A method and system for multi-time scale collaborative optimization scheduling of power system

Abstract

The present invention discloses a multi-time scale collaborative optimization scheduling method and system for a power system, and belongs to the field of distributed power generation technology. The present invention provides a multi-time scale optimization scheduling strategy for a distribution network that combines day-ahead scheduling and intraday redispatching. In this strategy, day-ahead scheduling and intraday scheduling are solved under the framework of the same optimization problem. Therefore, day-ahead scheduling is subject to the predicted values ​​of intraday load and distributed resources, and a more balanced scheduling result can be obtained. In the intraday stage, various adjustable power equipment needs to be redistributed based on the day-ahead scheduling result, which can be restricted by the equipment action amount adjustment constraint. The strategy also takes into account the participation of mobile energy storage and SOP, so as to reduce power grid power fluctuations, network losses and wind and solar abandonment, and improve the low-carbon nature and safety of distribution system operation.

Description

Technical Field

[0001] The present invention relates to the field of distributed power generation technology, and in particular to a multi-time scale collaborative optimization scheduling method and system for a power system. Background Art

[0002] With the advancement of distributed power generation technology, the proportion of various distributed resources such as distributed photovoltaic and wind power connected to the grid has been increasing, making the power fluctuation and network loss problems of active distribution networks more serious. The characteristics of new energy sources make them unable to be fully absorbed by the power grid. Therefore, it is urgent to reduce the abandonment of wind and solar power and improve the absorption level of new energy in the distribution network. Mobile energy storage has the ability to flexibly adjust the time and space of power and energy. Its rational use in the distribution network can effectively reduce the power fluctuation and network loss of the power grid. Mobile energy storage is connected to the distribution network. When the output of new energy is large, it drives to the distributed energy access point to absorb power for energy storage. When the load of the power grid is heavy, it drives to the end of the line to reduce the pressure of the overloaded line and reduce network loss. SOP (Soft Open Point) is a new regulation method for active distribution networks. Based on the structural characteristics of fully controlled power electronic devices, it can flexibly control the flow of power and even optimize the adjustment of power in real time. It can effectively deal with the volatility and intermittency of distributed power sources, and can also play a role in reducing network losses and reducing wind and solar abandonment.

[0003] The multi-time scale scheduling problem of the power system is usually scheduled separately before the day and during the day. The optimization results obtained have poor connectivity and cannot be radiated from the results before the day to the day. The power system after optimized scheduling using this method still has the defects of large grid power fluctuations, large network losses and frequent wind and solar power abandonment. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-time scale collaborative optimization scheduling method and system for an electric power system, so as to reduce power fluctuations, network losses and wind and solar power abandonment in the power grid, and improve the low-carbon nature and safety of the distribution system operation.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The present invention provides a multi-time scale collaborative optimization scheduling method for a power system, the method comprising the following steps:

[0007] A mathematical model of mobile energy storage and a mathematical model of SOP equipment are established respectively; the mobile energy storage and the SOP equipment are equipment participating in the operation of active distribution network in the power system;

[0008] According to the mathematical model of mobile energy storage and the mathematical model of SOP equipment, a multi-time scale dispatching model of active distribution network is established with the goal of maximizing the low-carbon index value of system operation; the multi-time scale dispatching model of active distribution network includes: objective function, day-ahead constraint conditions and intra-day constraint conditions;

[0009] The multi-time scale dispatching model of active distribution network is transformed into a model that can be directly solved by the solver, and then solved to obtain the optimized dispatching plan.

[0010] Optionally, the mathematical model of the mobile energy storage includes an energy model of the mobile energy storage and a displacement model of the mobile energy storage;

[0011] The energy model of mobile energy storage is:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] in, represents the energy storage capacity of mobile energy storage m during period t, represents the charging and discharging power of mobile energy storage m during period t, η c , η d Indicates the charging and discharging efficiency of mobile energy storage, Indicates the charging and discharging mark of the mobile energy storage m. When its value is 1, it means that it is being charged or discharged. When its value is 0, it means that it is not being charged or discharged. min Indicates the minimum capacity of mobile energy storage, E N Indicates the rated capacity of mobile energy storage, P N Indicates the rated power of mobile energy storage, and They are the energy storage capacity of mobile energy storage m in the 0 period and 24 period of a day respectively;

