Electric railway flexible traction power supply light storage integrated capacity configuration method and device

Abstract

The invention discloses an electrified railway flexible traction power supply optical storage integrated capacity configuration method and device, and the method comprises the steps: taking the minimum sum of the total annual investment cost and the total annual operation cost as a target based on a daily operation model and a total annual investment cost model of a flexible traction substation, and combining with a constraint condition, carrying out the optical storage integrated capacity configuration of the flexible traction substation; establishing a hybrid integer nonlinear programming model based on photovoltaic and hybrid energy storage system and back-to-back converter integrated capacity optimization; converting the grid-side voltage imbalance constraint and the back-to-back converter power constraint in the constraint conditions into a second-order cone constraint form, and converting the mixed integer nonlinear programming model into a mixed integer second-order cone model; and solving the mixed integer second-order cone model to obtain a capacity optimization configuration result of the photovoltaic and hybrid energy storage system and the back-to-back converter. The energy utilization efficiency of the flexible traction substation is improved, the long-term operation cost of the system is reduced, and the electric energy quality and the power supply reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traction power supply systems, and in particular to a method and device for configuring the capacity of a flexible traction power supply system with integrated photovoltaic and storage capabilities for electrified railways. Background Art

[0002] With the development of railway electrification, grid energy demand has increased dramatically. High-power loads have exacerbated power quality issues such as negative sequence current and voltage fluctuations in traction networks. In recent years, energy conservation and emission reduction have garnered widespread attention worldwide, driven by the pursuit of low-carbon, high-efficiency, and grid-friendly energy conservation and reduction. Against this backdrop, flexible traction substations integrating photovoltaics, hybrid energy storage systems, and back-to-back converters have emerged.

[0003] In a flexible traction substation, a power flow controller serves as an energy router for integrated photovoltaic and hybrid energy storage systems, providing control over power quality issues. Photovoltaic power generation generates clean electricity to reduce carbon emissions and operating costs. Hybrid energy storage systems match fluctuating photovoltaic power generation with intense traction loads, recovering regenerative braking energy and improving energy efficiency and economic performance. Overall, flexible traction substations offer a promising solution for achieving the goals of low-carbon power supply, efficient energy utilization, improved power quality (PQ), and enhanced economic performance for electrified railways. These goals depend significantly on the installed capacity of photovoltaics, hybrid energy storage systems, and back-to-back converters. Therefore, optimally allocating the capacity of photovoltaics, storage systems, and back-to-back converters is crucial for achieving the economic and technical operational objectives of new traction substations. Currently, there is a lack of a comprehensive capacity optimization design technology for photovoltaics, hybrid energy storage systems, and back-to-back converters. Summary of the Invention

[0004] In order to solve at least one technical problem in the above-mentioned background technology, the present invention proposes a method and device for configuring the capacity of flexible traction power supply, photovoltaic storage integration for electrified railways.

[0005] To achieve the above-mentioned object, according to one aspect of the present invention, a method for configuring the capacity of an electrified railway flexible traction power supply, photovoltaic power storage integration is provided, the method comprising:

[0006] Based on historical data, a scenario reduction algorithm is used to obtain typical daily scenarios of the photovoltaic power generation system and traction load;

[0007] For each typical daily scenario, a flexible traction substation daily operation model is established with the goal of minimizing the daily operation cost of the flexible traction substation, taking into account power quality requirements and system operation-related constraints.

[0008] Using the equivalent annual value method, a total annualized investment cost model was established based on the capacity and estimated service life of the photovoltaic, hybrid energy storage system, and back-to-back converter.

[0009] Based on the daily operation model of the flexible traction substation and the total annualized investment cost model, with the goal of minimizing the sum of the total annualized investment cost and the total annualized operating cost, and in combination with constraints, a mixed integer nonlinear programming model based on the integrated capacity optimization of photovoltaic, hybrid energy storage systems, and back-to-back converters is established;

[0010] Using a second-order cone relaxation method, the grid-side voltage unbalance constraint and the back-to-back converter power constraint in the constraint conditions are converted into a second-order cone constraint form, thereby converting the mixed-integer nonlinear programming model into a mixed-integer second-order cone model;

[0011] The mixed integer second-order cone model is solved to obtain the capacity configuration results of photovoltaic, hybrid energy storage system and back-to-back converter.

[0012] Optionally, the daily operating costs of the flexible traction substation include: external grid billing, daily demand billing, ongoing operating costs, and additional penalty billing;

[0013] The objective function of the daily operation model of the flexible traction substation is:

[0014]

[0015] in, is the daily operating cost of the flexible traction substation, Billing for external grids, Billing for daily needs, For ongoing operating costs, Billing for additional penalties.

[0016] Optionally, the total annualized investment cost model aims to minimize the total annual investment cost, where the total annual investment cost includes: the investment cost of the photovoltaic power generation system, the investment cost of the supercapacitor, the investment cost of the battery energy storage system, and the investment cost of the back-to-back converter;

[0017] The objective function of the total annualized investment cost model is:

[0018] min C INV =C pv +C uc +C bt +C rpc

[0019] Among them, C INV is the total annual investment cost, C pv is the investment cost of the photovoltaic power generation system, C uc is the investment cost of supercapacitor, C bt is the investment cost of the battery energy storage system, C rpc is the investment cost of the back-to-back converter.

