Vehicle-network interaction optimization scheduling method and system for distributed photovoltaic power distribution network

Abstract

The invention belongs to the technical field of power distribution network operation scheduling, and particularly relates to a vehicle-network interaction optimization scheduling method and system for a distributed photovoltaic power distribution network, and the method comprises the steps: obtaining the basic data of the distributed photovoltaic power distribution network; according to the acquired data, constructing a vehicle-network interaction double-layer model comprising an upper-layer power optimization model taking the minimum total operation cost as a target and a lower-layer voltage out-of-limit risk management and control model taking the minimum total active power network loss as a target; solving the vehicle network interaction double-layer model to obtain a reference scheduling plan of the electric vehicle; constructing a vehicle network interaction decision correction model by considering the driving characteristic difference and the reference scheduling plan of the electric vehicle; solving the vehicle network interaction decision correction model to obtain a scheduling decision considering the electric vehicle driving characteristic difference; calculating an evaluation index of voltage out-of-limit risk management and control according to the scheduling decision, and adjusting parameters of the vehicle-network interaction decision correction model to obtain a scheduling decision scheme of vehicle-network interaction; and vehicle-network interaction optimization scheduling of the distributed photovoltaic power distribution network is completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network operation dispatching, and specifically relates to a vehicle-grid interactive optimization dispatching method and system for a distributed photovoltaic distribution network. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the large-scale access of distributed photovoltaic power generation equipment to the distribution network, the penetration rate of photovoltaic power generation has gradually increased, and the operating properties of the distribution network including distributed power grids have gradually strengthened. There is a certain lag in the construction of the distribution network network architecture and the upgrade of transmission capacity in township areas. In addition, the distribution network's acceptance and operation regulation capabilities for photovoltaic power generation are still insufficient, making it difficult to meet the operating needs of high-penetration distributed photovoltaic consumption. Due to the concentrated output of photovoltaic power generation during the daytime period of sufficient sunlight, the photovoltaic grid-connected points of the high-penetration distributed photovoltaic distribution network will have the problem of voltage exceeding the upper limit. In addition, the voltage amplitude of the photovoltaic grid-connected points in the distribution network will fluctuate greatly with the change of photovoltaic output, seriously affecting the normal power consumption of other nearby loads in the system. There are a large number of electric vehicles in the power system including distributed power grids. If electric vehicles are charged and discharged disorderly during the high-generation period of photovoltaic power generation during the day, the risk of voltage problems in the distribution network will be significantly increased, and the degree of voltage exceeding the limit at each node in the distribution network will gradually deepen.

[0004] At present, there have been studies on the voltage over-limit risk control of distributed photovoltaic grid-connected distribution networks. The conventional method is to add additional transmission lines and substation facilities to distributed photovoltaic equipment, or to configure adjustable capacitors and inductors at the connecting and key nodes in the distribution network to adjust the node voltage and achieve voltage over-limit risk control.

[0005] However, the existing methods can only achieve effective voltage over-limit control for distributed photovoltaic distribution networks under conventional access levels; when it is necessary to solve the voltage over-limit problem in distribution networks with higher penetration rates, a substantial increase in network architecture and auxiliary equipment investment will lead to a significant increase in the cost of voltage over-limit risk control. At the same time, most of the existing methods rely on the addition of auxiliary equipment, and have not yet considered the coordinated scheduling of a considerable number of electric vehicles in the distribution network, transforming disordered interference resources into orderly management resources, and achieving voltage over-limit management at each node. At the same time, the driving characteristics of electric vehicles in real operating scenarios are different. When scheduling electric vehicles, it is also necessary to comply with the driving habits of electric vehicles. The penetration rate of distributed photovoltaic grid-connected in the distribution network is gradually increasing. If the voltage over-limit problem in the distribution network is not effectively controlled, it will seriously affect the power quality of the high-penetration distributed photovoltaic distribution network and restrict the application and development space of distributed photovoltaic power generation. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a vehicle-grid interaction optimization scheduling method and system for a distributed photovoltaic distribution network, constructs and solves a two-layer model of vehicle-grid interaction of the distribution network, and obtains a reference scheduling plan for electric vehicles; considers the driving characteristics differences of electric vehicles and the reference scheduling plan to construct and solve a decision correction model for vehicle-grid interaction, and obtains a scheduling decision plan for vehicle-grid interaction, thereby achieving effective control of voltage over-limit risks during the operation of a high-penetration distributed photovoltaic distribution network.