[0019] The displacement model of mobile energy storage is:

[0020]

[0021]

[0022]

[0023]

[0024] in, Represents the elements in the three-dimensional access matrix, when is equal to 1, indicating that the mobile energy storage m is connected to the distribution network node j during period t, Ω J represents the candidate access node set, n represents the number of mobile energy storage participating in the active distribution network operation in the power system, cap j represents the maximum number of mobile energy storage that can be connected to the distribution network node j, Indicates the driving status of the mobile energy storage m in period t. When it is equal to 1, it means that the mobile energy storage m is in driving state during period t.

[0025] Optionally, the mathematical model of the SOP device is:

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] in, and are the active power injected into the SOP of distribution network node i and distribution network node j during period t, and are the reactive power injected into the SOP of the distribution network node i and the distribution network node j during period t, and The active power loss of the converter connected to the distribution network node i and the distribution network node j in the period t, η SOP is the loss coefficient of the converter, and are the converter capacities connected to distribution network node i and distribution network node j in period t respectively.

[0032] Optionally, the objective function is:

[0033] maxf=f 1 -f 2 -f 3 ;

[0034]

[0035]

[0036]

[0037] Among them, f 1 is the peak-valley power regulation index of mobile energy storage, f 2 is the power generation index of thermal power units, f 3 is the distribution network loss index, c t is the weight of period t, is the active power value of thermal power unit g at node j in the distribution network during period t, Ω g is the set of thermal power units, a is the quadratic polynomial coefficient of the thermal power unit power generation index, b is the linear polynomial coefficient of the thermal power unit power generation index, C is the constant coefficient of the thermal power unit power generation index, Ω L is the set of distribution network branches, r ij is the resistance of branch ij, I ij,t is the current of branch ij during period t, and Δt represents the time interval.

[0038] Optionally, the day-ahead constraints include: day-ahead mobile energy storage operation constraints, day-ahead SOP operation constraints, day-ahead distribution network flow constraints, day-ahead safety constraints, day-ahead power output constraints, and day-ahead power capacitor operation constraints; the day-ahead mobile energy storage operation constraints include mobile energy storage access power constraints, mobile energy storage energy model constraints, and mobile model constraints of mobile energy storage; the day-ahead SOP operation constraints include mathematical model constraints of SOP equipment;

[0039] The intraday constraints include: intraday mobile energy storage operation constraints, intraday SOP operation constraints, intraday distribution network flow constraints, intraday safety constraints, intraday power capacitor operation constraints and intraday power output constraints.

[0040] Optionally, the multi-time scale dispatching model of the active distribution network is converted into a model that can be directly solved by the solver, and the model is solved to obtain an optimized dispatching solution, which specifically includes:

[0041] The multi-time-scale dispatching model of active distribution network is transformed into a second-order cone, and the big M method is used to simplify the bilinear terms in the model to obtain the transformed model.

[0042] The transformed model is solved by a solver to obtain the optimal scheduling solution.

[0043] Optionally, the active distribution network multi-time scale dispatch model is transformed into a second-order cone, and the big M method is used to simplify the bilinear terms in the model to obtain the transformed model, which specifically includes:

[0044] The day-ahead distribution network power flow constraints with nonlinear terms in the multi-time-scale dispatching model of the active distribution network are converted into first- and second-order cone constraints, and the intraday distribution network power flow constraints with nonlinear terms in the multi-time-scale dispatching model of the active distribution network are converted into second-order cone constraints, and a model after second-order cone conversion is obtained;

[0045] The big M method is used to simplify the bilinear terms of the mobile energy storage access power constraint in the model after the second-order cone transformation to obtain the transformed model.

[0046] Optionally, the first second-order cone constraint is:

[0047]

[0048] Among them, l ij,t is the intermediate conversion variable of branch ij in period t, v i,t is the intermediate conversion variable of the distribution network node i in period t; ij,t and Q ij,t are the active power and reactive power of branch ij in period t respectively;

[0049]

[0050] Among them I ij,t is the current of branch ij during period t, U i,t is the voltage of the node i in the distribution network during period t.

