Peak shaving auxiliary service-oriented electric vehicle aggregation and bidding method and system

Abstract

The invention relates to an electric vehicle aggregation and bidding method and system for peak regulation auxiliary service, and the method comprises the following steps: building a dynamic model of a single electric vehicle based on the operation parameter data of the electric vehicle; based on the dynamic model of the single electric vehicle, performing approximate optimization solution through an aggregation algorithm to obtain a dynamic model of electric vehicle cluster aggregation; based on the dynamic model of the electric vehicle cluster aggregation, establishing a double-layer strategy model of an electric vehicle aggregator participating in a peak regulation market; and converting the double-layer strategy model of the electric vehicle aggregator participating in the peak regulation market into a single-layer mathematical optimization function containing equality constraints through a Karush-Kuhn-Tucker condition and a strong duality principle, and solving to obtain an electricity purchase plan participating in the electricity market and the report quantity and quotation of the peak regulation auxiliary service market. According to the method provided by the invention, large-scale electric vehicle clusters with heterogeneous parameters can be rapidly aggregated, and an aggregator not only can optimize the income of the aggregator in the electricity market, but also can effectively participate in the power grid peak regulation auxiliary service to play the regulation role of the aggregator.

Description

Technical Field

[0001] The present invention relates to the field of demand-side resource management of power systems, and in particular to a method and system for aggregating and bidding electric vehicles for peak load auxiliary services. Background Art

[0002] In recent years, the proportion of new energy power generation represented by wind power and photovoltaic power has increased year by year. Although these new energy sources have the advantages of being renewable and having low carbon emissions, the intermittent and volatile nature of their power generation has brought severe challenges to the stable operation of the power grid. Especially during the peak load period of the power grid, the traditional peak-shaving method relies on the expansion of peak power sources and transmission channels, which not only significantly increases the operating cost of the power grid, but also fails to give full play to the flexibility resources of the power grid.

[0003] In the demand side management of the power grid, electric vehicles are considered to be a key resource for alleviating grid volatility and improving system flexibility due to their potential large-scale energy storage capacity and flexible load regulation characteristics. It is predicted that the number of electric vehicles will reach 83 million, the equivalent energy storage capacity will reach 5TW·h, the demand for electric charging will account for 6% to 7% of the total electricity consumption in society, and the maximum charging load will account for 11% to 12% of the grid load. However, due to the wide distribution of electric vehicles, heterogeneous parameters and limited single-unit regulation capabilities, it is difficult for a single electric vehicle to meet the requirements of direct participation in the electricity market.

[0004] To meet the above challenges, the introduction of electric vehicle aggregators is key. By aggregating a large number of scattered demand-side electric vehicles into a whole, the requirements for regulating capacity can be met, so that they can effectively participate in the interaction between the power market and the power grid. Electric vehicle aggregators are responsible for directly regulating these resources to achieve the goals of peak load regulation, new energy consumption, and peak load shaving in the power system.

[0005] Existing aggregation methods face challenges such as high computational complexity and insufficient approximation accuracy when dealing with the heterogeneity of electric vehicle parameters and large-scale aggregation problems. In particular, when faced with the non-full-dimensional characteristics of the electric vehicle power domain, the traditional Minkowski sum (M-sum) solution method is difficult to achieve efficient and accurate aggregation. Summary of the invention

[0006] Aiming at the problem that the power domain polyhedron is not full-dimensional due to the heterogeneity of electric vehicle parameters and is difficult to approximate, the present invention provides an electric vehicle aggregation and bidding method for peak-shaving auxiliary services. Through the improved approximate solution strategy, while solving the non-full-dimensional approximation problem, the computational efficiency and approximation accuracy are improved, thereby releasing more electric vehicle regulation flexibility and being able to quickly aggregate large-scale, parameter-heterogeneous electric vehicle clusters. Further considering the cost of purchasing electricity, a bidding strategy for electric vehicle aggregators in the auxiliary service market is designed, which can not only optimize their revenue in the electricity market, but also effectively participate in the power grid peak-shaving auxiliary services and play their regulatory role.

[0007] The present invention solves the above technical problems with the following solution: an electric vehicle aggregation and bidding method for peak load auxiliary services, comprising the following steps:

[0008] Based on the electric vehicle operating parameter data, a dynamic model of a single electric vehicle is established;

[0009] Based on the dynamic model of a single electric vehicle, the dynamic model of electric vehicle cluster aggregation is obtained through approximate optimization and solution of the aggregation algorithm;

[0010] Based on the dynamic model of electric vehicle cluster aggregation, a two-tier strategy model for electric vehicle aggregators to participate in the peak-shaving market is established;

[0011] Through the Karush-Kuhn-Tucker condition and the strong duality principle, the two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market is converted into a single-layer mathematical optimization function with equality constraints for solution, and the power purchase plan for participating in the power market and the quotation and quotation in the peak-shaving ancillary service market are obtained.

[0012] Preferably, the step of establishing a dynamic model of a single electric vehicle based on the electric vehicle operating parameter data comprises:

[0013] Based on the electric vehicle operating parameter data, including the electric vehicle power, state of charge, and charging and discharging time limit, a dynamic model of a single electric vehicle is established, as follows:

[0014]

[0015] Among them, E i (t) is the electric energy state of the i-th electric vehicle at time t, P i ch (t) / P i dis (t) is the charging / discharging power of the i-th electric vehicle at time t, is the upper limit of the charging / discharging power of the i-th electric vehicle at time t, is the lower / upper limit of the energy state of the i-th electric vehicle at time t, α i is the energy dissipation rate of the i-th electric vehicle, Δt is the sampling time interval, t i,a / t i,l is the arrival / departure time of the electric vehicle at time t, It is the desired energy state when EVi leaves.

[0016] Preferably, the dynamic model based on a single electric vehicle is solved by approximate optimization using an aggregation algorithm to obtain a dynamic model of electric vehicle cluster aggregation, which specifically includes:

[0017] The dynamic model of a single electric vehicle is converted into a polyhedral half-space form as follows:

[0018]

[0019] The power constraints of electric vehicles are converted into a closed region representation in a multidimensional linear space, also called the power feasible region, which represents the power set of electric vehicles within a certain period of time, facilitating the subsequent Minkowski and aggregation solutions.

[0020] Where P i represents the power of the D time nodes of the i-th electric vehicle, is the set of power curves of the ith electric vehicle, indicating the operating power range of the ith electric vehicle, that is, the power feasible region of EVi. i The actual input / output power, when P i ≥0 means charging state, When P i ≤0 means discharge state, The content after the symbol is a further explanation of the content before the symbol.

