Electric bus and VSC coordinated optimization method, system, device and medium

Abstract

The invention belongs to the technical field of optimization scheduling strategy formulation of a power distribution network, and discloses an electric bus and VSC coordinated optimization method, system, device and medium, the method comprises the following steps: calculating the passing time of an electric bus on each road based on the movement characteristics and traffic flow information of the electric bus, modeling all the electric buses by using a multi-layer random space-time network, generating a scheduling model of the electric buses, adding public traffic task constraints, and analyzing traffic tasks in which each electric bus can participate; and analyzing the voltage quality and power flow distribution of the flexible interconnection power distribution network based on the output power of the voltage source converter and the charging and discharging time and position of the electric bus. Aiming at the characteristic that an electric bus needs to execute a public traffic task, an original multi-layer random TSN model is improved, so that the model can be used for mobile scheduling of the electric bus, the condition of traffic jam is considered, and the influence of traffic flow change is integrated into the model.

Description

Technical Field

[0001] The present invention relates to the technical field of optimizing dispatching strategies for distribution networks, and in particular to a method, system, device and medium for optimizing coordination between electric buses and VSCs. Background Art

[0002] In order to build a new power system with new energy as the main body, new sources and loads represented by distributed photovoltaics and electric vehicles have developed rapidly in various provinces and cities in recent years. With the continuous improvement of electric vehicle charging facilities, charging piles have been quickly installed, and a large number of pure electric buses have begun to be put into operation. However, due to the limited control means and insufficient flexibility of traditional AC distribution networks, in the distribution network with large-scale access to new sources and loads, the randomness and volatility of distributed photovoltaic output lead to frequent power quality problems such as power flow return and voltage over-limit in the distribution network. Electric vehicles have high charging power and strong uncertainty in charging behavior, which has further impacted the stable operation of the distribution network. Therefore, the scale of new sources and loads connected to the distribution network is limited. The insufficient capacity of the distribution network to absorb new sources and loads has restricted the development of new sources and loads.

[0003] Flexible interconnection technology can improve the flexibility of power regulation in distribution networks, and the complementarity of power between stations can effectively improve the distribution network's ability to absorb new sources and loads. However, the regulation capacity of flexible interconnection equipment is limited by its own capacity and installation location. It only relies on the power transfer capability of flexible equipment, and has limited improvement in the absorption of new sources and loads in the distribution network. With the continuous development of V2G technology, electric vehicles can also participate in the power dispatch of distribution networks as mobile energy storage. Electric buses (EB) have the advantages of large capacity, strong mobility and are managed by a unified transportation department. By signing an agreement with the bus company and using the idle time of EB to participate in the power dispatch of the distribution network, the power grid company can further improve the flexibility of the distribution network and reduce the load impact caused by the charging of electric vehicle clusters.

[0004] However, EB is different from ordinary electric vehicles. The distribution network needs to dispatch it without affecting the operation of public transportation. In addition, existing research has not considered the impact of traffic flow on the dispatch of electric vehicles. During periods of heavy traffic, the travel time of EB will be longer, affecting the normal dispatch plan.

[0005] Most existing studies focus only on the power regulation capability of flexible interconnected devices themselves, and rarely consider the coordination between flexible interconnected devices and other devices. Some studies consider the coordination between flexible devices and energy storage. However, compared with energy storage devices, EB has strong mobility and can simultaneously realize power scheduling at the temporal and spatial levels. Therefore, existing control strategies are difficult to use in distribution networks where VSC and EB participate in scheduling at the same time.

[0006] In summary, the current research has the following shortcomings: (1) The existing research on power regulation of electric vehicles assisted distribution networks mainly targets ordinary electric vehicles, while EBs need to perform their own public transportation tasks. The existing scheduling strategies cannot be used for EBs, and few studies have considered the impact of traffic congestion on EBs. (2) The existing research does not consider the coordination between VSCs and electric buses, which may lead to overcompensation of a certain node in the distribution network, which in turn aggravates the power quality problem.

[0007] Therefore, how to provide a method, system, equipment and medium for the coordinated optimization of electric buses and VSC is a problem that needs to be solved urgently. Summary of the invention

[0008] The embodiments of the present invention provide an electric bus and VSC coordinated optimization method, system, device and medium to solve the above-mentioned technical problems existing in the prior art.

[0009] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0010] According to a first aspect of an embodiment of the present invention, a method for coordinated optimization of an electric bus and a VSC is provided.

[0011] In one embodiment, the electric bus and VSC coordination optimization method includes:

[0012] Based on the mobility characteristics and traffic flow information of electric buses, the travel time of electric buses on various roads is calculated. All electric buses are modeled using a multi-layer random space-time network to generate an electric bus scheduling model. Public transportation task constraints are added to analyze the traffic tasks that each electric bus can participate in.

[0013] Based on the output power of the voltage source converter and the time and location of the charging and discharging of the electric bus, the voltage quality and power flow distribution of the flexible interconnected distribution network are analyzed. By setting decision variables and optimization objectives, a coordinated optimization model of the voltage source converter and the electric bus is established.

[0014] The coordinated optimization model is simplified by the second-order cone convexification method and converted into a cone optimization model. The model is solved by the solver to achieve the coordinated scheduling of voltage source converter and electric bus charging and discharging efficiency.

[0015] In one embodiment, based on the mobility characteristics and traffic flow information of electric buses, the travel time of electric buses on various roads is calculated, all electric buses are modeled using a multi-layer random time-space network, a scheduling model for electric buses is generated, and public transportation task constraints are added. The traffic tasks that each electric bus can participate in are analyzed, including:

[0016] Obtain the set of all nodes and all roads in the traffic network, express the distance between adjacent nodes in the traffic network in the form of an adjacency matrix, set the average speed of the electric bus, calculate the travel time of the electric bus on each road, and express the distance between any two nodes;

[0017] Obtain the traffic flow information of each road in the dispatching area, identify the capacity and traffic flow of each road at the current moment, and use the BPR function to calculate the travel time of the electric bus between the nodes at both ends of the road based on the capacity and traffic flow of the selected road at the current moment;

[0018] A multi-layer random space-time network is used to model all electric buses in the dispatching area, set the optimization cycle and unit time interval of the optimal dispatch, calculate the total number of time spans in the optimization cycle, and connect different nodes in different layers to form arcs to generate a dispatch model for electric buses.

