Power distribution network optimization scheduling method considering schedulable capability of electric vehicle cluster

Abstract

The invention discloses a power distribution network optimization scheduling method considering the schedulable capability of an electric vehicle cluster, and belongs to the field of power system optimization scheduling. The method comprises the following steps: establishing a virtual energy storage operation domain of the electric vehicle cluster; according to the electric vehicle cluster virtual energy storage operation domain, establishing a collaborative optimization scheduling model by taking the lowest operation cost of a power distribution network operator and the lowest operation cost of an electric vehicle aggregator as a target; depicting prediction uncertainty of new energy output and electric vehicle response energy, and establishing an uncertainty set based on a Wasserstein distance; and according to the collaborative optimization scheduling model and the uncertainty set based on the Wasserstein distance, establishing a distribution robust optimization scheduling model based on the Wasserstein distance, and converting the distribution robust optimization scheduling model into a mixed integer linear programming problem for solving, thereby completing the optimization scheduling of the power distribution network. According to the method, the problems of flexible dispatching and economic operation of the power distribution network after large-scale electric vehicles and distributed energy sources are connected to the grid are solved.

Description

Technical Field

[0001] The present invention belongs to the field of power system optimization and dispatching, and in particular relates to a distribution network optimization and dispatching method taking into account the dispatchable capacity of an electric vehicle cluster. Background Art

[0002] With the advancement of the dual carbon goals, the coordinated development of new power systems based on new energy and electric vehicles has become an inevitable trend. However, the randomness and volatility of new energy output and the randomness of electric vehicle charging have brought burdens to the flexible dispatching and safe and economical operation of the distribution network. Therefore, it is necessary to reasonably utilize the dispatching flexibility of electric vehicles and enable them to participate in the dispatching plan of the distribution network.

[0003] On the one hand, electric vehicles are connected to the distribution network as traffic loads. On the other hand, they can be regarded as distributed energy storage, providing flexible resources for the distribution network and participating in the dispatching and management of the distribution network. Due to the large number of electric vehicles, wide distribution, and small individual capacity, they need to be uniformly managed by electric vehicle aggregators and participate in the dispatching of distribution networks in the form of energy clusters. Existing studies mostly consider the impact of new energy and basic load forecasting errors, ignoring the uncertainty of electric vehicle charging behavior. As the scale of electric vehicles entering the grid continues to expand, the accurate assessment of the dispatchability of electric vehicle clusters will also greatly affect the formulation of system dispatch plans. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a distribution network optimization scheduling method taking into account the dispatchability of electric vehicle clusters, which solves the problems of flexible scheduling and economic operation of distribution networks after large-scale electric vehicles and distributed energy are connected to the grid.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a distribution network optimization scheduling method considering the dispatchable capacity of electric vehicle clusters, comprising:

[0006] Establish an individual charging and discharging model for electric vehicles, and based on the individual charging and discharging model for electric vehicles, establish a virtual energy storage operation domain for electric vehicle clusters using Minkowski summation based on the power boundary and energy boundary of a single electric vehicle;

[0007] According to the operation domain of the virtual energy storage of the electric vehicle cluster, a collaborative optimization dispatch model is established with the goal of minimizing the operating costs of the distribution network operator and the electric vehicle aggregator respectively;

[0008] Characterize the uncertainty of new energy output and electric vehicle response energy prediction, and establish an uncertainty set based on Wasserstein distance;

[0009] According to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a distributed robust optimization scheduling model based on Wasserstein distance is established and converted into a mixed integer linear programming problem for solution to complete the optimal scheduling of the distribution network.

[0010] Furthermore, the individual charging and discharging model of electric vehicles is established, and based on the individual charging and discharging model of electric vehicles, the virtual energy storage operation domain of the electric vehicle cluster is established by using Minkowski summation based on the power boundary and energy boundary of a single electric vehicle, specifically:

[0011] Establish an individual charging and discharging model for electric vehicles:

[0012]

[0013] in, is the charging power of the nth electric car at time t; P n cm is the rated charging power of the nth electric vehicle; t is the time; t ar is the time when the electric vehicle enters the grid; t eq Off-grid moments for electric vehicles; is the discharge power of the nth electric car at time t; P n dm is the rated discharge power of the nth electric vehicle; E n,t is the power of the nth electric car at time t; E n,t-1 is the power of the nth electric car at time t-1; η c is the charging efficiency of the electric vehicle; Δt is the time step; η d is the discharge efficiency of electric vehicles; is the minimum power of the nth electric car; is the maximum power of the nth electric car; E n,t＝0 is the power of the nth electric car at t=0; E n,0 E is the power consumption of the nth electric car when it is connected to the grid; n,T is the power of the nth electric car at time T; T is the time when the electric car is off the grid; E n,e is the expected power of the nth electric vehicle when it is off-grid;

[0014] According to the individual charging and discharging model of electric vehicles, the power boundary and energy boundary of a single electric vehicle are calculated:

[0015]

[0016] in, is the upper energy limit of the electric vehicle at time t; E0 is the power of the electric vehicle when it is connected to the grid; Et-1 is the power of the electric vehicle at time t-1; P cm is the rated charging power of the electric vehicle; E e is the expected power of the electric vehicle when it is off-grid; P dm is the rated discharge power of the electric vehicle; S max is the maximum power of the electric vehicle; is the lower bound of the energy of the electric vehicle at time t; S min is the minimum power of the electric vehicle; P t max is the upper limit of the power of the electric vehicle at time t; P t min is the lower power limit of the electric vehicle at time t;

[0017] According to the power boundary and energy boundary of a single electric vehicle, each charging station is regarded as an electric vehicle cluster. The energy and power boundary of the electric vehicles in the charging station are superimposed using Minkowski summation to obtain the virtual energy storage operation domain of the electric vehicle cluster:

[0018]

[0019] in, is the maximum charging power of the electric vehicle cluster w at time t; w is the electric vehicle cluster; u n,t It is a Boolean variable. When it is 0, it means that the electric vehicle is off-grid, and when it is 1, it means that the electric vehicle is on-grid. is the maximum discharge power of the electric vehicle cluster w at time t; are the upper and lower energy boundaries of the electric vehicle cluster w at time t, respectively; are the upper and lower energy boundaries of the nth electric car at time t respectively.

