Power transmission line congestion relieving method and system based on electric vehicle aggregation model

Abstract

The invention belongs to the technical field of electric power systems, and particularly relates to a power transmission line congestion relieving method and system based on an electric vehicle aggregation model, and the method comprises the steps: firstly constructing an electric vehicle monomer model, and constructing the electric vehicle aggregation model based on the electric vehicle monomer model; secondly, for a power transmission line which is accessed into the electric power system and is congested, comprehensively considering the electric vehicle aggregation model, a thermal power generating unit and the electric power system with the flexible adjustment capability of a new energy unit, and constructing a coordinated scheduling model by taking the total cost of system operation as a target; and finally, solving the coordinated dispatching model to obtain an optimal coordinated dispatching scheme of the power system. According to the method, various adjustable resources of the power grid are considered, the tide congestion phenomenon of the important power transmission section is relieved through the electric vehicle, the safety and stability of the system are ensured, the available transmission capacity of the power grid is improved, meanwhile, the consumption rate of regional renewable energy sources is increased, and load shedding is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a method, system, device and medium for alleviating transmission line congestion based on an electric vehicle aggregation model. Background Art

[0002] With the acceleration of the construction of new power systems, the total installed capacity of new energy has increased significantly, and the proportion of new energy power generation in the power system has increased rapidly. However, new energy is intermittent and uncertain, and large-scale grid connection will cause fluctuations in line flows. At the same time, with the gradual increase in terminal electrification rate and the increase in grid load demand, the uncertainty of grid flows has increased, and the congestion phenomenon in the dispatching and operation of the power system has become more serious, which has brought major challenges to the stable operation of the power grid. The power system can be optimized by tapping into a variety of adjustable resources of the power grid to form available transmission capacity. Therefore, how to accurately optimize a large number of electric vehicles and new energy output under controllable computing conditions, reduce the transmission burden of grid flows, increase the regional renewable energy absorption rate, and reduce load shedding is an urgent problem to be solved. Summary of the invention

[0003] The purpose of the present invention is to address the above-mentioned problems existing in the prior art and to provide a method, system, device and medium for alleviating transmission line congestion based on an electric vehicle aggregation model, which can accurately optimize the output of a large number of electric vehicles and new energy under controllable computing conditions, reduce the transmission burden of power grid trends, improve the regional renewable energy absorption rate, and reduce load shedding.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows:

[0005] In a first aspect, the present invention proposes a method for alleviating transmission line congestion based on an electric vehicle aggregation model, the method comprising:

[0006] S1. Build an electric vehicle monomer model, and build an electric vehicle aggregation model based on the electric vehicle monomer model;

[0007] S2. For the transmission lines that are congested when the electric vehicle aggregation model is connected to the power system, a coordinated dispatching model of the power system is constructed with the goal of reducing the total system operation cost by comprehensively considering the electric vehicle aggregation model, the thermal power unit and the flexible adjustment capability of the new energy unit; the total system operation cost includes fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost and load reduction cost;

[0008] S3. Solve the coordinated dispatch model to obtain the optimal coordinated dispatch plan for the power system.

[0009] The S1 includes:

[0010] S11. Construct the following electric vehicle monomer model:

[0011]

[0012]

[0013] A i =I q +diag(-1;-1) q×q ;

[0014] In the above formula, Ψ i Represents the single-unit model of electric vehicle; R i , Y i All are polyhedral system matrices of electric vehicles; is the power matrix of the electric vehicle; q is the length of the scheduling period T; I q is the q-dimensional identity matrix; are the q-dimensional matrices of the charging and discharging power of electric vehicle i; are the charging and discharging power of electric vehicle i respectively; are the q-dimensional matrices of the maximum charging and discharging power of electric vehicle i; are the maximum charging and discharging power of electric vehicle i; A i is the transformation matrix; A i The inverse matrix of are the q-dimensional matrices of the charging and discharging efficiencies of electric vehicle i; are the charging and discharging efficiencies of electric vehicle i respectively; diag is the function for constructing a diagonal matrix; are the q-dimensional matrices of the maximum and minimum available capacities of electric vehicle i, respectively; is the maximum and minimum available capacity of electric vehicle i; is the q-dimensional matrix of the initial capacity of electric vehicle i; is the initial capacity of electric vehicle i;

[0015]

[0016]

[0017] In the above formula, are the charging and discharging power of electric vehicle i in period t respectively; is the current capacity of electric vehicle i in time periods t and t-1; ΔT is a single time slot; N EV represents the total number of electric vehicles;

[0018] S12. Based on the electric vehicle monomer model constructed in S11, the following electric vehicle aggregation model is constructed:

[0019]

[0020] In the above formula, represents the electric vehicle aggregation model; represents the Minkowski sum; is the power curve of the electric vehicle aggregation model; Ω s It is the aggregation area for electric vehicles.

