CN117522020A - Electric automobile optimized dispatching method based on dispatching feasible domain boundary identification - Google Patents
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Abstract
An electric automobile optimized dispatching method based on dispatching feasible domain boundary identification is characterized by acquiring basic information of an electric automobile, traffic network topology information and basic information of a grid frame of an electric power system; establishing an electric vehicle information physical model and an electric vehicle aggregate scheduling feasible domain boundary identification model, and solving to obtain a time-varying scheduling feasible domain boundary of the electric vehicle cluster aggregate by a self-adaptive robust method; and carrying out equipment-level decomposition on the instruction through the instruction fed back by the dispatching center. According to the invention, the multi-dimensional flexible adjustment potential of the external characteristic parameters of the electric automobile aggregate is excavated, the boundary of the dynamic time-varying scheduling feasible region of the aggregate power is delineated, the regulation and control capability of the centralized electric automobile cluster aggregate participating in the electric power system is further excavated, and the high-efficiency integration of the electric automobile is improved.
Description
Technical Field
The invention relates to a technology in the field of power dispatching, in particular to an electric automobile optimized dispatching method based on dispatching feasible domain boundary identification.
Background
The conventional electric automobile polymer scheduling boundary identification method only describes linear superposition of electric automobile polymer power, and cannot accurately fit the power time-varying characteristic of the polymer, so that scheduling or transaction strategies based on the result are inaccurate, and efficient integration and stable participation of the electric automobile in a power system are not facilitated.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an electric vehicle optimized dispatching method based on dispatching feasible region boundary identification, which is characterized by digging multidimensional flexible adjustment potential of external characteristic parameters of an electric vehicle aggregate, describing the dynamic time-varying dispatching feasible region boundary of the aggregate power, further digging the regulation and control capability of a centralized electric vehicle cluster aggregate in a power system, and improving the high-efficiency integration of the electric vehicle.
The invention is realized by the following technical scheme:
the invention relates to an electric vehicle optimized dispatching method based on dispatching feasible domain boundary identification, which comprises the steps of obtaining basic information of an electric vehicle, traffic network topology information and basic information of a grid frame of an electric power system; establishing an electric vehicle information physical model and an electric vehicle aggregate scheduling feasible domain boundary identification model formed by the electric vehicle physical model and a traffic network model, and solving by a self-adaptive robust method to obtain a time-varying scheduling feasible domain boundary of the electric vehicle cluster aggregate; and carrying out equipment-level decomposition on the instruction through the instruction fed back by the dispatching center.
The electric automobile basic information comprises: battery capacity and endurance mileage of an electric vehicle, acceleration performance and corresponding power of the electric vehicle, maximum speed limit of the vehicle, charging time, charging interface and standard, and size and weight of the vehicle.
The traffic network topology information includes: the geographical position nodes of the area, the path links among the roads, traffic flow data, namely the number of vehicles at different time periods of each node or road, traffic control equipment such as traffic lights and traffic signs, traffic demand, travel time and cost.
The basic information of the grid frame of the power system comprises: network topology of the power system, substation or other important node and branch information, load requirements, positions, capacities, transformation ratios and impedance values of transformers, settings and parameters of a protection and control system, voltage and power flow data of each node, and power grid fault and accident data.
The information physical model of the electric automobile is as follows: wherein: />For battery capacity,/->To start charging time, < >>To end the charging time, < >>For initial charge>To end the desired charge, +.>Is the state of charge of the battery(SOC) upper limit, ">Is battery SOC lower limit, ">For maximum charging power, < >>Is the maximum discharge power.
The information physical model of the electric automobile meets the following constraint: electric automobile charging and discharging upper and lower limit constraintState constraint of electric automobile in one working periodElectric automobile state of charge constraintRequiring electric vehicle devices to be charged or discharged only at the same time>Wherein: />Charging and discharging power of electric automobile equipment at t time respectively,/>And->Charging and discharging efficiency of electric automobile is->The state of charge of the electric automobile at the time t is obtained.