[0020] Furthermore, the objective function of the collaborative optimization scheduling model is:

[0021]

[0022] in, is the optimization target of the upper distribution network; x is the decision variable; X U is the set of decision variables of the upper model; C G the cost of generating electricity for the distribution network; is the power generated by the mth generator set at time t; C MG Cost of purchasing electricity for the distribution network; is the power purchase power of power purchase node k at time t; C EVA Energy cost for electric vehicle clusters; is the charging or discharging power of the electric vehicle cluster w at time t; Optimize the target for the lower electric vehicle cluster; X L is the set of decision variables of the lower model; T is the time when the electric vehicle is off-grid; N a Assemble for electric vehicle clusters; is the charging electricity price of electric vehicles at time t; is the charging power of the electric vehicle cluster w at time t; is the discharge electricity price of the electric vehicle at time t; is the discharge power of the electric vehicle cluster w at time t; N G is the generator set; a m , b m and c m N is the power generation cost coefficient of the mth generator set; k It is a collection of power purchasing nodes; is the electricity purchase price at time t; is the reactive power of the mth generator set at time t; P t PV is the photovoltaic power generation in the distribution network at time t; P t WT is the wind power generation power in the distribution network at time t.

[0023] Furthermore, the constraints of the collaborative optimization scheduling model include power balance constraints, safety constraints, power generation constraints, power purchase constraints, new energy output constraints, electric vehicle electricity price constraints, and electric vehicle charging and discharging power constraints:

[0024]

[0025]

[0026] Among them, P j,t is the active injected power at node j at time t; P jq,t is the active power flowing from i to q in branch iq at time t; h(j) is the set of branch end nodes with node j as the head node; e(j) is the set of branch head node with node j as the end node; P ij,t is the active power flowing from branch i to branch j at time t; r ij is the resistance of branch ij at time t; I ij,t Q is the square of the current amplitude flowing from i to j in branch ij at time t; j,t is the reactive power injected at node j at time t; Q jq,t is the reactive power flowing from i to q in branch iq at time t; Q ij,t is the reactive power flowing from branch i to branch j at time t; ij is the reactance of branch ij at time t; is the active power generated by the generator at node j at time t; is the purchased power of node j at time t; is the photovoltaic power generation power of node j at time t; is the wind power generation power at node j at time t; is the charging and discharging power of the electric vehicle cluster at node j at time t; is the conventional active load at node j at time t; is the reactive power generated by the generator at node j at time t; is the normal reactive load at node j at time t; V j,t is the square of the voltage amplitude at node j at time t; V i,t is the square of the voltage amplitude at node i at time t; U i,min is the lower limit of the node voltage amplitude; U i,max is the upper limit of the node voltage amplitude; I ij,max is the upper limit of branch current amplitude; is the minimum active output of generator j; is the maximum active output of generator j; is the minimum reactive power output of generator j; is the maximum reactive power output of generator j; is the ramp-down rate of the generator; is the active power generated by the generator at node j at time t-1; is the ramp-up rate of the generator; is the power purchase limit of node j at time t; is the predicted maximum output value of photovoltaic power; is the predicted maximum output value of wind power; is the electricity purchase price at time t; is the charging electricity price of electric vehicles at time t; is the discharge electricity price of the electric vehicle at time t; is the charging power of the electric vehicle cluster w at time t; is the maximum charging power of the electric vehicle cluster w at time t; is the discharge power of the electric vehicle cluster w at time t; u w,t is a Boolean variable, indicating that the electric vehicle cluster w has only one behavior, charging or discharging, at any time; is the maximum discharge power of the electric vehicle cluster w at time t; E w,t is the total energy of the electric vehicle cluster w at time t; E w,t-1 is the total energy of the electric vehicle cluster w at time t-1; η c is the charging efficiency of electric vehicles; η d is the discharge efficiency of electric vehicles; is the upper energy boundary of the electric vehicle cluster w at time t; is the lower bound of the energy of the electric vehicle cluster w at time t; E w,T is the total energy of the electric vehicle cluster w at time T; is the expected power consumption when cluster w is off-grid; is the lower bound of the energy of the electric vehicle cluster w at time T.

[0027] Furthermore, the expression of the uncertainty set based on Wasserstein distance is:

[0028]

[0029] Among them, F is the uncertainty set of new energy output prediction error based on Wasserstein distance; D is the empirical distribution of uncertainty; is the real distribution; is the uncertainty in the empirical distribution D; is the empirical distribution D with The corresponding auxiliary variables; is the expected value of the auxiliary variable; ε is the radius of the Wasserstein sphere; is the constraint on the upper bound of the expected value of the auxiliary variable; is the support set V from the empirical distribution D l Taking out any D and the corresponding u from the equation also satisfies the true distribution of the uncertainty; v is the uncertain variable; u is the auxiliary variable; V l is the support set of sample l; l is the lth sample extracted from the sample data; is the distribution of sample l drawn from the empirical distribution D; is the probability of each distribution; The probability of each distribution is equal, and they are M is the total number of sample data; is the sample data; v is the lower bound of the uncertainty; v is the upper bound of the uncertainty.

[0030] Furthermore, according to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a distributed robust optimization scheduling model based on Wasserstein distance is established and converted into a mixed integer linear programming problem for solution to complete the distribution network optimization scheduling, specifically:

[0031] According to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a two-stage distributed robust optimization scheduling model is proposed.

[0032] The two-stage distributed robust optimization scheduling model is transformed into a mixed integer programming problem;

[0033] Solve the mixed integer programming problem and complete the optimal dispatch of the distribution network.