[0007] According to some embodiments, a first solution of the present invention provides a vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network, which adopts the following technical solutions:

[0008] A vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network, comprising:

[0009] Obtain basic data of distributed photovoltaic distribution network;

[0010] Based on the acquired data, a two-layer vehicle-grid interaction model is constructed, which includes an upper-layer power optimization model with the goal of minimizing the total operating cost and a lower-layer voltage over-limit risk control model with the goal of minimizing the total active network loss.

[0011] Solve the constructed vehicle-grid interaction two-layer model to obtain the reference dispatch plan for electric vehicles;

[0012] Considering the driving characteristics differences of electric vehicles and the reference dispatch plan, a vehicle-grid interactive decision correction model is constructed;

[0013] Solve the constructed vehicle-grid interaction decision correction model to obtain the dispatch decision that takes into account the differences in driving characteristics of electric vehicles;

[0014] The evaluation index of voltage over-limit risk control is calculated according to the obtained dispatching decision, the parameters of the vehicle-grid interaction decision correction model are adjusted, the dispatching decision plan of vehicle-grid interaction is obtained, and the vehicle-grid interaction optimization dispatching of the distributed photovoltaic distribution network is completed.

[0015] As a further technical limitation, the constructed vehicle-grid interaction decision correction model is Among them, α N is the weight coefficient of the objective function component composed of the deviation between the charging and discharging state of each node and the reference scheduling plan; α γ is the weight coefficient of the objective function component composed of the deviation between the electric vehicle's driving power and the charging expectation. N , α γ The relative size of is used to modify the vehicle-grid interaction decision-making modification model; The number of electric vehicles charged and discharged at each node is planned for charge and discharge reference scheduling; The number of electric vehicles charged and discharged at the node in the scheduling decision is considered to take into account the differences in driving characteristics; The expected driving power of electric vehicles; To provide electric vehicle with driving away power.

[0016] Furthermore, according to the weight coefficient adjustment criterion of the vehicle-grid interaction decision correction model and the obtained voltage over-limit risk control evaluation index, the weight coefficient of the vehicle-grid interaction decision correction model is continuously corrected until it meets the voltage over-limit risk control and electric vehicle differentiated scheduling demand standards, and the vehicle-grid interaction decision correction model parameter adjustment is completed to obtain the vehicle-grid interaction scheduling decision plan and complete the vehicle-grid interaction optimization scheduling of the distributed photovoltaic distribution network.

[0017] As a further technical limitation, a binary particle swarm algorithm is used to solve the constructed vehicle-grid interaction decision correction model to obtain the scheduling decision that takes into account the differences in driving characteristics of electric vehicles at each node in each time period; based on the obtained scheduling decision and distribution network operation data information, an evaluation index for voltage over-limit risk control is obtained; among them, the larger the obtained voltage over-limit risk control evaluation index, the better the voltage over-limit risk control effect.

[0018] As a further technical limitation, the decision variables of the two-layer model of vehicle-grid interaction are solved, and the obtained number of electric vehicles charged and discharged at each node is used as a reference scheduling plan for electric vehicles; wherein the decision variables at least include the start and stop status and output size of distributed generators in each time period, the output size of energy storage equipment, the amount of abandoned photovoltaic power generation equipment and the number of electric vehicles charged and discharged at each node.

[0019] As a further technical limitation, the upper-level power optimization model uses the comprehensive start-stop status and output size of distributed generator sets in each photovoltaic output scenario, the output size of energy storage equipment, the number of electric vehicles charged and discharged, the power generation of generators in each photovoltaic output scenario in different time periods, and the amount of abandoned photovoltaic power generation equipment as selection decision variables, and uses electric vehicles, distributed photovoltaic equipment, grid-side generators and energy storage equipment as controlled resources, with the optimization goals of increasing the local consumption rate of photovoltaic power generation during high photovoltaic periods in the system and the autonomous supply ratio of electricity during peak load periods in the evening, and the objective function of minimizing the total operating cost in the distributed photovoltaic distribution network.

