Energy output control method based on power electronic converter, controller and medium

Abstract

The invention relates to the technical field of track power supply, particularly provides an energy output control method based on a power electronic converter, a controller and a storage medium, and aims to solve the problem of how to control new energy output of a railway power system. In order to achieve the purpose, the method comprises the steps that at the starting moment of each time step, a first power generation power prediction value of a new energy power generation unit in the time step and a first traction load power prediction value needed by a railway power system are obtained, and a new energy output plan of the time step is determined, and obtaining a second power generation power prediction value and a second traction load power prediction value in the preset time period at the starting moment of each preset time period in the time step length, and adjusting the new energy output plan so as to control the output power of the new energy power generation unit in real time. Thus, dependence of a railway power system on an external power grid can be reduced, new energy is directly utilized for power supply, the energy consumption requirement of a railway is met, and the utilization rate of the new energy is increased.

Description

Technical Field

[0001] The present application relates to the technical field of track power supply, and specifically relates to an energy output control method, a controller, and a storage medium based on a power electronic converter. Background Art

[0002] The railway power and electrical industry has experienced significant growth in recent years. With the expansion of the high-speed railway network and the increase in the electrification rate, the demand for electric traction power supply systems and electric locomotives continues to grow. Due to the large amount of power required for the operation of high-speed railways and ordinary railways, this high power demand makes it impossible for the railway system to rely on its own power generation capacity to meet the demand and must rely on an external power supply system.

[0003] However, the high dependence of the railway traction power supply system on the external power grid will result in the occupation of a huge amount of electricity consumption in the surrounding areas, which will affect the stability of the local power grid and bring a greater burden to the local power supply system. Moreover, in some weak grid or off-grid areas, it is difficult to match or adapt the railway road network with the power grid layout, which may affect the normal operation of railway trains. Therefore, the application of new energy with flexible layout is beneficial to autonomous energy supply and improvement of the self-consistency level, and is the primary choice for realizing high-quality operation and maintenance and green railways.

[0004] At present, the new energy power generation technology for railway infrastructure mainly provides electrical energy for non-traction parts, mainly in the form of micro-power generation systems, and the power supply objects are mostly trackside monitoring devices with high integration and low power. The proportion of renewable energy used in railways in China's overall railway energy consumption is still low, and there is currently a lack of energy management for railway new energy systems. Therefore, how to optimize the dispatching strategy of the railway power supply system and realize the management and control of the new energy output of the railway power supply system has become an urgent problem to be solved.

[0005] Correspondingly, a new solution is needed in this field to solve the above problems. Application Content

[0006] The present application aims to solve the above technical problems, that is, to solve the problem of how to realize the management and control of the new energy output of the railway power system.

[0007] In a first aspect, the present application provides an energy output control method based on a power electronic converter. The method is applied to a traction power supply system, and the traction power supply system includes a new energy power generation unit for supplying power to the railway power system. Among them, the new energy power generation unit is connected to the railway power system through a power electronic converter. The method includes:

[0008] At the start of each preset time step, obtain the first predicted power generation value of the new energy power generation unit within the time step and the first predicted traction load power value required by the railway power system; wherein, at least one continuous preset time period is included within the time step.

[0009] Determine the new energy output plan for the time step according to the first predicted power generation value and the first predicted traction load power value.

[0010] At the start of each preset time period when implementing the new energy output plan, obtain the second predicted power generation value of the new energy power generation unit within the preset time period and the second predicted traction load power value required by the railway power system.

[0011] Adjust the new energy output plan according to the second predicted power generation value and the second predicted traction load power value, so as to control the output power of the new energy power generation unit in real time within the preset time period.

[0012] In a technical solution of the above energy output control method based on a power electronic converter,

[0013] The determining the new energy output plan for the time step according to the first predicted power generation value and the first predicted traction load power value includes:

[0014] Based on a preset target optimization function, according to the first predicted power generation value and the first predicted traction load power value, with the minimum operating cost of the traction power supply system as the optimization target, perform target optimization to determine the new energy output plan.

[0015] In a technical solution of the above energy output control method based on a power electronic converter, the traction power supply system further includes a distribution network and an energy storage module, and the new energy power generation unit includes a photovoltaic power generation module;

[0016] The performing target optimization based on a preset target optimization function, according to the first predicted power generation value and the first predicted traction load power value, with the minimum operating cost of the traction power supply system as the optimization target, to determine the new energy output plan includes:

[0017] Determine the target optimization function according to the first predicted traction load power value and the following formula:

[0018]

[0019] wherein, J c (t) is the operating cost of the railway power system and the new energy power generation unit, J NFor the construction cost of the new energy power generation unit, J e For the potential profit of charging and discharging of the energy storage module, N is the number of preset time periods contained in the time step, k is the start time of the time step, and M is the time length of the time step;

[0020] Among them, J c (t) = c g (t)P g (t) + c pv (t)P pv (t) + c e (t)(P ch (t) - P dch (t)) - c(t)P(t)

[0021] c g (t) is the grid power purchase price, P g (t) is the output power of the distribution network, c pv (t) is the photovoltaic operation and maintenance cost, P pv (t) is the output power of the photovoltaic power generation module, c e (t) is the operation and maintenance cost of the energy storage module, P ch (t) is the charging power of the energy storage module, P dch (t) is the discharging power of the energy storage module, c(t) is the operation and maintenance cost of the traction power supply system, and P(t) is the predicted value of the first traction load power;

[0022] According to the first power generation prediction value, determine the constraint conditions of the target optimization function;

[0023] According to the target optimization function and the constraint conditions, perform target optimization to obtain the new energy output plan.