[0021] The objective function of the coordinated scheduling model includes:

[0022] min C=C g +C cs +C wind +C loadcut ;

[0023]

[0024] In the above formula, C is the total cost of system operation; C g , C cs , C wind , C loadcut They include fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost, and load reduction cost; S is the number of segments of the coal consumption curve; are the cost coefficients of each segment in the coal consumption curve; is the output of thermal power units in each section of the coal consumption curve; They are the start and stop state variables of the thermal power unit in period t and period t-1 respectively. A value of 1 indicates that the thermal power unit is in the start state, and a value of 0 indicates that the thermal power unit is in the shutdown state; is the unit start-up and shutdown cost of thermal power units; W t , P t w are the available wind power and actual power generation of the wind farm respectively; c w is the penalty cost for wind abandonment; c cut Penalty costs for load shedding; is the load reduction power in period t; N W is the number of wind farms; N G is the number of thermal power units;

[0025] The constraints of the coordinated scheduling model include:

[0026] System constraints:

[0027]

[0028] In the above formula, Pt g is the output of the thermal power unit during period t; P t loadcut is the load power during period t; P t jk is the power flow of transmission line jk, where transmission line jk refers to the transmission line connecting node j and node k in the power grid; P t load is the load power in period t; l is the total number of transmission lines in the power grid;

[0029] Constraints on thermal power units:

[0030]

[0031] In the above formula, are the maximum and minimum output power of thermal power units respectively; They are the ramp-up rate and ramp-down rate of thermal power units respectively; is the output of the thermal power unit in the t-1 period; is the total system reserve during period t; T t g,on , T t g,off They are the continuous operation time and continuous shutdown time of thermal power units in period t respectively; They are the shortest startup time and shortest shutdown time of the unit respectively;

[0032] Wind power constraints:

[0033]

[0034] Power flow constraints:

[0035]

[0036] In the above formula, g jk 、b jk are the conductivity and susceptance of the transmission line jk respectively; V t j 、V t k are the voltage amplitudes of node j and node k in period t respectively; are the voltage phase angles of node j and node k in period t respectively; β represents the maximum allowable power limit parameter of the line; is the rated power handling capacity of the transmission line jk; V max 、V min are the upper and lower limits of the voltage amplitude of node j respectively, and N represents the total number of nodes in the power grid.

[0037] The optimal coordinated dispatching scheme of the power system includes the power curve of the electric vehicle aggregation model, the output of thermal power units and new energy units; the dispatching power of a single electric vehicle is obtained by decomposing the power curve based on the electric vehicle aggregation model, and the decomposition formula is:

[0038]

[0039] In the above formula, P i EV,dis To decompose the dispatch power to a single electric vehicle; are the scaling factor and translation factor respectively.

[0040] In a second aspect, the present invention proposes a transmission line congestion relief system based on an electric vehicle aggregation model, wherein the transmission line congestion relief system comprises:

[0041] An electric vehicle aggregation model building module is used to build an electric vehicle monomer model, and to build an electric vehicle aggregation model based on the electric vehicle monomer model;

[0042] A coordinated dispatch model building module is used to connect the electric vehicle aggregation model to the congested transmission line in the power system, comprehensively consider the electric vehicle aggregation model, the thermal power unit and the flexible adjustment capability of the new energy unit, and build a coordinated dispatch model of the power system with the goal of reducing the total system operation cost; the total system operation cost includes fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost, and load reduction cost;

[0043] The calculation module is used to solve the coordinated dispatch model to obtain the optimal coordinated dispatch plan for the power system.

[0044] The electric vehicle aggregation model building module is used to build the following electric vehicle monomer model:

[0045]

[0046]

[0047] A i =I q +diag(-1;-1) q×q ;

[0048] In the above formula, Ψ i Represents the single-unit model of electric vehicle; R i , Y i All of them are polyhedral system matrix of electric vehicles; P i EV is the power matrix of the electric vehicle; q is the length of the scheduling period T; I q is the q-dimensional identity matrix; are the q-dimensional matrices of the charging and discharging power of electric vehicle i; are the charging and discharging power of electric vehicle i respectively; are the q-dimensional matrices of the maximum charging and discharging power of electric vehicle i; are the maximum charging and discharging power of electric vehicle i; A i is the transformation matrix; A i The inverse matrix of are the q-dimensional matrices of the charging and discharging efficiencies of electric vehicle i; are the charging and discharging efficiencies of electric vehicle i respectively; diag is the function for constructing a diagonal matrix; are the q-dimensional matrices of the maximum and minimum available capacities of electric vehicle i, respectively; is the maximum and minimum available capacity of electric vehicle i; is the q-dimensional matrix of the initial capacity of electric vehicle i; is the initial capacity of electric vehicle i;

[0049]

[0050]

[0051] In the above formula, are the charging and discharging power of electric vehicle i in period t respectively; is the current capacity of electric vehicle i in time periods t and t-1; ΔT is a single time slot; N EV represents the total number of electric vehicles;

[0052] The electric vehicle aggregation model building module is also used to build the following electric vehicle aggregation model based on the electric vehicle monomer model:

[0053]

[0054] In the above formula, represents the electric vehicle aggregation model; represents the Minkowski sum; is the power curve of the electric vehicle aggregation model; Ω s It is the aggregation area for electric vehicles.

