A day-ahead scheduling method and system for railway station AC-DC microgrid

Abstract

The application provides a day-ahead scheduling method and system for a railway station AC-DC micro-grid, and relates to the technical field of power grid scheduling; the method comprises the following steps: obtaining photovoltaic output and AC-DC load in the railway station AC-DC micro-grid; modeling the probability distribution of the photovoltaic output and the AC-DC load by using a norm distance uncertainty set to obtain a three-layer two-stage distribution robust optimization model of the AC-DC micro-grid; and solving the three-layer two-stage distribution robust optimization model to obtain a day-ahead scheduling result. The application achieves a good balance between the robustness and the economy of the railway station AC-DC micro-grid, and well balances the robustness and the economy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid dispatching, and in particular to a day-ahead dispatching method and system for a railway station AC / DC microgrid. BACKGROUND

[0002] As a typical large public building, the energy consumption of a railway station is huge, and a distributed photovoltaic power generation system is usually installed on a large area of the roof to reduce the dependence on the power supply of the distribution network. Since the lighting, variable frequency air conditioning and charging piles in the station area in the railway station building are DC loads, if conventional AC power supply is used, they need to be rectified to DC for dispatching, and the output of the photovoltaic power generation system is DC, and part of the load is directly supplied with DC, which not only reduces the conversion loss between AC and DC and improves the power supply efficiency, but also reduces the AC / DC conversion device and the power supply cost. Therefore, using an AC / DC microgrid as the power supply and distribution mode of the railway station can improve the local consumption capacity of photovoltaic power generation, reduce the influence of photovoltaic output fluctuation on the reliability of the power supply of the railway station, and reduce the electrical energy loss in the conversion process, thereby better integrating distributed power sources, energy storage devices, AC / DC loads, charging piles and other resources.

[0003] In order to reduce the influence of the random characteristics of photovoltaic and other distributed power sources and loads on the AC / DC microgrid and realize the reliability and economy of the operation of the AC / DC microgrid, currently, in the face of uncertain factors in the AC / DC microgrid, random programming and two-stage robust optimization methods are usually used to model uncertain variables, and based on the random probability distribution of the distributed power sources and loads, an optimal solution with the expected minimum operation cost is sought to obtain the optimal dispatching.

[0004] However, the accuracy of the probability distribution of the random variable depends on the random programming, which is usually difficult to accurately estimate with limited historical data; although the two-stage robust optimization does not require accurate probability distribution, it needs to use an uncertainty set to describe the uncertainty of the random variable, and then find the optimal solution for a given uncertainty set. It can be seen that the two-stage robust optimization method considers the worst case, and the solution provided is too conservative, which not only leads to a large error in the day-ahead dispatching result, but also cannot balance the robustness and economy of the railway station AC / DC microgrid, affecting the user's power consumption experience. SUMMARY

[0005] Therefore, it is necessary to provide a day-ahead dispatching method and system for a railway station AC / DC microgrid to solve the problem that the robustness and economy of the railway station AC / DC microgrid cannot be considered at the same time.

[0006] In a first aspect, the present application provides a day-ahead dispatching method for a railway station AC / DC microgrid, which comprises:

[0007] The photovoltaic output and AC / DC load in the AC / DC microgrid of the railway passenger station are obtained. The probability distribution of the photovoltaic output and the AC / DC load is modeled using the uncertainty set of the norm distance, and a three-layer two-stage sub-Bruker optimization model of the AC / DC microgrid is obtained.

[0008] The establishment of the three-layer two-stage sub-Bruker optimization model includes: establishing a first-stage model based on the photovoltaic output and AC / DC load under the day-ahead forecast scenario, with the minimum daily operating cost as the first objective function and the equipment connected to the DC bus as the first constraint; and establishing a second-stage model based on the photovoltaic output and AC / DC load under the typical intraday scenario, with the minimum intraday adjustment cost as the second objective function and the equipment connected to the DC bus as the second constraint.

[0009] The three-layer, two-stage distributed bar optimization model was solved to obtain the day-ahead scheduling results.

[0010] Furthermore, the three-layer, two-stage sub-Bruker optimization model is as follows:

[0011] ;

[0012] in, These represent the decision variables of the first-stage model; This represents the cost coefficient vector in the objective function; These represent the decision variables of the second-stage model; Indicates classic intraday scenarios The probability of occurrence; Represents an uncertain set; Indicates the total number of discrete scenes; , , This represents the coefficient matrix or vector in the constraint conditions.

[0013] Furthermore, solving the three-layer two-stage sub-Bruker optimization model includes: decomposing the three-layer two-stage sub-Bruker optimization model into a main problem and sub-problems in the form of mixed integer linear programming, and solving them using a column and constraint generation algorithm to obtain the day-ahead scheduling results of the AC / DC microgrid of the railway passenger station.