[0024] In a technical solution of the above energy output control method based on a power electronic converter, the determining the constraint conditions of the target optimization function according to the first power generation prediction value includes:

[0025] According to the first power generation prediction value, determine the upper limit value of the output power of the photovoltaic power generation module;

[0026] According to the following formula, determine the constraint conditions for the photovoltaic power generation module:

[0027] P pv_min (t) ≤ P pv (t) ≤ P pv_max (t)

[0028] Among them, P pv_min(t) is the lower limit value of the output power of the preset photovoltaic power generation module, P pv (t) is the output power of the photovoltaic power generation module, P pv_max (t) is the upper limit value of the output power of the photovoltaic power generation module.

[0029] In a technical solution of the above energy output control method based on a power electronic converter, the traction power supply system includes an interconnection unit, and the constraint conditions of the target optimization function include constraint conditions for the energy storage module, power balance constraint conditions, and constraint conditions for the transmission power of the interconnection unit;

[0030] Determining the constraint conditions of the target optimization function further includes:

[0031] According to the following formula, determine the constraint conditions for the energy storage module:

[0032] P ch_min (t) ≤ P ch (t) ≤ P ch_max (t)

[0033] P dch_min (t) ≤ P dch (t) ≤ P dch_max (t)

[0034] SOC min (t) ≤ SOC(t) ≤ SOC max (t)

[0035] Among them, P ch (t) is the charging power of the energy storage module, P ch_min (t) is the lower limit value of the charging power of the energy storage module, P ch_max (t) is the upper limit value of the charging power of the energy storage module; P dch (t) is the discharging power of the energy storage module, P dch_min (t) is the lower limit value of the discharging power of the energy storage module, P dch_max (t) is the upper limit value of the discharging power of the energy storage module; SOC(t) is the state of charge of the energy storage module, SOC min (t) is the lower limit value of the state of charge of the energy storage module, SOC max (t) is the upper limit value of the state of charge of the energy storage module; and / or,

[0036] According to the following formula, determine the power balance constraint conditions:

[0037] P g (t) + P pv (t) - P ch (t) + Pdch P(t) = P(t)

[0038] Wherein, P g (t) is the output power of the distribution network, P pv (t) is the output power of the photovoltaic power generation module, P dch (t) is the discharge power of the energy storage module, P ch (t) is the charging power of the energy storage module, and P(t) is the predicted value of the first traction load power; and / or,

[0039] Determine the constraint condition for the active power of the interconnection unit according to the following formula, where the active power is the power of the alternating current energy actually transmitted by the interconnection unit:

[0040]

[0041] Wherein, P ACDC is the active power of the interconnection unit, is the upper limit value of the active power of the interconnection unit, is the upper limit value of the active power of the interconnection unit;

[0042] Determine the constraint condition for the reactive power of the interconnection unit according to the following formula, where the reactive power is the power of the alternating current energy required to maintain the electromagnetic field or voltage of the interconnection unit:

[0043]

[0044] Wherein, Q ACDC is the reactive power of the interconnection unit, is the preset lower limit value of the reactive power of the interconnection unit, is the preset upper limit value of the reactive power of the interconnection unit;

[0045] Determine the constraint condition for the transmission power of the interconnection unit according to the constraint condition for the active power of the interconnection unit and the constraint condition for the reactive power of the interconnection unit.

[0046] In a technical solution of the above energy output control method based on a power electronic converter,

[0047] The new energy power generation unit includes a photovoltaic power generation module;

[0048] Obtaining the first power generation power prediction value of the new energy power generation unit within the time step includes:

[0049] Obtain the average solar irradiance received by the photovoltaic power generation module in the previous time step;

[0050] Determine a predicted value of the power generation of the photovoltaic power generation module by using the average solar irradiance according to the following formula:

[0051]

[0052] Wherein, P pv_a (t) is the predicted value of the power generation of the photovoltaic power generation module, h pv (t) is the average solar irradiance, h N is a preset rated solar irradiance, P pvN is a preset rated photovoltaic power;

[0053] Determine the first power generation prediction value according to the predicted value of the power generation of the photovoltaic power generation module.

[0054] In a technical solution of the above energy output control method based on a power electronic converter, the obtaining of the first traction load power prediction value required by the railway power system within the time step includes:

[0055] Obtain the traction load data of the railway power system in the previous time step, where the traction load data includes the historical traction load powers of n train groups, and n is a positive integer;

[0056] Obtain the predicted value of the traction load power of each train group within the time step according to the historical traction load powers of the n train groups;

[0057] Determine the first traction load power prediction value by using the predicted values of the traction load powers of the n train groups according to the following formula:

[0058] P(t) = P1(t) + P2(t) +... + P n (t)

[0059] Wherein, P(t) is the first traction load power prediction value, P1(t) is the predicted value of the traction load power of the first train group, P2(t) is the predicted value of the traction load power of the second train group, P n (t) is the predicted value of the traction load power of the nth train group.

[0060] In a technical solution of the above energy output control method based on a power electronic converter, the adjusting of the new energy output plan according to the second power generation prediction value and the second traction load power prediction value so as to control the output power of the new energy generation unit in real time within the preset time period includes:

[0061] Determine the ultra-short-term planned output power of the new energy power generation unit within the preset time period according to the second power generation power prediction value and the second traction load power prediction value;

[0062] Determine a power error according to the difference between the planned output power and the ultra-short-term planned output power, where the planned output power is the output power of the new energy power generation unit in the new energy output plan within the time step;

[0063] Adjust the new energy processing plan according to the power error so as to increase or decrease the output power of the new energy power generation unit in real time.

[0064] In a second aspect, a controller is provided, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above energy output control method based on a power electronic converter is implemented.