[0051] Optionally, the simplified mobile energy storage access power constraint of the Big M method is:

[0052]

[0053]

[0054]

[0055]

[0056] in, is the access power of mobile energy storage j at time t, is an auxiliary variable, and M is a positive integer.

[0057] A multi-time scale collaborative optimization dispatching system for a power system, the system is applied to the above method, and the system comprises:

[0058] A mathematical model building module, used to respectively establish a mathematical model of mobile energy storage and a mathematical model of SOP equipment; the mobile energy storage and the SOP equipment are equipment participating in the operation of active distribution network in the power system;

[0059] The model building module is used to establish a multi-time scale dispatching model of the active distribution network with the goal of maximizing the low-carbon index value of system operation according to the mathematical model of mobile energy storage and the mathematical model of SOP equipment; the multi-time scale dispatching model of the active distribution network includes: objective function, day-ahead constraint conditions and intra-day constraint conditions;

[0060] The model solving module is used to transform the multi-time scale dispatching model of the active distribution network into a model that can be directly solved by the solver, and solve it to obtain the optimized dispatching plan.

[0061] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0062] The embodiment of the present invention provides a multi-time scale collaborative optimization scheduling method and system for a power system, the method comprising: establishing a mathematical model of mobile energy storage and a mathematical model of SOP equipment respectively; establishing a multi-time scale scheduling model of an active distribution network with the maximum low-carbon index value of system operation as the goal according to the mathematical model of mobile energy storage and the mathematical model of SOP equipment; converting the multi-time scale scheduling model of the active distribution network into a model that can be directly solved by a solver, and solving it to obtain an optimized scheduling scheme. The present invention provides a multi-time scale optimization scheduling strategy for a distribution network combining day-ahead scheduling and intraday redispatching, in which day-ahead scheduling and intraday scheduling are solved under the framework of the same optimization problem, so day-ahead scheduling is subject to the predicted values ​​of intraday load and distributed resources, and a more balanced scheduling result can be obtained. In the intraday stage, various adjustable power equipment needs to be redistributed on the day-ahead scheduling result, which can be restricted by the equipment action amount adjustment constraint. The strategy takes into account the participation of mobile energy storage and SOP at the same time, thereby reducing power grid power fluctuations, network losses and wind and light abandonment, and improving the low-carbon and safety of distribution system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0064] Figure 1 A flowchart of a multi-time scale collaborative optimization scheduling method for a power system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] The purpose of the present invention is to provide a multi-time scale collaborative optimization scheduling method and system for an electric power system, so as to reduce power fluctuations, network losses and wind and solar power abandonment in the power grid, and improve the low-carbon nature and safety of the distribution system operation.

[0067] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] Example 1

[0069] Embodiment 1 of the present invention provides a multi-time scale collaborative optimization scheduling method for a power system, the method comprising the following steps:

[0070] Step 101, respectively establish a mathematical model of mobile energy storage and a mathematical model of SOP equipment; the mobile energy storage and the SOP equipment are equipment participating in the operation of active distribution network in the power system.

[0071] Step 101 in the embodiment of the present invention respectively establishes mathematical models describing mobile energy storage and SOP devices participating in the operation of the active distribution network, specifically including:

[0072] Step 1.1: Build an energy model for mobile energy storage

[0073] The energy model of mobile energy storage is:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] in, represents the energy storage capacity during period t, represents the charging and discharging power of energy storage during period t, η c , η d Indicates the charging and discharging efficiency of mobile energy storage. Indicates the mobile energy storage charging and discharging mark. When its value is 1, it means it is charging or discharging. When its value is 0, it means it is not charging or discharging. min Indicates the minimum capacity of mobile energy storage, E N Indicates the rated capacity of mobile energy storage, P N Indicates the rated power of mobile energy storage.