[0020] Optionally, the constraints include: power balance constraint, demand power constraint, photovoltaic output constraint, hybrid energy storage device constraint, back-to-back converter constraint, voltage imbalance constraint, power factor constraint, device installation constraint and photovoltaic output wind curtailment ratio constraint.

[0021] Optionally, the objective function of the mixed integer nonlinear programming model is:

[0022]

[0023] Among them, C TAC is the sum of the total annual investment cost and the total annual operating cost, C INV is the total annual investment cost, is the number of days of source-load interaction, is the daily operating cost of the flexible traction substation, C AOC is the annual operating cost, Γ is the set of traction load scenarios, and Λ is the set of photovoltaic output scenarios.

[0024] To achieve the above-mentioned object, according to another aspect of the present invention, a device for configuring the capacity of an electric railway flexible traction power supply, photovoltaic power supply and storage integration is provided, the device comprising:

[0025] A typical day scenario determination unit is used to obtain a typical day scenario of the photovoltaic power generation system and traction load based on historical data using a scenario reduction algorithm;

[0026] a flexible traction substation daily operation model establishment unit, configured to establish a flexible traction substation daily operation model for each typical daily scenario, with the goal of minimizing the daily operation cost of the flexible traction substation, taking into account power quality requirements and system operation-related constraints;

[0027] A total annualized investment cost model establishment unit is used to establish a total annualized investment cost model based on the capacity and estimated service life of photovoltaic, hybrid energy storage systems, and back-to-back converters using the equal annual value method;

[0028] A mixed integer nonlinear programming model establishment unit is configured to establish, based on the flexible traction substation daily operation model and the total annualized investment cost model, a mixed integer nonlinear programming model for optimizing the integrated capacity of photovoltaic, hybrid energy storage systems, and back-to-back converters, with the goal of minimizing the sum of the total annualized investment cost and the total annualized operating cost, in combination with constraints;

[0029] a mixed integer second-order cone model determination unit, configured to convert the grid-side voltage unbalance constraint and the back-to-back converter power constraint in the constraint conditions into a second-order cone constraint form using a second-order cone relaxation method, thereby converting the mixed integer nonlinear programming model into a mixed integer second-order cone model;

[0030] The capacity optimization configuration result determination unit is used to solve the mixed integer second-order cone model to obtain the capacity configuration results of the photovoltaic, hybrid energy storage system and back-to-back converter.

[0031] In order to achieve the above-mentioned purpose, according to another aspect of the present invention, a computer device is also provided, including a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for configuring the capacity of the flexible traction power supply, photovoltaic storage and electric railway are implemented.

[0032] In order to achieve the above-mentioned purpose, according to another aspect of the present invention, a computer-readable storage medium is further provided, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the above-mentioned method for configuring the capacity of the flexible traction power supply, photovoltaic storage and integrated circuit of the electrified railway are implemented.

[0033] In order to achieve the above-mentioned purpose, according to another aspect of the present invention, a computer program product is also provided, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for configuring the capacity of the flexible traction power supply, photovoltaic storage and electric railway integration.

[0034] The beneficial effects of the present invention are:

[0035] This invention provides a capacity optimization configuration method for an integrated photovoltaic, hybrid energy storage system, and back-to-back converter. By establishing a daily operating model for a flexible traction substation and a total annualized investment cost model for the photovoltaic, hybrid energy storage, and back-to-back converter, the invention uses an optimization method to configure the capacity of the photovoltaic, hybrid energy storage, and back-to-back converter in an integrated manner. This method balances the system's operational economics with equipment investment costs, enabling the flexible traction substation to efficiently utilize renewable energy, reducing the system's long-term operating costs, and improving power quality and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0037] Figure 1 This is a flow chart of a method for configuring the capacity of an electrified railway flexible traction power supply, photovoltaic power storage integration, according to an embodiment of the present invention;

[0038] Figure 2This is a structural block diagram of a device for configuring the capacity of an electrified railway flexible traction power supply, photovoltaic power supply, and storage integration according to an embodiment of the present invention;

[0039] Figure 3 Schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0041] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or apparatuses.

[0043] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] The present invention proposes a capacity configuration method that realizes the integrated optimization design of the photovoltaic, hybrid energy storage system and back-to-back converter capacity of the flexible traction substation. It can also use the active and reactive regulation flexibility of the back-to-back converter to improve the negative sequence and power factor, thereby reducing the capacity requirement of the back-to-back converter.

[0045] Figure 1 Flowchart of the method for configuring the capacity of flexible traction power supply, photovoltaic and storage integration for electrified railways according to an embodiment of the present invention. Figure 1As shown, in one embodiment of the present invention, the method for configuring the capacity of the electric railway flexible traction power supply photovoltaic storage integration of the present invention includes steps S101 to S106.

[0046] Step S101 : Based on historical data, a scenario reduction algorithm is used to obtain typical daily scenarios of the photovoltaic power generation system and traction load.

[0047] In the present invention, this step also obtains the probability corresponding to each typical day scene.

[0048] In the present invention, this step is based on historical data and uses a scenario reduction algorithm to process the data of the photovoltaic power generation system and traction load, extracting multiple representative typical daily scenarios to ensure that the time-varying characteristics of the photovoltaic output and traction load can be accurately reflected in the subsequent optimization process, thereby improving the computational efficiency and solution accuracy of the model.

