Day-ahead scheduling method and system for AC / DC micro-grid of railway station

Abstract

The invention provides a day-ahead scheduling method and system for an AC / DC micro-grid of a railway station, and relates to the technical field of power grid scheduling. The method comprises the following steps: acquiring photovoltaic output and AC / DC load in the AC / DC micro-grid of the railway station, and modeling probability distribution of the photovoltaic output and the AC / DC load by using an uncertain set of norm distance 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. According to the method, the robustness and the economical efficiency of the AC / DC micro-grid of the railway station are well balanced, and the robustness and the economical efficiency are well considered.

Description

Technical Field

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

[0002] Railway passenger stations are typical large-scale public buildings with huge energy consumption. They usually use large-area rooftops to install distributed photovoltaic power generation systems to reduce their dependence on power distribution networks. Since the lighting, variable-frequency air conditioners, and charging piles in the station parking lot in the railway passenger station are DC loads, if conventional AC power supply is used, they need to be rectified into DC before dispatching. The photovoltaic power generation system directly outputs DC power, and some of its loads use direct DC supply, which can not only reduce the conversion loss between AC and DC and improve power supply efficiency, but also reduce AC / DC conversion devices and reduce power supply costs. Therefore, using AC / DC microgrids as the power supply and distribution method of railway passenger stations can improve the local consumption capacity of photovoltaic power generation, reduce the impact of photovoltaic output volatility on the reliability of power supply of railway passenger stations, reduce power loss during the conversion process, and better integrate distributed power sources, energy storage equipment, AC / DC loads, charging piles and other resources.

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

[0004] However, stochastic planning depends on the accuracy of the probability distribution of random variables, which is usually difficult to accurately estimate with limited historical data; although two-stage robust optimization does not require an accurate probability distribution, it needs to use an uncertainty set to describe the uncertainty of random variables, and then find the optimal solution for a given uncertainty set. It can be seen that the two-stage robust optimization method takes into account the worst case scenario, and the solution it provides is too conservative, which not only leads to a large error in the day-ahead scheduling results, but also fails to strike a balance between the robustness and economy of the AC / DC microgrid at the railway passenger station, affecting the user's electricity experience. Summary of the invention

[0005] Based on this, it is necessary to provide a day-ahead scheduling method and system for the AC / DC microgrid of a railway passenger station in response to the above technical problems, which solves the problem that it is impossible to simultaneously take into account the robustness and economy of the AC / DC microgrid of the railway passenger station.

[0006] In a first aspect, the present invention provides a method for day-ahead scheduling of an AC / DC microgrid in a railway passenger station, the method comprising: The photovoltaic output and AC / DC load in the AC / DC microgrid of the railway passenger station are obtained, and the probability distribution of the photovoltaic output and the AC / DC load is modeled using the uncertainty set of the norm distance to obtain a three-layer two-stage distributed blue-robust optimization model of the AC / DC microgrid; The establishment of the three-layer two-stage distributed blue-rod optimization model includes: based on the photovoltaic output and the AC and DC loads in the day-ahead forecast scenario, taking the daily minimum operation cost as the first objective function and the equipment connected to the DC bus as the first constraint condition, a first-stage model is established; based on the photovoltaic output and the AC and DC loads in the typical intraday scenario, taking the intraday minimum adjustment cost as the second objective function and the equipment connected to the DC bus as the second constraint condition, a second-stage model is established; The three-layer two-stage distributed robust optimization model is solved to obtain the day-ahead scheduling result.

[0007] Furthermore, the three-layer two-stage distributed robust optimization model is: ; in, represents the decision variables of the first stage model; represents the cost coefficient vector in the objective function; represents the decision variables of the second stage model; Indicates a classic intraday scenario Probability of occurrence; represents an uncertain set; represents the total number of discrete scenarios; , , Represents the coefficient matrix or vector in the constraints.

[0008] Furthermore, solving the three-layer two-stage distributed robust optimization model includes: decomposing the three-layer two-stage distributed robust optimization model into a main problem and sub-problems in the form of mixed integer linear programming, and solving it using a column and constraint generation algorithm to obtain the day-ahead scheduling result of the railway passenger station AC / DC microgrid.

[0009] Further, solving using the column and constraint generation algorithm includes: Obtaining a lower bound of the three-layer two-stage distributed robust optimization model, and using the lower bound as the main problem; Obtaining an upper bound of the three-layer two-stage distributed robust optimization model, and using the upper bound as a subproblem; Divide the sub-problem into two problems and solve them sequentially to obtain the worst probability of occurrence of the classic scenario, and feed it back to the main problem; 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 the threshold The iteration is stopped when , and the day-ahead scheduling result is obtained.