[0019] The nodes within a time span are divided into two groups using the dividing line in the multi-layer random space-time network. The node constraints that the electric bus needs to meet are established according to the set of arcs passed by the dividing line. Based on the condition that the electric bus can participate in the charging and discharging scheduling when it does not perform traffic tasks, the charging and discharging constraints of the electric bus are established, and the energy consumption of the electric bus during the charging and discharging scheduling process is calculated.

[0020] In one embodiment, the expression for calculating the travel time of the electric bus between the nodes at both ends of the road using the BPR function is:

[0021]

[0022] In the formula, is the travel time of the electric bus through road ij at time t; a and b are the blocking coefficients; D ij R is the time required for a vehicle to pass through road ij without blocking; TN is the set of all roads in the transportation network; is the capacity of road ij at time t; is the traffic flow of road ij at time t.

[0023] In one embodiment, each layer in the scheduling model represents the movement of an electric bus, and the horizontal axis is time and the vertical axis is the location of the electric bus. The arcs of the scheduling model include stagnation arcs, transfer arcs and traffic task arcs.

[0024] The stagnation arc represents all possible situations in which any electric bus stops at any position in the sth time span, the transfer arc represents all possible paths for any electric bus to travel from one node to another node in the sth time span; and the traffic task arc represents the route passed by any electric bus in the sth time span when performing a traffic task.

[0025] In one embodiment, the expression for calculating the energy consumption of the electric bus during the charging and discharging scheduling process includes:

[0026]

[0027]

[0028] In the formula, is the mobility consumption of the mth electric bus at time s; is the charging and discharging consumption of the mth electric bus at time s; is the average consumption of the electric bus moving on the road section l; l,m,s is the state variable; N Total is the total number of electric buses; η charge With η discharge are charging efficiency and discharging efficiency respectively; and are the charging power and discharging power of the mth electric bus at charging station n at time s; E N is the rated capacity of the electric bus; soc m,t is the charge state of the mth electric bus at time t; ΔT is the time interval.

[0029] In one embodiment, the voltage quality and power flow distribution of the flexible interconnected distribution network are analyzed based on the output power of the voltage source converter and the time and location of the charging and discharging of the electric bus, and the coordinated optimization model of the voltage source converter and the electric bus is established by setting decision variables and optimization objectives, including:

[0030] The output power of the voltage source converter at each moment and the dispatching status of the electric bus are set as decision variables, and the minimization of network loss of the flexible interconnected distribution network is taken as the optimization goal to establish the objective function.

[0031] Acquire the active power outputted by the AC side and the DC side during the operation of the voltage source converter, as well as the reactive power outputted by the AC side, control the AC side and the DC side to maintain active power balance, and establish the capacity constraint of the voltage source converter according to the active power and reactive power outputted by the AC side;

[0032] Based on the active power and reactive power transmitted between nodes, the power flow constraints that the two AC lines need to meet during the operation of the flexible interconnected distribution network are established. According to the voltage distribution of the nodes, the voltage constraints that each node needs to meet are established. Combined with the public transportation task constraints of the electric bus scheduling model, a coordinated optimization model of voltage source converters and electric buses is generated.

[0033] In one embodiment, the objective function is expressed as:

[0034]

[0035] Where P loss is the network loss of the flexible interconnected distribution network; i, j are the node numbers of the low-voltage distribution network; N AC is the set of all nodes on the AC line; v(i) is the set of nodes directly connected to node i; I ij,t is the current flowing through branch ij at time t; r ij,t is the resistance of the current flowing through branch ij at time t; ΔT is the time interval; T max is the maximum number of time intervals.

[0036] According to a second aspect of an embodiment of the present invention, a coordinated optimization system for an electric bus and a VSC is provided.

[0037] In one embodiment, the electric bus and VSC coordination optimization system includes:

[0038] The task scheduling module is used to calculate the travel time of electric buses on various roads based on the mobility characteristics and traffic flow information of electric buses, model all electric buses using a multi-layer random space-time network, generate an electric bus scheduling model, add public transportation task constraints, and analyze the traffic tasks that each electric bus can participate in;

[0039] The coordination optimization module is used to analyze the voltage quality and power flow distribution of the flexible interconnected distribution network based on the output power of the voltage source converter and the time and location of the charging and discharging of the electric bus. By setting decision variables and optimization goals, a coordination optimization model of the voltage source converter and the electric bus is established;

[0040] The optimization solution module is used to simplify the coordinated optimization model using the second-order cone convexification method, convert it into a cone optimization model, and solve it using the solver to achieve coordinated scheduling of the voltage source converter and the charging and discharging efficiency of the electric bus.

[0041] In one embodiment, the task scheduling module includes: a set building module, a time calculation module, a model generation module and a constraint calculation module, wherein:

[0042] The set building module is used to obtain the set of all nodes and the set of all roads in the traffic network, express the distance between adjacent nodes in the traffic network in the form of an adjacency matrix, set the average speed of the electric bus, calculate the travel time of the electric bus on each road, and express the distance between any two nodes;

[0043] The time calculation module is used to obtain the traffic flow information of each road in the dispatching area, identify the capacity and traffic flow of each road at the current moment, and calculate the travel time of the electric bus between the nodes at both ends of the road using the BPR function according to the capacity and traffic flow of the selected road at the current moment;

[0044] The model generation module is used to model all electric buses in the call area using a multi-layer random space-time network, set the optimization cycle and unit time interval of the optimization scheduling, calculate the total number of time spans in the optimization cycle, and connect different nodes in different layers to form arcs to generate a scheduling model for electric buses;

[0045] The constraint calculation module is used to divide the nodes within a time span into two groups using a dividing line in a multi-layer random space-time network, and establish node constraints that the electric bus needs to satisfy according to the set of arcs passed by the dividing line; based on the condition that the electric bus can participate in charging and discharging scheduling when not performing traffic tasks, establish charging and discharging constraints for the electric bus, and calculate the energy consumption of the electric bus during the charging and discharging scheduling process.