[0049] Step S102 : for each typical daily scenario, with the goal of minimizing the daily operating cost of the flexible traction substation, and taking into account power quality requirements and system operation-related constraints, a flexible traction substation daily operation model is established.

[0050] In this invention, this step establishes a daily operation model for the flexible traction substation for each typical daily scenario, with the goal of minimizing daily operating costs, taking into account power quality requirements and system operating constraints (such as power balance, voltage imbalance, and power factor). This flexible traction substation daily operation model accurately simulates the economic efficiency and stability of the flexible traction substation under different operating scenarios, providing a reliable basis for operating cost assessment for subsequent capacity configuration optimization.

[0051] Step S103 , using the equal annual value method, establishes a total annualized investment cost model based on the capacity and estimated service life of the photovoltaic, hybrid energy storage system, and back-to-back converter.

[0052] In this method, this step uses the equivalent annual value method, combining the capacity and estimated service life of the photovoltaic, hybrid energy storage system, and back-to-back converter to establish a total annualized investment cost model. This model can reasonably assess the long-term investment costs of each device and, combined with operating costs, achieve a comprehensive analysis of the system's economic viability throughout its entire lifecycle, ensuring that the optimal configuration balances initial investment and long-term returns.

[0053] Step S104, based on the daily operation model of the flexible traction substation and the total annual investment cost model, with the goal of minimizing the sum of the total annual investment cost and the total annual operation cost, combined with constraints, establish a mixed integer nonlinear programming model based on the integrated capacity optimization of photovoltaic, hybrid energy storage system and back-to-back converter.

[0054] In the present invention, this step builds on the daily operation model of step S102 and the total annualized investment cost model of step S103, with the optimization objective of minimizing the sum of the total annualized investment cost and the total annualized operating cost. This model, combined with constraints such as power balance, equipment capacity, device installation, and voltage imbalance, establishes a mixed integer nonlinear programming (MINLP) model for the integrated capacity optimization of the photovoltaic, hybrid energy storage system, and back-to-back converter. This model comprehensively considers both long-term investment and short-term operating costs, achieving a coordinated and optimized configuration of the photovoltaic, energy storage, and power systems.

[0055] Step S105 , using a second-order cone relaxation method, converting the grid-side voltage imbalance constraint and the back-to-back converter power constraint in the constraints into a second-order cone constraint form, thereby converting the mixed-integer nonlinear programming model into a mixed-integer second-order cone model.

[0056] In this method, this step uses a second-order cone relaxation method to transform nonlinear constraints in the MINLP model, such as the grid-side voltage imbalance constraint and the back-to-back converter power constraint, into a second-order cone constraint form. This allows the overall optimization problem to be solved using a mixed-integer second-order cone (MISOCP) model. This transformation effectively reduces computational complexity, improves solution efficiency, and ensures that a feasible solution to the optimization problem is obtained within a reasonable time.

[0057] Step S106 , solving the mixed integer second-order cone model to obtain capacity configuration results of the photovoltaic system, the hybrid energy storage system, and the back-to-back converter.

[0058] In the present invention, this step solves the MISOCP model and ultimately obtains the capacity configuration scheme of the photovoltaic, hybrid energy storage system and back-to-back converter.

[0059] As can be seen from this, the present invention provides a capacity optimization configuration method for the integration of photovoltaic, hybrid energy storage systems, and back-to-back converters. By establishing a daily operation model for a flexible traction substation and a total annualized investment cost model based on photovoltaic, hybrid energy storage systems, and back-to-back converters, the present invention uses an optimization method to integrate the capacity of photovoltaic, hybrid energy storage, and back-to-back converters. This method takes into account the system's operating economy and equipment investment costs, enabling the flexible traction substation to efficiently utilize renewable energy, reduce the system's long-term operating costs, and improve power quality and power supply reliability.

[0060] Regarding the above step S101, in one embodiment of the present invention, the sets of typical daily scenes of the photovoltaic power generation system and traction load of the present invention are respectively recorded as:

[0061]

[0062] Among them, W Lrepresents a set of multiple typical daily scenarios of traction load, W pv Represents a set of multiple typical daily scenarios for photovoltaic power generation systems; are the active and reactive power of the α and β phase power supply arms at a certain moment in a typical day scenario of traction load, respectively; where κ, t, and s represent the index of a typical day in the set of typical day scenarios of traction load, the index of the optimized time interval in a typical day, and the index of a typical day in the set of typical day scenarios of photovoltaic power generation system, respectively; Γ, Υ, and Λ represent the index set. Represents the number of repeated days of a typical scenario of traction load and PV output respectively; N L 、N T 、N pv They represent the scenario days of traction load, a certain time period of a day, and the scenario days of photovoltaic output respectively.

[0063] Regarding the above step S102, in one embodiment of the present invention, the daily operating cost of the flexible traction substation includes: external grid billing, daily demand billing, continuous operating cost and additional penalty billing.

[0064] In one embodiment of the present invention, the objective function of the flexible traction substation daily operation model is:

[0065]

[0066] in, is the daily operating cost of the flexible traction substation, Billing for external grids, Billing for daily needs, For ongoing operating costs, Billing for additional penalties.