[0055] The objective function of the coordinated scheduling model includes:

[0056] min C=C g +C cs +C wind +C loadcut ;

[0057]

[0058] In the above formula, C is the total cost of system operation; C g , C cs , C wind , C loadcut They include fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost, and load reduction cost; S is the number of segments of the coal consumption curve; are the cost coefficients of each segment in the coal consumption curve; is the output of thermal power units in each section of the coal consumption curve; They are the start and stop state variables of the thermal power unit in period t and period t-1 respectively. A value of 1 indicates that the thermal power unit is in the start state, and a value of 0 indicates that the thermal power unit is in the shutdown state; is the unit start-up and shutdown cost of thermal power units; W t , P t w are the available wind power and actual power generation of the wind farm respectively; c w is the penalty cost for wind abandonment; c cut is the penalty cost for load reduction; P t loadcut is the load reduction power in period t; N W is the number of wind farms; N G is the number of thermal power units;

[0059] The constraints of the coordinated scheduling model include:

[0060] System constraints:

[0061]

[0062] In the above formula, P t g is the output of the thermal power unit during period t; P t loadcut is the load power during period t; P t jk is the power flow of transmission line jk, where transmission line jk refers to the transmission line connecting node j and node k in the power grid; P t load is the load power in period t; l is the total number of transmission lines in the power grid;

[0063] Constraints on thermal power units:

[0064]

[0065] In the above formula, are the maximum and minimum output power of thermal power units respectively; They are the ramp-up rate and ramp-down rate of thermal power units respectively; is the output of the thermal power unit in the t-1 period; is the total system reserve during period t; T t g,on , T t g,off They are the continuous operation time and continuous shutdown time of thermal power units in period t respectively; They are the shortest startup time and shortest shutdown time of the unit respectively;

[0066] Wind power constraints:

[0067]

[0068] Power flow constraints:

[0069]

[0070] In the above formula, g jk 、b jk are the conductivity and susceptance of the transmission line jk respectively; V t j 、V t k are the voltage amplitudes of node j and node k in period t respectively; are the voltage phase angles of node j and node k in period t respectively; β represents the maximum allowable power limit parameter of the line; is the rated power handling capacity of the transmission line jk; V max 、V min are the upper and lower limits of the voltage amplitude of node j respectively, and N represents the total number of nodes in the power grid.

[0071] The optimal coordinated dispatching scheme of the power system includes the power curve of the electric vehicle aggregation model, the output of thermal power units and new energy units; the dispatching power of a single electric vehicle is obtained by decomposing the power curve based on the electric vehicle aggregation model, and the decomposition formula is:

[0072]

[0073] In the above formula, P i EV,dis To decompose the dispatch power to a single electric vehicle; are the scaling factor and translation factor respectively.

[0074] In the third aspect, the present invention proposes a transmission line congestion relief device based on an electric vehicle aggregation model, wherein the transmission line congestion relief device comprises a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the aforementioned transmission line congestion relief method according to the instructions in the computer program code.

[0075] In a fourth aspect, the present invention proposes a computer-readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements the aforementioned method for alleviating transmission line congestion.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] The method for alleviating transmission line congestion based on the electric vehicle aggregation model of the present invention first constructs an electric vehicle monomer model, and then constructs an electric vehicle aggregation model based on the electric vehicle monomer model, and then for the transmission line where the electric vehicle aggregation model is connected to the congested power system, the electric vehicle aggregation model, the thermal power unit and the power system with flexible adjustment capabilities of the new energy unit are comprehensively considered, and a coordinated scheduling model of the power system is constructed with the goal of reducing the total system operation cost; the total system operation cost includes fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost, and load reduction cost, and finally the coordinated scheduling model is solved to obtain the optimal coordinated scheduling plan of the power system, and the optimal coordinated scheduling plan includes the scheduling plan of each adjustable subject in the power system; the above design takes into account the various adjustable resources of the power grid, and uses electric vehicles to alleviate the power flow congestion phenomenon of important transmission line sections, ensures the safety and stability of the system, improves the available transmission capacity of the power grid, and at the same time improves the regional renewable energy absorption rate and reduces load shedding. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 The present invention is a flow chart of the method for alleviating transmission line congestion.

[0079] Figure 2 This is a structural diagram of the IEEE 14-bus system.

[0080] Figure 3 The changes in the flow of lines 2-3 and 7-9 before and after the participation of electric vehicles.

[0081] Figure 4 It is a structural schematic diagram of the transmission line congestion relief system of the present invention.