[0014] Furthermore, solving the problem using the column and constraint generation algorithm includes:

[0015] Obtain the lower bound of the three-layer two-stage sub-Bruker optimization model, and take the lower bound as the main problem;

[0016] Obtain the upper bound of the three-layer two-stage sub-Bruker optimization model, and treat the upper bound as a subproblem;

[0017] The sub-problem is divided into two problems and solved sequentially to obtain the worst-case probability of the classic scenario, which is then fed back to the main problem.

[0018] Repeatedly iterate to obtain the lower bound and the upper bound until the difference between the lower bound and the upper bound is less than a threshold. Stop the iteration when the time comes, and obtain the day-ahead scheduling result.

[0019] Furthermore,

[0020] Obtaining the lower bound includes: calculating the minimum total cost of day-ahead economic scheduling given that the probability distributions of each classic scenario are known;

[0021] Obtaining the upper bound includes finding the worst-case probability of each classic scenario under the variables solved for the main problem.

[0022] Furthermore, the uncertain set for:

[0023] ;

[0024] in, Indicates classic intraday scenarios The probability of occurrence; Indicates the total number of discrete scenes; Indicates the first Experience probability of typical scenarios within a day; This represents the allowable offset for controlling the 1-norm probability distribution. , This represents the allowable offset for controlling the ∞-norm probability distribution. ; Indicates generation The number of original scenes used in a typical scenario; and This indicates the preset confidence level parameter.

[0025] Furthermore, the decision variables of the first-stage model and the decision variables of the second-stage model for:

[0026] ;

[0027] in, Indicating the scenario predicted in the previous day Electricity purchase capacity during a given time period; , These represent the current day prediction scenarios. The charging and discharging power of time-segmented energy storage; , respectively represent the charging and discharging power of the i-th electric vehicle in the day-ahead prediction scenario injection power of the AC / DC converter from the AC side and from the DC side in the time period; 、 respectively represent the charging and discharging power of the i-th electric vehicle in the day-ahead prediction scenario the state of charge of the i-th electric vehicle in the time period; represent the state of charge of the energy storage in the time period in the day-ahead prediction scenario the state of charge of the i-th electric vehicle in the time period; represent the state of charge of the energy storage in the time period in the day-ahead prediction scenario 、 respectively represent the charging and discharging state of the i-th electric vehicle in the day-ahead prediction scenario the charging and discharging state of the energy storage in the time period in the day-ahead prediction scenario 、 respectively represent the charging and discharging state of the energy storage in the time period in the day-ahead prediction scenario 、 respectively represent the flow direction of the AC / DC converter power in the time period in the day-ahead prediction scenario represent the electricity buying regulation power in the time period in the i-th day-intra-day classical scenario the charging and discharging regulation power of the energy storage in the time period in the i-th day-intra-day classical scenario 、 the charging and discharging regulation power of the energy storage in the time period in the i-th day-intra-day classical scenario injection regulation power of the AC / DC converter from the AC side and from the DC side in the time period in the i-th day-intra-day classical scenario the state of charge of the energy storage in the time period in the i-th day-intra-day classical scenario the flow direction of the AC / DC converter in the time period in the i-th day-intra-day classical scenario. 、 the flow direction of the AC / DC converter in the time period in the i-th day-intra-day classical scenario.

[0028] Further, the first objective function is:

[0029] ;

[0030] ;

[0031] ​​​​​​​​​​​​​​​ ;

[0032] ;

[0033] in, Indicates daily operating costs; Indicates the total number of daily scheduling periods; Predicted scenarios Electricity purchase cost for a given period of time; express Electricity prices during certain time periods; Indicating the scenario predicted in the previous day Electricity purchase capacity during a given time period; Indicating the scenario predicted in the previous day Operation and maintenance costs of time-limited energy storage; This represents the operation and maintenance cost coefficient for energy storage. , These represent the current day prediction scenarios. The charging and discharging power of time-segmented energy storage; Indicating the scenario predicted in the previous day Losses and maintenance costs of AC / DC converters during different time periods; This represents the operation and maintenance cost coefficient of the AC / DC converter; Indicates the conversion efficiency of the AC / DC converter; , These represent the current day prediction scenarios. The injected power of the AC / DC converter during the time period is injected from the AC side and injected from the DC side.

[0034] Furthermore, the second objective function is:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] in, Indicates the total number of discrete scenes; No. The probability of a classic scene appearing within a day Indicates the total number of daily scheduling periods; No. Classic scenarios within a day The cost of electricity purchase adjustment during certain time periods; express Electricity prices during certain time periods; represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the operation and adjustment cost of the energy storage in the classical scenario of the first day represent the operation and adjustment cost of the energy storage in the classical scenario of the first day represent the operation and adjustment cost of the energy storage in the classical scenario of the first day represent the operation and adjustment cost of the energy storage in the classical scenario of the first day represent the operation and adjustment cost of the energy storage in the classical scenario of the first day represent the operation and adjustment cost of the energy storage in the classical scenario of the first day , represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day , represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day represent the charging and discharging adjustment power of the energy storage in the classical scenario of the first day

[0040] In a second aspect, the present application provides a day-ahead scheduling system of a railway station AC / DC micro-grid, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to realize the steps of any of the above methods.