[0067] The flexible traction power supply system needs to obtain a certain amount of active power from the external grid for normal operation. The energy generated by this active power needs to be billed, that is, the external grid billing

[0068] In one embodiment of the present invention, external grid billing The specific formula is:

[0069]

[0070] Where c ec is the unit price of energy consumption, ￥ / kWh; is the active power consumed from the grid under a typical PV output and traction load scenario, MW; Δt represents the optimization time interval, min.

[0071] In one embodiment of the present invention, daily demand billing The specific formula is:

[0072]

[0073] Where c dm is the unit price of required power, ￥ / kWh; is the maximum required power under a typical PV output and traction load scenario, in MW.

[0074] In the present invention, the flexible traction power supply system requires photovoltaic, hybrid energy storage system and back-to-back converter to maintain continuous operation. The continuous operation cost of the back-to-back converter is mainly determined by the apparent power of the α and β phases. The continuous operation cost of the photovoltaic power generation system is mainly determined by its output power. The operation of the hybrid energy storage system is mainly affected by the charge and discharge power of the supercapacitor and battery. In one embodiment of the present invention, the continuous operation cost is The specific formula is:

[0075]

[0076] In the formula, o bat is the unit price of the battery energy storage system for continuous operation, ￥ / kWh; pv is the unit price of the photovoltaic energy storage system for continuous operation, ￥ / kWh; uc is the unit price of the supercapacitor energy storage system for continuous operation, ￥ / kWh; is the apparent power of the two phases of the back-to-back converter under a typical PV output and traction load scenario, MW; are the battery charge and discharge power, MW; are the supercapacitor charging and discharging power, MW respectively.

[0077] In the present invention, if the power factor on the grid side is lower than the standard requirement or the quality of the returned active power is poor, additional billing is required. In one embodiment of the present invention, additional penalty billing The specific formula is:

[0078]

[0079] Where c PF The unit price of penalty for power factor failure to meet requirements, ￥ / kWh; C EC is the daily energy consumption cost, ￥ / kWh; C DM is the daily electricity cost, ￥ / kWh; c rv is the price of active power fed back to the grid, ￥ / kWh; It is the active power returned to the grid by the system under a typical photovoltaic output and traction load scenario, in MW.

[0080] Regarding step S103 above, in one embodiment of the present invention, the total annualized investment cost model aims to minimize the total annual investment cost, where the total annual investment cost includes: the investment cost of the photovoltaic power generation system, the investment cost of the supercapacitor, the investment cost of the battery energy storage system, and the investment cost of the back-to-back converter.

[0081] In one embodiment of the present invention, the objective function of the total annualized investment cost model is:

[0082] min C INV =C pv +C uc +C bt +C rpc

[0083] Among them, C INV is the total annual investment cost, C pv is the investment cost of the photovoltaic power generation system, C uc is the investment cost of supercapacitor, C bt is the investment cost of the battery energy storage system, C rpc is the investment cost of the back-to-back converter.

[0084] In one embodiment of the present invention, the specific formula for each investment cost is:

[0085]

[0086] in, r is the discount rate, Represents the CRF coefficient based on service life; where c pv 、 c rpc They represent the photovoltaic array, ￥ / MW, the unit price of the power conversion system, k￥ / MW, the unit price of the battery, k￥ / MW, the supercapacitor group, k￥ / MW, the unit investment price of the back-to-back converter, k￥ / MVA; m pv 、m uc 、m bat 、m rpc Represents the annual maintenance unit price of photovoltaic array, battery, supercapacitor group and back-to-back converter respectively; Y pv 、Y uc 、Y bat 、Y rpc Represent the service life of photovoltaic array, battery, supercapacitor bank and back-to-back converter respectively, year.

[0087] With respect to the above-mentioned step S104, the present invention takes minimizing the sum of the total annualized investment cost and the total annual operating cost as the goal, combines the constraints of the daily operation model of the flexible traction substation and the photovoltaic and hybrid energy storage investment constraints, and establishes a mixed integer nonlinear programming model based on the integrated capacity optimization of the photovoltaic, hybrid energy storage system and back-to-back converter.

[0088] In one embodiment of the present invention, the objective function of the mixed integer nonlinear programming model is:

[0089]

[0090] Among them, C TAC is the sum of the total annual investment cost and the total annual operating cost, C INV is the total annual investment cost, is the number of days of source-load interaction, is the daily operating cost of the flexible traction substation, C AOC is the annual operating cost, Γ is the set of traction load scenarios, and Λ is the set of photovoltaic output scenarios.

[0091] In one embodiment of the present invention, the constraints include: power balance constraint, demand power constraint, photovoltaic output constraint, hybrid energy storage device constraint, back-to-back converter constraint, voltage imbalance constraint, power factor constraint, device installation constraint and photovoltaic output wind curtailment ratio constraint.