[0082] Figure 5 It is a structural schematic diagram of the transmission line congestion relief device of the present invention. DETAILED DESCRIPTION

[0083] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0084] Embodiment 1:

[0085] See also Figure 1, a transmission line congestion relief method based on electric vehicle aggregation model, the specific steps are:

[0086] S1. Build an electric vehicle monomer model, and build an electric vehicle aggregation model based on the electric vehicle monomer model;

[0087] S11. The plug-in and unplugging behavior of electric vehicles in the power grid can be regarded as an adjustable energy storage resource. Therefore, the following electric vehicle monomer model is constructed:

[0088]

[0089] In the above formula, are the charging and discharging power of electric vehicle i in period t respectively; are the maximum charging and discharging power of electric vehicle i respectively; is the current capacity of electric vehicle i in period t and period t-1; are the charging and discharging efficiencies of electric vehicle i respectively; is the maximum and minimum available capacity of electric vehicle i; ΔT is a single time slot; T is the scheduling period; N EV represents the total number of electric vehicles;

[0090] Convert the above equation into the following compact form:

[0091]

[0092] A i =I q +diag(-1;-1) q×q ;

[0093] In the above formula, Ψ i Represents the single-unit model of electric vehicle; R i , Y i All are polyhedral system matrices of electric vehicles; is the power matrix of the electric vehicle; q is the length of the scheduling period T; I q is the q-dimensional identity matrix; are the q-dimensional matrices of the charging and discharging power of electric vehicle i; are the charging and discharging power of electric vehicle i respectively; are the q-dimensional matrices of the maximum charging and discharging power of electric vehicle i; are the maximum charging and discharging power of electric vehicle i; A i is the transformation matrix; A i The inverse matrix of are the q-dimensional matrices of the charging and discharging efficiencies of electric vehicle i; are the charging and discharging efficiencies of electric vehicle i respectively; diag is a function for constructing a diagonal matrix; are the q-dimensional matrices of the maximum and minimum available capacities of electric vehicle i, respectively; is the maximum and minimum available capacity of electric vehicle i; is the q-dimensional matrix of the initial capacity of electric vehicle i; is the initial capacity of electric vehicle i;

[0094] S12. Based on the electric vehicle monomer model constructed in S11, the following electric vehicle aggregation model is constructed:

[0095]

[0096] In the above formula, represents the electric vehicle aggregation model; represents the Minkowski sum; is the power curve of the electric vehicle aggregation model; Ω s It is an aggregation area for electric vehicles;

[0097] When there are a large number of electric vehicles, it is more complicated to calculate their Minkowski sum. Therefore, the polyhedron-based inner approximation scheme (PBIA) is used to find the maximum inner (subset) approximation value for calculation; specifically:

[0098] First create a basic set in Specify the average values ​​of all parameters of all electric vehicles; then calculate the average value of each electric vehicle Expand and translate to generate a series of isomorphs:

[0099]

[0100] In the above formula, Yes i The interior approximate feasible region, They are the scaling factor and the translation factor, respectively, using Scaling primitives to determine the size of the feasible region, using Translate the basic type to determine the location of the feasible region;

[0101] After the above transformation, when all electric vehicles in the area are approximated, the approximate Minkowski sum of each electric vehicle can be calculated as follows:

[0102]

[0103] In order to obtain the maximum inner approximation region, the following problem is established:

[0104]

[0105] By solving the above problem, we can obtain the dispatch power decomposed into a single electric vehicle:

[0106]

[0107] In the above formula, P i EV,dis To decompose the dispatch power to a single electric vehicle; are scaling factor and translation factor respectively;

[0108] S2. For the transmission lines that are congested when the electric vehicle aggregation model is connected to the power system, a coordinated dispatching model of the power system is constructed with the goal of reducing the total system operation cost by comprehensively considering the electric vehicle aggregation model, the thermal power unit and the flexible adjustment capability of the new energy unit. The total system operation cost includes fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost and load reduction cost. The objective function of the coordinated dispatching model includes:

[0109] min C=C g +C cs +C wind +C loadcut ;

[0110]

[0111] In the above formula, C is the total cost of system operation; C g , C cs , C wind , C loadcut They include fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost, and load reduction cost; S is the number of segments of the coal consumption curve; are the cost coefficients of each segment in the coal consumption curve; is the output of thermal power units in each section of the coal consumption curve; They are the start and stop state variables of the thermal power unit in period t and period t-1 respectively. A value of 1 indicates that the thermal power unit is in the start state, and a value of 0 indicates that the thermal power unit is in the shutdown state; is the unit start-up and shutdown cost of thermal power units; W t , P t w are the available wind power and actual power generation of the wind farm respectively; c w is the penalty cost for wind abandonment; c cut is the penalty cost for load reduction; P t loadcut is the load reduction power in period t; N W is the number of wind farms; N G is the number of thermal power units;

[0112] The constraints of the coordinated scheduling model include:

[0113] System constraints:

[0114]

[0115]

[0116] In the above formula, P t g is the output of thermal power unit during period t; P t loadcut is the load power during period t; P t jk is the power flow of transmission line jk, where transmission line jk refers to the transmission line connecting node j and node k in the power grid; P t load is the load power in period t; l is the total number of transmission lines in the power grid;

[0117] Constraints on thermal power units:

[0118]