[0034] Furthermore, the expression of the two-stage distributed robust optimization scheduling model is:

[0035]

[0036] Among them, F is a two-stage distributed robust optimization scheduling model; It is the deterministic optimization target of the first stage, that is, the objective function of the collaborative optimization scheduling model; is the optimization target of the second stage, which indicates the adjustment items made by the system to the power generation, power purchase and charging power of the electric vehicle cluster in order to cope with the influence of uncertain variables based on the deterministic optimization of the first stage; is the optimization decision variable x when the objective function is minimized; x is the decision variable; X is the set of decision variables in the first stage, including X U and X L ;X U is the set of decision variables of the upper model; X L is the set of decision variables for the lower model; c T is the transpose of the coefficient vector of the decision variable x in the first stage objective function; To find the worst distribution of uncertain variables, so that the second stage optimization objective function takes the maximum value; E D To seek expectations; T is the transposition of the coefficient variable corresponding to the decision variable y in the second stage objective function; y is the second stage optimization decision variable, including C u,G , C u,MG and C u,EVA ; C u,G To cope with the impact of uncertain variables, the adjustment amount of the power generation cost of the distribution network; C u,MG To cope with the impact of uncertain variables, the adjustment amount of the power purchase cost of the distribution network; C u,EVA To cope with the impact of uncertain variables, the charging cost of electric vehicle clusters is adjusted; A is the coefficient matrix of the constraints in the first stage; b is the parameter vector of the constraints in the first stage; C is the coefficient matrix of the variable x in the constraints in the second stage; H is the coefficient matrix of the variable y in the constraints in the second stage; g(v) is the constraint formula related to the uncertain variable v.

[0037] Furthermore, the expression of the mixed integer programming problem is:

[0038]

[0039] in, To optimize the decision variables x, σ, find the minimum value of the objective function; c is the decision variable that has a linear relationship with the uncertainty v after the variable y is linearly transformed by the affine strategy; T is the transpose of the coefficient vector of the decision variable x in the first stage objective function; x is the decision variable; M is the total number of sample data; m is the sample data number; β m is the auxiliary variable; σ is the dual variable; ε is the radius of the Wasserstein sphere; d T is the transposition of the coefficient variable corresponding to the decision variable y in the second stage objective function; d T y(·) is the second stage objective function; To substitute the sample data of the uncertain quantity into the objective function and obtain the objective function value at this time, the uncertain quantity is the prediction error of the new energy output; In order to bring the upper bound of the uncertainty into the objective function and obtain the objective function value at this time; d T y(v) is to bring the lower bound of the uncertainty into the objective function and obtain the objective function value at this time; is the mth sample data; A is the coefficient matrix of the first-stage constraint conditions; b is the parameter vector of the first-stage constraint conditions; Take the upper bound value for the uncertain variable v y(v) is the decision variable when the uncertain variable v takes the lower bound value v; v is the lower bound of the uncertainty; is the upper bound of the uncertainty; C is the coefficient matrix of the variable x in the second stage constraint; H is the coefficient matrix of the variable y in the second stage constraint; For uncertain variables Decision variables with linear relationships; For the general Substitute the constraint g(v); is a decision variable that is linearly related to the uncertain variable v; g(v) is the result of substituting v into the constraint formula g(v).

[0040] The beneficial effects of the present invention are as follows: the dispatching model can maximize the interests of both distribution network operators and electric vehicle aggregators; the uncertainty set of prediction errors of new energy output and dispatchable energy of electric vehicle clusters constructed based on Wasserstein distance makes full use of historical sample data to make the formulation of dispatching plans more in line with the needs of actual conditions; further considering the uncertainty, the established distributed robust optimization model can achieve a balance between economy and robustness of the dispatching model. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The figure is a flow chart of the method of the present invention.

[0042] Figure 2 Schematic diagram of a simulation system in an embodiment of the present invention.

[0043] Figure 3 It is a schematic diagram of the day-ahead source-load forecast curve and the electricity price in the power purchasing market in an embodiment of the present invention.

[0044] Figure 4 It is a schematic diagram of the comparison results of scheduling plans under various strategies in an embodiment of the present invention.

[0045] Figure 5 It is a schematic diagram of the charging and discharging conditions and power changes of an electric vehicle cluster in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0047] like Figure 1 As shown,

[0048] In one embodiment of the present invention, a distribution network optimization scheduling method considering the dispatchability of electric vehicle clusters includes:

[0049] Establish an individual charging and discharging model for electric vehicles, and based on the individual charging and discharging model for electric vehicles, establish a virtual energy storage operation domain for electric vehicle clusters using Minkowski summation based on the power boundary and energy boundary of a single electric vehicle;

[0050] According to the operation domain of the virtual energy storage of the electric vehicle cluster, a collaborative optimization dispatch model is established with the goal of minimizing the operating costs of the distribution network operator and the electric vehicle aggregator respectively;

[0051] Characterize the uncertainty of new energy output and electric vehicle response energy prediction, and establish an uncertainty set based on Wasserstein distance;

[0052] According to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a distributed robust optimization scheduling model based on Wasserstein distance is established and converted into a mixed integer linear programming problem for solution to complete the optimal scheduling of the distribution network.

[0053] In this embodiment, three charging stations are set up for unified charging and discharging management by electric vehicle aggregators. Three types of electric vehicles are set up in each charging station to participate in the distribution network dispatching.

[0054] The individual charging and discharging model of electric vehicles is established, and based on the individual charging and discharging model of electric vehicles, the virtual energy storage operation domain of electric vehicle clusters is established by using Minkowski summation based on the power boundary and energy boundary of a single electric vehicle, specifically:

[0055] Establish an individual charging and discharging model for electric vehicles:

[0056]

[0057] in, is the charging power of the nth electric vehicle at time t; is the rated charging power of the nth electric vehicle; t is the time; t ar is the time when the electric vehicle enters the grid; t eq Off-grid moments for electric vehicles; is the discharge power of the nth electric vehicle at time t; is the rated discharge power of the nth electric vehicle; E n,t is the power of the nth electric car at time t; E n,t-1 is the power of the nth electric car at time t-1; η c is the charging efficiency of the electric vehicle; Δt is the time step; η d is the discharge efficiency of electric vehicles; is the minimum power of the nth electric car; is the maximum power of the nth electric car; E n,t＝0 is the power of the nth electric car at t=0; E n,0 E is the power consumption of the nth electric car when it is connected to the grid; n,T is the power of the nth electric car at time T; T is the time when the electric car is off the grid; E n,e is the expected power of the nth electric vehicle when it is off-grid;