[0092] In one embodiment of the present invention, the power balance constraint is specifically:

[0093]

[0094] Where, is the active power consumed from the grid under a typical PV output and traction load scenario, MW; is the active power returned to the grid, MW; Represent the active power of α and β phases, MW; Represents the reactive power exchanged between the flexible traction substation and the grid, MW; Represents the reactive power of α and β phases, MW; S TT Indicates the capacity of the traction transformer, MVA; is the active power of phase i (α or β) of the traction substation, MW; is the active power demand of the traction load phase i, MW; Active power provided by the energy storage system phase i, MW; is the reactive power of phase i (α or β) of the traction substation, MW; is the reactive power demand of the traction load phase i, MW; Reactive power provided to phase i of the energy storage device, MW; W L A collection of several typical daily scenarios representing traction loads.

[0095] In one embodiment of the present invention, the required power constraint is specifically:

[0096]

[0097] Where, is the minimum required power of the system under typical PV output and traction load scenarios, MW; is the active power consumed from the grid at time t, MW; is the active power returned to the grid at time t, MW; Δt is the time step; N T is the total number of time steps.

[0098] This demand power constraint ensures that within each sliding 15-minute time window, the net power provided by the grid meets the system minimum demand power.

[0099] In one embodiment of the present invention, the photovoltaic output constraint is specifically:

[0100]

[0101] Where, is the active power output of the PV array under a typical PV output and traction load scenario, MW; is the rated output power of the PV array, MW; Represents the normalized photovoltaic array output power; W pv It is a collection of multiple typical daily scenes of photovoltaic power generation systems.

[0102] In one embodiment of the present invention, the hybrid energy storage device constraints are specifically:

[0103]

[0104] Where j represents a battery or supercapacitor, is the discharge power of the battery or supercapacitor at time t, is the charging power of the battery or supercapacitor at time t, Indicates the rated power of the battery or supercapacitor, MW; Represents the remaining energy of the battery or supercapacitor at time t and time t-1, MVA; Respectively represent the self-discharge rate coefficient, charging efficiency coefficient, and discharge efficiency coefficient of the battery or supercapacitor; The lower and upper limits of the Soc of the battery or supercapacitor respectively; Represent the initial and final Soc of the battery or supercapacitor respectively; Indicates the Soc of the battery or supercapacitor at the initial and final moments.

[0105] In one embodiment of the present invention, the back-to-back converter constraints are specifically:

[0106]

[0107] Where, They represent the active power of the α and β phases of the back-to-back converter, MW; represent the charging power of supercapacitor (uc) and battery (bat), MW; is the photovoltaic power generation power; represent the discharge power of supercapacitor (uc) and battery (bat), MW; represents the capacity of phase i of the back-to-back converter, is the active power of phase i, is the reactive power of phase i, Indicates the rated capacity of phase i of the back-to-back converter, in MVA.

[0108] In one embodiment of the present invention, the voltage imbalance constraint is specifically:

[0109] According to the power quality standard of IEC / TR 61000-3-13, the three-phase voltage unbalance shall not exceed 2%. α =U β =U T , the three-phase voltage unbalance constraint is:

[0110]

[0111] Where, is the three-phase voltage unbalance, Respectively represent the active power of phases α and β on the traction side of the traction transformer, MW; Respectively represent the reactive power of phases α and β on the traction side of the traction transformer, MW; S sc Indicates the short-circuit capacity of the three-phase line, MVA.

[0112] In one embodiment of the present invention, the power factor constraint is specifically:

[0113]

[0114] in, represents the daily average power factor of the flexible traction substation, They are the energy corresponding to the active power and reactive power consumed by the flexible traction substation in one day, MVA.

[0115] In one embodiment of the present invention, the device installation constraints are specifically:

[0116]

[0117]

[0118] Where, F ava Represents the area available for building photovoltaic arrays, m 2 ; Indicates the area required to build a 1MW photovoltaic power generation system, m 2 ; is the rated energy of the battery energy storage system, Represent the lower and upper limits of the energy rating of the battery energy storage system, MWh; is the rated energy of the supercapacitor energy storage system, Respectively represent the lower and upper limits of the energy rating of the supercapacitor energy storage system, MWh; is the rated power of the battery energy storage system, is the rated power of the supercapacitor energy storage system, Indicates the power of the minimum power conversion system of the battery and supercapacitor energy storage system, MW; CR bat , CR uc Represent the limiting current rate of battery and supercapacitor energy storage system, C; are the rated capacities of the α and β sides of the converter, MVA; C ini is the total investment cost of the equipment, c pv is the unit power cost of the photovoltaic system, is the unit power cost of supercapacitor, is the unit energy cost of supercapacitor, is the unit power cost of the battery, is the unit energy cost of the battery, c rpc is the unit capacity cost of the converter, The budget cap.

[0119] In one embodiment of the present invention, the photovoltaic output wind curtailment ratio constraint is specifically:

[0120]

[0121] Where, is the actual output of the PV system at time t under scenario (κ,s); is the weight factor of PV output under scenario (κ,s); PV unit output ratio under scenario s at time t; Rated installed capacity of the photovoltaic power station; The probability weight of scenario s occurring.

[0122] For step S105, the present invention uses a second-order cone relaxation method to convert the grid-side voltage imbalance constraint and the back-to-back converter power constraint in the constraint conditions of step S104 into the form of second-order cone constraints, thereby converting the mixed integer nonlinear programming model into a mixed integer second-order cone model.

[0123] The model established in step S104 of the present invention is a nonlinear optimization problem due to the existence of back-to-back converter operation constraints and grid-side voltage imbalance constraints. Since the above two constraints are quadratic and convex, they can be restated in the form of second-order cone constraints through the second-order cone relaxation method.