[0010] Furthermore, The acquisition of the lower bound includes: calculating the minimum value of the total cost of day-ahead economic dispatch when the probability distribution of each classic scenario is known; The acquisition of the upper bound includes: finding the worst occurrence probability of each classic scenario under the variables solved by the main problem.

[0011] Furthermore, the uncertain set for: ; in, Indicates a classic intraday scenario Probability of occurrence; represents the total number of discrete scenarios; Indicates Empirical probability of typical scenarios within a day; represents the allowable offset of the control 1-norm probability distribution, , represents the permissible offset of the control ∞-norm probability distribution, ; Indicates generation The number of original scenes used in a typical scene; and Represents the preset confidence level parameter.

[0012] Furthermore, the decision variables of the first stage model are and the decision variables of the second stage model for: ; in, In the day-ahead forecast scenario The power purchased during the time period; , They represent the day-ahead forecast scenario. The charging and discharging power of the time-slot energy storage; , They represent the day-ahead forecast scenario. The injected power of the AC / DC converter from the AC side and the DC side during the period; , They represent the day-ahead forecast scenario. Time period The charging and discharging power of electric vehicles; In the day-ahead forecast scenario Time period The state of charge of an electric vehicle; In the day-ahead forecast scenario The state of charge of the time storage; , They represent the day-ahead forecast scenario. Time period The charging and discharging status of electric vehicles; , They represent the day-ahead forecast scenario. The charging and discharging status of the time storage; , They represent the day-ahead forecast scenario. The direction of power flow of the AC / DC converter during the time period; Indicated in Classic scene within a day The power purchase adjustment power in the time period; , Respectively expressed in Classic scene within a day Charging and discharging regulation power of time-slot energy storage; , Respectively expressed in Classic scene within a day The period AC / DC converter injects the regulated power from the AC side and from the DC side; Indicated in Classic scene within a day The state of charge of the time storage; , Respectively expressed in Classic scene within a day The flow direction of the AC / DC converter during the time period.

[0013] Furthermore, the first objective function is: ; ; ; ; in, represents the daily operating cost; Indicates the total number of daily scheduling periods; In the day-ahead forecast scenario The cost of buying electricity during the time period; express The electricity purchase price during the time period; In the day-ahead forecast scenario The power purchased during the time period; In the day-ahead forecast scenario Operation and maintenance costs of time-slot energy storage; Represents the operation and maintenance cost coefficient of energy storage; , They represent the day-ahead forecast scenario. The charging and discharging power of the time-slot energy storage; In the day-ahead forecast scenario The losses and operation and maintenance costs of AC / DC converters during the period; represents the operation and maintenance cost coefficient of the AC / DC converter; Indicates the conversion efficiency of the AC / DC converter; , They represent the day-ahead forecast scenario. The period of time during which the AC / DC converter injects power from the AC side and from the DC side.

[0014] Furthermore, the second objective function is: ; ; ; ; in, represents the total number of discrete scenarios; No. The probability of classic scenarios occurring within a day Indicates the total number of daily scheduling periods; No. Classic scene within a day The adjustment cost of electricity purchase in different time periods; express The electricity purchase price during the time period; Indicated in Classic scene within a day The power purchase adjustment power in the time period; Indicated in Classic scene within a day The operation and maintenance adjustment costs of time-slot energy storage; Represents the operation and maintenance cost coefficient of energy storage; , Respectively expressed in Classic scene within a day Charging and discharging regulation power of time-slot energy storage; Indicated in Classic scene within a day The losses of AC / DC converters during the period and the operation and maintenance adjustment costs; represents the operation and maintenance cost coefficient of the AC / DC converter; Indicates the conversion efficiency of the AC / DC converter; , Respectively expressed in Classic scene within a day The AC / DC converter injects regulated power from the AC side and from the DC side during the period.

[0015] In a second aspect, the present invention provides a day-ahead dispatching system for an AC / DC microgrid at a railway passenger station, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods.

[0016] In general, the present invention provides a method and system for day-ahead scheduling of an AC / DC microgrid in a railway passenger station, which can achieve the following beneficial effects compared with the prior art: The present invention takes into account the uncertainty of distributed photovoltaic output and AC / DC load in the AC / DC microgrid of the railway passenger station, and generates a three-layer two-stage distributed robust optimization model of the AC / DC microgrid of the railway passenger station by coordinating the scheduling of charging piles, energy storage, AC / DC converters and other equipment connected to the DC bus. After solving the model, the day-ahead scheduling result is obtained. The model can effectively handle the uncertainty of distributed photovoltaic output and AC / DC load in the AC / DC microgrid of the railway passenger station, which not only reduces the error of day-ahead scheduling, but also achieves a better balance between the robustness and economy of the AC / DC microgrid of the railway passenger station, thereby improving the user's electricity experience.

