Power distribution network optimization scheduling method and device based on participation of electric vehicles in peak load shifting

Abstract

The invention discloses a power distribution network optimization scheduling method and device based on participation of electric vehicles in peak clipping and valley filling. The method comprises the following steps: constructing a power flow model, a demand response model and an electric vehicle cluster schedulable model of power distribution network equipment; constructing a relation function between node branch current of the power distribution network and power distribution network loss, a relation function between active power of distributed energy and power abandoning loss of the distributed energy, and a relation function between electricity purchasing power at an electric vehicle charging station and electric vehicle electricity purchasing loss; the function relationship between the actual load demand power of each node of the power distribution network, the node load power participating in demand response, the electricity purchasing power from the electric vehicle charging station and the load peak-valley difference loss is obtained; and taking the minimum sum of the power distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as an objective function, and combining the power flow model of the power distribution network equipment, the demand response model and the electric vehicle cluster schedulable model to solve and obtain a power distribution network optimization scheduling result.

Description

Technical Field

[0001] The present invention relates to the field of distribution network dispatching, and in particular to a distribution network optimization dispatching method and equipment based on electric vehicles participating in peak shaving and valley filling. Background Art

[0002] With the continuous development of distributed generation (DG), the proportion of DG participating in active distribution network (ADN) as power supply terminal is increasing. The power consumption of active distribution network is gradually increasing, which leads to the increasing peak-to-valley difference of load at each node. At the same time, the uncertainty of DG output and the volatility of each node increase the risk and challenge of optimizing the scheduling of active distribution network, that is, when there are a large number of DG in the active distribution network, the scheduling capacity of the distribution network is reduced. Summary of the invention

[0003] The embodiment of the present invention provides a distribution network optimization scheduling method and device based on electric vehicles participating in peak shaving and valley filling, so as to solve the problem that the scheduling capacity of the distribution network is reduced when a large number of distributed power sources are contained in the active distribution network.

[0004] A distribution network optimization dispatching method based on electric vehicles participating in peak load shaving and valley filling, comprising:

[0005] Constructing a power flow model, a demand response model and an electric vehicle cluster dispatchable model for distribution network equipment; the power flow model of the distribution network equipment includes: a distributed energy model, the distributed energy model includes active power constraints and reactive power constraints of distributed energy; the demand response model includes: demand response power constraints of each node in the distribution network and user satisfaction constraints of demand response; the electric vehicle cluster dispatchable model includes: power constraints for purchasing electricity from electric vehicle charging stations, power constraints for selling electricity to electric vehicle charging stations, and state of charge constraints of electric vehicle charging stations;

[0006] Construct the relationship function between the node branch current of the distribution network and the distribution network loss, the relationship function between the active power of distributed energy and the abandoned power loss of distributed energy, the relationship function between the power purchased from the electric vehicle charging station and the power purchased by the electric vehicle loss, and the functional relationship between the actual load demand power of each node in the distribution network, the load power of the nodes participating in the demand response, the power purchased from the electric vehicle charging station and the load peak-valley difference loss;

[0007] Taking the minimization of the sum of the distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as the objective function, combined with the power flow model of the distribution network equipment, the demand response model and the electric vehicle cluster dispatchable model, the distribution network optimization scheduling result is solved, including: the branch current scheduling value of each node, the active power scheduling value of distributed energy, the actual load demand power scheduling value of each node in the distribution network, the node load power scheduling value participating in the demand response and the power scheduling value of power purchase from the electric vehicle charging station.

[0008] Optionally, the demand response power constraint condition of each node in the distribution network is expressed as follows:

[0009]

[0010] In the formula, j is the node, t is the sampling time, are respectively the active power and reactive power in the node load power participating in demand response; They are the upper and lower limits of the node load power participating in demand response, respectively; are respectively the active power and reactive power in the actual load demand power of each node; B DR It is the set of nodes participating in demand response in the active distribution network;

[0011] The expression of the user satisfaction constraint condition of the demand response is as follows:

[0012]

[0013] In the formula, is the user satisfaction of demand response; T is the sampling value at sampling time t.