[0081] Step 1.2: Build a displacement model for mobile energy storage

[0082] When the output of new energy is high, the mobile energy storage will travel to the distributed power generation node to absorb electricity; when the grid load is heavy, the mobile energy storage will connect to the end node of the line to output electricity to reduce network loss. Therefore, a three-dimensional access matrix is ​​established. To describe the displacement constraints of mobile energy storage, the details are as follows:

[0083]

[0084]

[0085]

[0086]

[0087] when When it is equal to 1, it means that the mobile energy storage m is connected to the distribution network node j in time period t. Where m represents the serial number of the mobile energy storage, j represents the access node, t represents the time period, and Ω J Indicates the candidate access node set. j represents the maximum number of mobile energy storage that can be connected to node j. When it is equal to 1, it means that the mobile energy storage m is in the driving state. Formula (7) constrains a mobile energy storage to only be connected to one node at the same time, formula (8) constrains the number of mobile energy storage connected to the node, formula (9) indicates that the mobile energy storage cannot be in the driving and charging and discharging state at the same time, and formula (10) constrains the driving time of the mobile energy storage between nodes to be 1 time interval.

[0088] Step 1.3: Build a mathematical model of the SOP device

[0089] The mathematical model of the SOP device is:

[0090]

[0091]

[0092]

[0093] Where P iSOP and are the active power and reactive power of the SOP injected into node i respectively; P i SOP,L is the active power loss of the converter connected to the i node; η SOP is the loss factor of the converter.

[0094]

[0095]

[0096] In the formula, and are the capacities of the converters connected at nodes i and j respectively.

[0097] Step 102, based on the mathematical model of mobile energy storage and the mathematical model of SOP equipment, establish an active distribution network multi-time scale scheduling model with the goal of maximizing the low-carbon index value of system operation; the active distribution network multi-time scale scheduling model includes: objective function, day-ahead constraints and intra-day constraints.

[0098] For example, the system operation low-carbon index is composed of the peak-valley power regulation index of mobile energy storage, the power generation index of thermal power units and the network loss index of distribution network. The constraints include the distribution network flow constraints, safety constraints, equipment operation constraints and power output constraints on two time scales, day-ahead and intra-day, as follows:

[0099] Step 2.1: Establish the objective function

[0100] The multi-time-scale dispatching model of active distribution network considering mobile energy storage and SOP aims to maximize the low-carbon index value of system operation, including the peak-valley power regulation index of mobile energy storage, the power generation index of thermal power units and the network loss index of distribution network.

[0101] max f=f 1 -f 2 -f 3 (16)

[0102]

[0103]

[0104]

[0105] In the formula, f 1 is the peak-valley power regulation index of mobile energy storage, c t is the weight of period t. 2 is the power generation index of thermal power units, Ω g It is the thermal power access node. 3is the distribution network loss index, Ω L is the set of distribution network branches. Ω g is the set of thermal power units, a is the quadratic polynomial coefficient of the thermal power unit power generation index, b is the linear polynomial coefficient of the thermal power unit power generation index, C is the constant coefficient of the thermal power unit power generation index, r ij is the resistance of branch ij, I ij,t is the current of branch ij during period t, and Δt represents the time interval.

[0106] Step 2.2: Establish day-ahead constraints

[0107] The day-ahead constraints include: day-ahead mobile energy storage operation constraints, day-ahead SOP operation constraints, day-ahead distribution network flow constraints, intraday safety constraints, day-ahead power capacitor operation constraints, and day-ahead power supply output constraints.

[0108] Step 2.2.1: Establish day-ahead mobile energy storage operation constraints

[0109] The day-ahead mobile energy storage operation constraints include: mobile energy storage access power constraints, mobile energy storage energy model constraints, and mobile energy storage mobility model constraints.

[0110] The access power constraint of mobile energy storage is:

[0111]

[0112] The energy model constraints of mobile energy storage include equations (1) to (6), and the mobility model constraints of mobile energy storage include equations (7) to (10).

[0113] Step 2.2.2: Establish the day-ahead SOP operation constraints

[0114] The SOP operation constraints include the mathematical model constraints of the SOP equipment, and the mathematical model constraints of the SOP equipment include equations (11) to (15).