[0017] In addition, the present invention transforms the two-stage distributed robust optimization model into a main problem and sub-problems in the form of mixed integer linear programming, without the need for dual decomposition, and adopts a column and constraint generation algorithm for solving it. It can effectively deal with the uncertainty of distributed photovoltaic output and AC / DC loads in railway passenger stations, while taking into account the robustness and economy of the AC / DC microgrid of the railway passenger station, and improves the accuracy of the day-ahead scheduling results, making the electricity use of the railway passenger station safer and more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. The drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a method flow diagram of a method and system for day-ahead dispatching of an AC / DC microgrid in a railway passenger station provided by the present invention; Figure 2It is a schematic diagram of the structure of an AC / DC microgrid in a railway passenger station, which is a method and system for day-ahead dispatching of an AC / DC microgrid in a railway passenger station provided by the present invention; Figure 3 It is a schematic diagram of photovoltaic output and AC / DC loads in a day-ahead prediction scenario of a day-ahead dispatching method and system for an AC / DC microgrid of a railway passenger station provided by the present invention; Figure 4 It is a schematic diagram of photovoltaic output and AC / DC loads in a typical scenario within a day of a railway passenger station AC / DC microgrid day-ahead scheduling method and system provided by the present invention; Figure 5 The present invention provides a method for dispatching a railway passenger station AC / DC microgrid and a schematic diagram of the day-ahead dispatching results of the system. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in combination with the drawings and embodiments of the present invention. Obviously, the described embodiments are partial embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0021] It should be noted that, in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a method, step or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such method, step or system. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the method, step or system including the element.

[0022] The day-ahead dispatch of AC / DC microgrids refers to the electricity consumption plan arranged in advance in the electricity consumption plan, usually within a week or a month. AC / DC microgrids are an effective way to supply and distribute electricity at railway passenger stations. However, due to the randomness of distributed photovoltaic output and AC / DC loads, the day-ahead dispatch results usually have certain errors, which brings considerable challenges to the formulation of production plans.

[0023] To this end, the present invention proposes a day-ahead scheduling method for AC / DC microgrids in railway passenger stations. Considering the uncertainty of distributed photovoltaic output and AC / DC loads in AC / DC microgrids in railway passenger stations, a three-layer two-stage distributed robust optimization model is established and solved, so that a better balance is achieved between the robustness and economy of AC / DC microgrids in railway passenger stations. Specifically, Figure 1 As shown, the method includes: Step 101: Obtain the photovoltaic output and AC / DC load in the AC / DC microgrid of the railway passenger station, use the uncertainty set of the norm distance to model the probability distribution of the photovoltaic output and the AC / DC load, and obtain a three-layer two-stage distributed robust optimization model of the AC / DC microgrid.

[0024] The AC / DC microgrid structure of a railway station is as follows: Figure 2 As shown in the figure, distributed photovoltaics are connected to the DC bus, and the photovoltaic output is preferentially supplied to the charging piles and DC loads on the DC side; the excess photovoltaic output is transmitted to the AC bus through the AC / DC converter to ensure that all photovoltaic output is consumed locally. In addition, for safety reasons, the microgrid is not allowed to reverse power to the distribution network. Among them, DC loads include chilled water pumps, cooling water pumps, public area lighting and other equipment; AC loads include air conditioners, escalators, electric water heaters and other equipment; the interconnection line serves as a bridge between the AC bus and the distribution network. The AC / DC microgrid achieves power balance of the entire microgrid through power interaction with the distribution network and coordinated scheduling of charging piles and energy storage.

[0025] More specifically, the establishment of the three-layer two-stage distributed robust optimization model includes establishing a first-stage model and establishing a second-stage model.

[0026] Based on the PV output and AC / DC loads in the day-ahead forecast scenario, the first-stage model is established with the minimum daily operating cost as the first objective function and the equipment connected to the DC bus as the first constraint.

[0027] As a specific embodiment, the first objective function is: ; ; ; .