[0020] As a further technical limitation, the lower-level voltage over-limit risk control model takes the number of electric vehicles charged and discharged at each node in each time period, the voltage amplitude at each node in each time period and the active power loss on each interconnection line as selection decision variables, and based on the optimization solution of the upper-level power optimization model, takes the number of electric vehicles charged and discharged at each node in each time period as the controlled resource, and takes the management of the problem of node voltage exceeding the upper limit during the photovoltaic high-generation period and the node voltage exceeding the lower limit during the evening load peak period in the distribution network as the optimization goal, and takes the minimum sum of voltage deviations of each node in the photovoltaic distribution network and the active network loss in the system as the objective function.

[0021] According to some embodiments, a second solution of the present invention provides a vehicle-grid interactive optimization scheduling system for a distributed photovoltaic distribution network, which adopts the following technical solutions:

[0022] A vehicle-grid interactive optimization dispatching system for a distributed photovoltaic distribution network, comprising:

[0023] An acquisition module, which is configured to acquire basic data of a distributed photovoltaic distribution network;

[0024] A first construction module is configured to construct a vehicle-grid interaction double-layer model including an upper-layer power optimization model with a goal of minimizing total operating cost and a lower-layer voltage over-limit risk control model with a goal of minimizing total active network loss according to the acquired data;

[0025] A first solving module is configured to solve the constructed vehicle-grid interaction double-layer model to obtain a reference dispatch plan for electric vehicles;

[0026] A second building module is configured to build a vehicle-grid interaction decision correction model taking into account the driving characteristic differences of electric vehicles and a reference scheduling plan;

[0027] A second solving module is configured to solve the constructed vehicle-grid interaction decision correction model to obtain a scheduling decision that takes into account the differences in driving characteristics of electric vehicles;

[0028] The optimization scheduling module is configured to calculate the evaluation index of voltage over-limit risk control according to the obtained scheduling decision, adjust the parameters of the vehicle-grid interaction decision correction model, obtain the scheduling decision plan of the vehicle-grid interaction, and complete the vehicle-grid interaction optimization scheduling of the distributed photovoltaic distribution network.

[0029] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0030] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as described in the first solution of the present invention.

[0031] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0032] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as described in the first solution of the present invention are implemented.

[0033] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0034] A computer program product includes software code, wherein the program in the software code executes the steps in the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as described in the first embodiment of the present invention.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention takes into account the operating characteristics of various types of power generation and consumption entities in the distributed photovoltaic distribution network and the differentiated driving characteristics of electric vehicles, and successively establishes a vehicle-grid interaction double-layer model and a vehicle-grid interaction decision correction model considering the differences in driving characteristics of electric vehicles to solve the vehicle-grid interaction scheduling decision plan, and relies on the existing equipment resources in the distribution network to achieve effective control of the risk of voltage over-limit in the operation of the distributed photovoltaic distribution network.

[0037] The present invention considers the classification standard of electric vehicle driving habits and classifies a large number of electric vehicles into four categories, which is convenient for collective scheduling of a large number of electric vehicles, improves the demand response interaction efficiency of scheduling electric vehicles to participate in vehicle-grid interaction, and is conducive to realizing differentiated demand response of electric vehicles.

[0038] The present invention proposes a vehicle-grid interaction decision correction model that takes into account the differences in driving characteristics of electric vehicles, verifies the control effect based on the proposed voltage over-limit risk control effect evaluation index, adjusts the sub-item weights in the vehicle-grid interaction decision correction model according to the verification results, and obtains a vehicle-grid interaction scheduling decision plan that meets the voltage control requirements and conforms to the differences in driving characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0040] Figure 1 This is a flow chart of the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network in Embodiment 1 of the present invention;

[0041] Figure 2 This is a schematic diagram of a network topology structure for regional division of a distributed photovoltaic distribution network based on an IEEE 33-node system in Embodiment 1 of the present invention;

[0042] Figure 3 This is a schematic diagram of voltage over-limit control results in the system during the high volt period in the first embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of voltage over-limit control results in the system during the evening load peak period in the first embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0045] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0046] In the present invention, terms such as "fixed connection", "connected", "connection", etc. should be understood in a broad sense, indicating that it can be fixedly connected, integrally connected or detachably connected; it can be directly connected or indirectly connected through an intermediate medium. For relevant scientific research or technical personnel in this field, the specific meanings of the above terms in the present invention can be determined according to specific circumstances, and they cannot be understood as limitations on the present invention.