The electric motorThe boundary identification model of the automobile aggregate dispatching feasible region aims at searching the ICA maximum dispatching feasible region and is obtained by the following steps: using decision variable p 0 Description of feasible domainsObtaining the decoupling feasible interval of the total active power feasible domain using time +.>Aggregate active power p 0 As an upper bound->And lower bound->Linear combination of the two, and weight xi t ∈[0,1]The method comprises the steps of carrying out a first treatment on the surface of the When xi t When=0->When xi t When=1Aggregate time-varying active power->By controlling the weight xi t Is related to p in the adaptive robust model 0 Constraint of->Is converted into a form of xi t Constraint of-> As a set of cassette uncertainty:finally obtaining the electric automobile aggregate scheduling feasible region boundary identification modelWherein: />Scheduling feasible region for electric automobile polymer, < >>And->The active feasible region upper bound and the active feasible region lower bound are respectively at the moment t, the outermost layer max means that the scheduling feasible region is maximized, the middle layer min represents that the robust adjustment coefficient xi is minimized, and the inner layer max represents that the feasibility of the corresponding decomposition strategy x (xi) is maximized.
Constraint of the electric automobile aggregate scheduling feasible region boundary identification model comprises:
i) Meets the active linear approximate power flow model p 0 ＝Dx(p 0 ) +g, wherein: x (p) 0 ) The method is characterized in that the method is an electric automobile adjustable resource, and an uncontrollable resource is regarded as a given system parameter;
ii) Power capability constraint of Polymer internal device E l x(p 0 )||≤s l , Is a constraint index set;
iii) Constrained reconstruction of SOC equation to write Wx (p) in inequality form 0 )≤w；
iv) for the feasible regionAny active power p in 0 There must be a corresponding possible scheduling scheme x (p 0 ) Is satisfied thatRealizing the scheduling instruction on the premise of all operation constraints, thereby guaranteeing the decomposition feasibility of the obtained result, wherein: matrix D, E l W, vector g, W, scalar s l Are all aggregate system parameters.
The self-adaptive robust method specifically comprises the following steps: and importing an electric vehicle physical model and a traffic network model of the constructed electric vehicle aggregate scheduling feasible region boundary identification model into an objective function and constraint, constructing a self-adaptive robust model, wherein the objective function is a time-varying scheduling feasible region of the maximized electric vehicle aggregate, deciding the upper limit and the lower limit of the scheduling feasible region of the variable electric vehicle aggregate, and solving the model according to the constraint of the electric vehicle physical model, the constraint of the traffic network model and the constraint of a power grid by a Column-generated cutting plane method (Column-and-Cut Generation) to obtain the maximum feasible region of the aggregate.
The plane cutting method specifically comprises the following steps:
i) Solving an adaptive robust model using a simplified model comprising only a subset of variables;
ii) progressively increasing the variables in the current solution that have negative reduced costs or contribute to the objective function improvement, i.e. generating columns, to find the optimal solution for linear relaxation to cull the current non-integer solution, while not culling any integer feasible solutions;
iii) When the optimal solution of the linear relaxation does not meet the integer constraint, generating a new constraint by using a reduction constraint method, and returning to the step ii until an integer solution meeting all the constraints is found or no solution is determined to be a problem;
iv) when there are no new generated columns, i.e. all columns have non-negative reduced costs and the current solution satisfies all integer constraints, a scheduling policy scheme is obtained.
The electric automobile polymer scheduling feasible region boundary identification model refers to: the first stage aims at maximizing the overall aggregation flexibility, and the decision variables areWhen the value of the random variable ζ is determined, the second-stage objective function is at +.>And searching the optimal feasible DER scheduling strategy x (xi) under the worst scene. Thereby, the feasibility domain optimality is ensured, and meanwhile, the decomposition feasibility is ensured.
The instruction fed back by the dispatching center refers to: in the power grid dispatching process, a dispatching center generates and issues specific instructions to an electric automobile aggregate according to the operation and management requirements of the electric automobile aggregate. These instructions relate to the operation, control and other related operations of the aggregate, ensuring a stable and efficient operation of the grid.