[0028] in, represents the daily operating cost; Indicates the total number of daily scheduling periods, for example, it can be 24, each period is 1 hour; the subscript 0 in each variable indicates the day-ahead forecast scenario; the subscript In the day-ahead forecast scenario Microgrid related data for each time period; In the day-ahead forecast scenario The cost of buying electricity during the time period; express The electricity purchase price during the time period; In the day-ahead forecast scenario The power purchased during the time period; In the day-ahead forecast scenario Operation and maintenance costs of time-slot energy storage; Represents the operation and maintenance cost coefficient of energy storage; , They represent the day-ahead forecast scenario. The charging and discharging power of energy storage in a time period. The charging power of energy storage is positive, and the discharging power of energy storage is negative. In the day-ahead forecast scenario The losses and operation and maintenance costs of AC / DC converters during the period; represents the operation and maintenance cost coefficient of the AC / DC converter; Indicates the conversion efficiency of the AC / DC converter; , They represent the day-ahead forecast scenario. The injected power of the AC / DC converter from the AC side and from the DC side during the period. The injected power from the AC side is a negative value, and vice versa, the injected power from the DC side is a positive value.

[0029] It should be noted that in the day-ahead prediction scenario, the equipment connected to the DC bus is used as the first constraint condition, and the first constraint condition includes: the first charging pile constraint condition, the first energy storage constraint condition, the first AC / DC converter constraint condition, the first interconnection line power constraint condition and the first power balance constraint condition.

[0030] As an embodiment, a first charging pile constraint condition is obtained based on an electric vehicle that is charged and discharged at a bidirectional charging pile at a railway passenger station.

[0031] The first charging pile constraint condition can be: ; ; ; , , or ; ; ; ; .

[0032] in, , They represent the day-ahead forecast scenario. Time period The charging and discharging power of an electric vehicle, the charging power is positive and the discharging power is negative; , Respectively represent the rated charging and discharging power of the charging pile; , They represent the day-ahead forecast scenario. Time period The charging and discharging status of an electric vehicle. When it is 1, it means charging or discharging, and when it is 0, it means neither charging nor discharging. and They represent the first The time it takes for an electric vehicle to arrive at and leave a charging station; In the day-ahead forecast scenario Time period The state of charge of an electric vehicle; and They represent the maximum and minimum state of charge of the electric vehicle respectively; Indicates the scheduling duration, which can be 1 hour; Represents the battery capacity of electric vehicles. To simplify the analysis, the battery capacity of all electric vehicles can be taken as the same value; and Respectively represent the charging and discharging efficiency of electric vehicles; In the day-ahead forecast scenario Time period The state of charge of an electric vehicle when it leaves the charging station; Indicates the state of charge expected by electric vehicle users; In the day-ahead forecast scenario Total charging and discharging power of the charging pile during the time period; Represents the total number of electric vehicles.

[0033] The first energy storage constraint can be: ; ; ; ; ; .

[0034] in, , They represent the day-ahead forecast scenario. The charging and discharging power of energy storage in a time period. The charging power of energy storage is positive, and the discharging power of energy storage is negative. , Respectively represent the maximum charging and discharging power of energy storage; , They represent the day-ahead forecast scenario. The charging and discharging status of the energy storage during the time period. When it is 1, it means charging or discharging, and when it is 0, it means neither charging nor discharging. In the day-ahead forecast scenario The state of charge of the time storage; and Respectively represent the maximum and minimum values ​​of the energy storage charge state; Indicates the scheduling duration, which can be 1 hour; Indicates energy storage capacity; and Respectively represent the charging and discharging efficiency of energy storage; and They represent the initial state of charge and final state of charge of the energy storage respectively.

[0035] The first AC / DC converter constraint may be: ; ; .

[0036] in, Indicates the power limit transmitted by the AC / DC converter; , They represent the day-ahead forecast scenario. The direction of power flow of the AC / DC converter in each time period is a binary variable, for example, Indicates that the power of the AC / DC converter flows from AC to DC. Indicates that the power of the AC / DC converter flows from DC to AC.

[0037] The power constraint condition of the first tie line can be: .

[0038] in, In the day-ahead forecast scenario The power purchased during the time period; Indicates the power limit of the tie line transmission.

[0039] Since the AC / DC microgrid needs to satisfy the power balance constraints of both the DC side and the AC side, the first power balance constraint can be: ; .

[0040] in, In the day-ahead forecast scenario PV output during the time period; , They represent the day-ahead forecast scenario. DC load and AC load during the time period; , They represent the day-ahead forecast scenario. The injected power of the AC / DC converter from the AC side and the DC side during the period; Indicates the conversion efficiency of the AC / DC converter; In the day-ahead forecast scenario Total charging and discharging power of energy storage during the time period; In the day-ahead forecast scenario Total charging and discharging power of the charging pile during the time period; In the day-ahead forecast scenario The power purchased during the time period; In the day-ahead forecast scenario The power sold by the microgrid during the period.

[0041] Based on the PV output and AC / DC loads in typical daily scenarios, the second-stage model is established with the minimum cost of daily adjustment as the second objective function and the equipment connected to the DC bus as the second constraint.

[0042] As a specific embodiment, the second objective function is: ; ; ; .

