Distributed energy acceptance capability improving method and device, equipment and medium

Abstract

The invention belongs to the technical field of electric power automation, and particularly relates to a distributed energy acceptance capability improving method and device, equipment and a medium. According to the method, the maximum charging power value, the minimum discharging power value and the maximum discharging power value of each electric vehicle in each dispatching period are solved by taking the minimum charging cost and the minimum total battery loss cost of a single electric vehicle as a target, and the charging and discharging constraints of an electric vehicle cluster are determined; constructing a second target function by taking the minimum total cost of the system as a target, and determining constraint conditions corresponding to the second target function, including charge and discharge constraints of the electric vehicle cluster; and solving the second objective function to obtain the output of the peak regulation unit and the charging and discharging power of the electric vehicle cluster in each scheduling time period. According to the method, the double-layer optimization model and the constraint condition are constructed, the minimum total economic cost is taken as the target, the charging and discharging of the electric vehicle cluster are taken as the scheduling amount to participate in peak regulation of the power grid, and the total cost of the system is reduced while the distributed energy acceptance capability is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power automation, and specifically relates to a method, device, equipment and medium for improving the acceptance capacity of distributed energy. Background Art

[0002] With the massive growth of distributed photovoltaic, wind power and other distributed energy sources, when the total output of distributed energy is large and the load absorption capacity is insufficient, according to the principle of connecting distributed energy, the peak-shaving units of the transmission network need to reduce their output or even perform deep peak-shaving to accommodate distributed energy. Therefore, accepting massive distributed photovoltaic, wind power and other distributed energy sources has brought serious challenges to the peak-shaving of the transmission network. In particular, when the load absorption capacity of distributed energy is insufficient and the output of distributed energy is too large, the power grid may even need to perform deep peak-shaving, which will cause huge economic costs. Even if the deep peak-shaving reaches its limit, it cannot fully accommodate the output of massive distributed energy. Therefore, how to improve the transmission network's acceptance capacity for massive distributed energy is of great significance to the real-time operation and control of the transmission network. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, equipment and medium for improving the distributed energy acceptance capacity, so as to solve the problem in the prior art that when the load's capacity to absorb distributed energy is insufficient and the distributed energy output is too large, the power grid may even need to perform deep peak regulation, thus causing huge economic costs.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a method for improving the distributed energy acceptance capacity, comprising the following steps:

[0006] Determine the number of dispatch periods for each electric vehicle and the duration of the corresponding dispatch period;

[0007] Based on the number of scheduling periods and the duration of the corresponding scheduling periods, a first objective function is constructed with the goal of minimizing the charging cost of a single electric vehicle and the total cost of battery loss, and a first constraint condition corresponding to the first objective function is determined; based on the first constraint condition, the first objective function is solved to obtain the maximum charging power of each electric vehicle in each scheduling period, and the minimum and maximum discharge power;

[0008] Based on the maximum charging power, minimum and maximum discharging power of each electric vehicle in each scheduling period, the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period are determined, and the charging and discharging constraints of the electric vehicle cluster are determined based on the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period;

[0009] Determine each cost in the system, construct a second objective function with the goal of minimizing the total system cost, and determine a second constraint condition corresponding to the second objective function; wherein the second constraint condition includes a charging and discharging constraint of the electric vehicle cluster; solve the second objective function based on the second constraint condition to obtain the output of the peak-shaving unit and the charging and discharging power of the electric vehicle cluster in each scheduling period.

[0010] Furthermore, the first objective function is expressed as follows:

[0011]

[0012] Where: F i is the charging and discharging cost of the i-th electric vehicle; Ti is the dispatch period number of the i-th electric vehicle, t=1,…… Ti ; is the maximum value of the charging power of the i-th electric vehicle in the scheduling period t; The minimum value of the discharge power of the i-th electric vehicle in the dispatching period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; Δt is the duration of the scheduling period; α is the battery loss cost coefficient; The maximum value of the discharge power of the i-th electric vehicle in time period t; η dis is the discharge efficiency of electric vehicles.

[0013] Furthermore, the first constraint condition includes an electric vehicle charging and discharging cost constraint, an electric vehicle charging and discharging power constraint, an electric vehicle charging and discharging mutual exclusion constraint, an electric vehicle charge state constraint and an electric vehicle climbing constraint.