[0058] According to the individual charging and discharging model of electric vehicles, the power boundary and energy boundary of a single electric vehicle are calculated:

[0059]

[0060] in, is the upper energy limit of the electric vehicle at time t; E0 is the power of the electric vehicle when it is connected to the grid; E t-1 is the power of the electric vehicle at time t-1; P cm is the rated charging power of the electric vehicle; E e is the expected power of the electric vehicle when it is off-grid; P dm is the rated discharge power of the electric vehicle; S max is the maximum power of the electric vehicle; is the lower bound of the energy of the electric vehicle at time t; S min is the minimum power of the electric vehicle; P t max is the upper limit of the power of the electric vehicle at time t; P t min is the lower power limit of the electric vehicle at time t;

[0061] According to the power boundary and energy boundary of a single electric vehicle, each charging station is regarded as an electric vehicle cluster. The energy and power boundary of the electric vehicles in the charging station are superimposed using Minkowski summation to obtain the virtual energy storage operation domain of the electric vehicle cluster:

[0062]

[0063] in, is the maximum charging power of the electric vehicle cluster w at time t; w is the electric vehicle cluster; u n,t It is a Boolean variable. When it is 0, it means that the electric vehicle is off-grid, and when it is 1, it means that the electric vehicle is on-grid. is the maximum discharge power of the electric vehicle cluster w at time t; are the upper and lower energy boundaries of the electric vehicle cluster w at time t, respectively; are the upper and lower energy boundaries of the nth electric car at time t respectively.

[0064] In this embodiment, an individual charging and discharging model of the electric vehicle is established according to the charging and discharging power constraints and the safety power constraints of the individual electric vehicle.

[0065] The objective function of the collaborative optimization scheduling model is:

[0066]

[0067]

[0068] in, is the optimization target of the upper distribution network; x is the decision variable; X U is the set of decision variables of the upper model; C G the cost of generating electricity for the distribution network; is the power generated by the mth generator set at time t; C MG Cost of purchasing electricity for the distribution network; is the power purchase power of power purchase node k at time t; C EVA Energy cost for electric vehicle clusters; is the charging or discharging power of the electric vehicle cluster w at time t; Optimize the target for the lower electric vehicle cluster; X Lis the set of decision variables of the lower model; T is the time when the electric vehicle is off-grid; N a Assemble for electric vehicle clusters; is the charging electricity price of electric vehicles at time t; is the charging power of the electric vehicle cluster w at time t; is the discharge electricity price of the electric vehicle at time t; is the discharge power of the electric vehicle cluster w at time t; N G is the generator set; a m , b m and c m N is the power generation cost coefficient of the mth generator set; k is the set of electricity purchasing nodes; t MG is the electricity purchase price at time t; is the reactive power of the mth generator set at time t; P t PV is the photovoltaic power generation in the distribution network at time t; P t WT is the wind power generation power in the distribution network at time t.

[0069] The constraints of the collaborative optimization scheduling model include power balance constraints, safety constraints, power generation constraints, power purchase constraints, new energy output constraints, electric vehicle electricity price constraints, and electric vehicle charging and discharging power constraints:

[0070]

[0071]

[0072] Among them, P j,t is the active injected power at node j at time t; P jq,t is the active power flowing from i to q in branch iq at time t; h(j) is the set of branch end nodes with node j as the head node; e(j) is the set of branch head nodes with node j as the end node; P ij,t is the active power flowing from branch i to branch j at time t; r ij is the resistance of branch ij at time t; I ij,t Q is the square of the current amplitude flowing from i to j in branch ij at time t; j,t is the reactive power injected at node j at time t; Q jq,t is the reactive power flowing from i to q in branch iq at time t; Q ij,t is the reactive power flowing from branch i to branch j at time t; ij is the reactance of branch ij at time t; is the active power generated by the generator at node j at time t; is the purchased power of node j at time t; is the photovoltaic power generation power of node j at time t; is the wind power generation power at node j at time t; is the charging and discharging power of the electric vehicle cluster at node j at time t; is the conventional active load at node j at time t; is the reactive power generated by the generator at node j at time t; is the normal reactive load at node j at time t; V j,t is the square of the voltage amplitude at node j at time t; V i,t is the square of the voltage amplitude at node i at time t; U i,min is the lower limit of the node voltage amplitude; U i,max is the upper limit of the node voltage amplitude; I ij,max is the upper limit of branch current amplitude; is the minimum active output of generator j; is the maximum active output of generator j; is the minimum reactive power output of generator j; is the maximum reactive power output of generator j; is the ramp-down rate of the generator; is the active power generated by the generator at node j at time t-1; is the ramp-up rate of the generator; is the power purchase limit of node j at time t; is the predicted maximum output value of photovoltaic power; is the predicted maximum output value of wind power; is the electricity purchase price at time t; is the charging electricity price of electric vehicles at time t; is the discharge electricity price of the electric vehicle at time t; is the charging power of the electric vehicle cluster w at time t; is the maximum charging power of the electric vehicle cluster w at time t; is the discharge power of the electric vehicle cluster w at time t; u w,t is a Boolean variable, indicating that the electric vehicle cluster w has only one behavior, charging or discharging, at any time; is the maximum discharge power of the electric vehicle cluster w at time t; E w,t is the total energy of the electric vehicle cluster w at time t; E w,t-1 is the total energy of the electric vehicle cluster w at time t-1; η c is the charging efficiency of electric vehicles; η d is the discharge efficiency of electric vehicles; is the upper energy boundary of the electric vehicle cluster w at time t; is the lower bound of the energy of the electric vehicle cluster w at time t; E w,T is the total energy of the electric vehicle cluster w at time T; is the expected power consumption when cluster w is off-grid; is the lower bound of the energy of the electric vehicle cluster w at time T.