[0115] Step 2.2.3: Establish the day-ahead distribution network flow constraints

[0116]

[0117] Establish the Distflow power flow model of the distribution network, where P ij,t , Q ij,t ,I ij,t 、r ij 、x ij are respectively the active power, reactive power, current, resistance and reactance of branch ij. i,t is the node voltage, are the injected active and reactive power of node j respectively. j , jRespectively represent the parent and child node sets of node j. is the reactive power output of the distributed generation at access node j, are the active load and reactive load of node j respectively.

[0118] Step 2.2.4: Establish day-ahead security constraints

[0119]

[0120]

[0121] are the upper and lower limits of node voltage respectively, is the upper limit of branch current.

[0122] Step 2.2.5: Establish day-ahead power output constraints

[0123]

[0124]

[0125]

[0126] In the formula, represents the output of distributed energy, Represents the predicted output value of distributed energy, Indicates the active and reactive power values ​​of thermal power units. Indicates the upper limit of active and reactive output of thermal power units.

[0127] Step 2.2.6: Establish day-ahead power capacitor operation constraints

[0128]

[0129] In the formula, Indicates the output value of the power capacitor. Indicates the upper limit of the power capacitor output.

[0130] Step 2.3: Establish intraday constraints

[0131] Intraday constraints include: intraday mobile energy storage operation constraints, intraday SOP operation constraints, intraday distribution network flow constraints, intraday safety constraints, intraday power capacitor operation constraints, and intraday power supply output constraints.

[0132] Step 2.3.1: Establish intraday mobile energy storage operation constraints

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] Where s represents different intraday scenarios.

[0141] Step 2.3.2: Establish intraday SOP operation constraints

[0142]

[0143]

[0144]

[0145]

[0146]

[0147] Where s represents different intraday scenarios.

[0148] Step 2.3.3: Establish intraday distribution network flow constraints

[0149]

[0150] Where s represents different intraday scenarios.

[0151] Step 2.3.4: Establish intraday safety constraints

[0152]

[0153]

[0154] Where s represents different intraday scenarios.

[0155] Step 2.3.5: Establish intraday power output constraints

[0156]

[0157]

[0158]

[0159] In the formula, represents the predicted output of distributed generation under the intraday scenario s. Step 2.3.6: Establish the intraday power capacitor operation constraints

[0160]

[0161] Step 2.4: Create an actionable constraint

[0162] During the intraday stage, various adjustable power equipment needs to be reallocated based on the dispatch results of the previous day, so the equipment action volume adjustment constraints need to be met.

[0163]

[0164]

[0165] Step 103, converting the active distribution network multi-time scale scheduling model into a model that can be directly solved by a solver, and solving it to obtain an optimized scheduling solution.

[0166] The established multi-time-scale dispatch model of active distribution network considering mobile energy storage and SOP is transformed into a second-order cone, and the big M method is used to simplify the bilinear terms in the model so that the transformed model can be directly solved by the solver, including:

[0167] Step 3.1: Perform a second-order cone transformation on the model

[0168] Since the power flow equation (21) contains nonlinear terms, it is difficult to solve in the optimization model. Therefore, the following variables are defined:

[0169]

[0170] After substituting it into formula (21), we get

[0171]

[0172] After further second-order cone relaxation of equation (50), we can obtain the second-order cone constraint as shown in equation (51).

[0173]

[0174] The same is true for the intraday constraint formula (40).

[0175] Step 3.2: Simplify the bilinear terms of the model

[0176] Since there are bilinear terms in the mobile energy storage access power constraint (20), an auxiliary variable is introduced And the Big M method is used to transform it as follows:

[0177]

[0178]

[0179]

[0180]

[0181] Wherein, M is a relatively large positive integer. After the above transformation, the original mobile energy storage planning problem is transformed into a mixed integer second-order cone optimization problem.

[0182] In practical applications, the input conditions include the day-ahead and day-ahead forecasts of the loads and distributed resources at each node in the distribution network, the topological structure of the distribution network, and the impedance of the distribution lines. The dispatching model is solved by a solver to output dispatching information such as the day-ahead mobile energy storage driving route and charging and discharging plan, the day-ahead thermal power unit start and stop and output plan, the day-ahead distribution network flow information, and the change value of the mobile energy storage charging and discharging plan under the corresponding scenario within the day, the SOP flow transmission value, and the change value of the thermal power unit output.