[0043] in, Indicates the total number of discrete scenarios; the subscripts in each variable Indicates a classic intraday scenario; Indicates The probability of classic scenarios occurring within a day; Indicates the total number of daily scheduling periods; the subscripts in each variable Indicated in Classic scene within a day Microgrid related data for each time period; Indicated in Classic scene within a day The adjustment cost of electricity purchase in different time periods; express The electricity purchase price during the time period; Indicated in Classic scene within a day The power purchase adjustment power in the time period; Indicated in Classic scene within a day The operation and maintenance adjustment costs of time-slot energy storage; Represents the operation and maintenance cost coefficient of energy storage; , Respectively expressed in Classic scene within a day Charging and discharging regulation power of time-slot energy storage; Indicated in Classic scene within a day The losses of AC / DC converters during the period and the operation and maintenance adjustment costs; represents the operation and maintenance cost coefficient of the AC / DC converter; Indicates the conversion efficiency of the AC / DC converter; , Respectively expressed in Classic scene within a day The AC / DC converter injects regulated power from the AC side and from the DC side during the period.

[0044] It should be noted that in the classic intraday scenario, the equipment connected to the DC bus is used 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 interconnection line power constraint condition and the second power balance constraint condition.

[0045] Since the time when electric vehicles arrive at and leave the charging pile is random, the charging and discharging power of electric vehicles is not adjusted for each typical scenario. Therefore, the sum of the charging and discharging power in the day-ahead prediction scenario is taken as the first Classic scene within a day The total charging and discharging power of the charging pile during the time period, then the second charging pile constraint condition is equal to the first charging pile constraint condition.

[0046] That is: ;in, Indicated in Classic scene within a day Total charging and discharging power of the charging pile during the time period; In the day-ahead forecast scenario The total charging and discharging power of the charging pile during a period.

[0047] For typical scenarios within each day, it is necessary to keep the charging and discharging state of the energy storage consistent with the scenario predicted the day before. Therefore, as a specific embodiment, the second energy storage constraint condition can be: ; ; ; ; ; ; .

[0048] in, and Respectively expressed in Classic scene within a day The charging and discharging power of the time-slot energy storage; , They represent the day-ahead forecast scenario. The charging and discharging power of the time-slot energy storage; , Respectively expressed in Classic scene within a day Charging and discharging regulation power of time-slot energy storage; , Respectively represent the maximum charging and discharging power of energy storage; , They represent the day-ahead forecast scenario. The charging and discharging status of the time storage; Indicated in Classic scene within a day The state of charge of the time storage; and Respectively represent the maximum and minimum values ​​of the energy storage charge state; Indicates the scheduling duration; Indicates energy storage capacity; and Respectively represent the charging and discharging efficiency of energy storage; and They represent the initial state of charge and final state of charge of the energy storage respectively.

[0049] The second AC / DC converter constraint can be: ; ; ; ; .

[0050] in, , Respectively expressed in Classic scene within a day The injected power of the AC / DC converter from the AC side and the DC side during the period; , They represent the day-ahead forecast scenario. The injected power of the AC / DC converter from the AC side and the DC side during the period; , Respectively expressed in Classic scene within a day The period AC / DC converter injects the regulated power from the AC side and from the DC side; Indicates the power limit transmitted by the AC / DC converter; , Respectively expressed in Classic scene within a day The flow direction of the AC / DC converter during the time period.

[0051] The second tie line power constraint condition can be: ; .

[0052] in, Indicated in Classic scene within a day The power purchased by the microgrid during the period; In the day-ahead forecast scenario The power purchased during the time period; Indicated in Classic scene within a day The power purchase adjustment power in the time period; Indicates the power limit of the tie line transmission.

[0053] The second power balance constraint condition may be: ; .

[0054] in, Indicated in Classic scene within a day PV output during the time period; , Respectively expressed in Classic scene within a day DC load and AC load during the time period; , Respectively expressed in Classic scene within a day The injected power of the AC / DC converter from the AC side and the DC side during the period; Indicates the conversion efficiency of the AC / DC converter; Indicated in Classic scene within a day Total charging and discharging power of energy storage during the time period; Indicated in Classic scene within a day Total charging and discharging power of the charging pile during the time period; Indicated in Classic scene within a day Purchase electricity during the period.

[0055] Thus, the decision variables of the first stage model can be obtained and the decision variables of the second-stage model , specifically: .

[0056] The probability distribution of photovoltaic output and AC / DC load is modeled using the uncertainty set of norm distance, and a three-layer two-stage distributed robust optimization model of AC / DC microgrid min-max-min is obtained.