The device-level decomposition of the instruction means that: after receiving a dispatching instruction fed back by the power dispatching center, the instruction is refined and analyzed within the technical feasible range. The decomposition operation is to take the lowest cost as an objective function, and obtain specific values of the charging position, the charging time and the charging amount of each electric automobile by considering specific requirements and limiting conditions of each electric automobile. Such a decomposition ensures that each car gets an optimal charging strategy according to its actual situation.
Technical effects
The invention is based on the self-adaptive robust optimized electric vehicle polymer scheduling feasible region boundary identification technology, effectively describes the time-varying characteristics of the electric vehicle scheduling feasible region boundary, and can ensure the electric vehicle instruction decomposition feasibility under the boundary, which is not considered and adopted in the previous research; the invention realizes dynamic identification of the time-varying scheduling feasible domain boundary of the aggregate. The model not only can comprehensively describe time-varying characteristics of the dispatching feasibility domain of the electric automobile, but also ensures feasibility of electric automobile instruction decomposition under the boundary, provides more comprehensive data support for an electric automobile polymer when the electric automobile polymer is connected into an electric system, and therefore high-efficiency integration of the electric system and the electric automobile is improved. The method for identifying the boundary of the feasible region of the electric automobile polymer provided by the technology is applied by the inner Mongolian electric power limited liability company.
Drawings
FIG. 1 is a schematic diagram of a system according to the present invention;
FIG. 2 is a schematic flow chart of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a feasible region boundary;
FIG. 4 is an exploded view of an embodiment instruction.
Detailed Description
As shown in fig. 1, an electric vehicle polymer feasible region boundary identification system according to the present embodiment includes: the system comprises an information acquisition module, an information physical module, an optimizing identification module and an instruction decomposition module, wherein: the information acquisition module is connected with the electric automobile and transmits the running state and characteristic information of the electric automobile, the information physical module is connected with the optimizing identification module and transmits an information physical model based on the characteristics of the electric automobile, the optimizing identification module is connected with the instruction decomposition module and transmits an optimizing result of a feasible region boundary, and the instruction decomposition module outputs each scheduling instruction.
The information physical module, the optimization identification module and the instruction decomposition module establish a power dynamic change and scheduling feasible region model of the electric automobile aggregate according to the running state and characteristic information of the electric automobile, add the power dynamic change and scheduling feasible region model into an optimization target of the electric system for scheduling, obtain a scheduling strategy result, and calculate instruction decomposition regulation and control of the electric automobile aggregate according to the result.
As shown in fig. 2, the actual electric vehicle information is used as a detection system, 100 electric vehicles are classified into A, B, C types according to the charge and discharge characteristics, the upper limit of the electric vehicle SOC is 1.0, the lower limit of the electric vehicle SOC is 0.1, the maximum charge and discharge power of the electric vehicle is 7kw, the charge and discharge efficiency is 95%, and the battery capacity is 32kwh, so that the following three types of charge and discharge characteristics are obtained.
TABLE 1
As shown in fig. 3, in order to establish an information physical model and a self-adaptive robust optimization model of an electric vehicle according to the charge-discharge characteristics of the electric vehicle in table 1, a dual method is used to solve, and a feasible region boundary identification result of the electric vehicle is obtained, wherein: negative values provide flexible support for the electric vehicle polymer to the outside. For most of the time sections, the scheduling feasible region obtained by the linear superposition method is significantly larger than the feasible region obtained by the model, because the linear superposition method fails to consider the endophytic relevance among electric vehicles and the adjustment characteristic difference of different types of electric vehicles.
As shown in fig. 4, in order to decompose the instruction to the electric car level according to the dispatching instruction for the aggregate transmitted by the power system dispatching center, the charge and discharge plans of each vehicle are calculated, and the instruction decomposition result is obtained.
As shown in fig. 3, in order to obtain the electric vehicle aggregation feasible region with the maximum flexibility, the identification result of the linear superposition method is compared with the identification result of the linear superposition method.
Compared with the prior art such as a linear superposition method, the method and the system adopt a modeling method which fully considers the output condition and time coupling characteristic of electric vehicles of various types in the aggregate, and the scheduling feasible region boundary obtained by aggregation can ensure the aggregation optimality, and any scheduling scheme in the feasible region can be decomposed into specific instructions for each electric vehicle, so that the electric vehicle aggregate is promoted to consider the equipment-level power dynamic change and the synergy of the aggregate overall scheduling feasible region, and technical parameters are provided for the power system scheduling strategy which considers the electric vehicle aggregate.
