN is the transformer capacity; The capacity transmission constraint of the RPC is: Formula (5) In formula (5), P rm , Q rm are the active power and reactive power output by RPC respectively; S rN is the RPC port capacity; The active power non-feedback constraint can be expressed as: Formula (6) P bi ≥0,i=1,2; The power generation constraint is: Formula (7) 0≤P pvc ≤P pv , In formula (7), P pvc is the actual power generation of new energy; P pv Predicting power generation for renewable energy sources; The charge and discharge power constraints are: Formula (8) In formula (8), P e It is the energy storage charging and discharging power; are energy storage charging power and energy storage discharging power respectively; α is the charge and discharge sign, 0 means energy storage discharging, 1 means energy storage charging; The state of charge constraint is: Formula (9) In formula (9), S oc is the energy storage charge state; T is the control instruction interval; η ch , η dch are energy storage charging efficiency and energy storage discharging efficiency respectively; E ess is the energy storage capacity; The regenerative braking energy constraint is: Formula (10) 0≤P recs ≤P res ,s=1,2, In formula (10), P res P is the regenerative braking power generated by heavy-load trains on both sides of the traction substation; recs is the actual amount of regenerative braking power recovered; The power flow constraint of the entire system is: Formula (11) In formula (11), P bi , Q bi are the transformer output active power and reactive power respectively; P rm , Q rm are the active power and reactive power output by RPC respectively; P ti , Q ti are the active load and reactive load of heavy-load traction train respectively; P pvc P is the actual power generation of new energy; e It is the energy storage charging and discharging power.

8. The method for real-time energy optimization management of heavy-haul electrified railway stations according to claim 1, characterized in that: The working states include a two-side traction state, a right-side traction and left-side braking state, a right-side braking and left-side traction state, and a two-side braking state.

9. The method for real-time energy optimization management of heavy-duty electrified railway stations according to claim 6, characterized in that: The simplified collaborative power supply system global optimization model is solved, and an energy optimization data packet is generated according to the solution result, including: The preset solver is called to solve the simplified global optimization model of the coordinated power supply system to obtain the RPC transmission power, the actual output power of renewable energy power generation, and the energy storage charging and discharging power; An energy optimization data packet is generated according to the RPC transmission power, the actual output power of the new energy power generation and the energy storage charging and discharging power.

Images