[0065] In a third aspect, a computer-readable storage medium is provided, which stores multiple program codes, and the program codes are adapted to be loaded and run by a processor to execute the method described in any one of the technical solutions of the above energy output control method based on a power electronic converter.

[0066] One or more of the above technical solutions of the present application have at least one or more of the following beneficial effects:

[0001] In the case of adopting the above technical solutions, the present application can be applied to a traction power supply system, which includes a new energy power generation unit for supplying power to a railway power system connected to the traction power supply system.

[0002] Among them, the present application may include: at the start moment of each preset time step, obtain the first power generation power prediction value of the new energy power generation unit within the time step and the first traction load power prediction value required by the railway power system; determine the new energy output plan for the time step according to the first power generation power prediction value and the first traction load power prediction value. At the start moment of each preset time period within the time step of executing the new energy output plan, obtain the second power generation power prediction value of the new energy power generation unit within the preset time period and the second traction load power prediction value required by the railway power system; adjust the new energy output plan according to the second power generation power prediction value and the second traction load power prediction value so as to control the output power of the new energy power generation unit in real time within the preset time period.

[0003] Through the above configuration method, the present application can determine the new energy output plan by obtaining the first traction load power prediction value and the first power generation power prediction value of the new energy power generation unit within each time step, and then can control the new energy output power of the new energy power generation unit to the railway power system. In this way, the dependence of the railway power system on the external power grid can be reduced, and the renewable new energy can be directly used to supply power to the traction train to meet the energy consumption needs of different railway facilities. Moreover, ultra-short-term prediction is carried out within each preset time period of the time step to obtain the second traction load power prediction value and the second power generation power prediction value, and the new energy output plan is adjusted in real time to minimize the real-time mismatch between demand and supply, improve the utilization rate of the new energy output power, facilitate the realization of autonomous energy supply of the railway power system, enhance the energy self-consistency level, and achieve a high-quality green railway. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Referring to the accompanying drawings, the disclosure of the present application will become more understandable. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the protection scope of the present application. Among them:

[0068] Figure 1 is a schematic diagram of the main step flow of the energy output control method based on a power electronic converter according to an embodiment of the present application;

[0069] Figure 2 is a schematic diagram of the circuit topology of a traction power supply system according to an embodiment of the present application;

[0070] Figure 3 is a schematic diagram of the main step flow of the energy output control method based on a power electronic converter according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The following describes some embodiments of the present application with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present application and are not intended to limit the protection scope of the present application.

[0072] In the description of the present application, a "module" and a "processor" may include hardware, software, or a combination of both. A module may include a hardware circuit, various appropriate sensors, communication ports, a memory, and may also include a software part, such as program code, or may be a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, in hardware, or in a combination of both. A computer-readable storage medium includes any suitable medium that can store program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and so on. The term "A and / or B" represents all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one of A or B" or "at least one of A and B" has a meaning similar to "A and / or B" and may include only A, only B, or A and B. The singular terms "a" and "the" may also include the plural form.

[0073] Here, some terms related to the present application are explained first.

[0074] The new energy output plan refers to the planning or scheduling plan of the electric power generated by new energy generating units (such as solar energy, wind energy, or water energy, etc.) in the power system. Its core goal is to optimize the utilization efficiency of new energy power generation and reduce the dependence on traditional power generation energy.

[0075] Refer to the appendix Figure 1 , Figure 1 is a schematic diagram of the main steps of the energy output control method based on a power electronic converter according to an embodiment of the present application. As Figure 1 shown, the energy output control method based on a power electronic converter in the embodiment of the present application mainly includes the following steps S101 to step S104.

[0076] Step S101: At the start moment of each preset time step, obtain the first predicted power generation value of the new energy generating unit within the time step and the first predicted traction load power value required by the railway power system.

[0077] Wherein, the time step may include at least one continuous preset time period.

[0078] In this embodiment, the time step refers to the interval between two adjacent time points in a continuous time series. By way of example, in an actual application scenario, the time step may be 1 day or 1 week.

[0079] In this embodiment, the method can be applied to a traction power supply system, which includes a new energy power generation unit for supplying power to the railway power system. The new energy power generation unit is connected to the railway power system through a power electronic converter.

[0080] In one embodiment, the new energy power generation unit may further include one or more of a wind power generation module, a photovoltaic power generation module, a tidal power generation module, and other renewable energy power generation modules.

[0081] In one embodiment, reference may be made to the appendix Figure 2 , the appendix Figure 2 is a schematic diagram of the circuit topology of a traction power supply system according to an embodiment of the present application. As shown in the appendix Figure 2 As shown, the traction power supply system may include a distribution network 103, an energy storage module 102, and a photovoltaic power generation module 101. The traction power supply system is connected to the α-phase and β-phase of the traction network of the railway power system through an interconnection device 104. The direct current ports of the interconnection device 104 are respectively connected to the photovoltaic power generation module, the energy storage module, and the distribution network through different power electronic converters within the traction power supply system.

[0082] In this embodiment, the traction power supply system may further include at least one power electronic converter. The power electronic converter is a power electronic device for realizing the conversion of electrical energy forms, capable of converting alternating current to direct current or direct current to alternating current. By way of example, the power electronic converter may specifically be an inverter device 105. As Figure 2 shown, the interconnection device is connected to the distribution network through the inverter device 105.

[0083] In one embodiment, the traction power supply system can also use the interconnection device to recover the braking energy generated during the braking process of the traction train and store the braking energy in the energy storage module for subsequent power supply to the traction train, improving the energy utilization rate.

[0084] In one embodiment, the photovoltaic power generation module can be arranged on the slopes on both sides of the track subgrade to maximize the use of solar power generation.