[0119] In the above formula, are the maximum and minimum output power of thermal power units respectively; They are the ramp-up rate and ramp-down rate of thermal power units respectively; is the output of the thermal power unit in the t-1 period; is the total system reserve during period t; T t g,on , T t g,off They are the continuous operation time and continuous shutdown time of thermal power units in period t respectively; They are the shortest startup time and shortest shutdown time of the unit respectively;

[0120] Wind power constraints:

[0121]

[0122] Power flow constraints:

[0123]

[0124] In the above formula, g jk , b jk are the conductivity and susceptance of the transmission line jk respectively; V t j 、V t k are the voltage amplitudes of node j and node k in period t respectively; are the voltage phase angles of node j and node k in period t respectively; β represents the maximum allowable power limit parameter of the line; is the rated power handling capacity of the transmission line jk; V max 、V min are the upper and lower limits of the voltage amplitude of node j, respectively, and N represents the total number of nodes in the power grid;

[0125] S3. Solve the coordinated dispatch model on the MATLAB platform through the commercial software CPLEX to obtain the optimal coordinated dispatch plan of the power system; the optimal coordinated dispatch plan of the power system includes the power curve of the electric vehicle aggregation model, the output of the thermal power unit and the new energy unit; the dispatch power of a single electric vehicle is obtained by decomposing the power curve based on the electric vehicle aggregation model, and the decomposition formula is:

[0126]

[0127] In the above formula, P i EV,dis To decompose the dispatch power to a single electric vehicle; are the scaling factor and translation factor respectively.

[0128] Performance Verification:

[0129] The transmission line congestion relief method of the present invention is used to dispatch and operate various adjustable resources in the IEEE 14-bus system, and compare the changes in the congestion of the power grid lines with and without the participation of electric vehicles. Figure 2 As shown in Figure 1, the IEEE 14-bus system includes three thermal power units with a rated capacity of 200MW and three wind farms with a capacity of 60MW. The economic and technical parameters of each component and electric vehicle in the system are set. By analyzing the historical power flow of the IEEE 14-bus system, it can be concluded that line 2-3 and line 7-9 are two seriously congested lines. Line 2-3 refers to the transmission line connecting node 2 and node 3, and line 7-9 refers to the transmission line connecting node 7 and node 9. Therefore, three types of electric vehicle aggregation are set on line 2-3 and line 7-9, and electric vehicles with similar parameters are set as electric vehicles of the same type. The number of electric vehicles of each type is 500. The electric vehicle parameters of each node are shown in Table 1.

[0130] Table 1 Electric vehicle parameters

[0131]

[0132]

[0133] Get the power flow P of line 2-3 and line 7-9 before and after the electric vehicle participates tjk Changes such as Figure 3 As shown, the blocking time t of line 2-3 and line 7-9 block The changes are shown in Table 2:

[0134] Table 2 Line congestion

[0135]

[0136] From Table 2, we can see that the blocking time t of Line 2-3 and Line 7-9 after the participation of electric vehicles block has been greatly reduced and the line congestion has been improved; Figure 3 It can be seen that the participation of electric vehicles absorbs part of the line power, and the line flow is reduced when the flow exceeds the limit, which further reflects the flexible regulatory role of electric vehicles in improving the transmission capacity of the power grid.

[0137] Embodiment 2:

[0138] See also Figure 4 , a transmission line congestion relief system based on an electric vehicle aggregation model, comprising an electric vehicle aggregation model construction module, a coordination scheduling model construction module, and a calculation module; the electric vehicle aggregation model construction module is used to construct an electric vehicle monomer model, and to construct an electric vehicle aggregation model based on the electric vehicle monomer model; the electric vehicle aggregation model construction module is used to construct the following electric vehicle monomer models:

[0139] Ψ i = {R i P i EV ≤Y i};

[0140]

[0141]

[0142] A i =I q +diag(-1;-1) q×q ;

[0143] In the above formula, Ψ i Represents the single-unit model of electric vehicle; R i , Y i All of them are polyhedral system matrix of electric vehicles; P i EV is the power matrix of the electric vehicle; q is the length of the scheduling period T; I q is the q-dimensional identity matrix; are the q-dimensional matrices of the charging and discharging power of electric vehicle i; are the charging and discharging power of electric vehicle i respectively; are the q-dimensional matrices of the maximum charging and discharging power of electric vehicle i; are the maximum charging and discharging power of electric vehicle i; A i is the transformation matrix; A i The inverse matrix of are the q-dimensional matrices of the charging and discharging efficiencies of electric vehicle i; are the charging and discharging efficiencies of electric vehicle i respectively; diag is the function for constructing a diagonal matrix; are the q-dimensional matrices of the maximum and minimum available capacities of electric vehicle i, respectively; is the maximum and minimum available capacity of electric vehicle i; is the q-dimensional matrix of the initial capacity of electric vehicle i; is the initial capacity of electric vehicle i;

[0144]

[0145]

[0146] In the above formula, are the charging and discharging power of electric vehicle i in period t respectively; is the current capacity of electric vehicle i in time periods t and t-1; ΔT is a single time slot; N EV represents the total number of electric vehicles;