[0041] Overall, the present application provides a day-ahead scheduling method and system of a railway station AC / DC micro-grid, which can achieve the following beneficial effects compared with the prior art:

[0042] The present application takes into account the uncertainty of distributed photovoltaic output and AC / DC load in the railway station AC / DC micro-grid, coordinates and schedules devices connected to the DC bus such as charging piles, energy storage and AC / DC converters, generates a three-layer two-stage distribution robust optimization model of the railway station AC / DC micro-grid, and obtains a day-ahead scheduling result after solving the model. The model can effectively handle the uncertainty of distributed photovoltaic output and AC / DC load in the railway station AC / DC micro-grid, not only reduces the error of day-ahead scheduling, but also achieves a good balance between the robustness and economy of the railway station AC / DC micro-grid, and improves the user's power experience.

[0043] In addition, the two-stage distribution robust optimization model is converted into a master problem and a sub-problem in the form of a mixed integer linear programming by the present application, without dual decomposition, and is solved by a column and constraint generation algorithm, so that the uncertainty of distributed photovoltaic output and AC / DC load in the railway passenger station can be effectively processed, the robustness and economy of the AC / DC micro-grid in the railway passenger station are taken into account, the accuracy of the day-ahead scheduling result is improved, and the power consumption in the railway passenger station is safer and more reliable. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. The drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0045] Figure 1 is a method flow diagram of a railway passenger station AC / DC micro-grid day-ahead scheduling method and system provided by the present application;

[0046] Figure 2 is a railway passenger station AC / DC micro-grid structure diagram of a railway passenger station AC / DC micro-grid day-ahead scheduling method and system provided by the present application;

[0047] Figure 3 is a diagram of photovoltaic output and AC / DC load in a day-ahead prediction scenario of a railway passenger station AC / DC micro-grid day-ahead scheduling method and system provided by the present application;

[0048] Figure 4 is a diagram of photovoltaic output and AC / DC load in a day-ahead prediction scenario of a railway passenger station AC / DC micro-grid day-ahead scheduling method and system provided by the present application;

[0049] Figure 5 is a diagram of photovoltaic output and AC / DC load in a day-ahead prediction scenario of a railway passenger station AC / DC micro-grid day-ahead scheduling method and system provided by the present application; DETAILED DESCRIPTION

[0050] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in combination with the drawings and embodiments in the present application. Obviously, the described embodiments are some embodiments of the present application, but not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0051] It should be noted that in the description of the embodiments of the present application, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a method, step or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such method, step or system. Without more limitation, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the method, step or system comprising the element.

[0052] The day-ahead scheduling of the AC / DC microgrid refers to the power consumption plan arranged in advance in the power consumption plan, which is usually arranged within a time range of one week or one month. The AC / DC microgrid is an effective power supply and distribution mode for a railway station, however, due to the randomness of the distributed photovoltaic output and the AC / DC load, the day-ahead scheduling result usually has a certain error, which brings not small challenge to the formulation of the production plan.

[0053] Therefore, the present application proposes a day-ahead scheduling method for an AC / DC microgrid of a railway station, considers the uncertainty of the distributed photovoltaic output and the AC / DC load in the AC / DC microgrid of the railway station, establishes a three-layer two-stage distribution robust optimization model and solves it, so that a good balance is achieved between the robustness and the economy of the AC / DC microgrid of the railway station. Specifically, as shown in Figure 1 the method comprises:

[0054] Step 101: Obtain the photovoltaic output and the AC / DC load in the AC / DC microgrid of the railway station, model the probability distribution of the photovoltaic output and the AC / DC load by using the norm distance uncertainty set, and obtain a three-layer two-stage distribution robust optimization model of the AC / DC microgrid.

[0055] The AC / DC microgrid structure of the railway station is as shown in Figure 2 The distributed photovoltaic is connected to the DC bus, the photovoltaic output is preferentially supplied to the charging pile and the DC load on the DC side; and the excess photovoltaic output is transmitted to the AC bus through the AC / DC converter to ensure that the photovoltaic output is fully consumed on site, in addition, for safety, the microgrid is not allowed to send power to the distribution network. The DC load includes devices such as chilled water pumps, cooling water pumps, and public area lighting; the AC load includes devices such as air conditioners, escalators, and electric water boilers; and the tie line serves as a bridge between the AC bus and the distribution network. The AC / DC microgrid realizes power balance of the entire microgrid through power interaction with the distribution network and coordinated scheduling of the charging pile and the energy storage.

[0056] More specifically, the establishment of the three-layer two-stage distribution robust optimization model includes establishing a first stage model and establishing a second stage model.

[0057] Based on the photovoltaic output and AC / DC load in the day-ahead prediction scenario, a first-stage model is established with the daily operation minimum cost as a first objective function and devices connected to the DC bus as first constraint conditions.

[0058] As a specific embodiment, the first objective function is:

[0059] ;

[0060] ;

[0061] ;

[0062] .