The foregoing embodiments may be partially modified in numerous ways by those skilled in the art without departing from the principles and spirit of the invention, the scope of which is defined in the claims and not by the foregoing embodiments, and all such implementations are within the scope of the invention.
Claims (9)
1. An electric vehicle optimized dispatching method based on dispatching feasible domain boundary identification is characterized by comprising the steps of obtaining basic information of an electric vehicle, topology information of a traffic network and basic information of a grid frame of an electric power system; establishing an electric vehicle information physical model and an electric vehicle aggregate scheduling feasible domain boundary identification model formed by the electric vehicle physical model and a traffic network model, and solving by a self-adaptive robust method to obtain a time-varying scheduling feasible domain boundary of the electric vehicle cluster aggregate; performing equipment-level decomposition on the instruction through the instruction fed back by the dispatching center;
the electric automobile basic information comprises: battery capacity and endurance mileage of an electric vehicle, acceleration performance and corresponding power of the electric vehicle, highest speed limit of the vehicle, charging time, charging interface and standard, and size and weight of the vehicle;
the traffic network topology information includes: geographical position nodes of the area, path links among roads, traffic control equipment such as traffic flow data, namely the number of vehicles at different periods of each node or road, traffic lights, traffic signs and the like, traffic demand, travel time and cost;
the basic information of the grid frame of the power system comprises: network topology of the power system, substation or other important node and branch information, load requirements, positions, capacities, transformation ratios and impedance values of transformers, settings and parameters of a protection and control system, voltage and power flow data of each node, and power grid fault and accident data.
2. The electric vehicle optimizing and dispatching method based on the dispatching feasible domain boundary identification of claim 1, wherein the information physical model of the electric vehicle is as follows: wherein: />For battery capacity,/->To start charging time, < >>To end the charging time, < >>For initial charge>To end the desired charge, +.>Is the upper limit of the state of charge (SOC) of the battery,/-for>Is battery SOC lower limit, ">For maximum charging power, < >>Is the maximum discharge power.
3. The electric vehicle optimizing and dispatching method based on the dispatching feasible domain boundary identification according to claim 1 or 2, wherein the information physical model of the electric vehicle satisfies the following constraint: electric automobile charging and discharging upper and lower limit constraintState constraint of electric automobile in one working periodElectric automobile state of charge constraintRequiring electric vehicle devices to be charged or discharged only at the same time>Wherein: />Charging and discharging power of electric automobile equipment at t time respectively,/>And->Charging and discharging efficiency of electric automobile is->The state of charge of the electric automobile at the time t is obtained.
4. The electric vehicle optimized dispatching method based on dispatching feasible domain boundary identification of claim 1, wherein the electric vehicle aggregate dispatching feasible domain boundary identification model aims at finding an ICA maximum dispatching feasible region, and is specifically obtained by the following steps: using decision variable p 0 Description of feasible domainsObtaining the decoupling feasible interval of the total active power feasible domain using time +.>Aggregate active power p 0 As an upper bound->And lower bound->Linear combination of the two, and weight xi t ∈[0,1]The method comprises the steps of carrying out a first treatment on the surface of the When xi t When=0->When xi t When=1Aggregate time-varying active power->By controlling the weight xi t Is related to p in the adaptive robust model 0 Constraint of->Is converted into a form of xi t Constraint of-> As a set of cassette uncertainty:finally obtaining the electric automobile aggregate scheduling feasible region boundary identification modelWherein: />Scheduling feasible region for electric automobile polymer, < >>And->The upper bound and the lower bound of the active feasible region at the moment t are respectively, and the outermost layermax means maximizing the scheduling feasibility domain, middle layer min represents the feasibility of minimizing the robust adjustment coefficient xi, inner layer max represents the feasibility of maximizing the corresponding decomposition strategy x (xi), the first stage of the model aims at maximizing the overall aggregation flexibility, and decision variables are +.>When the value of the random variable xi is determined, the second stage objective function is as followsAnd searching the optimal feasible DER scheduling strategy x (xi) under the worst scene, thereby guaranteeing the decomposition feasibility of the feasible region optimality.