[0047] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0048] Embodiment 1

[0049] Embodiment 1 of the present invention introduces a vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network.

[0050] like Figure 1 A vehicle-grid interaction optimization scheduling method for a distributed photovoltaic distribution network is shown, including solving a reference scheduling plan for electric vehicles and correcting a vehicle-grid interaction decision; the specific steps are:

[0051] Step S01: Model the characteristics of the power generation and consumption entities in the high-penetration distributed photovoltaic distribution network, with the goal of improving the power utilization efficiency and voltage over-limit control in the system, construct a two-layer model of vehicle-grid interaction, and solve to obtain a reference dispatch plan for electric vehicles;

[0052] Step S02: analyzing the driving characteristics of electric vehicle users in a high-penetration distributed photovoltaic distribution network, making judgments based on driving habit classification criteria, and obtaining an electric vehicle classification including four driving habit categories;

[0053] Step S03: comprehensively consider the reference dispatch plan and the electric vehicle classification results, construct a vehicle-grid interactive decision correction model that considers the differences in electric vehicle driving characteristics, and use a binary particle swarm algorithm to solve it to obtain a dispatch decision that considers the differences in electric vehicle driving characteristics;

[0054] Step S04: Verify the control effect of the scheduling decision that takes into account the differences in driving characteristics of electric vehicles based on the proposed voltage over-limit risk control effect evaluation index, adjust the sub-item weights in the vehicle-grid interactive decision correction model according to the verification results, and repeat the iteration until a vehicle-grid interactive scheduling decision plan that meets the voltage over-limit risk control and differentiated scheduling needs of electric vehicles is obtained.

[0055] As one or more implementation modes, in step S01, in combination with the characteristics of the power generation and consumption entities in the distribution network, electric vehicles, distributed photovoltaic equipment, grid-side generators, and energy storage equipment are used as controlled resources to improve the local consumption rate of photovoltaic power generation during the high-generation period of photovoltaic power generation in the system and the autonomous supply ratio of electric energy during the peak load period in the evening as the optimization goal, and establish an upper-level power optimization model; specifically,

[0056] 1) Select decision variables

[0057] The decision variables to be solved in the upper power optimization model are: the comprehensive start-stop state u of the distributed generator set that meets each photovoltaic output scenario i,t With output size Energy storage equipment output Number of electric vehicle charging and discharging and Generator power generation in each photovoltaic output scenario at different time periods Abandoned photovoltaic power generation equipment

[0058] 2) Determine the objective function

[0059] In the upper power optimization model, the optimization target of the model is to minimize the total operating cost in the distributed photovoltaic distribution network. The optimization target expression is:

[0060]

[0061] Among them, f UP Total operating cost in the system; S is the set of photovoltaic output scenarios; T is the set of scheduling time periods throughout the day; I is the set of grid-side generators and distributed generators; E{g} is the expected cost throughout the day under all scenarios; s i,t is the generator startup cost; u i,t The start and stop status of the generator; is the output of the generator; is the output cost of the generator; The sewage costs for the generator; The cost of power supply support for energy storage equipment; the cost of charging and discharging electric vehicles; The cost of abandoned photovoltaic power.

[0062] Based on the optimization solution of the upper-level power optimization model, the number of electric vehicles charged and discharged at each node in each time period is taken as the controlled resource. The problem of node voltage exceeding the upper limit during the period of high photovoltaic generation in the distribution network and the problem of node voltage exceeding the lower limit during the peak load period in the evening is taken as the optimization goal, and a lower-level voltage over-limit risk management model is established.

[0063] 1) Select decision variables

[0064] Select the decision variables to be optimized. In the lower-level voltage over-limit risk control model, the decision variables to be optimized are: the number of electric vehicles charged and discharged at each node in each time period and The voltage amplitude V of each node in each period α,t,s , Active power loss on each tie line

[0065] 2) Determine the objective function

[0066] In the lower-level voltage over-limit risk control model, the model optimization objective is to minimize the sum of the voltage deviation of each node in the high-penetration distributed photovoltaic distribution network and the active network loss in the system. The optimization objective expression is:

[0067]

[0068] Among them, f DN is the equivalent cost function of the voltage over-limit management model of the lower-level nodes in the system; K is the set of nodes in the system; L is the set of branches in the system; E(g) is the expected cost of each time period under all scenarios; ΔV′ k,t,s is the node voltage deviation value after normalization calculation; is the branch active power loss after normalization calculation.