[0046] In one embodiment, the expression for calculating the travel time of the electric bus between the nodes at both ends of the road using the BPR function is:

[0047]

[0048] In the formula, is the travel time of the electric bus through road ij at time t; a and b are the blocking coefficients; D ij R is the time required for a vehicle to pass through road ij without blocking; TN is the set of all roads in the transportation network; is the capacity of road ij at time t; is the traffic flow of road ij at time t.

[0049] In one embodiment, each layer in the scheduling model represents the movement of an electric bus, and the horizontal axis is time and the vertical axis is the location of the electric bus. The arcs of the scheduling model include stagnation arcs, transfer arcs and traffic task arcs.

[0050] The stagnation arc represents all possible situations in which any electric bus stops at any position in the sth time span, the transfer arc represents all possible paths for any electric bus to travel from one node to another node in the sth time span; and the traffic task arc represents the route passed by any electric bus in the sth time span when performing a traffic task.

[0051] In one embodiment, the expression for calculating the energy consumption of the electric bus during the charging and discharging scheduling process includes:

[0052]

[0053] In the formula, is the mobility consumption of the mth electric bus at time s; is the charging and discharging consumption of the mth electric bus at time s; is the average consumption of the electric bus moving on the road section l; l,m,s is the state variable; N Total is the total number of electric buses; η charge With η discharge are charging efficiency and discharging efficiency respectively; and are the charging power and discharging power of the mth electric bus at charging station n at time s; E N is the rated capacity of the electric bus; soc m,t is the charge state of the mth electric bus at time t; ΔT is the time interval.

[0054] In one embodiment, the coordination optimization module includes: an optimization target module, a capacity constraint module and an integration generation module, wherein:

[0055] The optimization target module is used to set the output power of the voltage source converter at each moment and the dispatching status of the electric bus as decision variables, and to establish an objective function with minimizing the network loss of the flexible interconnected distribution network as the optimization target;

[0056] The capacity constraint is used to obtain the active power output by the AC side and the DC side during the operation of the voltage source converter, as well as the reactive power output by the AC side, control the AC side and the DC side to maintain active power balance, and establish the capacity constraint of the voltage source converter according to the active power and reactive power output by the AC side;

[0057] The integration generation module is used to establish the power flow constraints that the two AC lines need to meet during the operation of the flexible interconnected distribution network based on the active power and reactive power transmitted between the nodes, and to establish the voltage constraints that each node needs to meet according to the voltage distribution of the nodes. Combined with the scheduling public transportation task constraints of the scheduling model of the electric bus, a coordinated optimization model of the voltage source converter and the electric bus is generated.

[0058] In one embodiment, the objective function is expressed as:

[0059]

[0060] Where P loss is the network loss of the flexible interconnected distribution network; i, j are the node numbers of the low-voltage distribution network; N AC is the set of all nodes on the AC line; v(i) is the set of nodes directly connected to node i; I ij,t is the current flowing through branch ij at time t; r ij,t is the resistance of the current flowing through branch ij at time t; ΔT is the time interval; T max is the maximum number of time intervals.

[0061] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0062] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0063] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0064] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0065] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0066] 1. Aiming at the characteristics of electric buses that need to perform public transportation tasks, the present invention improves the original multi-layer random TSN model so that it can be used for mobile scheduling of electric buses, and takes into account the situation of traffic congestion, and incorporates the impact of traffic flow changes into the model.

[0067] 2. The present invention takes into account the spatial transfer capability of the flexible device VSC for the power of the distribution network and the spatiotemporal transfer capability of the electric bus for the power, as well as their respective regulation capability limitations, and establishes an optimization model for the coordinated cooperation between VSC and electric buses, which can maximize the distribution network's ability to absorb new sources and loads and improve the power quality of the distribution network.

[0068] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0070] Figure 1 is a flow chart showing a method for coordinating and optimizing an electric bus and a VSC according to an exemplary embodiment;

[0071] Figure 2 is a structural schematic diagram of an electric bus and VSC coordination optimization system according to an exemplary embodiment;

[0072] Figure 3 is a structural schematic diagram of establishing an electric bus dispatching model based on a multi-layer random space-time network according to an exemplary embodiment;

[0073] Figure 4 It is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0074] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0075] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0076] As used herein, unless otherwise specified, the term "plurality" means two or more than two.

[0077] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0078] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0079] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0080] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0081] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0082] Figure 1An embodiment of a coordinated optimization method for an electric bus and a VSC of the present invention is shown.

[0083] In this optional embodiment, the electric bus and VSC coordination optimization method includes:

[0084] Step S101: based on the mobility characteristics and traffic flow information of electric buses, the travel time of electric buses on various roads is calculated, all electric buses are modeled using a multi-layer random space-time network, a dispatching model of electric buses is generated, public transportation task constraints are added, and the traffic tasks that each electric bus can participate in are analyzed;

[0085] Step S103: Analyze the voltage quality and power flow distribution of the flexible interconnected distribution network based on the output power of the voltage source converter and the time and location of the charging and discharging of the electric bus, and establish a coordinated optimization model of the voltage source converter and the electric bus by setting decision variables and optimization objectives;

[0086] Step S105: Simplify the coordinated optimization model using the second-order cone convexification method, convert it into a cone optimization model, and solve it using a solver to achieve coordinated scheduling of the voltage source converter and the charging and discharging efficiency of the electric bus.