[0021] Half-space representation of a single electric vehicle dynamic model A i P i <=b i , A i , b i as follows:

[0022]

[0023] Among them, E i (0) is the electric energy state of the i-th electric vehicle at time t, D ≥ t i,l -t i,a , D is the number of moments aggregated by the aggregator, and the specific matrix form is as follows:

[0024]

[0025]

[0026] Based on the dynamic model transformation of a single electric vehicle and its polyhedral half-space form, the aggregate dynamic model of the electric vehicle cluster is approximately solved by Minkowski and M-Sum, as follows:

[0027] The aggregate dynamic model of the electric vehicle cluster is expressed as the Minkowski and M-Sum expressions of the dynamic model of N electric vehicles as follows:

[0028]

[0029] in, is a cluster of N electric vehicles, It is the aggregation dynamic model of electric vehicle clusters, and the result is still in the linear constraint form of Ax≤b. Represents direct sum.

[0030] Directly calculate N half-spaces to represent the polyhedron The exact M-Sum of is an NP-hard problem, so an approximate solution method is generally used to convert the NP-hard problem into an arithmetic sum problem to reduce the computational complexity. i,a / t i,l A new approximate solution method is used to solve the NP-hard problem of aggregate calculation of electric vehicles in N half-spaces, as follows:

[0031] The power feasible region of N electric vehicles Using the same matrix The form of approximation is Aggregate power feasible region of electric vehicle clusters Can be used Approximate representation, converting the complex exact M-sum calculation in EV aggregation into arithmetic and calculation.

[0032] Using Hausdorff distance To measure the approximation quality, the external and internal approximation problems can be described as:

[0033] or

[0034] in, is the outer approximation of the power feasible region of EV i, It is the internal approximation of the power feasible region of EVi.

[0035] The external approximate constraint means that the external approximate power feasible domain includes the entire set of electric vehicle's operable power curves and a small number of inoperable power curves. The internal approximate constraint narrows the power feasible domain of electric vehicles to include a partial set of electric operable power curves.

[0036] Using the triangular inequality and Lipschitz continuity of the Hausdorff distance, the objective function is transformed into a convex function in the form of a two-norm, as follows:

[0037] Lemma 1: There are two feasible regions of power for electric vehicles and If ||·|| p ,||·|| q For two arbitrary norms and d H (·,·) is given by the norm ||·|| p The Hausdorff distance is measured, so the Hausdorff distance of the power feasible region of the two electric vehicles satisfies:

[0038]

[0039] in,

[0040]

[0041] According to Lemma 1 and the Hausdorff distance triangle inequality;

[0042] right To simplify:

[0043] Construct a non-empty collection:

[0044] According to the Hausdorff distance triangle inequality, we can get:

[0045] because is a fixed value, L(A0) is the Lipschitz constant of matrix A0, so the objective function Convex function that can be equivalently converted into a two-norm form

[0046] According to Farkas'lemma, the set inclusion constraint in the approximate optimization problem is transformed into linear constraint and nonlinear constraint. For nonlinear constraint, the basic polyhedron scaling algorithm is used to solve it, as follows:

[0047] The constraints of the outer approximation are linear constraints, as follows:

[0048]

[0049] The inner approximation conditions are nonlinear constraints, as follows:

[0050]

[0051] For the case where the inner approximation condition is a nonlinear constraint, the outer approximation polyhedron is solved Considered as a basic polyhedron, the basic polyhedron scaling algorithm is used to solve the problem, and the internal approximation problem is simplified to:

[0052]

[0053] Among them, s i =1 / φ i ,r i =-s i ψ i ;

[0054] use Approximately represent the exact dynamic model of electric vehicles Then the aggregation dynamic model of electric vehicle cluster is: It is a logical symbol indicating that two conditions are equivalent.

[0055]

[0056] Preferably, the dynamic model based on electric vehicle cluster aggregation establishes a two-tier strategy model for electric vehicle aggregators to participate in the peak load regulation market, including:

[0057] Based on the dynamic model of electric vehicle cluster aggregation, a model of electric vehicle aggregators participating in the electricity market is established within the aggregation feasible domain of electric vehicle clusters, in which the charging cost of electric vehicle aggregators is minimized. The expression is as follows:

[0058]

[0059] It indicates that the charging cost of electric vehicle aggregators is minimized within the aggregation feasible domain of electric vehicle clusters.

[0060] in, is the decision variable, is the charging power of the electric vehicle aggregator, and EM (t) Publish time-of-use electricity prices for the electricity market. When , it means that the aggregator charges the grid reversely to get paid;

[0061] Based on the model of electric vehicle aggregators participating in the electricity market, the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving ancillary service market is established to quote the quantity of electric vehicle aggregators participating in the peak-shaving ancillary service market and minimize the overall cost of electric vehicle aggregators. The expression is as follows:

[0062]

[0063] The cost of an EV aggregator consists of two parts: EM (t)P agg (t) is the electricity purchase cost of the aggregator participating in the electricity market, The optimal power curve for electric vehicle aggregators to participate in the electricity market is calculated. Power adjustment for electric vehicle aggregators, is the revenue of electric vehicle aggregators participating in the peak-shaving ancillary service market. The peak-shaving ancillary service market settles revenue according to the adjustment amount of peak-shaving participants. λ(t) is the clearing electricity price of the peak-shaving ancillary service market. λ(t)>0 indicates that the peak-shaving demand is positive, and λ(t)<0 indicates that the peak-shaving demand is positive.

[0064] Establish an electric vehicle aggregator and large-scale battery to participate in the peak-shaving auxiliary service market. Under the premise of meeting the peak-shaving demand, establish an electric vehicle aggregator to participate in the peak-shaving market bottom-level strategy model that minimizes the overall peak-shaving cost. The expression is as follows:

[0065] Objective function:

[0066] The objective function is to minimize the overall peak-shaving cost, including the peak-shaving cost settled for electric vehicles. And the call cost of large peak-shaving equipment λ o (t)|P o (t)|;

[0067] Constraints: including the reported peak load range constraints of electric vehicle aggregators, according to A0P agg (t)<=b agg calculate; is the maximum power that can be achieved at each time node; is the minimum power that can be achieved at each time node, is the upward and downward adjustment range of the optimal power curve difference that only participates in the power market, and its corresponding dual variable is as follows:

[0068]

[0069] The adjustable range constraint of large peak-shaving equipment corresponds to the dual variable: as follows:

[0070]

[0071] The peak load supply and demand balance constraint, whose corresponding dual variable λ(t) is the clearing price of the peak load ancillary service market, is as follows:

[0072]

[0073] Preferably, the two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market is converted into a single-layer mathematical optimization function with equality constraints through the Karush-Kuhn-Tucker condition and the strong duality principle for solving, so as to obtain the power purchase plan for participating in the power market and the quantity and quotation of the peak-shaving auxiliary service market, specifically including:

[0074] The Lagrangian function is constructed for the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the objective function and constraints of the lower-level model are combined with the Lagrangian multiplier as shown below:

[0075]

[0076] Applying the KKT conditions to the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is as follows:

[0077]

[0078] The complementary relaxation conditions in the KKT condition are as follows:

[0079]

[0080] Through the strong duality principle, the optimal solution of the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is transformed into the constraints of the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the two-layer model is transformed into a single-layer model, as follows:

[0081]

[0082] According to the KKT condition and the strong duality principle, the two-layer strategy model of electric vehicle aggregators participating in the peak load auxiliary service market is transformed into a single-layer mathematical optimization problem with equality constraints, as follows:

[0083] The objective function can be obtained according to the complementary relaxation condition:

[0084]

[0085] According to the KKT condition, we can get:

[0086]

[0087] Substituting the above KKT condition results into the complementary relaxation condition results, we can get:

[0088]

[0089] Then bring in the strong duality principle and we get:

[0090]

[0091] Therefore, the objective function of the single-layer model of electric vehicle aggregators participating in the peak-shaving market is finally obtained as:

[0092]

[0093] The constraints include the adjustable range constraints of electric vehicle aggregators, the reported peak-shaving range constraints, the adjustable range constraints of peak-shaving equipment, and the KKT condition constraints, as shown below:

[0094] A0P agg (t)<=b agg

[0095]

[0096] The nonlinear function sign(P o (t)) and the complementary relaxation condition containing bilinear terms are linearized as follows:

[0097] The symbol function sign(P o Linearization of (t)):

[0098]

[0099] Complementary relaxation condition linearization:

[0100]

[0101] The mixed integer linear programming problem is transformed into the two-level strategy model of electric vehicle aggregators participating in the peak-shaving market, where the continuous decision variables are P agg (t),P o (t),α agg (t),λ(t), The decision variables are Obtain quotation for participating in the power purchase plan of the power market and the peak load ancillary service market and quote

[0102] The present invention also provides an electric vehicle aggregation and bidding system for peak load auxiliary services, comprising:

[0103] The electric vehicle dynamic model building module is used to build a dynamic model of a single electric vehicle based on the electric vehicle operating parameter data;

[0104] The dynamic model building module of electric vehicle cluster aggregation is used to obtain the dynamic model of electric vehicle cluster aggregation through approximate optimization solution based on the dynamic model of a single electric vehicle through aggregation algorithm;

[0105] A two-tier strategy model building module for aggregators to participate in the peak-shaving market, which is used to build a two-tier strategy model for electric vehicle aggregators to participate in the peak-shaving market based on a dynamic model of electric vehicle cluster aggregation;

[0106] The two-layer strategy model solving module for aggregators participating in the peak-shaving market is used to convert the two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market into a single-layer mathematical optimization function with equality constraints through the Karush-Kuhn-Tucker condition and the strong duality principle for solving, and obtain the power purchase plan for participating in the power market and the quantity and quotation in the peak-shaving auxiliary service market.

[0107] Preferably, the step of establishing a dynamic model of a single electric vehicle based on the electric vehicle operating parameter data comprises:

[0108] Based on the electric vehicle operating parameter data, including the electric vehicle power, state of charge, and charging and discharging time limit, a dynamic model of a single electric vehicle is established, as follows:

[0109]

[0110] Among them, E i (t) is the electric energy state of the i-th electric vehicle at time t, P i ch (t) / P i dis (t) is the charging / discharging power of the i-th electric vehicle at time t, is the upper limit of the charging / discharging power of the i-th electric vehicle at time t, is the lower / upper limit of the energy state of the i-th electric vehicle at time t, α i is the energy dissipation rate of the i-th electric vehicle, Δt is the sampling time interval, t i,a / t i,l is the arrival / departure time of the electric vehicle at time t, It is the desired energy state when EVi leaves.

[0111] Preferably, the dynamic model based on a single electric vehicle is solved by approximate optimization using an aggregation algorithm to obtain a dynamic model of electric vehicle cluster aggregation, which specifically includes:

[0112] The dynamic model of a single electric vehicle is converted into a polyhedral half-space form as follows:

[0113]

[0114] Where P i represents the power of the D time nodes of the i-th electric vehicle, is the set of power curves of the i-th electric vehicle, indicating the operating power range of the i-th electric vehicle;

[0115]

[0116] Among them, E i (0) is the electric energy state of the i-th electric vehicle at time t, D ≥ t i,l -t i,a , D is the number of moments aggregated by the aggregator, and the specific matrix form is as follows:

[0117]

[0118] Based on the dynamic model transformation of a single electric vehicle and its polyhedral half-space form, the aggregate dynamic model of the electric vehicle cluster is approximately solved by Minkowski and M-Sum, as follows:

[0119] The aggregate dynamic model of the electric vehicle cluster is expressed as the Minkowski and M-Sum expressions of the dynamic model of N electric vehicles as follows:

[0120]

[0121] in, is a cluster of N electric vehicles, An aggregation dynamic model for electric vehicle clusters;

[0122] The approximate solution method is used to calculate the NP-hard problem of N half-spaces, as follows:

[0123] The power feasible region of N electric vehicles Using the same matrix The form of approximation is Aggregate power feasible region of electric vehicle clusters Can be used Approximate representation;

[0124] Using Hausdorff distance To measure the approximation quality, the external and internal approximation problems can be described as:

[0125] or

[0126] in, is the outer approximation of the power feasible region of EV i, is the internal approximation of the power feasible region of EVi;

[0127] Using the triangular inequality and Lipschitz continuity of the Hausdorff distance, the objective function is transformed into a convex function in the form of a two-norm, as follows:

[0128] right To simplify:

[0129] Construct a non-empty collection:

[0130] According to the Hausdorff distance triangle inequality, we can get:

[0131] because is a fixed value, L(A0) is the Lipschitz constant of matrix A0, so the objective function Convex function that can be equivalently converted into a two-norm form

[0132] According to Farkas'lemma, the set inclusion constraint in the approximate optimization problem is transformed into linear constraint and nonlinear constraint. For nonlinear constraint, the basic polyhedron scaling algorithm is used to solve it, as follows:

[0133] The constraints of the outer approximation are linear constraints, as follows:

[0134]

[0135] The inner approximation conditions are nonlinear constraints, as follows:

[0136]

[0137] For the case where the inner approximation condition is a nonlinear constraint, the outer approximation polyhedron is solved Considered as a basic polyhedron, the basic polyhedron scaling algorithm is used to solve the problem, and the internal approximation problem is simplified to:

[0138]

[0139] Among them, s i =1 / φ i ,r i =-s i ψ i ;

[0140] use Approximately represent the exact dynamic model of electric vehicles Then the aggregation dynamic model of electric vehicle cluster is:

[0141]

[0142] Preferably, the dynamic model based on electric vehicle cluster aggregation establishes a two-tier strategy model for electric vehicle aggregators to participate in the peak load regulation market, including:

[0143] Based on the dynamic model of electric vehicle cluster aggregation, a model of electric vehicle aggregators participating in the electricity market is established within the aggregation feasible domain of electric vehicle clusters, in which the charging cost of electric vehicle aggregators is minimized. The expression is as follows:

[0144]

[0145] It indicates that the charging cost of electric vehicle aggregators is minimized within the aggregation feasible domain of electric vehicle clusters.