[0014] Optionally, the power purchase constraint condition from the electric vehicle charging station, the power sale constraint condition to the electric vehicle charging station, and the state of charge constraint condition of the electric vehicle charging station are expressed as follows:

[0015]

[0016] In the formula, To sell electricity to electric vehicle charging stations, is the power purchased from the electric vehicle charging station, They are the upper limits of the power that microgrid operators can purchase and sell from electric vehicles. They are the actual state of charge value and the expected state of charge value of the electric vehicle when it leaves the charging station; is the state of charge value of the electric vehicle charging station at time t, They are respectively the preset upper limit and lower limit of the state of charge; is the state of charge value of the electric vehicle charging station at time t-1; T is the number of electric vehicles participating in peak load shifting; N is the type of electric vehicles participating in peak load shifting; η EV The charging and discharging efficiency of electric vehicles; is the charging and discharging status of the electric vehicle; i and n are the numbers of the electric vehicle.

[0017] Optionally, the distributed energy model includes expressions of active power constraints and reactive power constraints of distributed energy as follows:

[0018]

[0019] In the formula, is the predicted active power value of distributed energy at node j of the distribution network, is the active power of distributed energy at node j of the distribution network; is the reactive power of distributed energy at node j of the distribution network; The adjustable ratio of the on-load tap-changing transformer in the distribution network is set; are the upper and lower limits of the adjustable ratio; are the upper and lower limits of the reactive power of distributed energy at node j in the distribution network.

[0020] Optionally, the functional relationship between the actual load demand power of each node in the distribution network, the load power of the nodes participating in the demand response, the power purchased from the electric vehicle charging station and the load peak-valley difference loss is expressed as follows:

[0021]

[0022] In the formula, F p-v is the load peak-to-valley loss, c p-v is the unit load peak-valley difference cost; are the sum of the peak and valley values ​​of the load active power, P t sum is the sum of all node load values ​​at time t; N Bus is the number of nodes, is the active power in the actual load demand power of each node, is the active power of the node load power participating in demand response; is the charging power of the input node, which is equal to the power purchased from the electric vehicle charging station.

[0023] Optionally, the expression of the relationship function between the power purchased from the electric vehicle charging station and the power purchase loss of the electric vehicle is as follows:

[0024]

[0025] In the formula, F buy is the power purchase loss of electric vehicles, c buy is the unit power purchase loss; N Bus is a set of substation nodes; is the power purchased from the electric vehicle charging station; t is the sampling time, T is the sampling period, and i is the node in the substation node set.

[0026] Optionally, the expression of the relationship function between the node branch current of the distribution network and the distribution network loss is as follows:

[0027]

[0028] In the formula, F loss is the power distribution network loss, c loss is the unit network loss; t is the sampling time, T is the sampling period, E line is the branch set in the distribution network; I ij,t is the branch current; r ij is the branch resistance; ij is the node number.

[0029] The expression of the relationship function between the active power of the distributed energy and the abandoned power loss of the distributed energy is as follows:

[0030]

[0031] In the formula, F DG is the power loss of distributed energy, t is the sampling time, T is the sampling period, c DG is the unit power abandonment cost; N DG A collection of nodes equipped with distributed energy; is the predicted active power value of distributed energy at node j of the distribution network, is the active power of distributed energy at node j of the distribution network.

[0032] Optionally, it also includes constructing a reactive compensation device model of the distribution network, wherein the reactive compensation device model includes reactive compensation power constraints of the switching capacitor group;

[0033] Taking the minimization of the sum of the distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as the objective function, combined with the power flow model of the distribution network equipment, the demand response model, the electric vehicle cluster dispatchable model and the reactive compensation device model, the distribution network optimization scheduling result is solved.

[0034] Optionally, the expression of the reactive compensation power constraint condition is as follows:

[0035]

[0036] In the formula, is the reactive compensation power of a group of switched capacitors, t is the sampling time, and j is the node; is the compensation power of a switched capacitor; B CB A node set for setting switching capacitors in the distribution network; is the total number of switched capacitors put into operation at sampling time t; is the upper limit of the number of units; is the total number of switched capacitors put into operation at sampling time t-1, It is the upper limit of the number of operations of switching capacitors.