[0087] In this optional embodiment, based on the mobility characteristics and traffic flow information of the electric bus, the travel time of the electric bus on each road is calculated, all electric buses are modeled using a multi-layer random space-time network, a scheduling model for the electric bus is generated, and public transportation task constraints are added. When analyzing the traffic tasks that each electric bus can participate in, the set of all nodes in the traffic network and the set of all roads can be obtained, the distance between adjacent nodes in the traffic network is expressed in the form of an adjacency matrix, and the average speed of the electric bus is set. The travel time of the electric bus on each road is calculated to represent the distance between any two nodes; the traffic flow information of each road in the scheduling area is obtained, the capacity and traffic flow of each road at the current moment are identified, and the selected road is selected according to the current The capacity and traffic flow at each moment are taken into account, and the travel time of electric buses between the nodes at both ends of the road is calculated using the BPR function; all electric buses in the access area are modeled using a multi-layer random space-time network, the optimization period and unit time interval of the optimization scheduling are set, the total number of time spans within the optimization period is calculated, and different nodes in different layers are connected to form arcs to generate a scheduling model for electric buses; the nodes within a time span are divided into two groups using the dividing line in the multi-layer random space-time network, and the node constraints that the electric bus needs to meet are established according to the set of arcs passed by the dividing line; based on the condition that the electric bus can participate in the charging and discharging scheduling when it is not performing traffic tasks, the charging and discharging constraints of the electric bus are established, and the energy consumption of the electric bus during the charging and discharging scheduling process is calculated.

[0088] In this optional embodiment, the expression for calculating the travel time of the electric bus between the nodes at both ends of the road using the BPR function is:

[0089]

[0090] In the formula, is the travel time of the electric bus through road ij at time t; a and b are the blocking coefficients; D ij R is the time required for a vehicle to pass through road ij without blocking; TN is the set of all roads in the transportation network; is the capacity of road ij at time t; is the traffic flow of road ij at time t.

[0091] In this optional embodiment, each layer in the scheduling model represents the movement of an electric bus, and the horizontal axis is time and the vertical axis is the location of the electric bus. The arcs of the scheduling model include stagnation arcs, transfer arcs and traffic task arcs; wherein the stagnation arc represents all possible situations in which any electric bus stops at any location in the sth time span, the transfer arc represents all possible paths for any electric bus to travel from one node to another node in the sth time span; the traffic task arc represents the route passed by any electric bus in the sth time span when performing a traffic task.

[0092] In this optional embodiment, the expression for calculating the energy consumption of the electric bus during the charging and discharging scheduling process includes:

[0093]

[0094] In the formula, is the mobility consumption of the mth electric bus at time s; is the charging and discharging consumption of the mth electric bus at time s; is the average consumption of the electric bus moving on the road section l; l,m,s is the state variable; N Total is the total number of electric buses; η charge With η discharge are charging efficiency and discharging efficiency respectively; and are the charging power and discharging power of the mth electric bus at charging station n at time s; E N is the rated capacity of the electric bus; soc m,t is the charge state of the mth electric bus at time t; ΔT is the time interval.

[0095] In this optional embodiment, when analyzing the voltage quality and power flow distribution of the flexible interconnected distribution network based on the output power of the voltage source converter and the time and position of the charging and discharging of the electric bus, and establishing the coordinated optimization model of the voltage source converter and the electric bus by setting the decision variables and the optimization target, the output power of the voltage source converter at each moment and the scheduling status of the electric bus can be set as the decision variables, and the objective function can be established with the minimization of the network loss of the flexible interconnected distribution network as the optimization target; the active power output of the AC side and the DC side during the operation of the voltage source converter, and the reactive power output of the AC side are obtained, the AC side and the DC side are controlled to maintain the active power balance, and the capacity constraint of the voltage source converter is established according to the active power and reactive power output of the AC side; based on the active power and reactive power transmitted between nodes, the power flow constraints that the two AC lines need to meet during the operation of the flexible interconnected distribution network are established, and the voltage constraints that each node needs to meet are established according to the voltage distribution of the nodes, and the coordinated optimization model of the voltage source converter and the electric bus is generated in combination with the scheduling public transportation task constraints of the scheduling model of the electric bus.

[0096] In this optional embodiment, the expression of the objective function is:

[0097]

[0098] Where P loss is the network loss of the flexible interconnected distribution network; i, j are the node numbers of the low-voltage distribution network; N AC is the set of all nodes on the AC line; v(i) is the set of nodes directly connected to node i; I ij,t is the current flowing through branch ij at time t; r ij,t is the resistance of the current flowing through branch ij at time t; ΔT is the time interval; T max is the maximum number of time intervals.

[0099] Figure 2 An embodiment of an electric bus and VSC coordinated optimization system of the present invention is shown.

[0100] In this optional embodiment, the electric bus and VSC coordination optimization system includes:

[0101] The task scheduling module 201 is used to calculate the travel time of electric buses on various roads based on the mobility characteristics and traffic flow information of electric buses, model all electric buses using a multi-layer random time-space network, generate a scheduling model for electric buses, add public transportation task constraints, and analyze the traffic tasks that each electric bus can participate in;

[0102] The coordination optimization module 203 is used to analyze the voltage quality and power flow distribution of the flexible interconnected distribution network based on the output power of the voltage source converter and the time and location of the charging and discharging of the electric bus, and establish a coordination optimization model of the voltage source converter and the electric bus by setting decision variables and optimization objectives;

[0103] The optimization solution module 205 is used to simplify the coordinated optimization model by using the second-order cone convexification method, convert it into a cone optimization model, and solve it using a solver to achieve coordinated scheduling of the voltage source converter and the charging and discharging efficiency of the electric bus.