[0069] Solve the decision variables in the two-layer model of vehicle-grid interaction, which include: the start-stop status and output of distributed generators in each period, the output of energy storage equipment, the amount of abandoned photovoltaic power generation equipment, and the number of electric vehicles charged and discharged at each node. The number of electric vehicles charged and discharged at each node obtained by the two-layer model of vehicle-grid interaction is the reference scheduling plan for electric vehicles.

[0070] As one or more implementation modes, in step S02, since the vehicle owners have different behavioral characteristics, the time when the electric vehicle enters and exits the charging node presents a relatively regular randomness. The probability density function of the normal distribution function is used to fit the statistical data of the time when each electric vehicle enters and exits the charging node.

[0071]

[0072] Among them, μ is the mean and σ is the standard deviation.

[0073] The probability distribution of each electric vehicle's entry time belongs to a positive skewed distribution, and its mean is The probability distribution of each electric vehicle's departure time belongs to a negatively skewed distribution, and its mean is

[0074] In order to facilitate the coordinated dispatch of electric vehicles according to driving characteristics, electric vehicles are classified according to the driving habits of electric vehicle users. The classification standard shown in the following formula is used to divide electric vehicles into four categories, which are: on-time entry and exit (category 1); delayed entry and on-time exit (category 2); delayed entry and early exit (category 3); on-time entry and early exit (category 4).

[0075]

[0076] Among them, R1, R2, R3, and R4 are EV sets of categories 1 to 4 respectively; are the EV entry and exit time respectively; t B ,t E The start and end time of the scheduling period respectively.

[0077] As one or more implementation methods, in step S03, when arranging charging and discharging of different types of electric vehicles, it is necessary to ensure that the vehicle-grid interaction power of each node in each period in the scheduling decision is as consistent as possible with the charging and discharging reference scheduling plan. At the same time, it is necessary to meet the differentiated charging and discharging needs of different types of electric vehicles. The following formula quantifies the vehicle-grid interaction decision correction model considering the differences in electric vehicle driving characteristics:

[0078]

[0079] Among them, α N is the weight coefficient of the objective function component composed of the deviation between the charging and discharging state of each node and the reference scheduling plan; α γ is the weight coefficient of the objective function component composed of the deviation between the electric vehicle's driving power and the charging expectation. N , α γ The relative size of the model is used to correct the target. The number of electric vehicles charged and discharged at each node is planned for charge and discharge reference scheduling; The number of electric vehicles charged and discharged at the node in the scheduling decision is considered to take into account the differences in driving characteristics; The expected driving power of electric vehicles; To provide electric vehicle with driving away power.

[0080] With α N , α γ The relative size of the target function component composed of the deviation between the charging and discharging status of each node and the reference scheduling plan and the target function component composed of the deviation between the EV driving power and the charging expectation are weighed. The binary particle swarm algorithm is used to solve the model shown in the above formula to obtain the scheduling decision considering the differences in the driving characteristics of electric vehicles at each node in each period.

[0081] As one or more implementation methods, in step S04, according to the scheduling decision and combined with the system operation data, the voltage over-limit risk control effect evaluation index V is calculated. og , in order to quantify the improvement level of node voltage over-limit management in the system:

[0082]

[0083] Among them, |g is the number of elements in the set; K Vo K is the node set whose voltage value exceeds the national standard voltage limit before the voltage over-limit risk control method is adopted in the system; Vog It is the set of nodes in the system whose voltage values ​​exceed the national standard voltage limit after the voltage over-limit risk control is performed using this method.

[0084] It should be noted that V og The larger the value, the better the voltage limit risk control effect.

[0085] Before control, determine whether the node belongs to K Vo The criteria are as follows:

[0086]

[0087] After control, determine whether the node belongs to K Vog The criteria are as follows:

[0088]

[0089] Among them, k is the node to be evaluated; V k is the voltage at node k; They are the upper and lower limits of the voltage amplitude specified by the national standard.

[0090] According to the weight coefficient adjustment criterion in the vehicle-grid interactive decision correction model, combined with the voltage over-limit risk control effect evaluation index, the weight coefficient α is adjusted. N , α γ resizing.