[0057] As an embodiment, the three-layer two-stage distributed robust optimization model is: ; in, represents the decision variables of the first-stage model; represents the cost coefficient vector in the objective function; represents the decision variables of the second-stage model; Indicates a classic intraday scenario Probability of occurrence; represents an uncertain set; represents the total number of discrete scenarios; , , Represents the coefficient matrix or vector in the constraints.

[0058] The uncertainty set is obtained by using the empirical distribution probability of the 1-norm and ∞-norm distance. As an example, the uncertainty set Can be: ; in, Indicates a classic intraday scenario Probability of occurrence; represents the total number of discrete scenarios; Indicates Empirical probability of typical scenarios within a day; represents the allowable offset of the control 1-norm probability distribution, , represents the permissible offset of the control ∞-norm probability distribution, ; Indicates generation The number of original scenes used in a typical scene; and Represents the preset confidence level parameter.

[0059] Step 102: Solve the three-layer two-stage distributed robust optimization model to obtain the day-ahead scheduling result.

[0060] As an embodiment, solving the three-layer two-stage distributed robust optimization model includes: decomposing the three-layer two-stage distributed robust optimization model into a main problem and sub-problems in the form of mixed integer linear programming, and solving it using a column and constraint generation algorithm to obtain the day-ahead scheduling result of the railway passenger station AC / DC microgrid.

[0061] Furthermore, solving using the column and constraint generation algorithm includes: S201: Obtain the lower bound of the three-layer two-stage distributed robust optimization model and use the lower bound as the main problem.

[0062] It should be noted that the main problem is to calculate the minimum total cost of day-ahead economic dispatch when the probability distribution of each classic scenario is known, and then obtain the lower bound of the three-layer two-stage distributed robust optimization model.

[0063] As a specific embodiment, the lower bound is: ; in, represents the cost coefficient vector in the objective function; represents the decision variables of the first-stage model; Represents the maximum value of the second stage objective function; represents the total number of discrete scenarios; Indicates After the iteration The worst probability of a scenario is obtained by solving the sub-problems; Indicates The decision variables of the second-stage model after iterations; , , Represents the coefficient matrix or vector in the constraints; Indicates the total number of iterations.

[0064] S202: Obtain the upper bound of the three-layer two-stage distributed robust optimization model, and use the upper bound as a sub-problem.

[0065] It should be noted that the sub-problem is to find the worst probability of occurrence of each classic scenario under the variables solved by the main problem, and return to the main problem for the next iteration.

[0066] As an example, the lower bound is: ; in, represents the total number of discrete scenarios; Indicates a classic intraday scenario Probability of occurrence; represents an uncertain set; represents the decision variables of the second stage model; represents the cost coefficient vector in the objective function; represents the variables solved from the main problem; , , Represents the coefficient matrix or vector in the constraints.

[0067] S203: Divide the sub-problem into two problems and solve them sequentially to obtain the worst probability of occurrence of the classic scenario, and feed it back to the main problem.

[0068] As an embodiment, dividing the sub-problem into two problems and solving them sequentially includes: first solving the inner minimization problem, and then solving the outer maximization problem.

[0069] Specifically, the minimization problem is: .

[0070] The maximization problem is: .

[0071] 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 the threshold The iteration is stopped when , and the day-ahead scheduling result is obtained.

[0072] In order to verify the effectiveness of the proposed day-ahead scheduling method for the AC / DC microgrid of a railway passenger station, the present invention takes an AC / DC microgrid of a railway passenger station in Guangdong Province as an example to verify its effectiveness.

[0073] AC / DC microgrid structure Figure 2 The parameters of the AC / DC microgrid are shown in Table 1, and the time-of-use electricity price of the AC / DC microgrid is shown in Figure 2 As shown in Figure 2, the time it takes for electric vehicles to arrive at and leave the charging station is statistically approximately normal distributed.

[0074] Table 1 AC / DC microgrid parameters

[0075] Table 2 Time-of-use electricity prices for AC / DC microgrids

[0076] The day-ahead forecast data of PV output and AC / DC load in the day-ahead forecast scenario are as follows: Figure 3 As shown in Figure 1, multiple random scenarios are generated through LHS based on the day-ahead forecast data. The historical forecast errors of the AC / DC microgrid are analyzed, and the day-ahead forecast errors of the PV output and AC / DC load are set to 20% and 15% respectively. 1,000 random scenarios are initially generated and reduced to 4 typical scenarios within a day, as shown in Figure 1. Figure 4 Then the day-ahead dispatching results of the AC / DC microgrid of the railway passenger station are obtained, as shown in the attached Figure 5 As shown ( =0.2, =0.9).