[0104] In this optional embodiment, the task scheduling module 201 includes: a set establishment module (not shown in the figure), a time calculation module (not shown in the figure), a model generation module (not shown in the figure) and a constraint calculation module (not shown in the figure), wherein the set establishment module is used to obtain the set of all nodes and the set of all roads in the traffic network, express the distance between adjacent nodes in the traffic network in the form of an adjacency matrix, set the average speed of the electric bus, calculate the travel time of the electric bus on each road, and express the distance between any two nodes; the time calculation module is used to obtain the traffic flow information of each road in the scheduling area, identify the capacity and traffic flow of each road at the current time, and use BP according to the capacity and traffic flow of the selected road at the current time. The R function calculates the travel time of the electric bus between the nodes at both ends of the road; the model generation module is used to use the multi-layer random space-time network to model all the electric buses in the call area, set the optimization cycle and unit time interval of the optimization scheduling, calculate the total number of time spans in the optimization cycle, and connect different nodes in different layers to form arcs to generate the scheduling model of the electric bus; the constraint calculation module is used to use the dividing line in the multi-layer random space-time network to divide the nodes in a time span into two groups, and establish the node constraints that the electric bus needs to meet according to the set of arcs passed by the dividing line; based on the condition that the electric bus can participate in the charging and discharging scheduling when it does not perform traffic tasks, the charging and discharging constraints of the electric bus are established, and the energy consumption of the electric bus during the charging and discharging scheduling process is calculated.

[0105] In this optional embodiment, the expression for calculating the travel time of the electric bus between the nodes at both ends of the road using the BPR function is:

[0106]

[0107] In the formula, is the travel time of the electric bus through road ij at time t; a and b are the blocking coefficients; D ij R is the time required for a vehicle to pass through road ij without blocking; TN is the set of all roads in the transportation network; is the capacity of road ij at time t; is the traffic flow of road ij at time t.

[0108] In this optional embodiment, each layer in the scheduling model represents the movement of an electric bus, and the horizontal axis is time and the vertical axis is the location of the electric bus. The arcs of the scheduling model include stagnation arcs, transfer arcs and traffic task arcs; wherein the stagnation arc represents all possible situations in which any electric bus stops at any location in the sth time span, the transfer arc represents all possible paths for any electric bus to travel from one node to another node in the sth time span; the traffic task arc represents the route passed by any electric bus in the sth time span when performing a traffic task.

[0109] In this optional embodiment, the expression for calculating the energy consumption of the electric bus during the charging and discharging scheduling process includes:

[0110]

[0111] In the formula, is the mobility consumption of the mth electric bus at time s; is the charging and discharging consumption of the mth electric bus at time s; is the average consumption of the electric bus moving on the road section l; l,m,s is the state variable; N Total is the total number of electric buses; η charge With η discharge are charging efficiency and discharging efficiency respectively; and are the charging power and discharging power of the mth electric bus at charging station n at time s; E N is the rated capacity of the electric bus; soc m,t is the charge state of the mth electric bus at time t; ΔT is the time interval.

[0112] In this optional embodiment, the coordination optimization module 203 includes: an optimization target module (not shown in the figure), a capacity constraint module (not shown in the figure) and an integration generation module (not shown in the figure), wherein the optimization target module is used to set the output power of the voltage source converter at each moment and the dispatching status of the electric bus as decision variables, and to establish an objective function with the minimization of the network loss of the flexible interconnected distribution network as the optimization target; the capacity constraint is used to obtain the active power output of the AC side and the DC side during the operation of the voltage source converter, and the reactive power output of the AC side, control the AC side and the DC side to maintain the active power balance, and establish the capacity constraint of the voltage source converter according to the active power and reactive power output of the AC side; the integration generation module is used to establish the power flow constraints that the two AC lines need to meet during the operation of the flexible interconnected distribution network based on the active power and reactive power transmitted between nodes, and establish the voltage constraints that each node needs to meet according to the voltage distribution of the node, and generate a coordination optimization model of the voltage source converter and the electric bus in combination with the dispatching public transportation task constraints of the dispatching model of the electric bus.

[0113] In this optional embodiment, the expression of the objective function is:

[0114]

[0115] Where P loss is the network loss of the flexible interconnected distribution network; i, j are the node numbers of the low-voltage distribution network; N AC is the set of all nodes on the AC line; v(i) is the set of nodes directly connected to node i; I ij,t is the current flowing through branch ij at time t; r ij,t is the resistance of the current flowing through branch ij at time t; ΔT is the time interval; T max is the maximum number of time intervals.

[0116] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0117] In order to achieve the above-mentioned invention purpose, the present invention proposes a coordinated optimization strategy for electric buses and VSCs for improving the capacity of new source load consumption. Considering the mobility characteristics of electric buses themselves and the influence of traffic flow, the EB scheduling model (electric bus scheduling model) is constructed using the multi-layer random time-space network (Time Space Network, TSN) method; secondly, considering the coordination of VSC (voltage source converter) and EB charging and discharging power, an optimization model is constructed with the goal of minimizing the network loss of the distribution network; finally, the proposed model is converted into a second-order cone optimization model that is easier to solve using the convex optimization method.

[0118] The access of AC / DC interconnection devices will have a relevant impact on network operation, but the optimization planning scheme for access to the network needs to fully consider the existing situation. The use of new AC / DC distribution networks can improve the transmission capacity of the distribution network and realize flexible power transfer between AC and DC lines. At the same time, the limited transmission power of branches is one of the important reasons that hinder the large-scale access of distributed photovoltaics to the distribution network. The DC distribution network can use the original lines to increase the transmission power of branches, physically break the barriers of line transmission capacity, and improve the ability of the distribution network to absorb the overflow power of distributed photovoltaics. On the other hand, the flexible optimization and scheduling of power between AC and DC lines can alleviate the problems of voltage over-limit and power return caused by photovoltaic power overflow.

[0119] 1. Electric bus dispatch modeling

[0120] 1) Consider the impact of traffic flow

[0121] The symbol M can be used for traffic network TN = {N TN ,R TN} means, where N TN For transportation network M TN The set of all nodes in R TN For transportation network M TN The distance between adjacent points in the traffic network can be calculated using the adjacency matrix Dist TN Assume that the average speed of EB is Dist TN The distance D between midpoints i and j ij It can be converted into driving time. However, when the road is congested, the driving time of EB through the road between i and j will be affected to a certain extent. The bus command center can obtain the traffic flow information of each road in the area in real time, so as to determine the driving time required on each road and plan the route reasonably in advance. Assuming that the capacity of road ij is The traffic flow at time t is Depending on the traffic flow on the road at this time, the travel time It can be calculated using the BPR (Bureau of Public Roads) function:

[0122]

[0123] Where: a, b are the retardation coefficients, usually 0.15, 4 respectively; D ij is the time required for a vehicle to pass through road ij without congestion.