[0091] The criterion for adjusting the weight coefficient in the vehicle-grid interactive decision correction model is:

[0092] Case 1: When V og Less than the lower limit of the rated voltage over-limit risk control effect evaluation index V og,N,dn When α N , α γ The adjustment calculation method is:

[0093]

[0094] Among them, α N′ is the weight coefficient of the objective function component composed of the deviation between the charging and discharging state of each node and the reference scheduling plan after adjustment; α γ ′ is the weight coefficient of the objective function component composed of the deviation between the electric vehicle’s driving-away power and the charging expectation after adjustment.

[0095] Case 2: When V og Greater than the rated limit upper limit V of the voltage over-limit risk control effect evaluation index og,N,up When α N , α γ The adjustment calculation method is:

[0096]

[0097] The α obtained by adjusting the criterion based on the weight coefficient in the vehicle-grid interaction decision correction model N ′、α γ ′The model is substituted into the modified vehicle-grid interaction decision model that takes into account the differences in driving characteristics of electric vehicles for iterative solution until a vehicle-grid interaction scheduling decision scheme that meets the standards of voltage over-limit risk control and differentiated scheduling requirements of electric vehicles is obtained.

[0098] The standards that meet the voltage over-limit risk control and differentiated dispatch requirements for electric vehicles are:

[0099] V og,N,min <V og <V og,N,max (11)

[0100] Among them, V og,N,min 、V og,N,max They are respectively the minimum and maximum rated limits of the voltage over-limit risk control effect evaluation index.

[0101] This embodiment is as follows Figure 2 The network topology after regional division of the high-penetration distributed photovoltaic distribution network based on the IEEE 33-node system upgrade shown in the figure verifies the limitations of the proposed optimization scheduling method; based on the proposed voltage over-limit control method, the proposed Figure 2 The high-penetration distributed photovoltaic distribution network system is dispatched as shown; among them, Table 1 is the number of electric vehicles charged at each node during the peak photovoltaic period, and Table 2 is the number of electric vehicles discharged at each node during the evening load peak period.

[0102] Table 1 Charging plan for photovoltaic high-generation period

[0103]

[0104] Table 2 Discharge plan during evening load peak period

[0105]

[0106] Based on the optimization scheduling method in this embodiment, the following is obtained: Figure 3 The results of voltage over-limit management in the system during the period of high photovoltaic power generation are shown; it can be obtained that after the electric vehicles participate in the voltage over-limit management, during the period of high photovoltaic power generation, although a very small number of nodes still have voltage over-limit due to limited absorption capacity, the voltage of most nodes has been controlled below the upper limit of the voltage deviation value; that is, it is verified that the optimization scheduling method introduced in this embodiment can better solve the problem of node voltage over-limit caused by distributed photovoltaic centralized power generation and grid connection in the system.

[0107] Based on the optimization scheduling method in this embodiment, we get Figure 4 The results of voltage over-limit control in the system during the evening load peak period are shown; it can be obtained that after the electric vehicles participate in the voltage over-limit control, the electric vehicles are discharged according to the plan in Table 2, and the voltage of the 30-33 nodes is significantly increased, all exceeding the lower limit of the voltage deviation amplitude of 0.93pu, and the voltage of these nodes is increased to 0.94-0.95pu; that is, it is verified that the optimization scheduling method introduced in this embodiment can better solve the problem of node voltage exceeding the lower limit caused by concentrated load consumption at the terminal nodes in the system.

[0108] This embodiment takes into account the operating characteristics of various types of power generation and consumption entities in the distributed photovoltaic distribution network and the differentiated needs of electric vehicle driving characteristics, and successively establishes a vehicle-grid interaction double-layer model and a vehicle-grid interaction decision correction model considering the differences in electric vehicle driving characteristics to solve the vehicle-grid interaction scheduling decision plan, and relies on the existing equipment resources in the distribution network to achieve effective control of the risk of voltage over-limit in the operation of the distributed photovoltaic distribution network.

[0109] This embodiment considers the classification standard of electric vehicle driving habits and classifies many electric vehicles into four categories, which is convenient for collective scheduling of a large number of electric vehicles, improves the demand response interaction efficiency of scheduling electric vehicles to participate in vehicle-grid interaction, and is conducive to realizing differentiated demand response for electric vehicles.