[0147] The electric vehicle aggregation model building module is also used to build the following electric vehicle aggregation model based on the electric vehicle monomer model:

[0148]

[0149] In the above formula, represents the electric vehicle aggregation model; represents the Minkowski sum; is the power curve of the electric vehicle aggregation model; Ω s It is an aggregation area for electric vehicles;

[0150] The coordinated dispatch model building module is used to connect the electric vehicle aggregation model to the congested transmission line in the power system, comprehensively consider the electric vehicle aggregation model, the thermal power unit and the flexible adjustment capability of the new energy unit, and build a coordinated dispatch model of the power system with the goal of reducing the total system operation cost; the total system operation cost includes fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost, and load reduction cost; the objective function of the coordinated dispatch model includes:

[0151] min C=C g +C cs +C wind +C loadcut ;

[0152]

[0153] In the above formula, C is the total cost of system operation; C g , C cs , C wind , C loadcut They include fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost, and load reduction cost; S is the number of segments of the coal consumption curve; are the cost coefficients of each segment in the coal consumption curve; is the output of thermal power units in each section of the coal consumption curve; They are the start and stop state variables of the thermal power unit in period t and period t-1 respectively. A value of 1 indicates that the thermal power unit is in the start state, and a value of 0 indicates that the thermal power unit is in the shutdown state; is the unit start-up and shutdown cost of thermal power units; W t , P t w are the available wind power and actual power generation of the wind farm respectively; c w is the penalty cost for wind abandonment; c cut is the penalty cost for load reduction; P t loadcut is the load reduction power in period t; N W is the number of wind farms; N G is the number of thermal power units;

[0154] The constraints of the coordinated scheduling model include:

[0155] System constraints:

[0156]

[0157] In the above formula, P t g is the output of the thermal power unit during period t; P t loadcut is the load power during period t; P t jk is the power flow of transmission line jk, where transmission line jk refers to the transmission line connecting node j and node k in the power grid; P t load is the load power in period t; l is the total number of transmission lines in the power grid;

[0158] Constraints on thermal power units:

[0159]

[0160] In the above formula, are the maximum and minimum output power of thermal power units respectively; They are the ramp-up rate and ramp-down rate of thermal power units respectively; is the output of the thermal power unit in the t-1 period; is the total system reserve during period t; T t g,on , T t g,off They are the continuous operation time and continuous shutdown time of thermal power units in period t respectively; They are the shortest startup time and shortest shutdown time of the unit respectively;

[0161] Wind power constraints:

[0162]

[0163] Power flow constraints:

[0164]

[0165] In the above formula, g jk 、b jk are the conductivity and susceptance of the transmission line jk respectively; V t j 、V t k are the voltage amplitudes of node j and node k in period t respectively; are the voltage phase angles of node j and node k in period t respectively; β represents the maximum allowable power limit parameter of the line; is the rated power handling capacity of the transmission line jk; V max 、V min are the upper and lower limits of the voltage amplitude of node j, respectively, and N represents the total number of nodes in the power grid;

[0166] The calculation module is used to solve the coordinated dispatch model to obtain the optimal coordinated dispatch plan of the power system; the optimal coordinated dispatch plan of the power system includes the power curve of the electric vehicle aggregation model, the output of the thermal power unit and the new energy unit; the dispatch power of a single electric vehicle is obtained by decomposing the power curve based on the electric vehicle aggregation model, and the decomposition formula is:

[0167]

[0168] In the above formula, P i EV,dis To decompose the dispatch power to a single electric vehicle; are the scaling factor and translation factor respectively.

[0169] Embodiment 3:

[0170] See also Figure 5 , a transmission line congestion relief device based on an electric vehicle aggregation model, comprising a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the transmission line congestion relief method described in Example 1 according to the instructions in the computer program code.

[0171] Embodiment 4:

[0172] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for alleviating transmission line congestion as described in Example 1.

[0173] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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 codes.

[0174] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes 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 generate 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.

[0175] 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.

[0176] 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.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A transmission line congestion relief method based on an electric vehicle aggregation model, characterized in that: The transmission line congestion relief method comprises: S1. Build an electric vehicle monomer model, and build an electric vehicle aggregation model based on the electric vehicle monomer model; S2. For the transmission lines that are congested when the electric vehicle aggregation model is connected to the power system, a coordinated dispatching model of the power system is constructed with the goal of reducing the total system operation cost by comprehensively considering the electric vehicle aggregation model, the thermal power unit and the flexible adjustment capability of the new energy unit; the total system operation cost includes fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost and load reduction cost; S3. Solve the coordinated dispatch model to obtain the optimal coordinated dispatch plan for the power system.