[0146] in, is the decision variable, is the charging power of the electric vehicle aggregator, and EM (t) Publish time-of-use electricity prices for the electricity market. When , it means that the aggregator charges the grid reversely to get paid;

[0147] Based on the model of electric vehicle aggregators participating in the electricity market, the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving ancillary service market is established to quote the quantity of electric vehicle aggregators participating in the peak-shaving ancillary service market and minimize the overall cost of electric vehicle aggregators. The expression is as follows:

[0148]

[0149] The cost of an EV aggregator consists of two parts: EM (t)P agg (t) is the electricity purchase cost of the aggregator participating in the electricity market, The optimal power curve for electric vehicle aggregators to participate in the electricity market is calculated. Power adjustment for electric vehicle aggregators, is the income of electric vehicle aggregators participating in the peak-shaving ancillary service market. The peak-shaving ancillary service market settles income according to the adjustment amount of peak-shaving participants. λ(t) is the clearing electricity price of the peak-shaving ancillary service market; λ(t)>0 indicates that the peak-shaving demand is positive, and λ(t)<0 indicates that the peak-shaving demand is positive.

[0150] Establish an electric vehicle aggregator and large-scale battery to participate in the peak-shaving auxiliary service market. Under the premise of meeting the peak-shaving demand, establish an electric vehicle aggregator to participate in the peak-shaving market bottom-level strategy model that minimizes the overall peak-shaving cost. The expression is as follows:

[0151] Objective function:

[0152] The objective function is to minimize the overall peak-shaving cost, including the peak-shaving cost settled for electric vehicles. And the call cost of large peak-shaving equipment λ o (t)|P o (t)|;

[0153] Constraints:

[0154] Contains the reported peak load range constraints of electric vehicle aggregators, according to A0P agg (t)<=b agg calculate;

[0155] is the maximum power that can be achieved at each time node; is the minimum power that can be achieved at each time node, is the upward and downward adjustment range of the optimal power curve difference that only participates in the power market, and its corresponding dual variable is as follows:

[0156]

[0157] The adjustable range constraint of large peak-shaving equipment corresponds to the dual variable: as follows:

[0158]

[0159] The peak load supply and demand balance constraint, whose corresponding dual variable λ(t) is the clearing price of the peak load ancillary service market, is as follows:

[0160]

[0161] Preferably, the two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market is converted into a single-layer mathematical optimization function with equality constraints through the Karush-Kuhn-Tucker condition and the strong duality principle for solving, so as to obtain the power purchase plan for participating in the power market and the quantity and quotation of the peak-shaving auxiliary service market, specifically including:

[0162] The Lagrangian function is constructed for the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the objective function and constraints of the lower-level model are combined with the Lagrangian multiplier as shown below:

[0163]

[0164] Applying the KKT conditions to the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is as follows:

[0165]

[0166] The complementary relaxation conditions in the KKT condition are as follows:

[0167]

[0168] Through the strong duality principle, the optimal solution of the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is transformed into the constraints of the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the two-layer model is transformed into a single-layer model, as follows:

[0169]

[0170] According to the KKT condition and the strong duality principle, the two-layer strategy model of electric vehicle aggregators participating in the peak load auxiliary service market is transformed into a single-layer mathematical optimization problem with equality constraints, as follows:

[0171] The objective function can be obtained according to the complementary relaxation condition:

[0172]

[0173] According to the KKT condition, we can get:

[0174]

[0175] Substituting the above KKT condition results into the complementary relaxation condition results, we can get:

[0176]

[0177] Then bring in the strong duality principle and we get:

[0178]

[0179] Therefore, the objective function of the single-layer model of electric vehicle aggregators participating in the peak-shaving market is finally obtained as:

[0180]

[0181] The constraints include the adjustable range constraints of electric vehicle aggregators, the reported peak-shaving range constraints, the adjustable range constraints of peak-shaving equipment, and the KKT condition constraints, as shown below:

[0182] A0P agg (t)<=b agg

[0183]

[0184] The nonlinear function sign(P o (t)) and the complementary relaxation condition containing bilinear terms are linearized as follows:

[0185] The symbol function sign(P o Linearization of (t)):

[0186]

[0187] Complementary relaxation condition linearization:

[0188]

[0189] The mixed integer linear programming problem is transformed into the two-level strategy model of electric vehicle aggregators participating in the peak-shaving market, where the continuous decision variables are P agg (t),P o (t),α agg (t),λ(t), The decision variables are Obtain quotation for participating in the power purchase plan of the power market and the peak load ancillary service market and quote

[0190] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the electric vehicle aggregation and bidding method for peak-shaving auxiliary services as described above are implemented.

[0191] The beneficial effects of the present invention are as follows: in view of the problem that the power domain polyhedron is not full-dimensional due to the heterogeneity of electric vehicle parameters, which makes it difficult to perform approximate solutions, the present invention improves the computational efficiency and approximation accuracy while solving the non-full-dimensional approximation problem through an improved approximate solution strategy, thereby releasing more flexibility in regulating electric vehicles and being able to quickly aggregate large-scale, parameter-heterogeneous electric vehicle clusters. It further involves a strategic bidding mechanism, which designs a bidding strategy for electric vehicle aggregators in the auxiliary service market taking into account the cost of purchasing electricity. Through this strategy, aggregators not only improve their market competitiveness, but also optimize their revenue in the electricity market, and can effectively participate in the power grid peak-shaving auxiliary services and play their regulatory role.

[0192] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0193] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0194] Figure 1 A flow chart of an electric vehicle aggregation and bidding method for peak load auxiliary services provided in Example 1;

[0195] Figure 2 A flow chart for constructing a dynamic model for electric vehicle cluster aggregation in Example 1;

[0196] Figure 3 This is a module diagram of an electric vehicle aggregation and bidding system for peak load auxiliary services in Example 2. DETAILED DESCRIPTION

[0197] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0198] Example 1

[0199] like Figure 1 As shown, the present invention provides an electric vehicle aggregation and bidding method for peak load auxiliary services, comprising the following steps:

[0200] S1. Based on the electric vehicle operating parameter data, a dynamic model of a single electric vehicle is established, including:

[0201] Based on the electric vehicle operating parameter data, including the electric vehicle power, state of charge, and charging and discharging time limit, a dynamic model of a single electric vehicle is established, as follows:

[0202]

[0203] Among them, E i (t) is the electric energy state of the i-th electric vehicle at time t, P i ch (t) / P i dis (t) is the charging / discharging power of the i-th electric vehicle at time t, is the upper limit of the charging / discharging power of the i-th electric vehicle at time t, is the lower / upper limit of the energy state of the i-th electric vehicle at time t, α i is the energy dissipation rate of the i-th electric vehicle, Δt is the sampling time interval, t i,a / t i,l is the arrival / departure time of the electric vehicle at time t, It is the desired energy state when EVi leaves.