[0183] Example 2

[0184] Embodiment 2 of the present invention provides a multi-time scale collaborative optimization dispatching system for a power system, the system is applied to the above method, and the system includes:

[0185] The mathematical model building module is used to respectively establish a mathematical model of mobile energy storage and a mathematical model of SOP equipment; the mobile energy storage and the SOP equipment are equipment participating in the operation of the active distribution network in the power system.

[0186] The model building module is used to establish a multi-time scale dispatching model of an active distribution network with the goal of maximizing the low-carbon index value of system operation according to the mathematical model of mobile energy storage and the mathematical model of SOP equipment; the multi-time scale dispatching model of an active distribution network includes: objective function, day-ahead constraints and intraday constraints.

[0187] The model solving module is used to transform the multi-time scale dispatching model of the active distribution network into a model that can be directly solved by the solver, and solve it to obtain the optimized dispatching plan.

[0188] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0189] In summary, the embodiments of the present invention utilize the dual flexible adjustment capabilities of mobile energy storage in time and space for energy and the characteristics of SOP that can control the power flow in real time, and consider the multi-time scale optimization scheduling strategy of the distribution network that combines day-ahead scheduling and intraday redispatching. An active distribution network scheduling model is established with the goal of maximizing the low-carbon index value of system operation, and with the distribution network flow constraints, safety constraints, equipment operation constraints, and power output at two time scales of day-ahead and intraday as constraints. It can effectively reduce grid power fluctuations, network losses, and wind and solar power abandonment, and improve the low-carbon and safety of distribution system operation.