[0110] This embodiment considers the vehicle-grid interaction decision correction model based on the differences in driving characteristics of electric vehicles, verifies the control effect based on the proposed voltage over-limit risk control effect evaluation index, adjusts the sub-item weights in the vehicle-grid interaction decision correction model according to the verification results, and obtains a vehicle-grid interaction scheduling decision plan that meets the voltage control requirements and conforms to the differences in driving characteristics.

[0111] Embodiment 2

[0112] Embodiment 2 of the present invention introduces a vehicle-grid interactive optimization scheduling system for a distributed photovoltaic distribution network.

[0113] A vehicle-grid interactive optimization dispatching system for a distributed photovoltaic distribution network, comprising:

[0114] An acquisition module, which is configured to acquire basic data of a distributed photovoltaic distribution network;

[0115] A first construction module is configured to construct a vehicle-grid interaction double-layer model including an upper-layer power optimization model with a goal of minimizing total operating cost and a lower-layer voltage over-limit risk control model with a goal of minimizing total active network loss according to the acquired data;

[0116] A first solving module is configured to solve the constructed vehicle-grid interaction double-layer model to obtain a reference dispatch plan for electric vehicles;

[0117] A second building module is configured to build a vehicle-grid interaction decision correction model taking into account the driving characteristic differences of electric vehicles and a reference scheduling plan;

[0118] A second solving module is configured to solve the constructed vehicle-grid interaction decision correction model to obtain a scheduling decision that takes into account the differences in driving characteristics of electric vehicles;

[0119] The optimization scheduling module is configured to calculate the evaluation index of voltage over-limit risk control according to the obtained scheduling decision, adjust the parameters of the vehicle-grid interaction decision correction model, obtain the scheduling decision plan of the vehicle-grid interaction, and complete the vehicle-grid interaction optimization scheduling of the distributed photovoltaic distribution network.

[0120] The detailed steps are the same as the vehicle-grid interactive optimization scheduling method for the distributed photovoltaic distribution network provided in Example 1, and will not be repeated here.

[0121] Embodiment 3

[0122] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0123] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as described in Embodiment 1 of the present invention.

[0124] The detailed steps are the same as the vehicle-grid interactive optimization scheduling method for the distributed photovoltaic distribution network provided in Example 1, and will not be repeated here.

[0125] Embodiment 4

[0126] A fourth embodiment of the present invention provides an electronic device.

[0127] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as described in Embodiment 1 of the present invention are implemented.

[0128] The detailed steps are the same as the vehicle-grid interactive optimization scheduling method for the distributed photovoltaic distribution network provided in Example 1, and will not be repeated here.

[0129] Embodiment 5

[0130] Embodiment 5 of the present invention provides a computer program product.

[0131] A computer program product includes software code, wherein the program in the software code executes the steps in the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as described in the first embodiment of the present invention.

[0132] The detailed steps are the same as the vehicle-grid interactive optimization scheduling method for the distributed photovoltaic distribution network provided in Example 1, and will not be repeated here.

[0133] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network, characterized in that: include: Obtain basic data of distributed photovoltaic distribution network; Based on the acquired data, a two-layer vehicle-grid interaction model is constructed, which includes an upper-layer power optimization model with the goal of minimizing the total operating cost and a lower-layer voltage over-limit risk control model with the goal of minimizing the total active network loss. Solve the constructed vehicle-grid interaction two-layer model to obtain the reference dispatch plan for electric vehicles; Considering the driving characteristics differences of electric vehicles and the reference dispatch plan, a vehicle-grid interaction decision correction model is constructed; Solve the constructed vehicle-grid interaction decision correction model to obtain the dispatch decision that takes into account the differences in driving characteristics of electric vehicles; The evaluation index of voltage over-limit risk control is calculated according to the obtained dispatching decision, the parameters of the vehicle-grid interaction decision correction model are adjusted, the dispatching decision plan of vehicle-grid interaction is obtained, and the vehicle-grid interaction optimization dispatching of the distributed photovoltaic distribution network is completed.