[0204] S2, such as Figure 2 As shown, based on the dynamic model of a single electric vehicle, the dynamic model of electric vehicle cluster aggregation is obtained through approximate optimization solution using aggregation algorithm;

[0205] The dynamic model of a single electric vehicle is converted into a polyhedral half-space form as follows:

[0206]

[0207] Where P i represents the power of the D time nodes of the i-th electric vehicle, is the set of power curves of the i-th electric vehicle, indicating the operating power range of the i-th electric vehicle;

[0208]

[0209] Among them, E i (0) is the electric energy state of the i-th electric vehicle at time t, D ≥ t i,l -t i,a , D is the number of moments aggregated by the aggregator, and the specific matrix form is as follows:

[0210]

[0211] Based on the dynamic model transformation of a single electric vehicle and its polyhedral half-space form, the aggregate dynamic model of the electric vehicle cluster is approximately solved by Minkowski and M-Sum, as follows:

[0212] The aggregate dynamic model of the electric vehicle cluster is expressed as the Minkowski and M-Sum expressions of the dynamic model of N electric vehicles as follows:

[0213]

[0214] in, is a cluster of N electric vehicles, An aggregation dynamic model for electric vehicle clusters;

[0215] The approximate solution method is used to calculate the NP-hard problem of N half-spaces, as follows:

[0216] The power feasible region of N electric vehicles Using the same matrix The form of approximation is Aggregate power feasible region of electric vehicle clusters Can be used Approximate representation;

[0217] Calculate the parameter matrix A within the electric vehicle cluster i The average value of , we get the isomorphic parameter matrix A0;

[0218] Using Hausdorff distance To measure the approximation quality, the external and internal approximation problems can be described as:

[0219] or

[0220] in, is the outer approximation of the power feasible region of EV i, is the internal approximation of the power feasible region of EVi;

[0221] Using the triangular inequality and Lipschitz continuity of the Hausdorff distance, the objective function is transformed into a convex function in the form of a two-norm, as follows:

[0222] right To simplify:

[0223] Construct a non-empty collection:

[0224] According to the Hausdorff distance triangle inequality, we can get:

[0225] because is a fixed value, L(A0) is the Lipschitz constant of matrix A0, so the objective function Convex function that can be equivalently converted into a two-norm form

[0226] According to Farkas'lemma, the set inclusion constraint in the approximate optimization problem is transformed into linear constraint and nonlinear constraint. For nonlinear constraint, the basic polyhedron scaling algorithm is used to solve it, as follows:

[0227] The constraints of the outer approximation are linear constraints, as follows:

[0228]

[0229] The inner approximation conditions are nonlinear constraints, as follows:

[0230]

[0231] Calculate the external approximate parameter matrix for each electric vehicle

[0232] For the case where the inner approximation condition is a nonlinear constraint, the outer approximation polyhedron is solved Considered as a basic polyhedron, the basic polyhedron scaling algorithm is used to solve the problem, and the internal approximation problem is simplified to:

[0233]

[0234] Among them, s i =1 / φ i ,ri =-s i ψ i ;

[0235]

[0236] Calculate the approximate internal scaling factor φ for each electric vehicle i =1 / s i ,ψ i =-r i / s i Then, the parameter matrix is ​​calculated

[0237] use Approximately represent the exact dynamic model of electric vehicles Then the aggregation dynamic model of electric vehicle cluster is:

[0238]

[0239] S3. Based on the dynamic model of electric vehicle cluster aggregation, a two-tier strategy model for electric vehicle aggregators to participate in the peak load regulation market is established, including:

[0240] Based on the dynamic model of electric vehicle cluster aggregation, a model of electric vehicle aggregators participating in the electricity market is established within the aggregation feasible domain of electric vehicle clusters, in which the charging cost of electric vehicle aggregators is minimized. The expression is as follows:

[0241]

[0242] in, is the decision variable, is the charging power of the electric vehicle aggregator, and EM (t) Publish time-of-use electricity prices for the electricity market. When , it means that the aggregator reverse charges the grid to get paid.

[0243] Based on the model of electric vehicle aggregators participating in the electricity market, the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving ancillary service market is established to quote the quantity of electric vehicle aggregators participating in the peak-shaving ancillary service market and minimize the overall cost of electric vehicle aggregators. The expression is as follows:

[0244]

[0245] The cost of an EV aggregator consists of two parts: EM (t)P agg (t) is the electricity purchase cost of the aggregator participating in the electricity market, The optimal power curve for electric vehicle aggregators to participate in the electricity market is calculated. Power adjustment for electric vehicle aggregators, is the revenue of electric vehicle aggregators participating in the peak-shaving ancillary service market. The peak-shaving ancillary service market settles revenue according to the adjustment amount of peak-shaving participants. λ(t) is the clearing electricity price of the peak-shaving ancillary service market. λ(t)>0 indicates that the peak-shaving demand is positive, and λ(t)<0 indicates that the peak-shaving demand is positive.

[0246] Establish an electric vehicle aggregator and large-scale battery to participate in the peak-shaving auxiliary service market. Under the premise of meeting the peak-shaving demand, establish an electric vehicle aggregator to participate in the peak-shaving market bottom-level strategy model that minimizes the overall peak-shaving cost. The expression is as follows:

[0247] a. Objective function:

[0248] The objective function is to minimize the overall peak-shaving cost, including the peak-shaving cost settled for electric vehicles. And the call cost of large peak-shaving equipment λ o (t)|P o (t)|;

[0249] b. Constraints: including the reported peak load range constraints of electric vehicle aggregators, according to A0P agg (t)<=b agg calculate; is the maximum power that can be achieved at each time node; is the minimum power that can be achieved at each time node, is the upward and downward adjustment range of the optimal power curve difference that only participates in the power market, and its corresponding dual variable is as follows:

[0250]

[0251] The adjustable range constraint of large peak-shaving equipment corresponds to the dual variable: as follows:

[0252]

[0253] The peak load supply and demand balance constraint, whose corresponding dual variable λ(t) is the clearing price of the peak load ancillary service market, is as follows:

[0254]

[0255] S4. The two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market is converted into a single-layer mathematical optimization function with equality constraints through the Karush-Kuhn-Tucker condition and the strong duality principle to solve the problem, and the power purchase plan for participating in the power market and the quotation and price of the peak-shaving auxiliary service market are obtained, including:

[0256] The Lagrangian function is constructed for the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the objective function and constraints of the lower-level model are combined with the Lagrangian multiplier as shown below:

[0257]

[0258] Applying the KKT conditions to the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is as follows:

[0259]

[0260] The complementary relaxation conditions in the KKT condition are as follows:

[0261]

[0262] Through the strong duality principle, the optimal solution of the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is transformed into the constraints of the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the two-layer model is transformed into a single-layer model, as follows:

[0263]

[0264] According to the KKT condition and the strong duality principle, the two-layer strategy model of electric vehicle aggregators participating in the peak load auxiliary service market is transformed into a single-layer mathematical optimization problem with equality constraints, as follows:

[0265] The objective function can be obtained according to the complementary relaxation condition:

[0266]

[0267] According to the KKT condition, we can get:

[0268]

[0269] Substituting the above KKT condition results into the complementary relaxation condition results, we can get:

[0270]

[0271] Then bring in the strong duality principle and we get:

[0272]

[0273] a. Therefore, the objective function of the single-layer model of electric vehicle aggregators participating in the peak-shaving market is finally obtained as:

[0274]

[0275] b. Constraints include adjustable range constraints of electric vehicle aggregators, reported peak load range constraints, adjustable range constraints of peak load equipment, and KKT condition constraints, as shown below: A0P agg (t)<=b agg

[0276]

[0277] A0P agg (t)<=b agg

[0278]

[0279] The mixed integer linear programming problem is transformed into the two-level strategy model of electric vehicle aggregators participating in the peak-shaving market, where the continuous decision variables are P agg (t),P o (t),α agg (t),λ(t), The decision variables are Obtain quotation for participating in the power purchase plan of the power market and the peak load ancillary service market and quote

[0280] Example 2

[0281] This embodiment provides an electric vehicle aggregation and bidding system for peak load auxiliary services, including:

[0282] The electric vehicle dynamic model building module is used to build a dynamic model of a single electric vehicle based on the electric vehicle operating parameter data;

[0283] The dynamic model building module of electric vehicle cluster aggregation is used to obtain the dynamic model of electric vehicle cluster aggregation through approximate optimization solution based on the dynamic model of a single electric vehicle through aggregation algorithm;

[0284] A two-tier strategy model building module for aggregators to participate in the peak-shaving market, which is used to build a two-tier strategy model for electric vehicle aggregators to participate in the peak-shaving market based on a dynamic model of electric vehicle cluster aggregation;

[0285] The two-layer strategy model solving module for aggregators participating in the peak-shaving market is used to convert the two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market into a single-layer mathematical optimization function with equality constraints through the Karush-Kuhn-Tucker condition and the strong duality principle for solving, and obtain the power purchase plan for participating in the power market and the quantity and quotation in the peak-shaving auxiliary service market.

[0286] Example 3

[0287] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the electric vehicle aggregation and bidding method for peak-shaving auxiliary services as described in Example 1 are implemented.

[0288] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0289] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0290] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0291] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. An electric vehicle aggregation and bidding method for peak load auxiliary services, characterized in that: The following steps are involved: Based on the electric vehicle operating parameter data, a dynamic model of a single electric vehicle is established; Based on the dynamic model of a single electric vehicle, the dynamic model of electric vehicle cluster aggregation is obtained through approximate optimization and solution of the aggregation algorithm; Based on the dynamic model of electric vehicle cluster aggregation, a two-tier strategy model for electric vehicle aggregators to participate in the peak-shaving market is established; Through the Karush-Kuhn-Tucker condition and the strong duality principle, the two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market is converted into a single-layer mathematical optimization function with equality constraints for solution, and the power purchase plan for participating in the power market and the quotation and quotation in the peak-shaving ancillary service market are obtained.

2. According to claim 1, an electric vehicle aggregation and bidding method for peak load auxiliary services is characterized in that: The method of establishing a dynamic model of a single electric vehicle based on the electric vehicle operating parameter data includes: Based on the electric vehicle operating parameter data, including the electric vehicle power, state of charge, and charging and discharging time limit, a dynamic model of a single electric vehicle is established, as follows: Among them, E i (t) is the electric energy state of the i-th electric vehicle at time t, P i ch (t) / P i dis (t) is the charging / discharging power of the i-th electric vehicle at time t, is the upper limit of the charging / discharging power of the ith electric vehicle at time t, E i (t) / is the lower / upper limit of the energy state of the i-th electric vehicle at time t, α i is the energy dissipation rate of the i-th electric vehicle, Δt is the sampling time interval, t i,a / t i,l is the arrival / departure time of the electric vehicle at time t, It is the desired energy state when EVi leaves.

3. According to claim 2, an electric vehicle aggregation and bidding method for peak load auxiliary services is characterized in that: The dynamic model based on a single electric vehicle is solved by approximate optimization of the aggregation algorithm to obtain the dynamic model of electric vehicle cluster aggregation, which specifically includes: The dynamic model of a single electric vehicle is converted into a polyhedral half-space form as follows: Where P i represents the power of the D time nodes of the i-th electric vehicle, is the set of power curves of the i-th electric vehicle, indicating the operating power range of the i-th electric vehicle; Half-space representation of a single electric vehicle dynamic model A i P i <=b i , A i 、b i as follows: Among them, E i (0) is the electric energy state of the i-th electric vehicle at time t, D ≥ t i,l -t i,a , D is the number of moments aggregated by the aggregator, and the specific matrix form is as follows: Based on the dynamic model transformation of a single electric vehicle and its polyhedral half-space form, the aggregate dynamic model of the electric vehicle cluster is approximately solved by Minkowski and M-Sum, as follows: The aggregate dynamic model of the electric vehicle cluster is expressed as the Minkowski and M-Sum expressions of the dynamic model of N electric vehicles as follows: in, is a cluster of N electric vehicles, An aggregate dynamic model for electric vehicle clusters; The approximate solution method is used to calculate the NP-hard problem of N half-spaces, as follows: The power feasible region of N electric vehicles Using the same matrix The form of approximation is Aggregate power feasible region of electric vehicle clusters Can be used Approximate representation; Using Hausdorff distance To measure the quality of approximation, the external and internal approximation problems can be described as: or in, is the outer approximation of the power feasible region of EV i, is the internal approximation of the power feasible region of EVi; Using the triangular inequality and Lipschitz continuity of the Hausdorff distance, the objective function is transformed into a convex function in the form of a two-norm, as follows: right To simplify: Construct a non-empty collection: According to the Hausdorff distance triangle inequality, we can get: because is a fixed value, L(A0) is the Lipschitz constant of matrix A0, so the objective function Convex function that can be equivalently converted into a two-norm form According to Farkas'lemma, the set inclusion constraint in the approximate optimization problem is transformed into linear constraint and nonlinear constraint. For nonlinear constraint, the basic polyhedron scaling algorithm is used to solve it, as follows: The constraints of the outer approximation are linear constraints, as follows: The inner approximation conditions are nonlinear constraints, as follows: For the case where the inner approximation condition is a nonlinear constraint, the outer approximation polyhedron is solved Considered as a basic polyhedron, the basic polyhedron scaling algorithm is used to solve the problem, and the internal approximation problem is simplified to: among them, s i =1 / φ i ,r i =-s i ψ i ; use Approximately represent the exact dynamic model of electric vehicles Then the aggregation dynamic model of electric vehicle cluster is:

4. According to claim 3, an electric vehicle aggregation and bidding method for peak load auxiliary services is characterized in that: The dynamic model based on electric vehicle cluster aggregation establishes a two-tier strategy model for electric vehicle aggregators to participate in the peak load regulation market, including: Based on the dynamic model of electric vehicle cluster aggregation, a model of electric vehicle aggregators participating in the electricity market is established within the aggregation feasible domain of electric vehicle clusters, in which the charging cost of electric vehicle aggregators is minimized. The expression is as follows: in, is the decision variable, is the charging power of the electric vehicle aggregator, and EM (t) publish time-of-use electricity prices for the electricity market; Based on the model of electric vehicle aggregators participating in the electricity market, the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving ancillary service market is established to quote the quantity of electric vehicle aggregators participating in the peak-shaving ancillary service market and minimize the overall cost of electric vehicle aggregators. The expression is as follows: λ EM (t)P agg (t) is the electricity purchase cost of the aggregator participating in the electricity market, The optimal power curve for electric vehicle aggregators to participate in the electricity market is calculated. Power adjustment for electric vehicle aggregators, is the revenue of electric vehicle aggregators participating in the peak-shaving ancillary service market. The peak-shaving ancillary service market settles revenues according to the adjustment amount of the peak-shaving participants. λ(t) is the clearing electricity price of the peak-shaving ancillary service market. Establish an electric vehicle aggregator and large-scale battery to participate in the peak-shaving auxiliary service market. Under the premise of meeting the peak-shaving demand, establish an electric vehicle aggregator to participate in the peak-shaving market bottom-level strategy model that minimizes the overall peak-shaving cost. The expression is as follows: Objective function: Constraints: is the maximum power that can be achieved at each time node; is the minimum power that can be achieved at each time node, is the upward and downward adjustment range of the optimal power curve difference that only participates in the power market, and its corresponding dual variable is as follows: The adjustable range constraint of large peak-shaving equipment corresponds to the dual variable: as follows: The peak load supply and demand balance constraint, whose corresponding dual variable λ(t) is the clearing price of the peak load ancillary service market, is as follows:

5. The electric vehicle aggregation and bidding method for peak load auxiliary services according to claim 4 is characterized in that: The two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market is converted into a single-layer mathematical optimization function with equality constraints through the Karush-Kuhn-Tucker condition and the strong duality principle to solve the problem, and the power purchase plan for participating in the power market and the quantity and quotation of the peak-shaving auxiliary service market are obtained, which specifically includes: The Lagrangian function is constructed for the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the objective function and constraints of the lower-level model are combined with the Lagrangian multiplier as shown below: Applying the KKT conditions to the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is as follows: The complementary relaxation conditions in the KKT condition are as follows: Through the strong duality principle, the optimal solution of the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is transformed into the constraints of the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the two-layer model is transformed into a single-layer model, as follows: According to the KKT condition and the strong duality principle, the two-layer strategy model of electric vehicle aggregators participating in the peak load auxiliary service market is transformed into a single-layer mathematical optimization problem with equality constraints, as follows: The objective function can be obtained according to the complementary relaxation condition: According to the KKT conditions, we can get: Substituting the above KKT condition results into the complementary relaxation condition results, we can get: Then bring in the strong duality principle and we get: Therefore, the objective function of the single-layer model of electric vehicle aggregators participating in the peak-shaving market is finally obtained as: The constraints include the adjustable range constraints of electric vehicle aggregators, the reported peak-shaving range constraints, the adjustable range constraints of peak-shaving equipment, and the KKT condition constraints, as shown below: A0P agg (t)<=b agg The nonlinear function sign(P o (t)) and the complementary relaxation condition containing bilinear terms are linearized as follows: The symbol function sign(P o Linearization of (t)): Complementary relaxation condition linearization: The mixed integer linear programming problem is transformed into the two-level strategy model of electric vehicle aggregators participating in the peak-shaving market, where the continuous decision variables are P agg (t),P o (t),α agg (t),λ(t), The decision variables are Obtain quotation for participating in the power purchase plan of the power market and the peak load ancillary service market and quote 6. An electric vehicle aggregation and bidding system for peak load auxiliary services, comprising: The electric vehicle dynamic model building module is used to build a dynamic model of a single electric vehicle based on the electric vehicle operating parameter data; The dynamic model building module of electric vehicle cluster aggregation is used to obtain the dynamic model of electric vehicle cluster aggregation through approximate optimization solution based on the dynamic model of a single electric vehicle through aggregation algorithm; A two-tier strategy model building module for aggregators to participate in the peak-shaving market, which is used to build a two-tier strategy model for electric vehicle aggregators to participate in the peak-shaving market based on a dynamic model of electric vehicle cluster aggregation; The two-layer strategy model solving module for aggregators participating in the peak-shaving market is used to convert the two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market into a single-layer mathematical optimization function with equality constraints through the Karush-Kuhn-Tucker condition and the strong duality principle for solving, and obtain the power purchase plan for participating in the power market and the quantity and quotation in the peak-shaving auxiliary service market.