2. The method for alleviating transmission line congestion based on an electric vehicle aggregation model according to claim 1, characterized in that: The S1 includes: S11. Construct the following electric vehicle monomer model: Ai=Iq+diag(-1;-1)q × q; In the above formula, Ψi represents the single-body model of electric vehicles; Ri and Yi are both polyhedral system matrix of electric vehicles; is the power matrix of the electric vehicle; q is the length of the scheduling period T; Iq is the q-dimensional unit matrix; are the q-dimensional matrices of the charging and discharging power of electric vehicle i; are the charging and discharging power of electric vehicle i respectively; are the q-dimensional matrices of the maximum charging and discharging power of electric vehicle i; are the maximum charging and discharging power of electric vehicle i respectively; Ai is the transformation matrix; is the inverse matrix of Ai; are the q-dimensional matrices of the charging and discharging efficiencies of electric vehicle i; are the charging and discharging efficiencies of electric vehicle i respectively; diag is the function for constructing a diagonal matrix; are the q-dimensional matrices of the maximum and minimum available capacities of electric vehicle i, respectively; is the maximum and minimum available capacity of electric vehicle i; is the q-dimensional matrix of the initial capacity of electric vehicle i; is the initial capacity of electric vehicle i; In the above formula, are the charging and discharging power of electric vehicle i in period t respectively; is the current capacity of electric vehicle i in time periods t and t-1; ΔT is a single time slot; N EV represents the total number of electric vehicles; S12. Based on the electric vehicle monomer model constructed in S11, the following electric vehicle aggregation model is constructed: In the above formula, represents the electric vehicle aggregation model; represents the Minkowski sum; is the power curve of the electric vehicle aggregation model; Ωs is the aggregation area of ​​the electric vehicle.

3. The method for alleviating transmission line congestion based on an electric vehicle aggregation model according to claim 2 is characterized in that: The objective function of the coordinated scheduling model includes: minC=Cg+Ccs+Cwind+Cloadcut; In the above formula, C is the total cost of system operation; Cg, Ccs, Cwind, and Cloadcut respectively include fuel cost, start-up and shutdown cost of thermal power units, wind power reduction cost, and load reduction cost; S is the number of segments of the coal consumption curve; are the cost coefficients of each segment in the coal consumption curve; is the output of thermal power units in each section of the coal consumption curve; They are the start and stop state variables of the thermal power unit in period t and period t-1 respectively. A value of 1 indicates that the thermal power unit is in the start state, and a value of 0 indicates that the thermal power unit is in the shutdown state; is the unit start-up and shutdown cost of thermal power units; Wt, P t w are the available wind power and actual power generation of the wind farm respectively; cw is the penalty cost of wind abandonment; ccut is the penalty cost of load reduction; P t loadcut is the load reduction power in period t; N W is the number of wind farms; N G is the number of thermal power units; The constraints of the coordinated scheduling model include: System constraints: In the above formula, Ptg is the output of the thermal power unit in period t; Ptloadcut is the load power in period t; Ptjk is the power flow of transmission line jk, and transmission line jk refers to the transmission line connecting node j and node k in the power grid; P t load is the load power in period t; l is the total number of transmission lines in the power grid; Constraints on thermal power units: In the above formula, are the maximum and minimum output power of thermal power units respectively; They are the ramp-up rate and ramp-down rate of thermal power units respectively; is the output of the thermal power unit in the t-1 period; is the total system reserve in period t; Ttg,on and Ttg,off are the continuous operation time and continuous shutdown time of the thermal power unit in period t respectively; They are the shortest startup time and shortest shutdown time of thermal power units respectively; Wind power constraints: Power flow constraints: In the above formula, g jk bjk and bjk are the conductivity and susceptance of the transmission line jk respectively; V t j 、V t k are the voltage amplitudes of node j and node k in period t respectively; are the voltage phase angles of node j and node k in period t respectively; β represents the maximum allowable power limit parameter of the line; is the rated power handling capacity of transmission line jk; Vmax and Vmin are the upper and lower limits of the voltage amplitude of node j respectively, and N represents the total number of nodes in the power grid.

4. The method for alleviating transmission line congestion based on an electric vehicle aggregation model according to claim 2, characterized in that: The optimal coordinated dispatching scheme of the power system includes the power curve of the electric vehicle aggregation model, the output of thermal power units and new energy units; the dispatching power of a single electric vehicle is obtained by decomposing the power curve based on the electric vehicle aggregation model, and the decomposition formula is: In the above formula, Pi EV ,dis is the dispatch power decomposed into a single electric vehicle; are the scaling factor and translation factor respectively.

5. A transmission line congestion relief system based on an electric vehicle aggregation model, characterized by: The transmission line congestion relief system comprises: An electric vehicle aggregation model building module is used to build an electric vehicle monomer model, and to build an electric vehicle aggregation model based on the electric vehicle monomer model; A coordinated dispatch model building module is used to connect the electric vehicle aggregation model to the congested transmission line in the power system, comprehensively consider the electric vehicle aggregation model, the thermal power unit and the flexible adjustment capability of the new energy unit, and build a coordinated dispatch model of the power system with the goal of reducing the total system operation cost; the total system operation cost includes fuel cost, thermal power unit start-up and shutdown cost, wind power reduction cost, and load reduction cost; The calculation module is used to solve the coordinated dispatch model to obtain the optimal coordinated dispatch plan for the power system.