[0085] In this embodiment, the first power generation power prediction value refers to the predicted value of the maximum power generation of the new energy power generation unit, and the first traction power prediction value refers to the predicted value of the electric power required by all electric locomotives in the railway power system during operation.

[0086] In one embodiment, if the new energy power generation unit includes a photovoltaic power generation module, then "obtaining the first power generation power prediction value of the new energy power generation unit within the time step" in step S101 may include steps S1011 to S1013:

[0087] Step S1011: Obtain the average solar irradiance received by the photovoltaic power generation module at the previous time step.

[0088] Step S1012: Determine the predicted value of the power generation of the photovoltaic power generation module based on the average solar irradiance according to the following formula (1):

[0089]

[0090] where, P pv_a (t) is the predicted value of the power generation of the photovoltaic power generation module, h pv (t) is the average solar irradiance, h N is the preset rated solar irradiance, P pvN is the preset rated photovoltaic power. Where, t is used to represent the time variable within the time step, and N is used to represent the rated value corresponding to the photovoltaic power generation module.

[0091] Step S1013: Determine the first power generation prediction value according to the predicted value of the power generation of the photovoltaic power generation module.

[0092] In one embodiment, the predicted value of the output power of the photovoltaic power generation module can be determined based on the SARIMA model and the KF algorithm. Among them, the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) is a model specifically used for analyzing and predicting time series data with seasonal patterns. It can capture the seasonal characteristics of the time series by adding seasonal differences, seasonal autoregression, and seasonal moving average terms to obtain time series prediction data. The Kalman Filter (KF) is an algorithm used to estimate the current state of the traction power supply system. It obtains the optimal estimate of the state variables at the current moment by using the state estimate value at the previous moment and the observation value at the current moment.

[0093] In one embodiment, obtaining the "first traction load power prediction value required by the railway power system" in step S101 may further include:

[0094] Step S1013: Obtain the traction load data of the railway power system within the previous time step.

[0095] In this embodiment, the traction load data includes the historical traction load powers of n train groups, where n is a positive integer.

[0096] Step S1014: Obtain the predicted value of the traction load power of each train group within the time step according to the historical traction load powers of the n train groups.

[0097] In this embodiment, trains of the same model operating within the same time step can be grouped into one train group according to the train operation schedule or train timetable of the railway power system, and then n train groups within this time step can be obtained.

[0098] In this embodiment, when the train is in braking, the traction load power of the train can be negative.

[0099] Step S1015: According to the following formula (2), determine the first traction load power prediction value by using the predicted values of the traction load powers of the n train groups:

[0100] P(t) = P1(t) + P2(t) +... + P n (t) (2)

[0101] where P(t) is the first traction load power prediction value, P1(t) is the predicted value of the traction load power of the first train group, P2(t) is the predicted value of the traction load power of the second train group, and P n (t) is the predicted value of the traction load power of the nth train group.

[0102] Step S102: Determine the new energy output plan for this time step according to the first power generation prediction value and the first traction load power prediction value.

[0103] In this embodiment, the new energy output plan is a plan for setting the output power of each module in the traction power supply system. By way of example, the new energy output plan may specifically include the output power plan curve of the distribution network within this time step, the output power plan curve of the energy storage module within this time step, and the output power plan curve of the new energy discharge module within this time step.

[0104] In one embodiment, step S102 may further include:

[0105] Step S1021: Based on a preset target optimization function, according to the first power generation prediction value and the first traction load power prediction value, with the minimum operating cost of the traction power supply system as the optimization target, perform target optimization to determine the new energy output plan.

[0106] In this embodiment, the target optimization function can be determined in real time by using the first traction load power prediction value, and the constraint conditions of the target optimization function can be determined in real time by using the first power generation prediction value to determine the final target optimization function corresponding to this time step.

[0107] In one embodiment, the traction power supply system further includes a distribution network and an energy storage module, and the new energy power generation unit includes a photovoltaic power generation module.

[0108] In this embodiment, step S1021 may further include:

[0109] Step S10211: Determine the objective optimization function according to the first traction load power prediction value and the following formula (3):

[0110]

[0111] where, J c (t) is the operating cost of the railway power system and the new energy generation unit, J N is the construction cost of the new energy generation unit, J e is the potential profit of the energy storage module for charging and discharging, N is the number of preset time periods contained in the time step, k is the start time of the time step, and M is the time length of the time step.

[0112] where, J c (t) can be expressed according to the following formula (4): J c (t) = c g (t)P g (t) + c pv (t)P pv (t) + c e (t)(P ch (t) - P dch (t) - c(t)P(t) (4)

[0113] where, c g (t) is the grid power purchase price, P g (t) is the output power of the distribution network, c pv (t) is the photovoltaic operation and maintenance cost, P pv (t) is the output power of the photovoltaic power generation module, c e (t) is the operation and maintenance cost of the energy storage module, P ch (t) is the charging power of the energy storage module, P dch (t) is the discharge power of the energy storage module, c(t) is the operation and maintenance cost of the traction power supply system, and P(t) is the first traction load power prediction value.

[0114] where, t is used to represent the time variable within the time step.

[0115] Step S10212: Determine the constraint conditions of the objective optimization function according to the first power generation prediction value.

[0116] Step S10213: Perform objective optimization according to the objective optimization function and the constraint conditions to obtain the new energy output plan.

[0117] In this embodiment, the constraint conditions may include constraint conditions on the output power of each module in the traction power supply system, constraint conditions on the transmission power of the interconnection device, and power balance constraint conditions.