[0014] Furthermore, the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each dispatching period are determined, including:

[0015] Determine the number of electric vehicles N on the grid in the mth electric vehicle cluster m ;

[0016] According to the number of electric vehicles on the grid N m and the maximum value of the charging power of a single electric vehicle in the scheduling period t, determine the maximum value of the charging power of the mth electric vehicle cluster in the scheduling period t;

[0017] According to the number of electric vehicles on the grid N m and the minimum value of the discharge power of a single electric vehicle in the dispatch period t, determine the minimum value of the discharge power of the mth electric vehicle cluster in the dispatch period t;

[0018] According to the number of electric vehicles on the grid N mThe maximum value of the discharge power of the mth electric vehicle cluster in the scheduling period t is determined by the maximum value of the discharge power of a single electric vehicle in the scheduling period t.

[0019] Furthermore, the second objective function is expressed as follows:

[0020]

[0021] Where: F is the total system cost, F1 is the charging and discharging cost of the electric vehicle cluster, F2 is the peak load cost of the thermal power unit; M is the number of electric vehicle clusters, T is the number of dispatching periods; P ch,m,t and P dis,m,t are the charging and discharging power of electric vehicle cluster m in the scheduling period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; δ is the battery loss cost coefficient; U1 and U2 represent the number of conventional peak-shaving units and deep peak-shaving units respectively; W u1,t is the output of the u1th conventional peak-shaving unit in the dispatching period t, C u1 is the cost of the u1th conventional peak-shaving unit in the dispatching period t; W u2,t is the output of the u2-th deep peak-shaving unit in the dispatching period t, C u2 is the cost of the u2-th deep peak load unit in the dispatching period t; η ch , η dis are the charging and discharging efficiencies of electric vehicles, respectively.

[0022] Furthermore, the second constraint condition also includes: power balance constraint, upper and lower limit constraints of generator set output power, and DC power flow constraint; wherein the generator set includes conventional peak-shaving units and deep peak-shaving units.

[0023] In a second aspect of the present invention, a device for improving the acceptance capacity of distributed energy is provided, comprising:

[0024] A first determination module is used to determine the number of dispatch periods for each electric vehicle and the duration of the corresponding dispatch period;

[0025] A first optimization module is used to construct a first objective function based on the number of scheduling periods and the duration of the corresponding scheduling periods, with the goal of minimizing the charging cost of a single electric vehicle and the total cost of battery loss, and determine a first constraint condition corresponding to the first objective function; solve the first objective function based on the first constraint condition to obtain the maximum charging power of each electric vehicle in each scheduling period, and the minimum and maximum discharge power;

[0026] A second determination module is used to determine the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period based on the maximum charging power, and the minimum and maximum discharging power of each electric vehicle in each scheduling period, and determine the charging and discharging constraints of the electric vehicle cluster based on the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period;

[0027] The second optimization module is used to determine the various costs in the system, construct a second objective function with the goal of minimizing the total system cost, and determine the second constraint condition corresponding to the second objective function; wherein the second constraint condition includes the charging and discharging constraint of the electric vehicle cluster; the second objective function is solved based on the second constraint condition to obtain the output of the peak-shaving unit and the charging and discharging power of the electric vehicle cluster in each scheduling period.

[0028] Furthermore, in the second optimization module, the second objective function is expressed as follows:

[0029]

[0030] Where: F is the total system cost, F1 is the charging and discharging cost of the electric vehicle cluster, F2 is the peak load cost of the thermal power unit; M is the number of electric vehicle clusters, T is the number of dispatching periods; P ch,m,t and P dis,m,t are the charging and discharging power of electric vehicle cluster m in the scheduling period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; δ is the battery loss cost coefficient; U1 and U2 represent the number of conventional peak-shaving units and deep peak-shaving units respectively; W u1,t is the output of the u1th conventional peak-shaving unit in the dispatching period t, C u1 is the cost of the u1th conventional peak-shaving unit in the dispatching period t; W u2,t is the output of the u2-th deep peak-shaving unit in the dispatching period t, C u2 is the cost of the u2-th deep peak load unit in the dispatching period t; η ch , η dis are the charging and discharging efficiencies of electric vehicles, respectively.

[0031] According to a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the method for improving the distributed energy acceptance capacity as described above.