[0124] 2) Multi-layer random TSN model for public buses

[0125] The modeling method of multi-layer random TSN can more accurately describe the movement of vehicles at each moment. However, unlike the usual situation, the scheduling of EB needs to be carried out without affecting its normal traffic tasks. Therefore, in order to ensure that the public transportation tasks of EB can be performed normally, public transportation task constraints are further added to the multi-layer random TSN model. Figure 3 shown.

[0126] Using the multi-layer random TSN model to Total EB is modeled, TSN is divided into N Total Layers, each layer represents the movement of an EB. The horizontal axis of TSN is time, and the vertical axis is the location of the EB. Suppose the total time of the optimized scheduling is T, and the selected unit time interval is ΔT, then the total number of unit time spans in the optimization cycle is The total number of moments is |t|=|s|+1, and the nth time span is s n At time t n With t n+1 between.

[0127] There are three types of arcs in TSN: stagnation arc, transfer arc and traffic task arc.

[0128] Stagnation Arc Ap m,s : represents all possible situations where the mth EB stops at a certain place in the sth time span, as shown by the black solid line in the figure, {(t1,CS1),(t2,CS1)} means that the EB is located at CS1 from t1 to t2.

[0129] Transfer Arc At m,s : represents all possible paths for the mth EB to travel from one node to another node in the sth time span, as shown by the orange solid line in the figure, {(t1,CS2),(t3,CS1)} means that EB starts moving from CS2 at time t1 and arrives at CS1 at time t3. Due to the different traffic flow distribution at each moment, the travel time of the same road at different moments is also different, so the transfer arc at different moments may change. For example, at time t0, it only takes one ΔT to go from CS2 to CS1, but at time t1, it takes two ΔTs.

[0130] Traffic mission arc Af m,s,p : represents the route that the mth EB passes through in the sth time span when performing the traffic task p, as shown by the blue solid line in the figure, Af m,s,p ∈At m,s When performing transportation tasks, EB will ensure that tourists are sent to their destinations in the shortest possible time. Therefore, if the starting and ending points of the transportation task are the same, EB may choose different driving routes at different times. EB performing transportation tasks needs to meet the following requirements:

[0131]

[0132] Where: N Total is the total number of EBs; m,s,p is a state variable, which is 1 if EBm performs traffic task p at time s; (n1,n2),m,s is a state variable, which is 1 when TEBm is located on the arc (n1, n2) in the sth time span.

[0133] Formula (2) ensures that each public transportation task is executed by an EB, and formula (3) ensures that the EB that executes the public transportation task is located on the corresponding transportation task arc.

[0134] The dividing line in multi-layer random TSN divides the spatiotemporal network nodes within a time span into two groups. and is the set of spatiotemporal network nodes on the left side of the dividing line, is the set of spatiotemporal network nodes on the right side of the dividing line; the set of arcs passed by the dividing line can be expressed as EB needs to meet the following constraints in the TSN model:

[0135]

[0136] Where: m,n,t is a state variable, which is 1 when car m is located at node n at time t; (n, *) is the set of all arcs starting from node n, and (*, n) is the set of all arcs ending at node n.

[0137] Formula (4) indicates that EB can only be located on one arc at the same time; Formula (5) and Formula (6) ensure the continuity of EB's movement.

[0138] EBs that do not perform traffic tasks can be dispatched by the power grid to participate in charging and discharging. EBs that participate in charging and discharging need to meet the following requirements:

[0139]

[0140] Where: and is a state variable, which is 1 when EBm is charging / discharging at charging station n at time s; N C is the set of all charging station nodes; and are the charging power and discharging power of EBm at charging station n at time s respectively; and are the rated charging power and the rated discharging power of charging station n respectively; C n is the capacity of charging station n.

[0141] The energy consumption of EB during the scheduling process is:

[0142]

[0143] Where: is the mobility consumption of EBm (the mth electric bus) at time s; is the charge and discharge consumption of EBm at time s; is the average consumption of EB moving on section l; η charge With η discharge E is the charging efficiency and discharging efficiency respectively; N is the rated capacity of EB; soc m,t is the charge state of EB at time t.

[0144] In addition, in order to extend the service life of the EB battery, the SOC of the EB should not be too high or too low:

[0145]

[0146] 2. Coordination optimization model of VSC and EB

[0147] 1) Establishment of optimization model

[0148] By adjusting the output power of VSC and the time and position of EB charging and discharging, the power of the flexible interconnected distribution network can be flexibly controlled, the voltage quality and power flow distribution of the flexible interconnected distribution network can be improved, and the distribution network's ability to absorb new sources and loads can be improved. The decision variables of the proposed optimization model are the output power of VSC and the dispatching of EB at each moment, and the objective function is to minimize the network loss of the flexible interconnected distribution network:

[0149]

[0150] Where: i, j are the node numbers of the low-voltage distribution network; N AC is the set of all nodes on the AC line; v(i) is the set of nodes directly connected to node i; I ij,t is the current flowing through branch ij at time t.

[0151] During operation, VSC needs to meet the power balance and capacity constraints on the AC and DC sides:

[0152]

[0153] In the formula, and The distribution is the active power output by the VSC connected to the i-node on the AC side and the DC side; is the reactive power output by VSC on the AC side; is the capacity of the VSC.

[0154] During the operation of the flexible interconnected distribution network, the two AC lines need to meet the power flow constraints:

[0155]

[0156] Where: P ji With Q ji are the active power and reactive power flowing from node j to node i respectively; P i and Q i are the active power and reactive power injected into node i respectively; R ij With X ij is the resistance and reactance of branch ij; U i is the voltage of node i; I ji is the current flowing from node j to node i; ψ i is the set of branches with the first node being i; Φ i is the set of branches whose end node is i.