[0118] In one embodiment, step S10212 may further include:

[0119] Step S102121: Determine the upper limit value of the output power of the photovoltaic power generation module according to the first predicted power generation value.

[0120] Step S102122: Determine the constraint conditions for the photovoltaic power generation module according to the following formula (5):

[0121] P pv_min (t) ≤ P pv (t) ≤ P pv_max (t) (5)

[0122] Wherein, P pv_min (t) is the lower limit value of the output power of the preset photovoltaic power generation module, P pv (t) is the output power of the photovoltaic power generation module, P pv_max (t) is the upper limit value of the output power of the photovoltaic power generation module.

[0123] Wherein, t is used to represent the time variable within the time step.

[0124] In other embodiments, the new energy power generation unit may also be a wind power generation module, a nuclear power generation module, and other renewable energy power generation modules, and the corresponding constraint conditions may also be set according to actual needs, without affecting the normal implementation of this embodiment.

[0125] In one embodiment, step S10212 may further include:

[0126] Step 10213: Determine the constraint conditions for the energy storage module according to the following formulas (6) to (8):

[0127] P ch_min (t) ≤ P ch (t) ≤ P ch_max (t) (6)

[0128] P dch_min (t) ≤ P dch (t) ≤ P dch_max (t) (7)

[0129] SOC min (t) ≤ SOC(t) ≤ SOC max (t) (8)

[0130] Among them, P in formula (6) ch (t) is the charging power of the energy storage module, P ch_min (t) is the lower limit value of the charging power of the energy storage module, P ch_max (t) is the upper limit value of the charging power of the energy storage module; P in formula (7) dch (t) is the discharging power of the energy storage module, P dch_min (t) is the lower limit value of the discharging power of the energy storage module, P dch_max (t) is the upper limit value of the discharging power of the energy storage module; SOC(t) in formula (8) is the state of charge of the energy storage module, SOC min (t) is the lower limit value of the state of charge of the energy storage module, SOC max (t) is the upper limit value of the state of charge of the energy storage module.

[0131] Among them, t is used to represent the time variable within the time step.

[0132] In this embodiment, the state of charge (SOC) of the energy storage module mainly refers to the state of charge of the battery in the energy storage module, that is, the ratio of the remaining battery power to the total capacity, usually expressed as a percentage. The discharging power refers to the electric energy released by the energy storage module per unit time, usually in kilowatts (kW) or megawatts (MW). The charging power refers to the power of the energy storage module during the charging or energy absorption process.

[0133] In one embodiment, step S10212 may further include:

[0134] Step S10214: Determine the power balance constraint condition according to the following formula (9):

[0135] P g (t)+P pv (t)-P ch (t)+P dch (t)=P(t) (9)

[0136] Among them, P g (t) is the output power of the distribution network, P pv (t) is the output power of the photovoltaic power generation module, P dch (t) is the discharging power of the energy storage module, P ch (t) is the charging power of the energy storage module, and P(t) is the predicted value of the first traction load power.

[0137] Among them, t is used to represent the time variable within the time step.

[0138] In one embodiment, the traction power supply system includes an interconnection unit, and the constraint conditions of the objective optimization function include the constraint conditions for the energy storage module, the power balance constraint conditions, and the constraint conditions for the transmission power of the interconnection unit.

[0139] In this embodiment, the "interconnection unit" is equivalent to the "interconnection device" in the above appendix Figure 2 and is used to connect the distribution network, the energy storage module, the photovoltaic power generation module, and the traction network of the railway power system.

[0140] In this embodiment, step S10212 may further include:

[0141] Step S10214: Determine the constraint condition for the active power of the interconnection unit according to the following formula:

[0142]

[0143] where P ACDC is the active power of the interconnection unit, is the upper limit value of the active power of the interconnection unit, is the upper limit value of the active power of the interconnection unit.

[0144] Step S10215: Determine the constraint condition for the reactive power of the interconnection unit according to the following formula (11):

[0145]

[0146] where Q ACDC is the reactive power of the interconnection unit, is the preset lower limit value of the reactive power of the interconnection unit, is the preset upper limit value of the reactive power of the interconnection unit;

[0147] Step S10216: Determine the constraint condition for the transmission power of the interconnection unit according to the constraint condition for the active power of the interconnection unit and the constraint condition for the reactive power of the interconnection unit.

[0148] In this embodiment, the active power is the power of the alternating current energy actually transmitted by the interconnection unit, and the reactive power is the power of the alternating current energy required to maintain the electromagnetic field or voltage required by the interconnection unit.

[0149] Step S103: At the start moment of each preset time period when implementing the new energy output plan, obtain the second power prediction value of the new energy power generation unit and the second traction load power prediction value required by the railway power system within the preset time period.

[0150] In this embodiment, the time step for implementing the new energy output plan can be divided into multiple preset time periods with the same duration. At the start time of each preset time period, the predicted value of the second power generation and the predicted value of the second traction load power within the preset time period are obtained. Among them, the predicted value of the second power generation can be the predicted value of the power generation that the new energy power generation unit can generate within the preset time period, and the predicted value of the second traction load power can be the predicted value of the traction load power required by the railway power system within the preset time period.

[0151] In one implementation, based on the factual detection data in the previous preset time period within the time step, the predicted value of the second power generation of the new energy power generation unit and the predicted value of the second traction load power required by the railway power system for the current preset time period can be obtained.

[0152] As an example, if the new energy power generation unit is a photovoltaic power generation module, the actual solar irradiance in the previous preset time period and the actual traction load power actually required by the railway power system can be obtained. The actual solar irradiance is used as h pv (t), substituting it into the above formula (1) to obtain the predicted value of the second power, and substituting the actual traction load power of each train group in the previous preset time period into the above formula (2) to obtain the predicted value of the second traction load power.