5. The electric vehicle optimizing and dispatching method based on the dispatching feasible domain boundary identification according to claim 1 or 4, wherein the constraint of the electric vehicle aggregate dispatching feasible domain boundary identification model comprises:
i) Meets the active linear approximate power flow model p 0 ＝Dx(p 0 ) +g, wherein: x (p) 0 ) The method is characterized in that the method is an electric automobile adjustable resource, and an uncontrollable resource is regarded as a given system parameter;
ii) Power Capacity constraints for Polymer internal devices Is a constraint index set;
iii) Constrained reconstruction of SOC equation to write Wx (p) in inequality form 0 )≤w；
iv) for the feasible regionAny active power p in 0 There must be a corresponding possible scheduling scheme x (p 0 ) Real on the premise of meeting all operation constraintsNow scheduling instructions, thereby guaranteeing the resolution feasibility of the resulting results, wherein: matrix D, E l W, vector g, W, scalar s l Are all aggregate system parameters.
6. The electric vehicle optimized dispatching method based on the dispatching feasible domain boundary identification of claim 1, wherein the self-adaptive robust method specifically comprises the following steps: and importing the electric vehicle physical model and the traffic network model of the constructed electric vehicle aggregate dispatching feasible region boundary identification model into an objective function and constraint, constructing a self-adaptive robust model, wherein the objective function is a time-varying dispatching feasible region of the maximized electric vehicle aggregate, deciding the upper limit and the lower limit of the dispatching feasible region of the variable electric vehicle aggregate, and solving the model according to the constraint of the electric vehicle physical model, the constraint of the traffic network model and the constraint of a power grid by a cutting plane method generated by a column to obtain the maximum feasible region of the aggregate.
7. The electric vehicle optimized dispatching method based on the dispatching feasible domain boundary identification of claim 6, wherein the cutting plane method specifically comprises the following steps:
i) Solving an adaptive robust model using a simplified model comprising only a subset of variables;
ii) progressively increasing the variables in the current solution that have negative reduced costs or contribute to the objective function improvement, i.e. generating columns, to find the optimal solution for linear relaxation to cull the current non-integer solution, while not culling any integer feasible solutions;
iii) When the optimal solution of the linear relaxation does not meet the integer constraint, generating a new constraint by using a reduction constraint method, and returning to the step ii until an integer solution meeting all the constraints is found or no solution is determined to be a problem;
iv) when there are no new generated columns, i.e. all columns have non-negative reduced costs and the current solution satisfies all integer constraints, a scheduling policy scheme is obtained.
8. The method for optimizing and dispatching electric vehicles based on the boundary recognition of the dispatching feasible region according to claim 1, wherein the device-level decomposition of the instruction is as follows: after a dispatching instruction fed back by the power dispatching center is received, the instruction is refined and analyzed within the technical feasible range, the decomposing operation is to take the lowest cost as an objective function, and the specific values of the charging position, the charging time and the charging amount of each automobile are obtained by considering the specific requirements and the limiting conditions of each electric automobile, so that the decomposing ensures that each automobile can obtain the optimal charging strategy according to the actual situation.
9. An electric vehicle aggregate viable domain boundary identification system implementing the method of any of claims 1-8, comprising: the system comprises an information acquisition module, an information physical module, an optimizing identification module and an instruction decomposition module, wherein: the information acquisition module is connected with the electric automobile and transmits the running state and characteristic information of the electric automobile, the information physical module is connected with the optimizing identification module and transmits an information physical model based on the characteristics of the electric automobile, the optimizing identification module is connected with the instruction decomposition module and transmits an optimizing result of a feasible region boundary, and the instruction decomposition module outputs each scheduling instruction.
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| CN117811053B (en) * | 2024-02-29 | 2024-05-07 | 北京中电普华信息技术有限公司 | Source network charge storage coordination interaction regulation and control method, device and system |
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