2. A vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as claimed in claim 1, characterized in that: The constructed vehicle-grid interaction decision correction model is Among them, α N is the weight coefficient of the objective function component composed of the deviation between the charging and discharging state of each node and the reference scheduling plan; α γ is the weight coefficient of the objective function component composed of the deviation between the electric vehicle's driving power and the charging expectation. N , α γ The relative size of is used to modify the vehicle-grid interaction decision-making modification model; The number of electric vehicles charged and discharged at each node is planned for charge and discharge reference scheduling; The number of electric vehicles charged and discharged at the node in the scheduling decision is considered to take into account the differences in driving characteristics; The expected driving power of electric vehicles; To charge electric vehicles.

3. A vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as claimed in claim 2, characterized in that: According to the weight coefficient adjustment criterion of the vehicle-grid interaction decision correction model, combined with the obtained voltage over-limit risk control evaluation index, the weight coefficient of the vehicle-grid interaction decision correction model is continuously corrected until it meets the voltage over-limit risk control and electric vehicle differentiated scheduling demand standards, and the vehicle-grid interaction decision correction model parameter adjustment is completed. The scheduling decision plan for vehicle-grid interaction is obtained, and the vehicle-grid interaction optimization scheduling of the distributed photovoltaic distribution network is completed.

4. A vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as claimed in claim 1, characterized in that: The binary particle swarm algorithm is used to solve the constructed vehicle-grid interactive decision correction model to obtain the dispatching decision considering the differences in driving characteristics of electric vehicles at each node in each time period; based on the obtained dispatching decision and distribution network operation data information, the evaluation index of voltage over-limit risk control is obtained; among them, the larger the obtained voltage over-limit risk control evaluation index is, the better the voltage over-limit risk control effect is.

5. A vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as claimed in claim 1, characterized in that: Solve the decision variables of the vehicle-grid interaction double-layer model, and use the obtained number of electric vehicles charged and discharged at each node as the reference scheduling plan for electric vehicles; wherein the decision variables at least include the start and stop status and output size of distributed generators in each time period, the output size of energy storage equipment, the amount of abandoned photovoltaic power generation equipment and the number of electric vehicles charged and discharged at each node.

6. A vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as claimed in claim 1, characterized in that: The upper-level power optimization model takes electric vehicles, distributed photovoltaic equipment, grid-side generators and energy storage equipment as controlled resources, with the optimization goals of increasing the local consumption rate of photovoltaic power generation during the period of high photovoltaic generation in the system and the autonomous supply ratio of electricity during the peak load period in the evening, and the objective function of minimizing the total operating cost in the distributed photovoltaic distribution network; based on the optimization solution of the upper-level power optimization model, the lower-level voltage over-limit risk control model takes the number of electric vehicles charged and discharged at each node in each time period as a controlled resource, with the optimization goal of addressing the problem of node voltage exceeding the upper limit during the period of high photovoltaic generation in the distribution network and the problem of node voltage exceeding the lower limit during the peak load period in the evening, and the objective function of minimizing the sum of voltage deviations of each node in the photovoltaic distribution network and active network losses in the system.

7. A vehicle-grid interactive optimization dispatching system for a distributed photovoltaic distribution network, characterized in that: include: An acquisition module, which is configured to acquire basic data of a distributed photovoltaic distribution network; A first construction module is configured to construct a vehicle-grid interaction double-layer model including an upper-layer power optimization model with a goal of minimizing total operating cost and a lower-layer voltage over-limit risk control model with a goal of minimizing total active network loss according to the acquired data; A first solving module is configured to solve the constructed vehicle-grid interaction double-layer model to obtain a reference dispatch plan for electric vehicles; A second building module is configured to build a vehicle-grid interaction decision correction model taking into account the driving characteristic differences of electric vehicles and a reference scheduling plan; A second solving module is configured to solve the constructed vehicle-grid interaction decision correction model to obtain a scheduling decision that takes into account the differences in driving characteristics of electric vehicles; The optimization scheduling module is configured to calculate the evaluation index of voltage over-limit risk control according to the obtained scheduling decision, adjust the parameters of the vehicle-grid interaction decision correction model, obtain the scheduling decision plan of the vehicle-grid interaction, and complete the vehicle-grid interaction optimization scheduling of the distributed photovoltaic distribution network.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as described in any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising software code, characterized in that The program in the software code executes the steps of the vehicle-grid interactive optimization scheduling method for a distributed photovoltaic distribution network as described in any one of claims 1-6.

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