[0153] In this implementation, at the start time of the first preset time period within the time step, based on the factual detection data of the previous time step, the predicted value of the second power generation of the new energy power generation unit and the predicted value of the second traction load power required by the railway power system for the current preset time period can be obtained, which does not affect the normal implementation of this implementation either.

[0154] Step S104: Adjust the new energy output plan according to the predicted value of the second power generation and the predicted value of the second traction load power, so as to control the output power of the new energy power generation unit in real time within the preset time period.

[0155] In this embodiment, adjusting the new energy output plan means adjusting the new energy output plan within the preset time period. As an example, adjusting the new energy output plan can specifically be adjusting the planned output curve of the new energy power generation unit within the preset time period, the planned output curve of the energy storage module within the preset time period, and the planned output curve of the distribution network within the preset time period.

[0156] In one implementation, step S104 can further include:

[0157] Step S1041: Determine the ultra-short-term planned output power of the new energy power generation unit within the preset time period according to the predicted value of the second power generation and the predicted value of the second traction load power.

[0158] Step S1042: Determine the power error according to the difference between the planned output power and the ultra-short-term planned output power.

[0159] Step S1043: Adjust the new energy processing plan according to the power error so as to increase or decrease the output power of the new energy power generation unit in real time.

[0160] In this embodiment, the planned output power is the output power of the new energy power generation unit in the new energy output plan within the time step.

[0161] In an application scenario according to an embodiment of the present application, reference can be made to the attached Figure 3 attachment Figure 3 is a schematic diagram of the main step flow of an energy output control method based on a power electronic converter according to an embodiment of the present application. As Figure 3 shown, the traction power supply system may include a distribution network, an interconnection unit, a photovoltaic power generation module, and an energy storage module. This method may specifically include:

[0162] Step S201: Obtain system parameters.

[0163] In this embodiment, the system parameters may include the installed capacity of the photovoltaic power generation module in the traction power supply system, the geographical location of the photovoltaic power generation module and the meteorological information of the surrounding environment, the construction cost and operation and maintenance cost of the photovoltaic power generation unit, the potential profit of the energy storage module for charging and discharging, the grid power purchase price, the operation and maintenance cost of the energy storage unit, the operation and maintenance cost of the traction power supply system, and other parameters required for data prediction.

[0164] Step S202: Obtain the actual operation data of the previous time step.

[0165] In this embodiment, the actual operation data may include the actual traction load power required by the railway power system in the previous time step, the actual solar irradiance received by the photovoltaic power generation module in the previous time step, and other operation data of the traction power supply system.

[0166] In this embodiment, in the first time step, only the historical data of the traction load power required by the railway power system may be obtained, and in the subsequent data prediction process, the first photovoltaic power prediction value may be directly obtained according to the installed capacity of the photovoltaic power generation unit.

[0167] Step S203: Save the actual operation data as historical data.

[0168] Step S204: Perform data prediction according to the saved historical data.

[0169] In this embodiment, the data prediction method may be the same as the process of obtaining the "first traction load power prediction value" and the "first power generation power prediction value" in the above embodiment.

[0170] Step S206: Obtain a new energy output plan according to a preset target optimization function, constraint conditions, and data prediction results.

[0171] In this embodiment, the data prediction results may include a first traction load power prediction value and a first power generation power prediction value.

[0172] In this embodiment, it may be as Figure 3 shown to obtain a new energy output plan according to the constraint conditions and the objective function. Among them, before step S206, it may further include:

[0173] Step S2051: Determine the target optimization function within this time step according to the system parameters and the first traction load power prediction value.

[0174] In this embodiment, the target optimization function takes the minimum operating cost of the traction power supply system as the optimization goal for target optimization, which is the same as the target optimization function in the above embodiment and will not be elaborated here.

[0175] Step S2052: Determine the constraint conditions according to the first power generation power prediction value.

[0176] In this embodiment, the constraint conditions are the same as those in the above embodiment and will not be elaborated here. Among them, the upper and lower limits of the charge and discharge of the energy storage module, the upper and lower limits of the active power and reactive power of the interconnection unit, and the upper and lower limits of the photovoltaic power generation unit in the constraint conditions can all be obtained according to the system parameters.

[0177] In this embodiment, the new energy output plan may include multiple sets of time series data. For example, it may include time series data for setting the planned output power of the photovoltaic power generation module within this time step, time series data for setting the planned output power of the energy storage module within this time step, and so on.

[0178] Step S207: Perform ultra-short-term prediction within each preset time period in the time step.

[0179] In one embodiment, the actual operation data of executing the new energy output plan in the previous preset time period may be obtained, and ultra-short-term prediction may be performed based on the SARIMA model, KF algorithm, or other new energy power prediction technologies, which will not be elaborated here.

[0180] Step S208: Obtain a prediction error according to the ultra-short-term prediction results.

[0181] In this embodiment, the ultra-short-term planned output power within the corresponding preset time period can be obtained according to the ultra-short-term prediction result, and then the prediction error can be obtained based on the difference between the ultra-short-term planned output power and the original planned output power in the new energy output plan.

[0182] Among them, to obtain the ultra-short-term planned output power within the corresponding preset time period according to the ultra-short-term prediction result, specifically, k in the optimization objective function in the above embodiment can be modified to the initial moment of this preset time period, m is the time length of this preset time period, k + m is the end moment of this preset time period, and N is set to 1 to obtain the optimization objective function of this preset time period, and substituting the ultra-short-term prediction result to obtain the ultra-short-term planned output power.

[0183] Among them, the ultra-short-term prediction result can include the "second traction load power prediction value" and the "second power generation power prediction value" in the above embodiment.