[0032] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the method for improving the distributed energy acceptance capacity as described above is implemented.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] In the context of massive distributed energy access to the power grid, in order to accept distributed energy and improve the peak-shaving capacity of the power grid, the distributed energy acceptance capacity improvement method provided by this solution, by constructing a two-layer optimization model and constraints, takes the minimum total economic cost as the goal, and uses the charging and discharging of electric vehicle clusters as the dispatching amount to participate in the peak-shaving of the power grid, thus realizing the cost-benefit analysis of the dispatching strategy, and improving the overall acceptance capacity of the power grid for distributed energy, while improving the distributed energy acceptance capacity and reducing the total system cost. A distributed energy acceptance capacity improvement device, electronic device and computer-readable storage medium provided by the present invention also solve the problems raised in the background technology section.

[0035] Due to the intermittent and uncertain nature of distributed energy, the grid is subject to many restrictions when accepting this energy. Traditional grid planning and dispatching methods are often unable to cope with the large-scale access of distributed energy, resulting in limited grid acceptance capacity. This solution optimizes the charging and discharging behavior of electric vehicle clusters and uses electric vehicles as distributed energy storage units, effectively improving the grid's acceptance capacity for distributed energy, and can achieve large-scale access to distributed energy without affecting the stable operation of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings in the specification, which constitute a part of this application, are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0037] In the attached picture:

[0038] Figure 1 A schematic diagram of a process for improving the distributed energy acceptance capacity according to an embodiment of the present invention;

[0039] Figure 2 This is a structural block diagram of a device for improving the acceptance capacity of distributed energy sources according to an embodiment of the present invention;

[0040] Figure 3 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0042] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0043] Example 1

[0044] The acceptance of massive distributed energy by the transmission network has brought certain pressure and challenges to the peak load regulation of the transmission network, especially when the load capacity of distributed energy is insufficient and the output of distributed energy is too large, the power grid may even need to carry out deep peak load regulation, which will cause huge economic costs. Even if the deep peak load regulation reaches its limit, it cannot fully accept the output of massive distributed energy. Therefore, how to improve the acceptance capacity of the transmission network for massive distributed energy is of great significance to the real-time operation and control of the transmission network. Electric vehicles, as a kind of electric energy storage, can be charged and discharged, with high efficiency and high speed. On the premise of meeting the charging needs of electric vehicles, a large number of electric vehicles on the grid can provide certain active assistance and support for the peak load regulation of the power grid. Therefore, this plan takes electric vehicle clusters as units to participate in the peak load regulation of the power grid, with the goal of minimizing the total economic cost, mobilizing a large number of electric vehicles on the grid to participate in the peak load regulation of the power grid, and calculating the charging and discharging power of the electric vehicle cluster in each dispatching period, so as to support the peak load regulation capacity of the power grid through the reasonable charging and discharging of the electric vehicle cluster, improve the acceptance capacity of the power grid for distributed energy, and ensure the safe and stable operation of the power grid.

[0045] like Figure 1 As shown, a method for improving the acceptance capacity of distributed energy includes the following steps:

[0046] S1. Determine the number of dispatch periods for each electric vehicle and the duration of the corresponding dispatch period.

[0047] S2. Based on the number of scheduling periods and the duration of the corresponding scheduling periods, a first objective function is constructed with the goal of minimizing the charging cost of a single electric vehicle and the total battery loss cost, and a first constraint condition corresponding to the first objective function is determined; based on the first constraint condition, the first objective function is solved to obtain the maximum charging power of each electric vehicle in each scheduling period, as well as the minimum and maximum discharge power.

[0048] S3. Based on the maximum charging power, and the minimum and maximum discharging power of each electric vehicle in each scheduling period, determine the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period, and determine the charging and discharging constraints of the electric vehicle cluster based on the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period.

[0049] S4. Determine each cost in the system, construct a second objective function with the goal of minimizing the total system cost, and determine the second constraint corresponding to the second objective function; wherein the second constraint includes the charging and discharging constraint of the electric vehicle cluster; solve the second objective function based on the second constraint to obtain the output of the peak-shaving unit and the charging and discharging power of the electric vehicle cluster in each scheduling period.

[0050] The distributed energy acceptance capacity improvement method provided by the above scheme improves the distributed energy acceptance capacity by optimizing the charging and discharging behavior of electric vehicles (EVs), taking into account the charging cost and battery loss cost of a single electric vehicle, and realizing the optimal charging and discharging strategy of electric vehicle clusters in multiple scheduling periods by constructing objective functions and constraints. It effectively utilizes the energy storage capacity of electric vehicles, provides flexible adjustment means for the power grid, and enhances the power grid's acceptance capacity for distributed energy.