[0063] wherein, represents the daily operation cost; represents the total number of daily scheduling periods, which can be 24, for example, and each period is 1 hour; the subscript 0 in each variable represents the day-ahead prediction scenario; the subscript represents the microgrid-related data in the day-ahead prediction scenario period; represents the electricity purchase cost in the day-ahead prediction scenario period; represents the electricity purchase price in the day-ahead prediction scenario period; represents the electricity purchase power in the day-ahead prediction scenario period; represents the operation and maintenance cost of the energy storage in the day-ahead prediction scenario period; represents the operation and maintenance cost coefficient of the energy storage; , respectively represent the charging and discharging power of the energy storage in the day-ahead prediction scenario period, the charging power of the energy storage is positive, and the discharging power of the energy storage is negative; represents the loss and operation and maintenance cost of the AC / DC converter in the day-ahead prediction scenario period; represents the operation and maintenance cost coefficient of the AC / DC converter; represents the conversion efficiency of the AC / DC converter; , respectively represent the injection power of the AC / DC converter from the AC side and from the DC side in the day-ahead prediction scenario period, the injection power from the AC side is negative, and the injection power from the DC side is positive.

[0064] It should be noted that, in the current forecast scenario, the equipment connected to the DC bus is used as the first constraint condition. The first constraint conditions include: the first charging pile constraint condition, the first energy storage constraint condition, the first AC / DC converter constraint condition, the first tie line power constraint condition, and the first power balance constraint condition.

[0065] As an example, the first charging pile constraint condition is obtained based on an electric vehicle charging and discharging at a bidirectional charging pile in a railway passenger station.

[0066] The constraints for the first charging pile can be:

[0067] ;

[0068] ;

[0069] ;

[0070] , , or ;

[0071] ;

[0072] ;

[0073] ;

[0074] .

[0075] in, , These represent the current day prediction scenarios. Time period The charging and discharging power of an electric vehicle, with charging power being positive and discharging power being negative; , These represent the rated charging and discharging power of the charging pile, respectively. , These represent the current day prediction scenarios. Time period The charging and discharging status of an electric vehicle is represented by a value of 1, indicating charging or discharging, and a value of 0, indicating neither charging nor discharging. and These represent the nth prediction scenario in the previous day. The time it takes for an electric vehicle to arrive at and leave a charging station; Indicating the scenario predicted in the previous day Time period The state of charge of an electric vehicle; and These represent the maximum and minimum values ​​of the electric vehicle's state of charge, respectively. This indicates the scheduling duration, which can be set to 1 hour. This represents the battery capacity of an electric vehicle. To simplify the analysis, the battery capacity of all electric vehicles can be taken as the same value. and These represent the charging and discharging efficiencies of electric vehicles, respectively. Indicating the scenario predicted in the previous day Time period The state of charge of an electric vehicle when it leaves a charging station; This indicates the desired state of charge for electric vehicle users. Indicating the scenario predicted in the previous day Total charging and discharging power of the charging station during the specified time period; This represents the total number of electric vehicles.

[0076] The first energy storage constraint can be:

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] .

[0083] in, , These represent the current day prediction scenarios. The charging and discharging power of energy storage during a given time period; the charging power of energy storage is positive, and the discharging power of energy storage is negative. , These represent the maximum charging and discharging power of the energy storage, respectively. , These represent the current day prediction scenarios. The charging and discharging status of energy storage during a given period: 1 indicates charging or discharging, and 0 indicates neither charging nor discharging. Indicating the scenario predicted in the previous day State of charge of energy stored over a period of time; and These represent the maximum and minimum values ​​of the energy storage state of charge, respectively; This indicates the scheduling duration, which can be set to 1 hour. Indicates energy storage capacity; and respectively represent the charging and discharging efficiency of the energy storage; and respectively represent the initial state of charge and the final state of charge of the energy storage.

[0084] The first AC / DC converter constraint condition can be:

[0085] ;

[0086] ;

[0087] .

[0088] wherein, represents the power limit value of the AC / DC converter transmission; , respectively represent the AC / DC converter power flow direction in the day-ahead prediction scenario , are all binary variables, for example, represents that the AC / DC converter power flows from alternating current to direct current, represents that the AC / DC converter power flows from direct current to alternating current.

[0089] The first tie line power constraint condition can be:

[0090] .

[0091] wherein, represents the electricity buying power in the day-ahead prediction scenario period; represents the power limit value of the tie line transmission.

[0092] Since the AC / DC microgrid needs to simultaneously satisfy the power balance constraints of the direct current side and the alternating current side respectively, the first power balance constraint condition can be:

[0093] ;

[0094] .

[0095] wherein, represents the photovoltaic output in the day-ahead prediction scenario period; , respectively represent the direct current load and the alternating current load in the day-ahead prediction scenario period; , respectively represent the injection power of the AC / DC converter from the alternating current side and from the direct current side in the day-ahead prediction scenario period; Indicates the conversion efficiency of the AC / DC converter; Indicating the scenario predicted in the previous day Total charging and discharging power of energy storage during a given time period; Indicating the scenario predicted in the previous day Total charging and discharging power of the charging station during the specified time period; Indicating the scenario predicted in the previous day Electricity purchase capacity during a given time period; Indicating the scenario predicted in the previous day Electricity sales power of the microgrid during a given time period.