[0124] In one embodiment of the present invention, the grid-side voltage unbalance constraint converted into a second-order cone constraint is specifically:

[0125]

[0126] In one embodiment of the present invention, the back-to-back converter operation constraints converted into the second-order cone constraints are specifically:

[0127]

[0128] For step S106, the present invention can use a commercial solver to solve the mixed integer second-order cone model obtained in step S105 to obtain the capacity optimization configuration results of the photovoltaic, hybrid energy storage system and back-to-back converter.

[0129] In one embodiment of the present invention, based on the mixed integer second-order cone model obtained in step S105, the capacity configuration input parameters and the technical parameters of the original traction substation are input into the GUROBI solver to obtain the capacity optimization configuration results of the photovoltaic, hybrid energy storage system and back-to-back converter.

[0130] The capacity configuration input parameters are shown in Table 1 below:

[0131]

[0132]

[0133] Table 1

[0134] The technical parameters of the original traction substation are shown in Table 2 below:

[0135]

[0136] Table 2

[0137] In one embodiment of the present invention, Case 1 (a conventional traction substation), Case 2 (a traction substation capacity design considering PQ), and Case 3 (the present invention's method) were compared. Traction load and photovoltaic data remained consistent across the three models. After simulation, the results are shown in Table 3.

[0138]

[0139] Table 3

[0140] Table 3 shows the calculation results for a single traction substation using the two methods. As shown in Table 3, the traction power supply system energy management optimization method with PV, hybrid energy storage, and a power flow regulator (Case 2 and Case 3) reduces the annual total cost by 10.01% and 15.30%, respectively, compared to the traditional traction power supply system optimization method (Case 1). In Case 2, the equality of two-phase active power and power factor results in the best three-phase unbalance performance, but this requires a larger back-to-back converter capacity (14.73 MVA). Compared to Case 2, a smaller back-to-back converter capacity (i.e., 2 × 6.14 MVA) can effectively control voltage imbalance and average grid-side power factor to meet standards. Given a limited initial budget, the smaller back-to-back converter capacity requirement means more resources can be invested in PV and energy storage systems, resulting in better economic benefits.

[0141] The present invention takes a flexible traction substation connected to photovoltaic, hybrid energy storage systems and back-to-back converters as its object. In the substation operation model, it fully considers the impact of the active and reactive regulation flexibility of the back-to-back converters on voltage imbalance and power factor, reducing the capacity demand for the power flow regulator. The capacity configuration process reduces the electricity cost of railway operations while ensuring that the three-phase voltage imbalance and power factor meet the standards. Therefore, the integrated capacity planning method of photovoltaic, hybrid energy storage systems and back-to-back converters in the flexible traction substation of the present invention is closer to reality and can provide a basis for the access and engineering application of energy storage systems and renewable energy in future electrified railways.

[0142] It can be seen from the above embodiments that the method of the present invention achieves at least the following beneficial effects:

[0143] 1. The present invention constructs a capacity configuration optimization model with the goal of minimizing annual investment and annual operating costs. By solving the model, the capacity of the photovoltaic, hybrid energy storage system and back-to-back converter of the flexible traction substation can be systematically designed.

[0144] 2. The present invention considers utilizing the flexibility of active and reactive regulation of the power flow regulator to improve voltage imbalance and power factor, thereby reducing the capacity requirement of the power flow regulator.

[0145] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0146] Based on the same inventive concept, an embodiment of the present invention also provides an electric railway flexible traction power supply photovoltaic storage integrated capacity configuration device, which can be used to implement the electric railway flexible traction power supply photovoltaic storage integrated capacity configuration method described in the above embodiment, as described in the following embodiment. Since the principle of solving the problem by the electric railway flexible traction power supply photovoltaic storage integrated capacity configuration device is similar to the electric railway flexible traction power supply photovoltaic storage integrated capacity configuration method, the embodiment of the electric railway flexible traction power supply photovoltaic storage integrated capacity configuration device can refer to the embodiment of the electric railway flexible traction power supply photovoltaic storage integrated capacity configuration method, and the repetitions will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceived.

[0147] Figure 2 This is a structural block diagram of the electric railway flexible traction power supply photovoltaic storage integrated capacity configuration device according to an embodiment of the present invention. Figure 2 As shown, in one embodiment of the present invention, the electric railway flexible traction power supply photovoltaic storage integrated capacity configuration device of the present invention includes:

[0148] A typical day scene determination unit 1 is used to obtain a typical day scene of the photovoltaic power generation system and the traction load based on historical data using a scene reduction algorithm;

[0149] The flexible traction substation daily operation model establishing unit 2 is configured to establish a flexible traction substation daily operation model for each typical daily scenario, with the goal of minimizing the daily operation cost of the flexible traction substation, taking into account power quality requirements and system operation-related constraints;

[0150] Total annualized investment cost model establishment unit 3, for establishing a total annualized investment cost model based on the capacity and estimated service life of the photovoltaic, hybrid energy storage system, and back-to-back converter using the equal annual value method;

[0151] A mixed integer nonlinear programming model establishment unit 4 is configured to establish, based on the flexible traction substation daily operation model and the total annualized investment cost model, a mixed integer nonlinear programming model for optimizing the integrated capacity of photovoltaic, hybrid energy storage systems, and back-to-back converters, with the goal of minimizing the sum of the total annualized investment cost and the total annualized operating cost, in combination with constraints;

[0152] a mixed integer second-order cone model determination unit 5, configured to convert the grid-side voltage unbalance constraint and the back-to-back converter power constraint in the constraints into a second-order cone constraint form using a second-order cone relaxation method, thereby converting the mixed integer nonlinear programming model into a mixed integer second-order cone model;

[0153] The capacity optimization configuration result determination unit 6 is used to solve the mixed integer second-order cone model to obtain the capacity configuration results of the photovoltaic, hybrid energy storage system and back-to-back converter.