[0077] In order to verify the economy and rationality of the day-ahead scheduling method proposed in the present invention, two commonly used methods for dealing with uncertainty, deterministic optimization, two-stage robust optimization and random programming, are selected to compare with the three-layer two-stage robust optimization model of the present invention. Among them, the four optimization methods select the same day-ahead forecast data, the present invention and random programming select the same typical intraday scene, the uncertainty adjustment parameters of the photovoltaic output and load power of the two-stage robust optimization are 6 and 12 respectively, and the confidence level of the present invention is and They are set to 0.9 and 0.99 respectively, and the solution results are shown in Table 3.

[0078] Table 3 Comparison of solution results of different optimization methods

[0079] As can be seen from Table 3, the total cost of economic dispatch for the day-ahead of deterministic optimization is the lowest. Since this method is optimized for the forecast scenario and does not consider the influence of uncertainty factors, there is no adjustment cost. Compared with random programming, although the intraday adjustment cost of the second stage is higher than that of random programming, the day-ahead dispatch cost of the first stage is lower than that of random programming, and is very close to deterministic optimization, resulting in that the total cost of economic dispatch for the day-ahead only increases by 1.29% compared with deterministic optimization, which can better meet the economic requirements of day-ahead dispatch.

[0080] This is because the present invention is divided into two stages. In the second stage, the worst probability distribution of each typical scenario within the day is considered. Although the adjustment cost is increased, the scheduling result is more robust. The objective function of stochastic programming is to minimize the sum of the day-ahead scheduling cost and the intraday adjustment cost. Overall optimization is required, so the day-ahead scheduling cost is high and the intraday adjustment cost is negative. The two-stage robust optimization scheduling takes into account the uncertain variable values ​​of the worst scenario, resulting in a high intraday adjustment cost. Therefore, the total cost of economic scheduling on the day-ahead is the highest, an increase of 26.41% compared with stochastic programming.

[0081] In summary, the two-stage robust optimization scheduling over-considers robustness, and the random planning over-idealizes the economy. Compared with the two-stage robust optimization and random planning methods, the present invention achieves a better balance between the two, can effectively handle the uncertainty of distributed photovoltaic output and AC / DC loads, and takes into account the robustness and economy of AC / DC microgrids.

[0082] In a second aspect, the present invention provides a day-ahead dispatching system for an AC / DC microgrid in a railway passenger station, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods. The system is consistent with the technical solution of the above method, and will not be described one by one here.

[0083] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, and according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0084] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed method or system can be implemented in other ways. For example, the embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0085] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0086] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A day-ahead dispatching method for an AC / DC microgrid in a railway passenger station, characterized in that: The method comprises: The photovoltaic output and AC / DC load in the AC / DC microgrid of the railway passenger station are obtained, and the probability distribution of the photovoltaic output and the AC / DC load is modeled using the uncertainty set of the norm distance to obtain a three-layer two-stage distributed blue-robust optimization model of the AC / DC microgrid; The establishment of the three-layer two-stage distributed blue-rod optimization model includes: based on the photovoltaic output and the AC and DC loads in the day-ahead forecast scenario, taking the daily minimum operation cost as the first objective function and the equipment connected to the DC bus as the first constraint condition, a first-stage model is established; based on the photovoltaic output and the AC and DC loads in the typical intraday scenario, taking the intraday minimum adjustment cost as the second objective function and the equipment connected to the DC bus as the second constraint condition, a second-stage model is established; The three-layer two-stage distributed robust optimization model is solved to obtain the day-ahead scheduling result.

2. The method for day-ahead dispatching of a railway passenger station AC / DC microgrid according to claim 1, characterized in that: The three-layer two-stage distributed robust optimization model is: ； in, represents the decision variables of the first stage model; represents the cost coefficient vector in the objective function; represents the decision variables of the second stage model; Indicates a classic intraday scenario Probability of occurrence; represents an uncertain set; represents the total number of discrete scenarios; , , Represents the coefficient matrix or vector in the constraints.

3. A method for day-ahead dispatching of an AC / DC microgrid in a railway passenger station according to claim 1 or 2, characterized in that: Solving the three-layer two-stage distributed robust optimization model includes: decomposing the three-layer two-stage distributed robust optimization model into a main problem and sub-problems in the form of mixed integer linear programming, and solving it using a column and constraint generation algorithm to obtain the day-ahead scheduling result of the railway passenger station AC / DC microgrid.

4. The method for day-ahead dispatching of an AC / DC microgrid in a railway passenger station according to claim 3, characterized in that: Solving using the column and constraint generation algorithm includes: Obtaining a lower bound of the three-layer two-stage distributed robust optimization model, and using the lower bound as the main problem; Obtaining an upper bound of the three-layer two-stage distributed robust optimization model, and using the upper bound as a subproblem; Divide the sub-problem into two problems and solve them sequentially to obtain the worst occurrence probability of the classic scenario, and feed it back to the main problem; 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 the threshold The iteration is stopped when , and the day-ahead scheduling result is obtained.