[0096] Based on the photovoltaic output and AC / DC load under typical intraday scenarios, a second-stage model is established with the intraday adjustment minimum cost as the second objective function and the equipment connected to the DC bus as the second constraint.

[0097] As a specific embodiment, the second objective function is:

[0098] ;

[0099] ;

[0100] ;

[0101] .

[0102] in, Represents the total number of discrete scenes; subscripts in each variable. This represents a classic scene from the day; Indicates the first The probability of a classic scene appearing within a day; Indicates the total number of daily scheduling periods; subscripts in each variable. Indicates the first Classic scenarios within a day Microgrid-related data for different time periods; Indicates the first Classic scenarios within a day The cost of electricity purchase adjustment during certain time periods; express Electricity prices during certain time periods; Indicates the first Classic scenarios within a day Power consumption regulation during different time periods; Indicates the first Classic scenarios within a day Operation and maintenance costs of time-of-use energy storage; This represents the operation and maintenance cost coefficient for energy storage. , respectively represent the charging and discharging regulation power of the energy storage in the time period under the first respectively represent the charging and discharging regulation power of the energy storage in the time period under the first represent the operation and maintenance cost coefficient of the AC / DC converter; represent the conversion efficiency of the AC / DC converter; 、 respectively represent the charging and discharging regulation power of the energy storage in the time period under the first

[0103] It should be noted that, under the intra-day typical scenario, the device connected with the DC bus is taken as the second constraint condition, and the second constraint condition includes: the second charging pile constraint condition, the second energy storage constraint condition, the second AC / DC converter constraint condition, the second tie line power constraint condition and the second power balance constraint condition.

[0104] Since the time when the electric vehicle arrives and leaves the charging pile is random, the charging and discharging power of the electric vehicle is not adjusted for each typical scenario, and therefore the sum of the charging and discharging power under the day-ahead prediction scenario is taken as the total charging and discharging power of the charging pile in the time period under the first

[0105] That is: ; wherein, respectively represent the charging and discharging regulation power of the energy storage in the time period under the first

[0106] For each intra-day typical scenario, it is necessary to keep the charging and discharging state of the energy storage consistent with the day-ahead prediction scenario. Therefore, as a specific embodiment, the second energy storage constraint condition can be:

[0107] ;

[0108] ;

[0109] ;

[0110] ; ​​​​​​​​​​​​

[0111] ;

[0112] ;

[0113] .

[0114] in, and They represent the first time. Classic scenarios within a day The charging and discharging power of time-segmented energy storage; , These represent the current day prediction scenarios. The charging and discharging power of time-segmented energy storage; , They represent the first time. Classic scenarios within a day The charging and discharging regulation power of time-limited energy storage; , These represent the maximum charging and discharging power of the energy storage, respectively. , These represent the current day prediction scenarios. The charging and discharging status of energy storage during a given time period; Indicates the first Classic scenarios within a day State of charge of energy stored over a period of time; and These represent the maximum and minimum values ​​of the energy storage state of charge, respectively; Indicates the scheduling duration; Indicates energy storage capacity; and These represent the charging and discharging efficiencies of energy storage, respectively. and These represent the initial state of charge and the final state of charge of the energy storage, respectively.

[0115] The constraints for the second AC / DC converter can be:

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] .

[0121] in, , They represent the first time. Classic scenarios within a day The injected power from the AC side and the DC side of the AC / DC converter during the time period; , These represent the current day prediction scenarios. The injected power from the AC side and the DC side of the AC / DC converter during the time period; , They represent the first time. Classic scenarios within a day The time-limited AC / DC converter injects regulated power from the AC side and from the DC side; Indicates the power limit that the AC / DC converter can transmit; , They represent the first time. Classic scenarios within a day The flow direction of the AC / DC converter during a given time period.

[0122] The power constraint condition for the second tie line can be:

[0123] ;

[0124] .

[0125] in, Indicates the first Classic scenarios within a day The power purchase capacity of the microgrid during a given time period; Indicating the scenario predicted in the previous day Electricity purchase capacity during a given time period; Indicates the first Classic scenarios within a day Power consumption regulation during different time periods; This indicates the power limit for transmission over the tie line.

[0126] The second power balance constraint can be:

[0127] ;

[0128] .

[0129] in, Indicates the first Classic scenarios within a day Solar power output during different time periods; , They represent the first time. Classic scenarios within a day DC and AC loads during different time periods; , denote the injection power injected from the AC side and the DC side of the AC / DC converter respectively in the classical scenario of the first day, denote the injection power injected from the AC side and the DC side of the AC / DC converter respectively in the classical scenario of the first day, denote the conversion efficiency of the AC / DC converter, denote the total charging and discharging power of the energy storage in the classical scenario of the first day, denote the total charging and discharging power of the energy storage in the classical scenario of the first day, denote the total charging and discharging power of the energy storage in the classical scenario of the first day, denote the total charging and discharging power of the energy storage in the classical scenario of the first day, denote the total charging and discharging power of the energy storage in the classical scenario of the first day,

[0130] Thus, the decision variables of the first-stage model and the decision variables of the second-stage model can be obtained, and are specifically:

[0131] .