[0184] Step S209: Adjust the new energy output plan according to the prediction error.

[0185] As an example, the output power of the photovoltaic power generation module in the new energy output plan can be adjusted according to the following equation (12):

[0186] P1 = P2 + P3 (12)

[0187] Among them, P1 can be the output power of the adjusted photovoltaic power generation module, P2 can be the planned output power of the photovoltaic power generation module in the new energy output plan, and P3 can be the prediction error.

[0188] Among them, the prediction error can be positive, which is used to indicate an increase in output power, or negative, which is used to indicate a decrease in output power.

[0189] Step S210: Determine whether the current time step ends. If so, obtain the actual data of the current time step, return to step S202. If not, obtain the actual data of the current preset time period, enter the next preset time period, and return to step S207.

[0190] In this embodiment, a two-layer control strategy can also be adopted for the traction power supply system. Among them, the first-layer control is used to predict the power generation power of the photovoltaic power generation module and the traction load power required by the railway power system according to steps S201 to S206. Considering the optimization objectives and constraints, with the goal of minimizing the operating cost of the traction power supply system, a new energy output plan corresponding to each time step is generated to achieve the dispatching control of the traction power supply system; the second-layer control is applied to adjust the new energy processing plan in real time based on the actual operating conditions of the new energy output plan in the previous preset time period within a preset time period in each time step according to steps S207 to S210.

[0191] Based on the method described in the above steps S101 to S104, the present application can determine the new energy output plan by obtaining the first traction load power prediction value and the first power generation power prediction value of the new energy power generation unit in each time step, and then can control the new energy output power of the new energy power generation unit to the railway power system. In this way, the dependence of the railway power system on the external power grid can be reduced, and the renewable new energy can be directly used to supply power to the traction train to meet the energy consumption needs of different railway facilities. Moreover, ultra-short-term prediction is carried out within each preset time period of the time step to obtain the second traction load power prediction value and the second power generation power prediction value, and the new energy output plan is adjusted in real time to minimize the real-time mismatch between demand and supply to the greatest extent, improve the utilization rate of the new energy output power, and is conducive to the railway power system to achieve autonomous energy supply, improve the energy self-consistency level, and realize a high-quality green railway.

[0192] It should be noted that although the above steps are described in a specific order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of the present application, it is not necessary to execute different steps in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent technical solutions to the technical solutions described in the present application, and therefore will also fall within the protection scope of the present application.

[0193] Those skilled in the art can understand that all or part of the processes in the methods of the above-mentioned embodiments of the present application can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium that can carry the computer program code, etc.

[0194] Another aspect of the present application also provides a computer-readable storage medium.

[0195] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium can be configured to store a program for executing the energy output control method based on a power electronic converter in the above-mentioned method embodiments. The program can be loaded and run by a processor to implement the above-mentioned energy output control method based on a power electronic converter. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiments of the present application is a non-transitory computer-readable storage medium.

[0196] Another aspect of the present application also provides a controller.

[0197] In an embodiment of a controller according to the present application, the controller can include at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any of the above-mentioned embodiments is implemented.

[0198] So far, the technical solution of the present application has been described in conjunction with one embodiment shown in the drawings. However, those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principle of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. An energy output control method based on a power electronic converter, characterized in that: The method is applied to a traction power supply system, the traction power supply system includes a new energy power generation unit, the new energy power generation unit is used to supply power to a railway power system, wherein the new energy power generation unit is connected to the railway power system through a power electronic converter; the method includes: At the start time of each preset time step, a first power generation prediction value of the new energy power generation unit and a first traction load power prediction value required by the railway power system within the time step are obtained; wherein the time step contains at least one continuous preset time period; Determine the new energy output plan for the time step according to the first power generation power prediction value and the first traction load power prediction value; At the start time of each preset time period of executing the new energy output plan, obtaining a second power generation prediction value of the new energy power generation unit within the preset time period and a second traction load power prediction value required by the railway power system; According to the second power generation power prediction value and the second traction load power prediction value, the new energy output plan is adjusted so as to control the output power of the new energy power generation unit in real time within the preset time period.

2. The energy output control method based on power electronic converter according to claim 1 is characterized in that: The step of determining the new energy output plan for the time step according to the first power generation power prediction value and the first traction load power prediction value includes: Based on a preset target optimization function, according to the first power generation power prediction value and the first traction load power prediction value, target optimization is performed with the minimum operating cost of the traction power supply system as the optimization target to determine the new energy output plan.

3. The energy output control method based on power electronic converter according to claim 2 is characterized in that: The traction power supply system also includes a distribution network and an energy storage module, and the new energy power generation unit includes a photovoltaic power generation module; The method of performing target optimization based on a preset target optimization function, according to the first power generation power prediction value and the first traction load power prediction value, and taking the minimization of the operating cost of the traction power supply system as the optimization target, and determining the new energy output plan includes: The objective optimization function is determined according to the first traction load power prediction value and the following formula: Among them, J c (t) is the operating cost of the railway power system and the new energy power generation unit, J N is the construction cost of the new energy power generation unit, J e is the potential profit of charging and discharging the energy storage module, N is the number of preset time periods contained in the time step, k is the starting time of the time step, and M is the time length of the time step; Among them, J c (t) = c g (t)P g (t) + c pv (t)P pv (t) + c e (t)(P ch (t) - P dch (t)) - c(t)P(t) c g (t) is the power purchase price from the power grid, P g (t) is the output power of the distribution network, c pv (t) is the photovoltaic operation and maintenance cost, P pv (t) is the output power of the photovoltaic power generation module, c e (t) is the operation and maintenance cost of the energy storage module, P ch (t) is the charging power of the energy storage module, P dch (t) is the discharge power of the energy storage module, c(t) is the operation and maintenance cost of the traction power supply system, and P(t) is the predicted value of the first traction load power; Determining the constraint conditions of the objective optimization function according to the first power generation prediction value; According to the objective optimization function and the constraint conditions, objective optimization is performed to obtain the new energy output plan.