[0051] In order to further explain and illustrate this solution, in a more specific embodiment of this solution, a method for improving the acceptance capacity of distributed energy is also provided, including the following steps:

[0052] S100: Determine the number of dispatching time periods for each electric vehicle and the duration of the corresponding dispatching time periods.

[0053] S200. Based on the number of scheduling periods and the duration of the corresponding scheduling periods, a first objective function is constructed with the goal of minimizing the charging cost of a single electric vehicle and the total battery loss cost, and a first constraint condition corresponding to the first objective function is determined; based on the first constraint condition, the first objective function is solved to obtain the maximum charging power of each electric vehicle in each scheduling period, as well as the minimum and maximum discharge power.

[0054] Specifically, the first objective function is expressed as follows:

[0055]

[0056] Where: F i is the charging and discharging cost of the i-th electric vehicle; Ti is the dispatch period number of the i-th electric vehicle, t=1, ...Ti; is the maximum value of the charging power of the i-th electric vehicle in the scheduling period t, The minimum discharge power of the i-th electric vehicle in the dispatch period t, The maximum value of the discharge power of the i-th electric vehicle in time period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; Δt is the duration of the scheduling period; α is the battery loss cost coefficient; η dis is the discharge efficiency of electric vehicles.

[0057] Specifically, the first constraint conditions include electric vehicle charging and discharging cost constraint, electric vehicle charging and discharging power constraint, electric vehicle charging and discharging mutual exclusion constraint, electric vehicle charge state constraint and electric vehicle climbing constraint.

[0058] S300, based on the maximum charging power, and the minimum and maximum discharging power of each electric vehicle in each scheduling period, determine the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period, and determine the charging and discharging constraints of the electric vehicle cluster based on the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period.

[0059] Specifically, the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each dispatching period are determined, including:

[0060] Determine the number of electric vehicles N on the grid in the mth electric vehicle cluster m ;

[0061] According to the number of electric vehicles on the grid N m and the maximum value of the charging power of a single electric vehicle in the scheduling period t, determine the maximum value of the charging power of the mth electric vehicle cluster in the scheduling period t;

[0062] According to the number of electric vehicles on the grid N m and the minimum value of the discharge power of a single electric vehicle in the dispatch period t, determine the minimum value of the discharge power of the mth electric vehicle cluster in the dispatch period t;

[0063] According to the number of electric vehicles on the grid N m The maximum value of the discharge power of the mth electric vehicle cluster in the scheduling period t is determined by the maximum value of the discharge power of a single electric vehicle in the scheduling period t.

[0064] S400. Determine each cost in the system, construct a second objective function with the goal of minimizing the total system cost, and determine a second constraint condition corresponding to the second objective function; wherein the second constraint condition includes a charging and discharging constraint of the electric vehicle cluster; solve the second objective function based on the second constraint condition to obtain the output of the peak-shaving unit and the charging and discharging power of the electric vehicle cluster in each scheduling period.

[0065] Specifically, the second objective function is expressed as follows:

[0066]

[0067] Where: F is the total system cost, F1 is the charging and discharging cost of the electric vehicle cluster, F2 is the peak load cost of the thermal power unit; M is the number of electric vehicle clusters, T is the number of dispatching periods; P ch,m,t and P dis,m,tare the charging and discharging power of electric vehicle cluster m in the scheduling period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; δ is the battery loss cost coefficient; U1 and U2 represent the number of conventional peak-shaving units and deep peak-shaving units respectively; W u1,t is the output of the u1th conventional peak-shaving unit in the dispatching period t, C u1 is the cost of the u1th conventional peak-shaving unit in the dispatching period t; W u2,t is the output of the u2-th deep peak-shaving unit in the dispatching period t, C u2 is the cost of the u2-th deep peak load unit in the dispatching period t; η ch , η dis are the charging and discharging efficiency of electric vehicles respectively.

[0068] Specifically, the second constraint condition also includes: power balance constraint, upper and lower limit constraints of generator set output power, and DC power flow constraint; wherein the generator set includes conventional peak-shaving units and deep peak-shaving units.