[0154] In one embodiment of the present invention, the daily operating cost of the flexible traction substation includes: external grid billing, daily demand billing, ongoing operating costs, and additional penalty billing;

[0155] The objective function of the daily operation model of the flexible traction substation is:

[0156]

[0157] in, is the daily operating cost of the flexible traction substation, Billing for external grids, Billing for daily needs, For ongoing operating costs, Billing for additional penalties.

[0158] In one embodiment of the present invention, the total annualized investment cost model aims to minimize the total annual investment cost, where the total annual investment cost includes: the investment cost of the photovoltaic power generation system, the investment cost of the supercapacitor, the investment cost of the battery energy storage system, and the investment cost of the back-to-back converter;

[0159] The objective function of the total annualized investment cost model is:

[0160] min C INV =C pv +C uc +C bt +C rpc

[0161] Among them, C INV is the total annual investment cost, C pv is the investment cost of the photovoltaic power generation system, C uc is the investment cost of supercapacitor, C bt is the investment cost of the battery energy storage system, C rpc is the investment cost of the back-to-back converter.

[0162] In one embodiment of the present invention, the constraints include: power balance constraint, demand power constraint, photovoltaic output constraint, hybrid energy storage device constraint, back-to-back converter constraint, voltage imbalance constraint, power factor constraint, device installation constraint and photovoltaic output wind curtailment ratio constraint.

[0163] In one embodiment of the present invention, the objective function of the mixed integer nonlinear programming model is:

[0164]

[0165] Among them, C TAC is the sum of the total annual investment cost and the total annual operating cost, C INV is the total annual investment cost, is the number of days of source-load interaction, is the daily operating cost of the flexible traction substation, C AOC is the annual operating cost, Γ is the set of traction load scenarios, and Λ is the set of photovoltaic output scenarios.

[0166] In order to achieve the above object, according to another aspect of the present application, a computer device is also provided. Figure 3 As shown, the computer device includes a memory, a processor, a communication interface and a communication bus. The memory stores a computer program that can be run on the processor. When the processor executes the computer program, the steps in the above embodiment method are implemented.

[0167] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0168] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the corresponding program units in the above-described method embodiments of the present invention. The processor executes the non-transitory software programs, instructions, and modules stored in memory to perform various processor functions and work data processing, thereby implementing the methods in the above-described method embodiments.

[0169] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0170] The one or more units are stored in the memory, and when executed by the processor, perform the method in the above embodiment.

[0171] The specific details of the above-mentioned computer device can be understood by referring to the corresponding descriptions and effects in the above-mentioned embodiments, and will not be repeated here.

[0172] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed in a computer processor, the steps in the above-mentioned method for configuring the capacity of the electric railway flexible traction power supply and photovoltaic storage integration are implemented. It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment method can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0173] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer program product is also provided, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for configuring the capacity of the flexible traction power supply, photovoltaic storage and integrated circuit of the electrified railway.

[0174] Obviously, those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0175] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for configuring the capacity of an integrated photovoltaic and storage system for flexible traction power supply on electrified railways, characterized in that: include: Based on historical data, a scenario reduction algorithm is used to obtain typical daily scenarios of the photovoltaic power generation system and traction load; For each typical daily scenario, a flexible traction substation daily operation model is established with the goal of minimizing the daily operation cost of the flexible traction substation, taking into account power quality requirements and system operation-related constraints. Using the equivalent annual value method, a total annualized investment cost model was established based on the capacity and estimated service life of the photovoltaic, hybrid energy storage system, and back-to-back converter. Based on the daily operation model of the flexible traction substation and the total annualized investment cost model, with the goal of minimizing the sum of the total annualized investment cost and the total annualized operating cost, and in combination with constraints, a mixed integer nonlinear programming model based on the integrated capacity optimization of photovoltaic, hybrid energy storage systems, and back-to-back converters is established; Using a second-order cone relaxation method, the grid-side voltage unbalance constraint and the back-to-back converter power constraint in the constraint conditions are converted into a second-order cone constraint form, thereby converting the mixed-integer nonlinear programming model into a mixed-integer second-order cone model; The mixed integer second-order cone model is solved to obtain the capacity configuration results of photovoltaic, hybrid energy storage system and back-to-back converter.

2. The method for configuring the capacity of flexible traction power supply, photovoltaic and storage integration for electrified railways according to claim 1, characterized in that: The daily operating costs of a flexible traction substation include: external grid charges, daily demand charges, ongoing operating costs, and additional penalty charges; The objective function of the daily operation model of the flexible traction substation is: in, is the daily operating cost of the flexible traction substation, Billing for external grids, Billing for daily needs, For ongoing operating costs, Billing for additional penalties.