6. The transmission line congestion relief system based on electric vehicle aggregation model according to claim 5 is characterized by: The electric vehicle aggregation model building module is used to build the following electric vehicle monomer model: Ai=Iq+diag(-1;-1)q × q; In the above formula, Ψi represents the single-body model of electric vehicles; Ri and Yi are both polyhedral system matrix of electric vehicles; is the power matrix of the electric vehicle; q is the length of the scheduling period T; Iq is the q-dimensional unit matrix; are the q-dimensional matrices of the charging and discharging power of electric vehicle i; are the charging and discharging power of electric vehicle i respectively; are the q-dimensional matrices of the maximum charging and discharging power of electric vehicle i; are the maximum charging and discharging power of electric vehicle i respectively; Ai is the transformation matrix; is the inverse matrix of Ai; are the q-dimensional matrices of the charging and discharging efficiencies of electric vehicle i; are the charging and discharging efficiencies of electric vehicle i respectively; diag is the function for constructing a diagonal matrix; are the q-dimensional matrices of the maximum and minimum available capacities of electric vehicle i, respectively; is the maximum and minimum available capacity of electric vehicle i; is the q-dimensional matrix of the initial capacity of electric vehicle i; is the initial capacity of electric vehicle i; In the above formula, are the charging and discharging power of electric vehicle i in period t respectively; is the current capacity of electric vehicle i in time periods t and t-1; ΔT is a single time slot; N EV represents the total number of electric vehicles; The electric vehicle aggregation model building module is also used to build the following electric vehicle aggregation model based on the electric vehicle monomer model: In the above formula, represents the electric vehicle aggregation model; represents the Minkowski sum; is the power curve of the electric vehicle aggregation model; Ωs is the aggregation area of ​​the electric vehicle.

7. The transmission line congestion relief system based on electric vehicle aggregation model according to claim 6 is characterized by: The objective function of the coordinated scheduling model includes: minC=Cg+Ccs+Cwind+Cloadcut; In the above formula, C is the total cost of system operation; Cg, Ccs, Cwind, and Cloadcut respectively include fuel cost, start-up and shutdown cost of thermal power units, wind power reduction cost, and load reduction cost; S is the number of segments of the coal consumption curve; are the cost coefficients of each segment in the coal consumption curve; is the output of thermal power units in each section of the coal consumption curve; They are the start and stop state variables of the thermal power unit in period t and period t-1 respectively. A value of 1 indicates that the thermal power unit is in the start state, and a value of 0 indicates that the thermal power unit is in the shutdown state; is the unit start-up and shutdown cost of thermal power units; Wt, P t w are the available wind power and actual power generation of the wind farm respectively; cw is the penalty cost of wind abandonment; ccut is the penalty cost of load reduction; P t loadcut is the load reduction power in period t; N W is the number of wind farms; N G is the number of thermal power units; The constraints of the coordinated scheduling model include: System constraints: In the above formula, Ptg is the output of the thermal power unit in period t; Ptloadcut is the load power in period t; Ptjk is the power flow of transmission line jk, and transmission line jk refers to the transmission line connecting node j and node k in the power grid; P t load is the load power in period t; l is the total number of transmission lines in the power grid; Constraints on thermal power units: In the above formula, are the maximum and minimum output power of thermal power units respectively; They are the ramp-up rate and ramp-down rate of thermal power units respectively; is the output of the thermal power unit in the t-1 period; is the total system reserve in period t; Ttg,on and Ttg,off are the continuous operation time and continuous shutdown time of the thermal power unit in period t respectively; They are the shortest startup time and shortest shutdown time of the unit respectively; Wind power constraints: Power flow constraints: In the above formula, g jk bjk and bjk are the conductivity and susceptance of the transmission line jk respectively; V t j 、V t k are the voltage amplitudes of node j and node k in period t respectively; are the voltage phase angles of node j and node k in period t respectively; β represents the maximum allowable power limit parameter of the line; is the rated power handling capacity of transmission line jk; Vmax and Vmin are the upper and lower limits of the voltage amplitude of node j, respectively; N represents the total number of nodes in the power grid.

8. The transmission line congestion relief system based on electric vehicle aggregation model according to claim 6 is characterized by: The optimal coordinated dispatching scheme of the power system includes the power curve of the electric vehicle aggregation model, the output of thermal power units and new energy units; the dispatching power of a single electric vehicle is obtained by decomposing the power curve based on the electric vehicle aggregation model, and the decomposition formula is: In the above formula, Pi EV ,dis is the dispatch power decomposed into a single electric vehicle; are the scaling factor and translation factor respectively.

9. Transmission line congestion relief equipment based on electric vehicle aggregation model, characterized by: The transmission line congestion relief device includes a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the transmission line congestion relief method as described in claims 1-4 according to the instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for alleviating transmission line congestion as described in claims 1-4 is implemented.

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