[0069] Under the premise of meeting the travel needs of electric vehicles, this scheme takes the minimum charging and discharging cost of electric vehicles within the grid time as the optimization goal, considers the charging and discharging constraints of electric vehicles themselves, and establishes an optimization model to solve the maximum charging power of electric vehicles in each scheduling period, the minimum and maximum values ​​of the discharging power, so as to obtain the maximum charging power of the electric vehicle cluster, the minimum and maximum discharging power. Taking the minimum total charging and discharging cost of the grid peak-shaving units and electric vehicles as the optimization goal, considering the grid network constraints, and taking the acceptance of a certain amount of distributed energy output as the goal, the output of the distributed energy peak-shaving units and the charging and discharging power of the electric vehicle cluster are obtained, thereby improving the grid's acceptance of distributed energy.

[0070] The application of this solution is as follows:

[0071] (1) Can be applied to provincial and local smart grid dispatching and control systems;

[0072] (2) It can be applied to the engineering implementation of the control cloud platform;

[0073] (3) Can be applied to the engineering implementation of the new generation of dispatching control systems;

[0074] (4) It can provide basic services for the safe operation of power grids rich in new energy resources.

[0075] In summary, this scheme has good prospects for promotion and application.

[0076] In a more specific embodiment, this solution also provides a method for improving the distributed energy acceptance capacity, and the specific steps are as follows:

[0077] 1. Calculation of the limit value of charging and discharging power of electric vehicles in each dispatching period

[0078] The normal charging completion time for electric vehicles is:

[0079]

[0080] Where: is the normal protocol charging power of the i-th electric vehicle, t i,m The time required for the i-th electric vehicle to reach the desired state of charge according to the normal charging power protocol, is the expected state of charge of the ith electric vehicle, The charge state of the i-th electric vehicle at the beginning of the scheduling period t is: is the capacity of the battery of the i-th electric car.

[0081] If the electric vehicle leaves the grid after being fully charged, its network time is:

[0082] t i,p =t i,m (2)

[0083] If an electric vehicle has an estimated off-grid time, its on-grid time is:

[0084] t i,p =t i,s (3)

[0085] Where: t i,s is the expected off-grid time of the i-th electric vehicle.

[0086] The objective function is to minimize the total cost of charging a single electric vehicle and battery loss:

[0087]

[0088] Where: F i is the charging and discharging cost of the i-th electric vehicle, α is the battery loss cost coefficient; is the maximum value of the charging power of the i-th electric vehicle in the scheduling period t, The minimum discharge power of the i-th electric vehicle in the dispatch period t, The maximum discharge power of the i-th electric vehicle in time period t, Δt is the duration of the scheduling period.

[0089] The number of dispatch periods for electric vehicles is:

[0090]

[0091] Where: Ti is the number of dispatch periods for the i-th electric vehicle.

[0092] The constraints are:

[0093] (1) Electric vehicle charging and discharging cost constraints

[0094]

[0095] (2) Electric vehicle charging and discharging power constraints

[0096]

[0097] Where: P i,rated is the rated charging power of the i-th electric vehicle.

[0098] (3) Mutual Exclusive Constraints on Electric Vehicle Charging and Discharging

[0099]

[0100] (4) Electric vehicle state of charge constraints

[0101] At the end of the dispatch period, the state of charge of the electric vehicle should meet the following requirements:

[0102]

[0103] The physical meaning of formula (9) is that the state of charge can meet the expected state of charge of the electric vehicle owner.

[0104] (5) Electric vehicle climbing constraints

[0105] The single-period climbing amount of electric vehicle power should be less than the rated power:

[0106]

[0107] 2. The upper and lower limits of charging and discharging power of electric vehicle clusters in each dispatch period

[0108]

[0109] Where: For the m The maximum value of the charging power of an electric vehicle cluster in the scheduling period t, is the minimum discharge power of the mth electric vehicle cluster in the scheduling period t, is the maximum discharge power of the mth electric vehicle cluster in the scheduling period t, N m is the number of electric vehicles on the grid in the mth electric vehicle cluster.