4. The energy output control method based on power electronic converter according to claim 3 is characterized in that: Determining the constraint condition of the objective optimization function according to the first power generation prediction value includes: Determining an upper limit value of the output power of the photovoltaic power generation module according to the first power generation prediction value; The constraint condition on the photovoltaic power generation module is determined according to the following formula: P pv_min (t)≤P pv (t)≤P pv_max (t) Among them, P pv_min (t) is the preset lower limit of the output power of the photovoltaic power generation module, P pv (t) is the output power of the photovoltaic power generation module, P pv_max (t) is the upper limit of the output power of the photovoltaic power generation module.

5. The energy output control method based on power electronic converter according to claim 1, characterized in that: The traction power supply system includes an interconnection unit, and the constraint conditions of the objective optimization function include constraint conditions on the energy storage module, power balance constraint conditions and constraint conditions on the transmission power of the interconnection unit; The determining of the constraint conditions of the objective optimization function further includes: According to the following formula, the constraint conditions for the energy storage module are determined: P ch_min (t)≤P ch (t)≤P ch_max (t) P dch_min (t)≤P dch (t)≤P dch_max (t) SOC min (t)≤SOC(t)≤SOC max (t) Among them, P ch (t) is the charging power of the energy storage module, P ch_min (t) is the lower limit of the charging power of the energy storage module, P ch_max (t) is the upper limit of the charging power of the energy storage module; P dch (t) is the discharge power of the energy storage module, P dch_min (t) is the lower limit of the discharge power of the energy storage module, P dch_max (t) is the upper limit of the discharge power of the energy storage module; SOC(t) is the state of charge of the energy storage module, S6C min (t) is the lower limit of the state of charge of the energy storage module, SOC max (t) is the upper limit value of the state of charge of the energy storage module; and / or, The power balance constraint condition is determined according to the following formula: P g (t)+P pv (t)-P ch (t)+P dch (t)=P(t) Among them, P g (t) is the output power of the distribution network, P pv (t) is the output power of the photovoltaic power generation module, P dch (t) is the discharge power of the energy storage module, P ch (t) is the charging power of the energy storage module, P(t) is the predicted value of the first traction load power; and / or, The constraint condition on the active power of the interconnected unit is determined according to the following formula, where the active power is the power of the AC energy actually transmitted by the interconnected unit: Among them, P ACDC is the active power of the interconnected unit, is the upper limit value of the active power of the interconnected unit, is the upper limit value of the active power of the interconnected unit; According to the following formula, the constraint condition on the reactive power of the interconnected unit is determined, where the reactive power is the power of the alternating current energy required to maintain the electromagnetic field or voltage required by the interconnected unit: Among them, Q ACDC is the reactive power of the interconnected unit, is a preset lower limit value of reactive power of the interconnected unit, is a preset upper limit value of reactive power of the interconnected unit; According to the constraint conditions on the active power of the interconnected units and the constraint conditions on the reactive power of the interconnected units, the constraint conditions on the transmission power of the interconnected units are determined.

6. The energy output control method based on power electronic converter according to claim 1 is characterized in that: The new energy power generation unit includes a photovoltaic power generation module; Obtaining a first power generation prediction value of the new energy power generation unit within the time step, comprising: Obtaining the average solar irradiance received by the photovoltaic power generation module in the previous time step; The predicted value of the power generation of the photovoltaic power generation module is determined using the average solar irradiance according to the following formula: Among them, P pv_a (t) is the predicted value of the power generation of the photovoltaic power generation module, h pv (t) is the average solar irradiance, h N is the preset rated solar irradiance, P pvN is the preset PV rated power; The first power generation prediction value is determined according to the predicted value of the power generation of the photovoltaic power generation module.

7. The energy output control method based on power electronic converter according to claim 1 is characterized in that: The obtaining of the first traction load power prediction value required by the railway power system within the time step includes: Acquire traction load data of the railway power system in the previous time step, wherein the traction load data includes historical traction load power of n train sets, where n is a positive integer; Obtaining a predicted value of the traction load power of each train set within the time step according to the historical traction load power of the n train sets; The first traction load power prediction value is determined using the traction load power prediction values ​​of the n train sets according to the following formula: P(t)=P1(t)+P2(t)+...+P n (t) Wherein, P(t) is the first traction load power prediction value, P1(t) is the traction load power prediction value of the first train group, P2(t) is the traction load power prediction value of the second train group, P n (t) is the predicted value of the traction load power of the nth train set.

8. The energy output control method based on power electronic converter according to claim 1 is characterized in that: The step of adjusting the new energy output plan according to the second power generation power prediction value and the second traction load power prediction value so as to control the output power of the new energy power generation unit in real time within the preset time period includes: Determining the ultra-short-term planned output power of the new energy power generation unit within the preset time period according to the second power generation power prediction value and the second traction load power prediction value; Determining a power error according to a difference between a planned output power and the ultra-short-term planned output power, wherein the planned output power is the output power of a new energy power generation unit in the new energy output plan within the time step; The new energy processing plan is adjusted according to the power error so as to increase or decrease the output power of the new energy power generation unit in real time.

9. A controller, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the energy output control method based on the power electronic converter according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the energy output control method based on a power electronic converter according to any one of claims 1 to 8.

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