[0190] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0191] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A multi-time scale collaborative optimization scheduling method for a power system, characterized in that: The method comprises the following steps: A mathematical model of mobile energy storage and a mathematical model of SOP equipment are established respectively; the mobile energy storage and the SOP equipment are equipment participating in the operation of active distribution network in the power system; According to the mathematical model of mobile energy storage and the mathematical model of SOP equipment, a multi-time scale dispatching model of active distribution network is established with the goal of maximizing the low-carbon index value of system operation; the multi-time scale dispatching model of active distribution network includes: objective function, day-ahead constraint conditions and intra-day constraint conditions; The objective function is: maxf=f1-f2-f3; Among them, f1 is the peak-valley power regulation index of mobile energy storage, f2 is the power generation index of thermal power units, f3 is the distribution network loss index, c t is the weight of period t, is the active power value of thermal power unit g at node j in the distribution network during period t, Ω g is the set of thermal power units, a is the quadratic polynomial coefficient of the thermal power unit power generation index, b is the linear polynomial coefficient of the thermal power unit power generation index, C is the constant coefficient of the thermal power unit power generation index, Ω L is the set of distribution network branches, r ij is the resistance of branch ij, I ij,t is the current of branch ij in period t, Δt represents the time interval; The day-ahead constraints include: day-ahead mobile energy storage operation constraints, day-ahead SOP operation constraints, day-ahead distribution network flow constraints, day-ahead safety constraints, day-ahead power output constraints, and day-ahead power capacitor operation constraints; the day-ahead mobile energy storage operation constraints include mobile energy storage access power constraints, mobile energy storage energy model constraints, and mobile energy storage mobility model constraints; the day-ahead SOP operation constraints include SOP equipment mathematical model constraints; The intraday constraints include: intraday mobile energy storage operation constraints, intraday SOP operation constraints, intraday distribution network flow constraints, intraday safety constraints, intraday power capacitor operation constraints and intraday power output constraints; The multi-time scale dispatch model of the active distribution network is converted into a model that can be directly solved by the solver, and then solved to obtain the optimal dispatch plan; The mathematical model of mobile energy storage includes the energy model of mobile energy storage and the displacement model of mobile energy storage; The energy model of mobile energy storage is: in, represents the energy storage capacity of mobile energy storage m during period t, represents the charging and discharging power of mobile energy storage m during period t, η c , η d Indicates the charging and discharging efficiency of mobile energy storage, Indicates the charging and discharging mark of the mobile energy storage m. When its value is 1, it means that it is being charged or discharged. When its value is 0, it means that it is not being charged or discharged. min Indicates the minimum capacity of mobile energy storage, E N Indicates the rated capacity of mobile energy storage, P N Indicates the rated power of mobile energy storage, and They are the energy storage capacity of mobile energy storage m in the 0 period and 24 period of a day respectively; The displacement model of mobile energy storage is: in, Represents the elements in the three-dimensional access matrix, when When it is equal to 1, it means that the mobile energy storage m is connected to the distribution network node j during the period t, Ω J represents the candidate access node set, n represents the number of mobile energy storage participating in the active distribution network operation in the power system, cap j represents the maximum number of mobile energy storage that can be connected to the distribution network node j, Indicates the driving status of the mobile energy storage m in period t. When it is equal to 1, it means that the mobile energy storage m is in the driving state during the period t; The multi-time scale dispatch model of the active distribution network is converted into a model that can be directly solved by the solver, and then solved to obtain the optimal dispatching solution, including: The multi-time-scale dispatching model of active distribution network is transformed into a second-order cone, and the big M method is used to simplify the bilinear terms in the model to obtain the transformed model. The transformed model is solved by a solver to obtain an optimized scheduling solution; The multi-time-scale dispatching model of active distribution network is transformed into a second-order cone, and the big M method is used to simplify the bilinear terms in the model to obtain the transformed model, which specifically includes: The day-ahead distribution network power flow constraints with nonlinear terms in the multi-time-scale dispatching model of the active distribution network are converted into first- and second-order cone constraints, and the intraday distribution network power flow constraints with nonlinear terms in the multi-time-scale dispatching model of the active distribution network are converted into second-order cone constraints, and a model after second-order cone conversion is obtained; The big M method is used to simplify the bilinear terms of the mobile energy storage access power constraint in the model after the second-order cone transformation to obtain the transformed model. The first and second order cone constraints are: Among them, l ij,t is the intermediate conversion variable of branch ij in period t, v i,t is the intermediate conversion variable of the distribution network node i in period t; ij,t and Q ij,t are the active power and reactive power of branch ij in period t respectively; Among them I ij,t is the current of branch ij during period t, U i,t is the voltage of the distribution network node i during period t; The simplified mobile energy storage access power constraint of the Big M method is: in, is the access power of mobile energy storage j at time t, is an auxiliary variable, M is a positive integer; Day-ahead dispatching and intraday dispatching are solved within the same optimization problem framework. Day-ahead dispatching is subject to the predicted values ​​of intraday load and distributed resources. During the intraday stage, various adjustable power equipment needs to be reallocated based on the day-ahead dispatching results and is restricted by the equipment action volume adjustment constraints. The strategy also takes into account the participation of mobile energy storage and SOP.

2. The multi-time scale collaborative optimization scheduling method for power system according to claim 1 is characterized in that: The mathematical model of the SOP device is: in, and are the active power injected into the SOP of distribution network node i and distribution network node j during period t, and are the reactive power injected into the SOP of the distribution network node i and the distribution network node j during period t, and are the active power losses of the converters connected to the distribution network nodes i and j in period t, respectively, and η SOP is the loss coefficient of the converter, and are the converter capacities connected to distribution network node i and distribution network node j in period t respectively.

3. A multi-time scale collaborative optimization dispatching system for power systems, characterized in that: The system is applied to the method described in any one of claims 1 to 2, and the system comprises: A mathematical model building module, used to respectively establish a mathematical model of mobile energy storage and a mathematical model of SOP equipment; the mobile energy storage and the SOP equipment are equipment participating in the operation of active distribution network in the power system; A model building module is used to build a multi-time scale dispatching model of active distribution network with the goal of maximizing the low-carbon index value of system operation based on the mathematical model of mobile energy storage and the mathematical model of SOP equipment; The multi-time scale dispatch model of active distribution network includes: objective function, day-ahead constraints and intra-day constraints; The model solving module is used to convert the multi-time scale scheduling model of the active distribution network into a model that can be directly solved by the solver, and solve it to obtain the optimized scheduling plan.

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