[0110] 3. Methods for electric vehicle clusters to participate in peak load regulation of transmission grids and improve the capacity of distributed energy acceptance

[0111] 3.1 Objective Function

[0112] The dispatching cost includes the charging and discharging cost of electric vehicle clusters and the peak load regulation cost of thermal power units, with the goal of minimizing the total dispatching cost:

[0113]

[0114] Where: F is the total system cost, F1 is the charging and discharging cost of the electric vehicle cluster, and F2 is the peak load regulation cost of the thermal power unit, including conventional peak load regulation and deep peak load regulation; C ch,t and C dis,t P is the charging and discharging electricity price of electric vehicles in period t; ch,m,t and P dis,m,t is the charge and discharge power of electric vehicle cluster m in the dispatch period t; M is the number of electric vehicle clusters, T is the number of dispatch periods; δ is the battery loss cost coefficient; W u1,t is the output of the u1th conventional peak-shaving unit in the dispatching period t, C u1 is the cost of the u1th conventional peak-shaving unit in the dispatching period t; W u2,t is the output of the u2-th deep peak-shaving unit in the dispatching period t, C u2 is the cost of the u2th deep peak-shaving unit in the dispatch period t.

[0115] 3.2 Constraints

[0116] 1) Power balance constraints

[0117]

[0118] Where: W u3,t is the total output of non-peaking units except the peaking units during the dispatching period t, W u4,t is the sum of the outputs of all distributed generation sources in the dispatch period t, P t load is the sum of the powers of all loads in the dispatch period t.

[0119] 2) Upper and lower limits of generator set output power

[0120] Conventional peak load units:

[0121] W u1,t,min ≤W u1,t ≤W u1,t,max (14)

[0122] Deep peak load unit:

[0123] W u2,t,min ≤W u2,t ≤W u2,t,max (15)

[0124] Where: W u1,t,min and Wu1,t,max are the minimum technical output and maximum output of unit u1 respectively; W u2,t,min and W u2,t,max They are the minimum technical output and maximum output of unit u2 respectively.

[0125] 3) DC power flow constraints

[0126]

[0127] Where: M l,i is the DC power flow transfer distribution factor of node i to line l, M l,j is the DC power flow transfer distribution factor of node j to line l; is the transmission power limit of line l; I is the number of power nodes, N D is the number of load nodes; W i,t is the power generation injection of node i in scheduling period t, is the load injection of node j in scheduling period t.

[0128] 4) Charging and discharging constraints of electric vehicle clusters

[0129]

[0130] Calculation steps

[0131] 1. Taking formula (4) as the objective function and formulas (5) to (10) as the constraint conditions, a constrained optimization model is constructed to solve the maximum value of the charging power, the minimum value and the maximum value of the discharging power of each electric vehicle in each scheduling period;

[0132] 2. Calculate the maximum value of charging power, minimum value and maximum value of discharging power of each electric vehicle cluster in each period according to formula (11);

[0133] 3. Taking formula (12) as the objective function and formulas (13) to (18) as constraints, a constrained optimization model is constructed to solve the output of the peak-shaving units and the charging and discharging power of the electric vehicle cluster in each dispatching period when accepting a certain amount of distributed energy output.

[0134] Example 2

[0135] like Figure 2 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a distributed energy acceptance capacity improvement device, comprising:

[0136] A first determination module is used to determine the number of dispatch periods for each electric vehicle and the duration of the corresponding dispatch period;

[0137] A first optimization module is used to construct a first objective function based on the number of scheduling periods and the duration of the corresponding scheduling periods, with the goal of minimizing the charging cost of a single electric vehicle and the total cost of battery loss, and determine a first constraint condition corresponding to the first objective function; solve the first objective function based on the first constraint condition to obtain the maximum charging power of each electric vehicle in each scheduling period, and the minimum and maximum discharge power;

[0138] A second determination module is used to determine the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period based on the maximum charging power, and the minimum and maximum discharging power of each electric vehicle in each scheduling period, and determine the charging and discharging constraints of the electric vehicle cluster based on the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period;

[0139] The second optimization module is used to determine the various costs in the system, construct a second objective function with the goal of minimizing the total system cost, and determine the second constraint condition corresponding to the second objective function; wherein the second constraint condition includes the charging and discharging constraint of the electric vehicle cluster; the second objective function is solved based on the second constraint condition to obtain the output of the peak-shaving unit and the charging and discharging power of the electric vehicle cluster in each scheduling period.