[0157] In order to prevent serious voltage over-limit situations, each node in the flexible interconnected distribution network must also meet the voltage constraints:

[0158] U imin <U t,i <U imax (twenty two)

[0159] Where: U imin with U imax are the minimum and maximum voltages of the nodes, respectively.

[0160] Considering the coordination between VSC and EB, the optimization model also needs to include the scheduling model constraints (1)-(15) of EB.

[0161] 2) Solving the optimization model

[0162] In order to simplify the calculation, the model can be simplified by using the second-order cone convexification method. 2,ji with U 2,j Respectively replace (I ji ) 2 with (U j ) 2 Then, according to formula (19) (20), we can get:

[0163]

[0164] Formula (18) can also be transformed into a second-order cone form:

[0165]

[0166] At the same time, the objective function can be simplified as:

[0167]

[0168] Therefore, the objective function of the proposed VSC and EB coordinated optimization model can be expressed as formula (28). This model is a cone optimization model and can be solved by calling the solver.

[0169]

[0170] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0171] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0172] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0173] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0174] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0175] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A coordinated optimization method for electric buses and VSCs, characterized in that: include: Based on the mobility characteristics and traffic flow information of electric buses, the travel time of electric buses on various roads is calculated. All electric buses are modeled using a multi-layer random space-time network to generate an electric bus scheduling model. Public transportation task constraints are added to analyze the traffic tasks that each electric bus can participate in. Based on the output power of the voltage source converter and the time and location of the charging and discharging of the electric bus, the voltage quality and power flow distribution of the flexible interconnected distribution network are analyzed. By setting decision variables and optimization objectives, a coordinated optimization model of the voltage source converter and the electric bus is established. The coordinated optimization model is simplified by the second-order cone convexification method and converted into a cone optimization model. The model is solved by the solver to achieve the coordinated scheduling of voltage source converter and electric bus charging and discharging efficiency.

2. The electric bus and VSC coordination optimization method according to claim 1 is characterized in that: Based on the mobility characteristics and traffic flow information of electric buses, the travel time of electric buses on various roads is calculated, and all electric buses are modeled using a multi-layer random time-space network to generate an electric bus scheduling model, and public transportation task constraints are added to analyze the traffic tasks that each electric bus can participate in, including: Obtain the set of all nodes and all roads in the traffic network, express the distance between adjacent nodes in the traffic network in the form of an adjacency matrix, set the average speed of the electric bus, calculate the travel time of the electric bus on each road, and express the distance between any two nodes; Obtain the traffic flow information of each road in the dispatching area, identify the capacity and traffic flow of each road at the current moment, and use the BPR function to calculate the travel time of the electric bus between the nodes at both ends of the road based on the capacity and traffic flow of the selected road at the current moment; A multi-layer random space-time network is used to model all electric buses in the dispatching area, set the optimization cycle and unit time interval of the optimal dispatch, calculate the total number of time spans in the optimization cycle, and connect different nodes in different layers to form arcs to generate a dispatch model for electric buses. The nodes within a time span are divided into two groups using the dividing line in the multi-layer random space-time network. The node constraints that the electric bus needs to meet are established according to the set of arcs passed by the dividing line. Based on the condition that the electric bus can participate in the charging and discharging scheduling when it does not perform traffic tasks, the charging and discharging constraints of the electric bus are established, and the energy consumption of the electric bus during the charging and discharging scheduling process is calculated.

3. The electric bus and VSC coordination optimization method according to claim 2 is characterized in that: The expression for calculating the travel time of the electric bus between the nodes at both ends of the road using the BPR function is: In the formula, is the travel time of the electric bus through road ij at time t; a and b are the blocking coefficients; D ij R is the time required for a vehicle to pass through road ij without blocking; TN is the set of all roads in the transportation network; is the capacity of road ij at time t; is the traffic flow of road ij at time t.

4. The electric bus and VSC coordination optimization method according to claim 2 is characterized in that: Each layer in the scheduling model represents the movement of an electric bus, and the horizontal axis is time and the vertical axis is the location of the electric bus. The arcs of the scheduling model include stagnation arcs, transfer arcs and traffic task arcs; The stagnation arc represents all possible situations in which any electric bus stops at any position in the sth time span, the transfer arc represents all possible paths for any electric bus to travel from one node to another node in the sth time span; and the traffic task arc represents the route passed by any electric bus in the sth time span when performing a traffic task.

5. The electric bus and VSC coordination optimization method according to claim 4 is characterized in that: The expression for calculating the energy consumption of the electric bus during the charging and discharging scheduling process includes: In the formula, is the mobility consumption of the mth electric bus at time s; is the charging and discharging consumption of the mth electric bus at time s; is the average consumption of the electric bus moving on the road section l; l,m,s is the state variable; N Total is the total number of electric buses; η charge With η discharge are charging efficiency and discharging efficiency respectively; and are the charging power and discharging power of the mth electric bus at charging station n at time s; E N is the rated capacity of the electric bus; soc m,t is the charge state of the mth electric bus at time t; ΔT is the time interval.

6. The electric bus and VSC coordination optimization method according to claim 1 is characterized in that: The output power of the voltage source converter and the time and position of the charging and discharging of the electric bus are analyzed to analyze the voltage quality and power flow distribution of the flexible interconnected distribution network, and the coordinated optimization model of the voltage source converter and the electric bus is established by setting decision variables and optimization objectives, including: The output power of the voltage source converter at each moment and the dispatching status of the electric bus are set as decision variables, and the minimization of network loss of the flexible interconnected distribution network is taken as the optimization goal to establish the objective function. Acquire the active power outputted by the AC side and the DC side during the operation of the voltage source converter, as well as the reactive power outputted by the AC side, control the AC side and the DC side to maintain active power balance, and establish the capacity constraint of the voltage source converter according to the active power and reactive power outputted by the AC side; Based on the active power and reactive power transmitted between nodes, the power flow constraints that the two AC lines need to meet during the operation of the flexible interconnected distribution network are established. According to the voltage distribution of the nodes, the voltage constraints that each node needs to meet are established. Combined with the public transportation task constraints of the electric bus scheduling model, a coordinated optimization model of voltage source converters and electric buses is generated.