[0073] The expression of the uncertainty set based on Wasserstein distance is:

[0074]

[0075] Among them, F is the uncertainty set of new energy output prediction error based on Wasserstein distance; D is the empirical distribution of uncertainty; is the real distribution; is the uncertainty in the empirical distribution D; is the empirical distribution D with The corresponding auxiliary variables; is the expected value of the auxiliary variable; ε is the radius of the Wasserstein sphere; is the constraint on the upper bound of the expected value of the auxiliary variable; is the support set V from the empirical distribution D l Taking out any D and the corresponding u from the equation also satisfies the true distribution of the uncertainty; v is the uncertain variable; u is the auxiliary variable; V l is the support set of sample l; l is the lth sample extracted from the sample data; is the distribution of sample l drawn from the empirical distribution D; is the probability of each distribution; The probability of each distribution is equal, and they are M is the total number of sample data; is the sample data; v is the lower bound of the uncertainty; is the upper bound of the uncertainty.

[0076] In this embodiment, the support set V l On the one hand, it captures the distance between the true distribution and the empirical distribution, and on the other hand, it limits the fluctuation range of the uncertainty.

[0077] According to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a distributed robust optimization scheduling model based on Wasserstein distance is established and converted into a mixed integer linear programming problem for solution to complete the distribution network optimization scheduling, specifically:

[0078] According to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a two-stage distributed robust optimization scheduling model is proposed.

[0079] The two-stage distributed robust optimization scheduling model is transformed into a mixed integer programming problem;

[0080] Solve the mixed integer programming problem and complete the optimal dispatch of the distribution network.

[0081] The expression of the two-stage distributed robust optimization scheduling model is:

[0082]

[0083] Among them, F is a two-stage distributed robust optimization scheduling model; It is the deterministic optimization target of the first stage, that is, the objective function of the collaborative optimization scheduling model; is the optimization target of the second stage, which indicates the adjustment items made by the system to the power generation, power purchase and charging power of the electric vehicle cluster in order to cope with the influence of uncertain variables based on the deterministic optimization of the first stage; is the optimization decision variable x when the objective function is minimized; x is the decision variable; X is the set of decision variables in the first stage, including X U and X L ;X U is the set of decision variables of the upper model; X L is the set of decision variables for the lower model; c T is the transpose of the coefficient vector of the decision variable x in the first stage objective function; To find the worst distribution of uncertain variables, so that the second stage optimization objective function takes the maximum value; E D To seek expectations; T is the transposition of the coefficient variable corresponding to the decision variable y in the second stage objective function; y is the second stage optimization decision variable, including C u,G , C u,MG and C u,EVA ; C u,G To cope with the impact of uncertain variables, the adjustment amount of the power generation cost of the distribution network; C u,MG To cope with the impact of uncertain variables, the adjustment amount of the power purchase cost of the distribution network; C u,EVA To cope with the impact of uncertain variables, the charging cost of electric vehicle clusters is adjusted; A is the coefficient matrix of the constraints in the first stage; b is the parameter vector of the constraints in the first stage; C is the coefficient matrix of the variable x in the constraints in the second stage; H is the coefficient matrix of the variable y in the constraints in the second stage; g(v) is the constraint formula related to the uncertain variable v.

[0084] In this embodiment, based on the collaborative optimization scheduling model, a two-stage distributed robust optimization scheduling model is proposed, taking into account the uncertainty of the output of new energy and the lower limit prediction error of the energy of the electric vehicle cluster. The goal of the second stage optimization is: in order to smooth the impact of system uncertainty, the adjustment cost of rescheduling system resources is the lowest under the worst distribution of uncertain variables.

[0085] The expression of the mixed integer programming problem is:

[0086]

[0087] in, To optimize the decision variables x, σ, find the minimum value of the objective function; c is the decision variable that has a linear relationship with the uncertainty v after the variable y is linearly transformed by the affine strategy; T is the transpose of the coefficient vector of the decision variable x in the first stage objective function; x is the decision variable; M is the total number of sample data; m is the sample data number; β m is the auxiliary variable; σ is the dual variable; ε is the radius of the Wasserstein sphere; d T is the transposition of the coefficient variable corresponding to the decision variable y in the second stage objective function; d T y(·) is the second stage objective function; To substitute the sample data of the uncertain quantity into the objective function and obtain the objective function value at this time, the uncertain quantity is the prediction error of the new energy output; In order to bring the upper bound of the uncertainty into the objective function and obtain the objective function value at this time; d T y(v) is to bring the lower bound of the uncertainty into the objective function and obtain the objective function value at this time; is the mth sample data; A is the coefficient matrix of the first-stage constraint conditions; b is the parameter vector of the first-stage constraint conditions; Take the upper bound value for the uncertain variable v y(v) is the decision variable when the uncertain variable v takes the lower bound value v; v is the lower bound of the uncertainty; is the upper bound of the uncertainty; C is the coefficient matrix of the variable x in the second stage constraint; H is the coefficient matrix of the variable y in the second stage constraint; For uncertain variables Decision variables with linear relationships; For the general Substitute the constraint g(v); is a decision variable that is linearly related to the uncertain variable v; g(v) is the result of substituting v into the constraint formula g(v).

[0088] In this embodiment, Figure 2 As shown in the figure, it is an improved IEEE 33-node system. Node 1 is selected as the balancing node, the system rated voltage level is 12.66kV, and the power reference value is 100MVA. A photovoltaic node, a wind power node, a distributed generator node, and three charging station nodes managed by electric vehicle aggregators are set in the system. The new energy output forecast value, basic load forecast value, and electricity purchase market price are shown in Figure 1. Figure 3 As shown in Figure 1. The sample data of the prediction error of the output of new energy and the prediction error of the lower limit of the energy of the electric vehicle cluster are generated by Latin hypercube sampling and selected by scene reduction. Three types of electric vehicles are set to participate in the dispatching management of the distribution network. The parameters of each type of electric vehicle and its distribution in each charging station are shown in Table 1.

[0089] Table 1

[0090]

[0091] Among them, t st is the network access time; N(·) is normal distribution; U(·) is uniform distribution.

[0092] Set the following four scenarios and compare and analyze the scheduling results of each scenario:

[0093] ① Scenario 1: Disorderly charging of electric vehicle clusters;

[0094] ② Scenario 2: The electric vehicle cluster participates in charging and discharging scheduling with the goal of maximizing the benefits of the distribution network only;

[0095] ③Scenario 3: The goal is to maximize the interests of the two entities, the distribution network and the electric vehicle cluster, and the electric vehicle cluster does not perform discharge scheduling;

[0096] ④Scenario 4: With the goal of maximizing the interests of the two main entities, the distribution network and the electric vehicle cluster, the electric vehicle cluster participates in charging and discharging scheduling.