[0140] In the second optimization module, the second objective function is expressed as follows:

[0141]

[0142] Where: F is the total system cost, F1 is the charging and discharging cost of the electric vehicle cluster, F2 is the peak load cost of the thermal power unit; M is the number of electric vehicle clusters, T is the number of dispatching periods; P ch,m,t and P dis,m,t are the charging and discharging power of electric vehicle cluster m in the scheduling period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; δ is the battery loss cost coefficient; U1 and U2 represent the number of conventional peak-shaving units and deep peak-shaving units respectively; W u1,t is the output of the u1th conventional peak-shaving unit in the dispatching period t, C u1 is the cost of the u1th conventional peak-shaving unit in the dispatching period t; W u2,t is the output of the u2-th deep peak-shaving unit in the dispatching period t, C u2 is the cost of the u2-th deep peak load unit in the dispatching period t; η ch , η dis are the charging and discharging efficiencies of electric vehicles, respectively.

[0143] Example 3

[0144] like Figure 3As shown, the present invention also provides an electronic device 100 for implementing a method for improving the acceptance capacity of distributed energy in Example 1;

[0145] The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0146] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of a method for improving the acceptance capacity of distributed energy in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0147] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0148] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0149] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for improving the acceptance capacity of distributed energy resources, and the processor 102 can execute the plurality of instructions to implement:

[0150] Determine the number of dispatch periods for each electric vehicle and the duration of the corresponding dispatch period;

[0151] Based on the number of scheduling periods and the duration of the corresponding scheduling periods, a first objective function is constructed with the goal of minimizing the charging cost of a single electric vehicle and the total cost of battery loss, and a first constraint condition corresponding to the first objective function is determined; based on the first constraint condition, the first objective function is solved to obtain the maximum charging power of each electric vehicle in each scheduling period, and the minimum and maximum discharge power;

[0152] Based on the maximum charging power, minimum and maximum discharging power of each electric vehicle in each scheduling period, the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period are determined, and the charging and discharging constraints of the electric vehicle cluster are determined based on the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period;

[0153] Determine each cost in the system, construct a second objective function with the goal of minimizing the total system cost, and determine a second constraint condition corresponding to the second objective function; wherein the second constraint condition includes a charging and discharging constraint of the electric vehicle cluster; solve the second objective function based on the second constraint condition to obtain the output of the peak-shaving unit and the charging and discharging power of the electric vehicle cluster in each scheduling period.

[0154] Example 4

[0155] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0156] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] 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.

[0159] 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.

[0160] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0161] 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 method for improving distributed energy acceptance capacity, characterized in that: The steps include: Determine the number of dispatch periods for each electric vehicle and the duration of the corresponding dispatch period; Based on the number of scheduling periods and the duration of the corresponding scheduling periods, a first objective function is constructed with the goal of minimizing the charging cost of a single electric vehicle and the total cost of battery loss, and a first constraint condition corresponding to the first objective function is determined; Solving the first objective function based on the first constraint condition to obtain the maximum charging power, and the minimum and maximum discharging power of each electric vehicle in each scheduling period; Based on the maximum charging power, minimum and maximum discharging power of each electric vehicle in each scheduling period, the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period are determined, and the charging and discharging constraints of the electric vehicle cluster are determined based on the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period; Determine each cost in the system, construct a second objective function with the goal of minimizing the total system cost, and determine a second constraint condition corresponding to the second objective function; wherein the second constraint condition includes a charging and discharging constraint of the electric vehicle cluster; The second objective function is solved based on the second constraint condition to obtain the output of the peak-shaving unit and the charging and discharging power of the electric vehicle cluster in each scheduling period.

2. The method for improving distributed energy acceptance capacity according to claim 1, characterized in that: The first objective function is expressed as follows: Where: F i is the charging and discharging cost of the i-th electric vehicle; Ti is the dispatch period number of the i-th electric vehicle, t=1,…… Ti ; is the maximum value of the charging power of the i-th electric vehicle in the scheduling period t, The minimum discharge power of the i-th electric vehicle in the dispatch period t, The maximum value of the discharge power of the i-th electric vehicle in time period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; Δt is the duration of the scheduling period; α is the battery loss cost coefficient; η dis is the discharge efficiency of electric vehicles.

3. The method for improving distributed energy acceptance capacity according to claim 1, characterized in that: The first constraint conditions include electric vehicle charging and discharging cost constraint, electric vehicle charging and discharging power constraint, electric vehicle charging and discharging mutual exclusion constraint, electric vehicle charge state constraint and electric vehicle climbing constraint.