7. The electric bus and VSC coordination optimization method according to claim 6 is characterized in that: The expression of the objective function is: Where P loss is the network loss of the flexible interconnected distribution network; i, j are the node numbers of the low-voltage distribution network; N AC is the set of all nodes on the AC line; v(i) is the set of nodes directly connected to node i; I ij,t is the current flowing through branch ij at time t; r ij,t is the resistance of the current flowing through branch ij at time t; ΔT is the time interval; T max is the maximum number of time intervals.

8. An electric bus and VSC coordination optimization system, characterized in that: include: The task scheduling module is used to calculate the travel time of electric buses on various roads based on the mobility characteristics and traffic flow information of electric buses, model all electric buses using a multi-layer random space-time network, generate an electric bus scheduling model, add public transportation task constraints, and analyze the traffic tasks that each electric bus can participate in; The coordination optimization module is used to analyze the voltage quality and power flow distribution of the flexible interconnected distribution network based on the output power of the voltage source converter and the time and location of the charging and discharging of the electric bus. By setting decision variables and optimization goals, a coordination optimization model of the voltage source converter and the electric bus is established; The optimization solution module is used to simplify the coordinated optimization model using the second-order cone convexification method, convert it into a cone optimization model, and solve it using the solver to achieve coordinated scheduling of the voltage source converter and the charging and discharging efficiency of the electric bus.

9. The electric bus and VSC coordination optimization system according to claim 8 is characterized in that: The task scheduling module includes: a set building module, a time calculation module, a model generation module and a constraint calculation module, wherein: The set building module is used to obtain the set of all nodes and the set of all roads in the traffic network, express the distance between adjacent nodes in the traffic network in the form of an adjacency matrix, set the average speed of the electric bus, calculate the travel time of the electric bus on each road, and express the distance between any two nodes; The time calculation module is used to obtain the traffic flow information of each road in the dispatching area, identify the capacity and traffic flow of each road at the current moment, and calculate the travel time of the electric bus between the nodes at both ends of the road using the BPR function according to the capacity and traffic flow of the selected road at the current moment; The model generation module is used to model all electric buses in the call area using a multi-layer random space-time network, set the optimization cycle and unit time interval of the optimization scheduling, calculate the total number of time spans in the optimization cycle, and connect different nodes in different layers to form arcs to generate a scheduling model for electric buses; The constraint calculation module is used to divide the nodes within a time span into two groups using a dividing line in a multi-layer random space-time network, and establish node constraints that the electric bus needs to satisfy according to the set of arcs passed by the dividing line; based on the condition that the electric bus can participate in charging and discharging scheduling when not performing traffic tasks, establish the charging and discharging constraints of the electric bus, and calculate the energy consumption of the electric bus during the charging and discharging scheduling process.

10. The electric bus and VSC coordination optimization system according to claim 9 is characterized in that: The expression for calculating the travel time of the electric bus between the nodes at both ends of the road using the BPR function is: In the formula, is the travel time of the electric bus through road ij at time t; a and b are the blocking coefficients; D ij R is the time required for a vehicle to pass through road ij without blocking; TN is the set of all roads in the transportation network; is the capacity of road ij at time t; is the traffic flow of road ij at time t.

11. The electric bus and VSC coordination optimization system according to claim 8, characterized in that: Each layer in the scheduling model represents the movement of an electric bus, and the horizontal axis is time and the vertical axis is the location of the electric bus. The arcs of the scheduling model include stagnation arcs, transfer arcs and traffic task arcs; The stagnation arc represents all possible situations in which any electric bus stops at any position in the sth time span, the transfer arc represents all possible paths for any electric bus to travel from one node to another node in the sth time span; and the traffic task arc represents the route passed by any electric bus in the sth time span when performing a traffic task.

12. The electric bus and VSC coordination optimization system according to claim 11, characterized in that: The expression for calculating the energy consumption of the electric bus during the charging and discharging scheduling process includes: In the formula, is the mobility consumption of the mth electric bus at time s; is the charging and discharging consumption of the mth electric bus at time s; is the average consumption of the electric bus moving on the road section l; l,m,s is the state variable; N Total is the total number of electric buses; η charge With η discharge are charging efficiency and discharging efficiency respectively; and are the charging power and discharging power of the mth electric bus at charging station n at time s; E N is the rated capacity of the electric bus; soc m,t is the charge state of the mth electric bus at time t; ΔT is the time interval.

13. The electric bus and VSC coordination optimization system according to claim 8, characterized in that: The coordination optimization module includes: an optimization target module, a capacity constraint module and an integration generation module, wherein: The optimization target module is used to set the output power of the voltage source converter at each moment and the dispatching status of the electric bus as decision variables, and to establish an objective function with minimizing the network loss of the flexible interconnected distribution network as the optimization target; The capacity constraint is used to obtain the active power output by the AC side and the DC side during the operation of the voltage source converter, as well as the reactive power output by the AC side, control the AC side and the DC side to maintain active power balance, and establish the capacity constraint of the voltage source converter according to the active power and reactive power output by the AC side; The integration generation module is used to establish the power flow constraints that the two AC lines need to meet during the operation of the flexible interconnected distribution network based on the active power and reactive power transmitted between the nodes, and to establish the voltage constraints that each node needs to meet according to the voltage distribution of the nodes. Combined with the scheduling public transportation task constraints of the scheduling model of the electric bus, a coordinated optimization model of the voltage source converter and the electric bus is generated.

14. The electric bus and VSC coordination optimization system according to claim 13, characterized in that: The expression of the objective function is: Where P loss is the network loss of the flexible interconnected distribution network; i, j are the node numbers of the low-voltage distribution network; N AC is the set of all nodes on the AC line; v(i) is the set of nodes directly connected to node i; I ij,t is the current flowing through branch ij at time t; r ij,t is the resistance of the current flowing through branch ij at time t; ΔT is the time interval; T max is the maximum number of time intervals.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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