[0097] Scenario ④ is the method proposed by the present invention. Four scenarios are simulated to obtain scheduling plans for different scenarios. Figure 4 The scheduling results are shown in Table 2.

[0098] Table 2

[0099] Scenario Distribution network revenue / 10,000 yuan Total system generation and power purchase cost / 10,000 yuan Electric vehicle charging cost / 10,000 yuan 1 11.9991 8.6422 3.0793 2 13.2018 7.3769 3.0167 3 12.0985 6.8462 1.3827 4 12.1160 6.7395 1.2935

[0100] The distribution network revenue in Table 2 refers to the difference between the energy cost of conventional loads and the distribution network cost. The electricity price of conventional loads is 1.1 times the electricity price in the power purchase market.

[0101] from Figure 4It can be seen that in scenarios 1 and 2, the electric vehicle cluster performs a large amount of charging during the peak hours of electricity consumption, when the electricity price is high, so the energy cost of the electric vehicle cluster is also high. At the same time, in order to meet the system load demand, the generator output in scenarios 1 and 2 also reaches the upper limit value in multiple time periods, so the system power generation cost is also high. In addition, in scenario 1, around 19:00 in the evening, the electric vehicle cluster has a large demand for charging. A large amount of disordered charging loads are superimposed on the basic load during the peak hours, which makes the system power generation capacity reach the upper limit, resulting in additional electricity purchase from the main grid, further increasing the system power generation and purchase costs. Scenarios 3 and 4 consider the maximization of the interests of the two main bodies of the distribution network and the electric vehicle cluster. The electric vehicle cluster chooses to perform a large amount of charging during the low electricity consumption period, so the energy cost of the electric vehicle cluster and the system power generation and purchase costs are low. In addition, scenario 4 considers the discharge scheduling of the electric vehicle cluster. During the peak electricity consumption period, the electric vehicle cluster performs an appropriate amount of discharge, which further reduces the energy cost of the electric vehicle cluster and the system power generation and purchase costs.

[0102] Among the four scenarios, the distribution network benefits in scenario 2 are the highest. This is because when only the distribution network benefits are maximized, the electric vehicle cluster charges during the peak electricity price period, and the distribution network obtains higher charging cost benefits. However, this situation is not realistic. If the distribution network only considers its own interests and sacrifices the interests of electric vehicles, the cooperative relationship between the electric vehicle cluster and the distribution network will be broken, and the charging strategy of the electric vehicle cluster will no longer be jointly formulated by the distribution network and the electric vehicle cluster. In summary, scenario 4, that is, the strategy proposed in this invention, can maximize the interests of the two entities of the distribution network and the electric vehicle cluster, and achieve peak shaving and valley filling to a certain extent.

[0103] The electric vehicle cluster charging and discharging strategy formulated by the method proposed in the present invention is as follows: Figure 5 As shown. In order to ensure the interests of both the distribution network and the electric vehicle cluster, the electric vehicle cluster generally chooses to charge during the off-peak period, and not charge or discharge appropriately during the peak period. The energy of the three clusters is always within the operating domain, indicating that the scheduling strategy can meet the charging needs of electric vehicles in each period, including the electric vehicles on the grid in each period meeting the minimum power requirements and the vehicles off the grid meeting the charging requirements during the period.

[0104] Charging station CS1 is located at a node close to the photovoltaic power plant. The photovoltaic power output is high during the noon period. Therefore, although this is the peak period of electricity consumption, it still supports the charging service of cluster 1. Cluster 1 reaches the cluster energy limit before dusk, and then discharges a lot during the peak period in the evening, and charges a lot during the low period at night to meet the charging needs of cluster 1.

[0105] Charging station CS2 is located at the leaf node of the distribution network, and the voltage amplitude is sensitive to active power injection. Therefore, it discharges during the peak period at noon and remains silent during the peak period in the evening to avoid undervoltage problems.

[0106] The lower limit of cluster 3 energy at charging station CS3 has a large growth trend during the period of 11:00-17:00, indicating that cluster 3 has a higher charging demand during this period.

[0107] The random optimization (SO), robust optimization (RO) and distributional robust optimization (DRO) based on Wasserstein distance were used for comparative analysis, and the optimization results of different methods are shown in Table 3.

[0108] Table 3

[0109]

[0110]

[0111] As can be seen from Table 3, the distribution network benefits are maximized by using SO optimization, but the SO optimization results are too correlated with the selected typical scenarios of uncertainty and the probability distribution of the scenarios, and are not representative, so they cannot cope with system uncertainty well; RO optimization is always optimized under the worst scenario, ignoring the probability distribution characteristics of each scenario, making the decision results too conservative; DRO optimization is optimized under the worst scenario distribution of uncertainty, integrating the characteristics of SO and RO. As can be seen from Table 3, the DRO optimization results based on Wasserstein distance are between the two, and the results become worse as the radius of the Wasserstein ball increases. Therefore, setting different ball radiuses can change the conservatism of the optimization results. At the same time, this method makes full use of historical sample data, so that the optimization results conform to historical statistical laws, rather than blindly seeking the worst situation of uncertainty, which makes the optimization results too conservative.

[0112] In summary, the method proposed in the present invention comprehensively considers the uncertainty of new energy output and dispatchable energy of electric vehicle clusters, and can maximize the interests of both distribution network and electric vehicle clusters. Compared with traditional SO and RO optimization methods, it achieves a balance between economy and robustness of the scheduling model.

Claims

1. A distribution network optimization scheduling method considering the dispatchability of electric vehicle clusters, characterized in that: include: Establish an individual charging and discharging model for electric vehicles, and based on the individual charging and discharging model for electric vehicles, establish a virtual energy storage operation domain for electric vehicle clusters using Minkowski summation based on the power boundary and energy boundary of a single electric vehicle; According to the operation domain of the virtual energy storage of the electric vehicle cluster, a collaborative optimization dispatch model is established with the goal of minimizing the operating costs of the distribution network operator and the electric vehicle aggregator respectively; Characterize the uncertainty of new energy output and electric vehicle response energy prediction, and establish an uncertainty set based on Wasserstein distance; According to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a distributed robust optimization scheduling model based on Wasserstein distance is established and converted into a mixed integer linear programming problem for solution to complete the optimal scheduling of the distribution network.