4. The method for improving the distributed energy acceptance capacity according to claim 1, characterized in that: Determine the upper and lower limits of charging and discharging power for each dispatch period of the electric vehicle cluster, including: Determine the number of electric vehicles N on the grid in the mth electric vehicle cluster m ; According to the number of electric vehicles on the grid N m and the maximum value of the charging power of a single electric vehicle in the scheduling period t, determine the maximum value of the charging power of the mth electric vehicle cluster in the scheduling period t; According to the number of electric vehicles on the grid N m and the minimum value of the discharge power of a single electric vehicle in the dispatch period t, determine the minimum value of the discharge power of the mth electric vehicle cluster in the dispatch period t; According to the number of electric vehicles on the grid N m The maximum value of the discharge power of the mth electric vehicle cluster in the scheduling period t is determined by the maximum value of the discharge power of a single electric vehicle in the scheduling period t.

5. The method for improving distributed energy acceptance capacity according to claim 1, characterized in that: The second objective function is expressed as follows: Where: F is the total system cost, F1 is the charging and discharging cost of the electric vehicle cluster, F2 is the peak load cost of the thermal power unit; M is the number of electric vehicle clusters, T is the number of dispatching periods; P ch,m,t and P dis,m,t are the charging and discharging power of electric vehicle cluster m in the scheduling period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; δ is the battery loss cost coefficient; U1 and U2 represent the number of conventional peak-shaving units and deep peak-shaving units respectively; W u1,t is the output of the u1th conventional peak-shaving unit in the dispatching period t, C u1 is the cost of the u1th conventional peak-shaving unit in the dispatching period t; W u2,t is the output of the u2-th deep peak-shaving unit in the dispatching period t, C u2 is the cost of the u2-th deep peak-shaving unit in the dispatch period t; η ch , η dis are the charging and discharging efficiencies of electric vehicles, respectively.

6. The method for improving the distributed energy acceptance capacity according to claim 1, characterized in that: The second constraint condition also includes: power balance constraint, upper and lower limit constraints of generator set output power, and DC power flow constraint; wherein the generator set includes conventional peak-shaving units and deep peak-shaving units.

7. A distributed energy acceptance capacity enhancement device, characterized in that: include: A first determination module is used to determine the number of dispatch periods for each electric vehicle and the duration of the corresponding dispatch period; A first optimization module is used to construct a first objective function based on the number of scheduling periods and the duration of the corresponding scheduling periods, with the goal of minimizing the charging cost of a single electric vehicle and the total cost of battery loss, and determine a first constraint condition corresponding to the first objective function; Solving the first objective function based on the first constraint condition to obtain the maximum charging power, and the minimum and maximum discharging power of each electric vehicle in each scheduling period; A second determination module is used to determine the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period based on the maximum charging power, and the minimum and maximum discharging power of each electric vehicle in each scheduling period, and determine the charging and discharging constraints of the electric vehicle cluster based on the upper and lower limits of the charging and discharging power of the electric vehicle cluster in each scheduling period; A second optimization module is used to determine various costs in the system, construct a second objective function with the goal of minimizing the total system cost, and determine a second constraint condition corresponding to the second objective function; wherein the second constraint condition includes a charging and discharging constraint of the electric vehicle cluster; The second objective function is solved based on the second constraint condition to obtain the output of the peak-shaving unit and the charging and discharging power of the electric vehicle cluster in each scheduling period.

8. The distributed energy acceptance capacity enhancement device according to claim 7, characterized in that: In the second optimization module, the second objective function is expressed as follows: Where: F is the total system cost, F1 is the charging and discharging cost of the electric vehicle cluster, F2 is the peak load cost of the thermal power unit; M is the number of electric vehicle clusters, T is the number of dispatching periods; P ch,m,t and P dis,m,t are the charging and discharging power of electric vehicle cluster m in the scheduling period t; C ch,t and C dis,t are the charging and discharging electricity prices of electric vehicles in time period t; δ is the battery loss cost coefficient; U1 and U2 represent the number of conventional peak-shaving units and deep peak-shaving units respectively; W u1,t is the output of the u1th conventional peak-shaving unit in the dispatching period t, C u1 is the cost of the u1th conventional peak-shaving unit in the dispatching period t; W u2,t is the output of the u2-th deep peak-shaving unit in the dispatching period t, C u2 is the cost of the u2-th deep peak-shaving unit in the dispatch period t; η ch , η dis are the charging and discharging efficiencies of electric vehicles, respectively.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the method for improving the distributed energy acceptance capacity as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the method for improving the distributed energy acceptance capacity as described in any one of claims 1 to 6 is implemented.

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