[0132] The probability distribution of photovoltaic output and AC / DC load is modeled by using the norm distance of the uncertainty set, and a three-layer two-stage distribution robust optimization model of AC / DC microgrid min-max-min is obtained.

[0133] As an embodiment, the three-layer two-stage distribution robust optimization model is:

[0134] ;

[0135] wherein, denotes the decision variable of the first-stage model; denotes the cost coefficient vector in the objective function; denotes the decision variable of the second-stage model; denotes the probability of the occurrence of the classical scenario of the first day; denotes the uncertainty set; denotes the total number of discrete scenarios; , , , denotes the coefficient matrix or vector in the constraint condition.

[0136] The uncertainty set is obtained by using the empirical distribution probability of 1-norm and ∞-norm distance, and as an embodiment, the uncertainty set may be:

[0137] ; ​​​​​

[0138] where, denotes the intra-day classical scenario occurrence probability; denotes the total number of discrete scenarios; denotes the empirical probability of the th intra-day typical scenario; denotes the admissible deviation from the control 1-norm probability distribution, , denotes the admissible deviation from the control ∞-norm probability distribution, ; denotes the number of original scenarios used to generate typical scenarios; and denote the pre-specified confidence level parameters.

[0139] Step 102: solving the three-layer two-stage distribution robust optimization model to obtain the day-ahead scheduling result.

[0140] As an embodiment, solving the three-layer two-stage distribution robust optimization model includes: decomposing the three-layer two-stage distribution robust optimization model into a master problem and a sub-problem in the form of a mixed integer linear programming, and solving the master problem and the sub-problem by using a column and constraint generation algorithm to obtain the day-ahead scheduling result of the railway station AC / DC microgrid.

[0141] Further, solving by using the column and constraint generation algorithm includes:

[0142] S201: obtaining a lower bound of the three-layer two-stage distribution robust optimization model, and taking the lower bound as the master problem.

[0143] It should be noted that the master problem is to calculate the minimum value of the total cost of day-ahead economic dispatch under the condition that the probability distribution of each classical scenario is known, and then obtain the lower bound of the three-layer two-stage distribution robust optimization model.

[0144] As a specific embodiment, the lower bound is:

[0145] ;

[0146] where, denotes the cost coefficient vector in the objective function; denotes the decision variable of the first-stage model; denotes the maximum value of the second-stage objective function; denotes the total number of discrete scenarios; denotes the empirical probability of the th iteration; denotes the worst occurrence probability of the th scenario after the decision variable of the second stage model after the next iteration; , , denotes a coefficient matrix or vector in a constraint condition; denotes the total number of iterations.

[0147] S202: Obtain an upper bound of the three-layer two-stage distribution robust optimization model, and take the upper bound as a sub-problem.

[0148] It should be noted that the sub-problem is to find the worst-case probability of occurrence of each classical scenario under the variables solved by the main problem, and return to the main problem for the next iteration.

[0149] As an embodiment, the lower bound is:

[0150] ;

[0151] wherein, denotes the total number of discrete scenarios; denotes the probability of occurrence of the intra-day classical scenario ; denotes an uncertainty set; denotes a decision variable of the second stage model; denotes a cost coefficient vector in an objective function; denotes a variable solved by the main problem; , , denotes a coefficient matrix or vector in a constraint condition.

[0152] S203: Divide the sub-problem into two problems and sequentially solve them to obtain the worst-case probability of occurrence of the classical scenario, and feed back to the main problem.

[0153] As an embodiment, dividing the sub-problem into two problems and sequentially solving them includes: first solving the inner minimization problem, and then solving the outer maximization problem.

[0154] Specifically, the minimization problem is:

[0155] .

[0156] The maximization problem is:

[0157] .

[0158] S204: Repeat the iteration to obtain the lower bound and the upper bound until the difference between the lower bound and the upper bound is less than a threshold , and stop the iteration to obtain the day-ahead scheduling result.

[0159] In order to verify the effectiveness of the proposed day-ahead scheduling method of the railway passenger station AC-DC microgrid, the application takes an AC-DC microgrid of a railway passenger station in Guangdong province as an example to verify the effectiveness.

[0160] The AC-DC microgrid structure is shown as Figure 2 , the parameters in the AC-DC microgrid are shown in Table 1, the time-of-use electricity price of the AC-DC microgrid is shown as Figure 2 , and the arrival and departure times of the electric vehicles to the charging piles approximately obey the normal distribution.