5. The method for day-ahead dispatching of an AC / DC microgrid in a railway passenger station according to claim 4, characterized in that: The acquisition of the lower bound includes: calculating the minimum value of the total cost of day-ahead economic dispatch when the probability distribution of each classic scenario is known; The acquisition of the upper bound includes: finding the worst occurrence probability of each classic scenario under the variables solved by the main problem.

6. A method for day-ahead dispatching of an AC / DC microgrid in a railway passenger station according to claim 1 or 2, characterized in that: The uncertainty set for: ； in, Indicates a classic intraday scenario Probability of occurrence; represents the total number of discrete scenarios; Indicates Empirical probability of typical scenarios within a day; represents the allowable offset of the control 1-norm probability distribution, , represents the permissible offset of the control ∞-norm probability distribution, ; Indicates generation The number of original scenes used in a typical scene; and Represents the preset confidence level parameter.

7. The method for day-ahead dispatching of a railway passenger station AC / DC microgrid according to claim 2, characterized in that: The decision variables of the first stage model and the decision variables of the second stage model for: ； in, In the day-ahead forecast scenario The power purchased during the time period; , They represent the day-ahead forecast scenario. The charging and discharging power of the time-slot energy storage; , They represent the day-ahead forecast scenario. The injected power of the AC / DC converter from the AC side and the DC side during the period; , They represent the day-ahead forecast scenario. Time period The charging and discharging power of electric vehicles; In the day-ahead forecast scenario Time period The state of charge of an electric vehicle; In the day-ahead forecast scenario The state of charge of the time storage; , They represent the day-ahead forecast scenario. Time period The charging and discharging status of electric vehicles; , They represent the day-ahead forecast scenario. The charging and discharging status of the time storage; , They represent the day-ahead forecast scenario. The direction of power flow of the AC / DC converter during the time period; Indicated in Classic scene within a day The power purchase adjustment power in the time period; , Respectively expressed in Classic scene within a day Charging and discharging regulation power of time-slot energy storage; , Respectively expressed in Classic scene within a day The period AC / DC converter injects the regulated power from the AC side and from the DC side; Indicated in Classic scene within a day The state of charge of the time storage; , Respectively expressed in Classic scene within a day The flow direction of the AC / DC converter during the time period.

8. The method for day-ahead dispatching of an AC / DC microgrid in a railway passenger station according to claim 1, characterized in that: The first objective function is: ； ； ； ； in, represents the daily operating cost; Indicates the total number of daily scheduling periods; In the day-ahead forecast scenario The cost of buying electricity during the time period; express The electricity purchase price during the time period; In the day-ahead forecast scenario The power purchased during the time period; In the day-ahead forecast scenario Operation and maintenance costs of time-slot energy storage; Represents the operation and maintenance cost coefficient of energy storage; , They represent the day-ahead forecast scenario. The charging and discharging power of the time-slot energy storage; In the day-ahead forecast scenario The losses and operation and maintenance costs of AC / DC converters during the period; represents the operation and maintenance cost coefficient of the AC / DC converter; Indicates the conversion efficiency of the AC / DC converter; , They represent the day-ahead forecast scenario. The period of time during which the AC / DC converter injects power from the AC side and from the DC side.

9. The method for day-ahead dispatching of an AC / DC microgrid in a railway passenger station according to claim 1, characterized in that: The second objective function is: ； ； ； ； in, represents the total number of discrete scenarios; Indicates The probability of classic scenarios occurring within a day; Indicates the total number of daily scheduling periods; Indicated in Classic scene within a day The adjustment cost of electricity purchase in different time periods; express The electricity purchase price during the time period; Indicated in Classic scene within a day The power purchase adjustment power in the time period; Indicated in Classic scene within a day The operation and maintenance adjustment costs of time-slot energy storage; Represents the operation and maintenance cost coefficient of energy storage; , Respectively expressed in Classic scene within a day Charging and discharging regulation power of time-slot energy storage; Indicated in Classic scene within a day The losses and operation and maintenance adjustment costs of AC / DC converters during the period; represents the operation and maintenance cost coefficient of the AC / DC converter; Indicates the conversion efficiency of the AC / DC converter; , Respectively expressed in Classic scene within a day The AC / DC converter injects regulated power from the AC side and from the DC side during the period.

10. A day-ahead dispatching system for an AC / DC microgrid in a railway passenger station, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 9.

Images