3. The method for configuring the capacity of flexible traction power supply, photovoltaic and storage integration for electrified railways according to claim 1, characterized in that: The total annualized investment cost model aims to minimize the total annual investment cost, which includes the investment cost of the photovoltaic power generation system, the investment cost of the supercapacitor, the investment cost of the battery energy storage system, and the investment cost of the back-to-back converter. The objective function of the total annualized investment cost model is: my C INV =C pv +C uc +C bt +C rpc Among them, C INV is the total annual investment cost, C pv is the investment cost of the photovoltaic power generation system, C uc is the investment cost of supercapacitor, C bt is the investment cost of the battery energy storage system, C rpc is the investment cost of the back-to-back converter.

4. The method for configuring the capacity of flexible traction power supply, photovoltaic and storage integration for electrified railways according to claim 1, characterized in that: The constraints include: power balance constraint, demand power constraint, photovoltaic output constraint, hybrid energy storage device constraint, back-to-back converter constraint, voltage imbalance constraint, power factor constraint, device installation constraint and photovoltaic output wind curtailment ratio constraint.

5. The method for configuring the capacity of flexible traction power supply, photovoltaic and storage integration for electrified railways according to claim 1, characterized in that: The objective function of the mixed integer nonlinear programming model is: Among them, C TAC is the sum of the total annual investment cost and the total annual operating cost, C INV is the total annual investment cost, is the number of days of source-load interaction, is the daily operating cost of the flexible traction substation, C AOC is the annual operating cost, Γ is the set of traction load scenarios, and Λ is the set of photovoltaic output scenarios.

6. A flexible traction power supply, photovoltaic and storage integrated capacity configuration device for electrified railways, characterized in that: include: A typical day scenario determination unit is used to obtain a typical day scenario of the photovoltaic power generation system and traction load based on historical data using a scenario reduction algorithm; a flexible traction substation daily operation model establishment unit, configured to establish a flexible traction substation daily operation model for each typical daily scenario, with the goal of minimizing the daily operation cost of the flexible traction substation, taking into account power quality requirements and system operation-related constraints; A total annualized investment cost model establishment unit is used to establish a total annualized investment cost model based on the capacity and estimated service life of photovoltaic, hybrid energy storage systems, and back-to-back converters using the equal annual value method; A mixed integer nonlinear programming model establishment unit is configured to establish, based on the flexible traction substation daily operation model and the total annualized investment cost model, a mixed integer nonlinear programming model for optimizing the integrated capacity of photovoltaic, hybrid energy storage systems, and back-to-back converters, with the goal of minimizing the sum of the total annualized investment cost and the total annualized operating cost, in combination with constraints; a mixed integer second-order cone model determination unit, configured to convert the grid-side voltage unbalance constraint and the back-to-back converter power constraint in the constraint conditions into a second-order cone constraint form using a second-order cone relaxation method, thereby converting the mixed integer nonlinear programming model into a mixed integer second-order cone model; The capacity optimization configuration result determination unit is used to solve the mixed integer second-order cone model to obtain the capacity configuration results of the photovoltaic, hybrid energy storage system and back-to-back converter.

7. The electric railway flexible traction power supply photovoltaic storage integrated capacity configuration device according to claim 6, characterized in that: The daily operating costs of a flexible traction substation include: external grid charges, daily demand charges, ongoing operating costs, and additional penalty charges; The objective function of the daily operation model of the flexible traction substation is: in, is the daily operating cost of the flexible traction substation, Billing for external grids, Billing for daily needs, For ongoing operating costs, Billing for additional penalties.

8. The electric railway flexible traction power supply, photovoltaic and storage integrated capacity configuration device according to claim 6, characterized in that: The total annualized investment cost model aims to minimize the total annual investment cost, which includes the investment cost of the photovoltaic power generation system, the investment cost of the supercapacitor, the investment cost of the battery energy storage system, and the investment cost of the back-to-back converter. The objective function of the total annualized investment cost model is: my C INV =C pv +C uc +C bt +C rpc Among them, C INV is the total annual investment cost, C pv is the investment cost of the photovoltaic power generation system, C uc is the investment cost of supercapacitor, C bt is the investment cost of the battery energy storage system, C rpc is the investment cost of the back-to-back converter.

9. The electric railway flexible traction power supply, photovoltaic and storage integrated capacity configuration device according to claim 6, characterized in that: The constraints include: power balance constraint, demand power constraint, photovoltaic output constraint, hybrid energy storage device constraint, back-to-back converter constraint, voltage imbalance constraint, power factor constraint, device installation constraint and photovoltaic output wind curtailment ratio constraint.

10. The electric railway flexible traction power supply, photovoltaic and storage integrated capacity configuration device according to claim 6, characterized in that: The objective function of the mixed integer nonlinear programming model is: Among them, C TAC is the sum of the total annual investment cost and the total annual operating cost, C INV is the total annual investment cost, is the number of days of source-load interaction, is the daily operating cost of the flexible traction substation, C AOC is the annual operating cost, Γ is the set of traction load scenarios, and Λ is the set of photovoltaic output scenarios.

11. A computer 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 computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

12. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

13. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images