[0161] Table 1 Parameters of the AC-DC microgrid

[0162]

[0163] Table 2 Time-of-use electricity price of the AC-DC microgrid

[0164]

[0165] The day-ahead prediction data of the photovoltaic output and the AC-DC load are shown as Figure 3 , on the basis of the day-ahead prediction data, a plurality of random scenarios are generated through LHS respectively. The historical prediction error of the AC-DC microgrid is analyzed, and the day-ahead prediction error of the photovoltaic output and the AC-DC load is set to 20% and 15% respectively, 1000 random scenarios are initially generated, and are reduced to 4 typical scenarios within a day, as shown in Figure 4 . Further, the day-ahead scheduling result of the railway passenger station AC-DC microgrid is obtained, as shown in the attached Figure 5 . =0.2, =0.9).

[0166] In order to verify the economy and rationality of the day-ahead scheduling method proposed in the application, the deterministic optimization, and the two commonly used methods for handling uncertainty, two-stage robust optimization and stochastic programming, are compared with the three-layer two-stage distribution robust optimization model of the application. Among them, the four optimization methods select the same day-ahead prediction data, the application and the stochastic programming select the same typical scenarios within a day, the uncertainty adjustment parameters of the photovoltaic output and the load power of the two-stage robust optimization are 6 and 12 respectively, and the confidence levels of the application are set to 0.9 and 0.99 respectively. and The solving results are shown in Table 3.

[0167] Table 3 Comparison of solving results of different optimization methods

[0168]

[0169] From Table 3, it can be seen that the total cost of day-ahead economic dispatch of the deterministic optimization is the lowest, and since the method is optimized for the predicted scenario without considering the influence of uncertain factors, there is no adjustment cost. Compared with the stochastic programming, although the day-ahead adjustment cost of the second stage is higher than that of the stochastic programming, the day-ahead dispatch cost of the first stage is lower than that of the stochastic programming and close to that of the deterministic optimization, resulting in that the total cost of day-ahead economic dispatch only increases by 1.29% compared with the deterministic optimization, and the economic requirement of day-ahead dispatch can be better met.

[0170] This is because the present application is divided into two stages, and the worst probability distribution of each typical scenario in the second stage is considered, although the adjustment cost is increased, but the dispatch result has stronger robustness. The objective function of the stochastic programming is the minimum sum of the day-ahead dispatch cost and the day-ahead adjustment cost, and the overall optimization is performed, and thus the day-ahead dispatch cost is relatively high, and the day-ahead adjustment cost is negative. The two-stage robust optimization dispatch considers the uncertain variable value of the worst scenario, resulting in a high day-ahead adjustment cost, and thus the total cost of day-ahead economic dispatch is the highest, and increases by 26.41% compared with the stochastic programming.

[0171] In summary, the two-stage robust optimization dispatch over-considered the robustness, and the stochastic programming is too idealized in economy, and compared with the two-stage robust optimization and the stochastic programming, the present application achieves a good balance between the two, and can effectively handle the uncertainty of distributed photovoltaic output and AC / DC load, and the robustness and economy of the AC / DC micro-grid are considered.

[0172] In the second aspect, the present application provides a day-ahead dispatch system of an AC / DC micro-grid of a railway passenger station, comprising a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to realize the steps of the method of any one of the above aspects. The system is consistent with the technical solution of the above method, and will not be described here.

[0173] It should be noted that, for the above-mentioned various embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, and according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0174] In the above embodiments, the description of each embodiment is focused on, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments. In the several embodiments provided in the present application, it should be understood that the disclosed method or system can be implemented in other ways. For example, the embodiments described above are merely schematic, for example, the division of the units is only a logical function division, and other division manners can be used in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0175] The above merely describes exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. Any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of other embodiments of the present disclosure after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses, or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