2. The distribution network optimization scheduling method considering the dispatchability of electric vehicle clusters according to claim 1 is characterized in that: The individual charging and discharging model of electric vehicles is established, and based on the individual charging and discharging model of electric vehicles, the virtual energy storage operation domain of electric vehicle clusters is established by using Minkowski summation based on the power boundary and energy boundary of a single electric vehicle, specifically: Establish an individual charging and discharging model for electric vehicles: in, is the charging power of the nth electric vehicle at time t; is the rated charging power of the nth electric vehicle; t is the time; t ar is the time when the electric vehicle enters the grid; t eq Off-grid moments for electric vehicles; is the discharge power of the nth electric vehicle at time t; is the rated discharge power of the nth electric vehicle; E n,t is the power of the nth electric car at time t; E n,t-1 is the power of the nth electric car at time t-1; η c is the charging efficiency of the electric vehicle; Δt is the time step; η d is the discharge efficiency of electric vehicles; is the minimum power of the nth electric car; is the maximum power of the nth electric car; E n,t＝0 is the power of the nth electric car at t=0; E n,0 E is the power consumption of the nth electric car when it is connected to the grid; n,T is the power of the nth electric car at time T; T is the time when the electric car is off the grid; E n,e is the expected power of the nth electric vehicle when it is off-grid; According to the individual charging and discharging model of electric vehicles, the power boundary and energy boundary of a single electric vehicle are calculated: in, is the upper energy limit of the electric vehicle at time t; E0 is the power of the electric vehicle when it is connected to the grid; E t-1 is the power of the electric vehicle at time t-1; P cm is the rated charging power of the electric vehicle; E e is the expected power of the electric vehicle when it is off-grid; P dm is the rated discharge power of the electric vehicle; S max is the maximum power of the electric vehicle; is the lower bound of the energy of the electric vehicle at time t; S min is the minimum power of an electric vehicle; is the upper limit of the power of the electric vehicle at time t; is the lower power limit of the electric vehicle at time t; According to the power boundary and energy boundary of a single electric vehicle, each charging station is regarded as an electric vehicle cluster. The energy and power boundary of the electric vehicles in the charging station are superimposed using Minkowski summation to obtain the virtual energy storage operation domain of the electric vehicle cluster: in, is the maximum charging power of the electric vehicle cluster w at time t; w is the electric vehicle cluster; u n,t It is a Boolean variable. When it is 0, it means that the electric vehicle is off-grid, and when it is 1, it means that the electric vehicle is on-grid. is the maximum discharge power of the electric vehicle cluster w at time t; are the upper and lower energy boundaries of the electric vehicle cluster w at time t, respectively; are the upper and lower energy boundaries of the nth electric car at time t respectively.

3. The distribution network optimization scheduling method considering the dispatchability of electric vehicle clusters according to claim 1 is characterized in that: The objective function of the collaborative optimization scheduling model is: in, is the optimization target of the upper distribution network; x is the decision variable; X U is the set of decision variables of the upper model; C G the cost of generating electricity for the distribution network; is the power generated by the mth generator set at time t; C MG Cost of purchasing electricity for the distribution network; is the power purchase power of power purchase node k at time t; C EVA Energy cost for electric vehicle clusters; is the charging or discharging power of the electric vehicle cluster w at time t; Optimize the target for the lower electric vehicle cluster; X L is the set of decision variables of the lower model; T is the time when the electric vehicle is off-grid; N a Assemble for electric vehicle clusters; is the charging electricity price of electric vehicles at time t; is the charging power of the electric vehicle cluster w at time t; is the discharge electricity price of the electric vehicle at time t; is the discharge power of the electric vehicle cluster w at time t; N G is the generator set; a m 、b m and c m N is the power generation cost coefficient of the mth generator set; k It is a collection of power purchasing nodes; is the electricity purchase price at time t; is the reactive power of the mth generator set at time t; is the photovoltaic power generation in the distribution network at time t; is the wind power generation power in the distribution network at time t.

4. The distribution network optimization scheduling method considering the dispatchability of electric vehicle clusters according to claim 1 is characterized in that: The constraints of the collaborative optimization scheduling model include power balance constraints, safety constraints, power generation constraints, power purchase constraints, new energy output constraints, electric vehicle electricity price constraints, and electric vehicle charging and discharging power constraints: Among them, P j,t is the active injected power at node j at time t; P jq,t is the active power flowing from i to q in branch iq at time t; h(j) is the set of branch end nodes with node j as the head node; e(j) is the set of branch head node with node j as the end node; P ij,t is the active power flowing from branch i to branch j at time t; r ij is the resistance of branch ij at time t; I ij,t Q is the square of the current amplitude flowing from i to j in branch ij at time t; j,t is the reactive power injected at node j at time t; Q jq,t is the reactive power flowing from i to q in branch iq at time t; Q ij,t is the reactive power flowing from branch i to branch j at time t; ij is the reactance of branch ij at time t; is the active power generated by the generator at node j at time t; is the purchased power of node j at time t; is the photovoltaic power generation power of node j at time t; is the wind power generation power at node j at time t; is the charging and discharging power of the electric vehicle cluster at node j at time t; is the conventional active load at node j at time t; is the reactive power generated by the generator at node j at time t; is the normal reactive load at node j at time t; V j,t is the square of the voltage amplitude at node j at time t; V i,t is the square of the voltage amplitude at node i at time t; U i,min is the lower limit of the node voltage amplitude; U i,max is the upper limit of the node voltage amplitude; I ij,max is the upper limit of branch current amplitude; is the minimum active output of generator j; is the maximum active output of generator j; is the minimum reactive power output of generator j; is the maximum reactive power output of generator j; is the ramp-down rate of the generator; is the active power generated by the generator at node j at time t-1; is the ramp-up rate of the generator; is the power purchase limit of node j at time t; is the predicted maximum output value of photovoltaic power; is the predicted maximum output value of wind power; is the electricity purchase price at time t; is the charging electricity price of electric vehicles at time t; is the discharge electricity price of the electric vehicle at time t; is the charging power of the electric vehicle cluster w at time t; is the maximum charging power of the electric vehicle cluster w at time t; is the discharge power of the electric vehicle cluster w at time t; u w,t is a Boolean variable, indicating that the electric vehicle cluster w has only one behavior, charging or discharging, at any time; is the maximum discharge power of the electric vehicle cluster w at time t; E w,t is the total energy of the electric vehicle cluster w at time t; E w,t-1 is the total energy of the electric vehicle cluster w at time t-1; η c is the charging efficiency of electric vehicles; η d is the discharge efficiency of electric vehicles; is the upper energy boundary of the electric vehicle cluster w at time t; is the lower bound of the energy of the electric vehicle cluster w at time t; E w,T is the total energy of the electric vehicle cluster w at time T; is the expected power consumption when cluster w is off-grid; is the lower bound of the energy of the electric vehicle cluster w at time T.