7. The electric vehicle aggregation and bidding system for peak load auxiliary services according to claim 6 is characterized in that: The method of establishing a dynamic model of a single electric vehicle based on the electric vehicle operating parameter data includes: Based on the electric vehicle operating parameter data, including the electric vehicle power, state of charge, and charging and discharging time limit, a dynamic model of a single electric vehicle is established, as follows: Among them, E i (t) is the electric energy state of the i-th electric vehicle at time t, P i ch (t) / P i dis (t) is the charging / discharging power of the i-th electric vehicle at time t, is the upper limit of the charging / discharging power of the ith electric vehicle at time t, E i (t) / is the lower / upper limit of the energy state of the i-th electric vehicle at time t, α i is the energy dissipation rate of the i-th electric vehicle, Δt is the sampling time interval, t i,a / t i,l is the arrival / departure time of the electric vehicle at time t, The desired energy state when EVi leaves; The dynamic model based on a single electric vehicle is solved by approximate optimization of the aggregation algorithm to obtain the dynamic model of electric vehicle cluster aggregation, which specifically includes: The dynamic model of a single electric vehicle is converted into a polyhedral half-space form as follows: Where P i represents the power of the D time nodes of the i-th electric vehicle, is the set of power curves of the i-th electric vehicle, indicating the operating power range of the i-th electric vehicle; Half-space representation of a single electric vehicle dynamic model A i P i <=b i , A i 、b i as follows: Among them, E i (0) is the electric energy state of the i-th electric vehicle at time t, D ≥ t i,l -t i,a , D is the number of moments aggregated by the aggregator, and the specific matrix form is as follows: Based on the dynamic model transformation of a single electric vehicle and its polyhedral half-space form, the aggregate dynamic model of the electric vehicle cluster is approximately solved by Minkowski and M-Sum, as follows: The aggregate dynamic model of the electric vehicle cluster is expressed as the Minkowski and M-Sum expressions of the dynamic model of N electric vehicles as follows: in, is a cluster of N electric vehicles, An aggregate dynamic model for electric vehicle clusters; The approximate solution method is used to calculate the NP-hard problem of N half-spaces, as follows: The power feasible region of N electric vehicles Using the same matrix The form of approximation is Aggregate power feasible region of electric vehicle clusters Can be used Approximate representation; Using Hausdorff distance To measure the quality of approximation, the external and internal approximation problems can be described as: or in, is the outer approximation of the power feasible region of EV i, is the internal approximation of the power feasible region of EVi; Using the triangular inequality and Lipschitz continuity of the Hausdorff distance, the objective function is transformed into a convex function in the form of a two-norm, as follows: right To simplify: Construct a non-empty collection: According to the Hausdorff distance triangle inequality, we can get: because is a fixed value, L(A0) is the Lipschitz constant of matrix A0, so the objective function Convex function that can be equivalently converted into a two-norm form According to Farkas'lemma, the set inclusion constraint in the approximate optimization problem is transformed into linear constraint and nonlinear constraint. For nonlinear constraint, the basic polyhedron scaling algorithm is used to solve it, as follows: The constraints of the outer approximation are linear constraints, as follows: The inner approximation conditions are nonlinear constraints, as follows: For the case where the inner approximation condition is a nonlinear constraint, the outer approximation polyhedron is solved Considered as a basic polyhedron, the basic polyhedron scaling algorithm is used to solve the problem, and the internal approximation problem is simplified to: among them, s i =1 / φ i ,r i =-s i ψ i ; use Approximately represent the exact dynamic model of electric vehicles Then the aggregation dynamic model of electric vehicle cluster is:

8. The electric vehicle aggregation and bidding system for peak load auxiliary services according to claim 7 is characterized in that: The dynamic model based on electric vehicle cluster aggregation establishes a two-tier strategy model for electric vehicle aggregators to participate in the peak load regulation market, including: Based on the dynamic model of electric vehicle cluster aggregation, a model of electric vehicle aggregators participating in the electricity market is established within the aggregation feasible domain of electric vehicle clusters, in which the charging cost of electric vehicle aggregators is minimized. The expression is as follows: in, is the decision variable, is the charging power of the electric vehicle aggregator, and EM (t) publish time-of-use electricity prices for the electricity market; Based on the model of electric vehicle aggregators participating in the electricity market, the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving ancillary service market is established to quote the quantity of electric vehicle aggregators participating in the peak-shaving ancillary service market and minimize the overall cost of electric vehicle aggregators. The expression is as follows: λ EM (t)P agg (t) is the electricity purchase cost of the aggregator participating in the electricity market, The optimal power curve for electric vehicle aggregators to participate in the electricity market is calculated. Power adjustment for electric vehicle aggregators, is the revenue of electric vehicle aggregators participating in the peak-shaving ancillary service market. The peak-shaving ancillary service market settles revenues according to the adjustment amount of the peak-shaving participants. λ(t) is the clearing electricity price of the peak-shaving ancillary service market. Establish an electric vehicle aggregator and large-scale battery to participate in the peak-shaving auxiliary service market. Under the premise of meeting the peak-shaving demand, establish an electric vehicle aggregator to participate in the peak-shaving market bottom-level strategy model that minimizes the overall peak-shaving cost. The expression is as follows: Objective function: Constraints: is the maximum power that can be achieved at each time node; is the minimum power that can be achieved at each time node, is the upward and downward adjustment range of the optimal power curve difference that only participates in the power market, and its corresponding dual variable is as follows: The adjustable range constraint of large peak-shaving equipment corresponds to the dual variable: as follows: The peak load supply and demand balance constraint, whose corresponding dual variable λ(t) is the clearing price of the peak load ancillary service market, is as follows:

9. The electric vehicle aggregation and bidding system for peak load auxiliary services according to claim 8, characterized in that: The two-layer strategy model of electric vehicle aggregators participating in the peak-shaving market is converted into a single-layer mathematical optimization function with equality constraints through the Karush-Kuhn-Tucker condition and the strong duality principle to solve the problem, and the power purchase plan for participating in the power market and the quantity and quotation of the peak-shaving auxiliary service market are obtained, which specifically includes: The Lagrangian function is constructed for the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the objective function and constraints of the lower-level model are combined with the Lagrangian multiplier as shown below: Applying the KKT conditions to the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is as follows: The complementary relaxation conditions in the KKT condition are as follows: Through the strong duality principle, the optimal solution of the lower-level strategy model of electric vehicle aggregators participating in the peak-shaving market is transformed into the constraints of the upper-level strategy model of electric vehicle aggregators participating in the peak-shaving market, and the two-layer model is transformed into a single-layer model, as follows: According to the KKT condition and the strong duality principle, the two-layer strategy model of electric vehicle aggregators participating in the peak load auxiliary service market is transformed into a single-layer mathematical optimization problem with equality constraints, as follows: The objective function can be obtained according to the complementary relaxation condition: According to the KKT conditions, we can get: Substituting the above KKT condition results into the complementary relaxation condition results, we can get: Then bring in the strong duality principle and we get: Therefore, the objective function of the single-layer model of electric vehicle aggregators participating in the peak-shaving market is finally obtained as: The constraints include the adjustable range constraints of electric vehicle aggregators, the reported peak-shaving range constraints, the adjustable range constraints of peak-shaving equipment, and the KKT condition constraints, as shown below: A0P agg (t)<=b agg The nonlinear function sign(P o (t)) and the complementary relaxation condition containing bilinear terms are linearized as follows: The symbol function sign(P o Linearization of (t)): Complementary relaxation condition linearization: The mixed integer linear programming problem is transformed into the two-level strategy model of electric vehicle aggregators participating in the peak-shaving market, where the continuous decision variables are P agg (t),P o (t),α agg (t),λ(t), The decision variables are Obtain quotation for participating in the power purchase plan of the power market and the peak load ancillary service market and quote 10. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by a processor, the steps of the electric vehicle aggregation and bidding method for peak-shaving auxiliary services as described in any one of claims 1 to 5 are implemented.

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