[0176] The technical features of the above embodiments can be combined in any way, and to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0177] Those skilled in the art can easily understand that the above only describes the preferred embodiments of the present application and is not intended to limit the present application, and any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A day-ahead scheduling method for AC / DC microgrid of railway station, characterized in that, The method comprises: acquiring photovoltaic output and AC-DC load in a railway station AC-DC microgrid, modeling probability distribution of the photovoltaic output and the AC-DC load by using norm distance uncertainty set, and obtaining a three-layer two-stage distribution robust optimization model of the AC-DC microgrid; wherein, the establishment of the three-layer two-stage distribution robust optimization model comprises: based on the photovoltaic output and the AC-DC load under day-ahead prediction scenarios, taking day operation minimum cost as a first objective function, and taking devices connected with a DC bus as a first constraint condition, establishing a first stage model; based on the photovoltaic output and the AC-DC load under typical scenarios within a day, taking day-to-day adjustment minimum cost as a second objective function, and taking devices connected with the DC bus as a second constraint condition, establishing a second stage model; solving the three-layer two-stage distribution robust optimization model to obtain day-ahead scheduling results; the first objective function is: ； ； ； ； the second objective function is: ； ； ； ； in, Indicates daily operating costs; Indicates the total number of daily scheduling periods; Indicating the scenario predicted in the previous day Electricity purchase cost for a given period of time; express Electricity prices during certain time periods; Indicating the scenario predicted in the previous day Electricity purchase capacity during a given time period; Indicating the scenario predicted in the previous day Operation and maintenance costs of time-limited energy storage; This represents the operation and maintenance cost coefficient for energy storage. , These represent the current day prediction scenarios. The charging and discharging power of time-segmented energy storage; Indicating the scenario predicted in the previous day Losses and maintenance costs of AC / DC converters during different time periods; This represents the operation and maintenance cost coefficient of the AC / DC converter; Indicates the conversion efficiency of the AC / DC converter; , These represent the current day prediction scenarios. The injected power from the AC side and the DC side of the AC / DC converter during the time period; Indicates the total number of discrete scenes; Indicates the first The probability of a classic scene appearing within a day; Indicates the first Classic scenarios within a day The cost of electricity purchase adjustment during certain time periods; Indicates the first Classic scenarios within a day Power consumption regulation during different time periods; Indicates the first Classic scenarios within a day Operation and maintenance costs of time-of-use energy storage; , They represent the first time. Classic scenarios within a day The charging and discharging regulation power of time-limited energy storage; Indicates the first Classic scenarios within a day Losses and maintenance / adjustment costs of AC / DC converters during different time periods; , They represent the first time. Classic scenarios within a day The period AC / DC converter injects the injection regulating power from the AC side and from the DC side. the three-layer two-stage distribution robust optimization model is: ； wherein, denotes the decision variables of the first stage model; denotes a cost coefficient vector in the objective function; denotes the decision variables of the second stage model; denotes the intra-day classical scenarios occurring with a probability; denotes the uncertainty set; denotes the total number of discrete scenarios; , , denotes a coefficient matrix or vector in the constraint. 2.The day-ahead scheduling method of the AC / DC microgrid of a railway station according to claim 1, characterized in that, solving the three-layer two-stage distribution robust optimization model comprises: decomposing the three-layer two-stage distribution robust optimization model into a main problem and a sub-problem in a mixed integer linear programming form, and solving by using a column and constraint generation algorithm to obtain day-ahead scheduling results of the railway station AC-DC microgrid. 3.The day-ahead scheduling method of the AC / DC microgrid of a railway station according to claim 2, characterized in that, solving by using the column and constraint generation algorithm comprises: obtaining a lower bound of the three-layer two-stage distribution robust optimization model, and taking the lower bound as the main problem; obtaining an upper bound of the three-layer two-stage distribution robust optimization model, and taking the upper bound as the sub-problem; dividing the sub-problem into two problems to solve sequentially, obtaining a worst-case probability of occurrence of a classical scenario, and feeding back to the main problem; repeating the iteration to obtain the lower bound and the upper bound until a difference between the lower bound and the upper bound is less than a threshold value and stopping the iteration to obtain the day-ahead dispatch result.

4. The day-ahead scheduling method of the railway station AC-DC microgrid according to claim 3, characterized in that, the lower bound acquisition comprises: under the condition that each classical scenario probability distribution is known, calculating a minimum value of day-ahead economic dispatch total cost; the upper bound acquisition comprises: under the variables solved by the main problem, finding a worst-case probability of occurrence of each classical scenario.

5. The day-ahead scheduling method of the AC / DC microgrid of a railway station according to claim 1, characterized in that, The uncertain set Is: ； where, denotes the number of intra-day classical scenarios occurrence probability; denotes the total number of discrete scenarios; denotes the empirical probability of the intra-day typical scenario; denotes the admissible deviation from the control 1-norm probability distribution, , denotes the admissible deviation from the control ∞-norm probability distribution, ; denotes the number of original scenarios used to generate typical scenarios; and denote the pre-set confidence level parameters. 6.The day-ahead scheduling method of the AC / DC microgrid of a railway station according to claim 1, characterized in that, decision variables of the first stage model and decision variables of the second stage model are: ； in, Indicating the scenario predicted in the previous day Electricity purchase capacity during a given time period; , These represent the current day prediction scenarios. The charging and discharging power of time-segmented energy storage; , These represent the current day prediction scenarios. The injected power from the AC side and the DC side of the AC / DC converter during the time period; , These represent the current day prediction scenarios. Time period The charging and discharging power of an electric vehicle; Indicating the scenario predicted in the previous day Time period The state of charge of an electric vehicle; Indicating the scenario predicted in the previous day State of charge of energy stored over a period of time; , These represent the current day prediction scenarios. Time period The charging and discharging status of an electric vehicle; , These represent the current day prediction scenarios. The charging and discharging status of energy storage during a given time period; , These represent the current day prediction scenarios. The direction of power flow in the AC / DC converter during a given time period; Indicates the first Classic scenarios within a day Power consumption regulation during different time periods; , They represent the first time. Classic scenarios within a day The charging and discharging regulation power of time-limited energy storage; , They represent the first time. Classic scenarios within a day The time-limited AC / DC converter injects regulated power from the AC side and from the DC side; Indicates the first Classic scenarios within a day State of charge of energy stored over a period of time; , They represent the first time. Classic scenarios within a day The flow direction of the AC / DC converter during a given time period. 7.A day-ahead scheduling system for AC / DC microgrid of railway station, comprising a memory, a processor and a computer program stored in the memory, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1-6.

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