5. The distribution network optimization scheduling method considering the dispatchability of electric vehicle clusters according to claim 1 is characterized in that: The expression of the uncertainty set based on Wasserstein distance is: Among them, F is the uncertainty set of new energy output prediction error based on Wasserstein distance; D is the empirical distribution of uncertainty; is the real distribution; is the uncertainty in the empirical distribution D; is the empirical distribution D with The corresponding auxiliary variables; is the expected value of the auxiliary variable; ε is the radius of the Wasserstein sphere; is the constraint on the upper bound of the expected value of the auxiliary variable; is the support set V from the empirical distribution D l Taking out any D and the corresponding u from the equation also satisfies the true distribution of the uncertainty; v is the uncertain variable; u is the auxiliary variable; V l is the support set of sample l; l is the lth sample extracted from the sample data; is the distribution of sample l drawn from the empirical distribution D; is the probability of each distribution; The probability of each distribution is equal, and they are M is the total number of sample data; is the sample data; v is the lower bound of the uncertainty; is the upper bound of the uncertainty.

6. The distribution network optimization dispatching method considering the dispatchability of electric vehicle clusters according to claim 1 is characterized in that: According to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a distributed robust optimization scheduling model based on Wasserstein distance is established and converted into a mixed integer linear programming problem for solution to complete the distribution network optimization scheduling, specifically: According to the collaborative optimization scheduling model and the uncertainty set based on Wasserstein distance, a two-stage distributed robust optimization scheduling model is proposed. The two-stage distributed robust optimization scheduling model is transformed into a mixed integer programming problem; Solve the mixed integer programming problem and complete the optimal dispatch of the distribution network.

7. The distribution network optimization dispatching method considering the dispatchable capacity of electric vehicle clusters according to claim 6 is characterized in that: The expression of the two-stage distributed robust optimization scheduling model is: Among them, F is a two-stage distributed robust optimization scheduling model; is the deterministic optimization objective of the first stage, i.e., the objective function of the collaborative optimization scheduling model; is the optimization target of the second stage, which indicates the adjustment items made by the system to the power generation, power purchase and charging power of the electric vehicle cluster in order to cope with the influence of uncertain variables based on the deterministic optimization of the first stage; is the optimization decision variable x when the objective function is minimized; x is the decision variable; X is the set of decision variables in the first stage, including X U and X L ;X U is the set of decision variables of the upper model; X L is the set of decision variables for the lower model; c T is the transpose of the coefficient vector of the decision variable x in the first stage objective function; To find the worst distribution of uncertain variables, so that the second stage optimization objective function takes the maximum value; E D To seek expectations; T is the transposition of the coefficient variable corresponding to the decision variable y in the second stage objective function; y is the second stage optimization decision variable, including C u,G , C u,MG and C u,EVA ; C u,G To cope with the impact of uncertain variables, the adjustment amount of the power generation cost of the distribution network; C u,MG To cope with the impact of uncertain variables, the adjustment amount of the power purchase cost of the distribution network; C u,EVA To cope with the impact of uncertain variables, the charging cost of electric vehicle clusters is adjusted; A is the coefficient matrix of the constraints in the first stage; b is the parameter vector of the constraints in the first stage; C is the coefficient matrix of the variable x in the constraints in the second stage; H is the coefficient matrix of the variable y in the constraints in the second stage; g(v) is the constraint formula related to the uncertain variable v.

8. The distribution network optimization dispatching method considering the dispatchable capacity of electric vehicle clusters according to claim 6 is characterized in that: The expression of the mixed integer programming problem is: in, To optimize the decision variables x, σ, find the minimum value of the objective function; c is the decision variable that has a linear relationship with the uncertainty v after the variable y is linearly transformed by the affine strategy; T is the transpose of the coefficient vector of the decision variable x in the first stage objective function; M is the total number of sample data; m is the sample data number; β m is the auxiliary variable; σ is the dual variable; ε is the radius of the Wasserstein sphere; d T is the transposition of the coefficient variable corresponding to the decision variable y in the second stage objective function; d T y(·) is the second stage objective function; To substitute the sample data of the uncertain quantity into the objective function and obtain the objective function value at this time, the uncertain quantity is the prediction error of the new energy output; In order to bring the upper bound of the uncertainty into the objective function and obtain the objective function value at this time; d T y(v) is to bring the lower bound of the uncertainty into the objective function and obtain the objective function value at this time; is the mth sample data; A is the coefficient matrix of the first-stage constraint conditions; b is the parameter vector of the first-stage constraint conditions; Take the upper bound value for the uncertain variable v y(v) is the decision variable when the uncertain variable v takes the lower bound value v; v is the lower bound of the uncertainty; is the upper bound of the uncertainty; C is the coefficient matrix of the variable x in the second stage constraint; H is the coefficient matrix of the variable y in the second stage constraint; For uncertain variables Decision variables with linear relationships; For the general Substitute the constraint g(v); is a decision variable that is linearly related to the uncertain variable v; g(v) is the result of substituting v into the constraint formula g(v).

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