[0037] A distribution network optimization dispatching device based on electric vehicles participating in peak load shaving and valley load filling, comprising:

[0038] A constraint condition construction module is used to construct a power flow model of distribution network equipment, a demand response model and an electric vehicle cluster dispatchable model; the power flow model of the distribution network equipment includes: a distributed energy model, and the distributed energy model includes active power constraints and reactive power constraints of distributed energy; the demand response model includes: demand response power constraints of each node in the distribution network and user satisfaction constraints of demand response; the electric vehicle cluster dispatchable model includes: power purchase constraints from electric vehicle charging stations, power sales constraints to electric vehicle charging stations, and state of charge constraints of electric vehicle charging stations;

[0039] An objective function construction module is used to construct a relationship function between the node branch current of the distribution network and the distribution network loss, a relationship function between the active power of distributed energy and the abandoned power loss of distributed energy, a relationship function between the power purchased from the electric vehicle charging station and the power purchased loss of the electric vehicle, and a functional relationship between the actual load demand power of each node in the distribution network, the load power of the nodes participating in the demand response, the power purchased from the electric vehicle charging station and the load peak-valley difference loss;

[0040] The scheduling value solving module is used to take the minimum sum of the distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as the objective function, and combine the power flow model of the distribution network equipment, the demand response model and the electric vehicle cluster dispatchable model to solve the distribution network optimization scheduling result, including: the branch current scheduling value of each node, the active power scheduling value of distributed energy, the actual load demand power scheduling value of each node in the distribution network, the node load power scheduling value participating in the demand response and the power scheduling value of power purchase from the electric vehicle charging station.

[0041] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned distribution network optimization scheduling method when executing the computer program.

[0042] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned distribution network optimization scheduling method is implemented.

[0043] The above-mentioned distribution network optimization scheduling method, device, computer equipment and storage medium utilize the equipment output and load scheduling of demand response and electric vehicles participating in the distribution network, and share energy storage with electric vehicle clusters through distributed power sources, which can greatly reduce system network losses; in order to solve the distribution network optimization scheduling result, the demand response model is taken into account, reducing the load difference of the active distribution network during peak and valley periods; and in order to solve the distribution network optimization scheduling result, the electric vehicle cluster dispatchable model is also taken into account, which can not only reduce the initial construction cost of the energy storage equipment of the active distribution network and improve the energy storage utilization rate, but also actively absorb the excess active load in the system while meeting the electricity demand of electric vehicle users, so as to better achieve the goal of peak shaving and valley filling; so that even if the active distribution network contains a large number of distributed power sources, the scheduling capacity of the distribution network can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0045] Figure 1 This is a schematic diagram of an application environment of a distribution network optimization scheduling method according to an embodiment of the present invention;

[0046] Figure 2 is a flow chart of a method for optimizing and dispatching a distribution network in one embodiment of the present invention;

[0047] Figure 3 is a schematic diagram of a load curve in one embodiment of the present invention;

[0048] Figure 4a , Figure 4b They are schematic diagrams of node voltage curves in scenario 1 and scenario 4 in one embodiment of the present invention;

[0049] Figure 5a , Figure 5b , Figure 5c , Figure 5dThey are respectively schematic diagrams of electric vehicle charging and discharging curves of charging station 1, charging station 2, charging station 3, and charging station 4 in one embodiment of the present invention;

[0050] Figure 6 is a schematic diagram of a total capacity curve of an electric vehicle in one embodiment of the present invention;

[0051] Figure 7 is a schematic diagram of a distribution network optimization dispatching device in one embodiment of the present invention;

[0052] Figure 8 is a schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The distribution network optimization scheduling method based on electric vehicles participating in peak shaving and valley filling provided in the embodiment of the present invention can be applied as follows: Figure 1 Specifically, the distribution network optimization dispatching method is applied in a distribution network dispatching system, and the distribution network dispatching system includes: Figure 1 The client and server shown in the figure communicate with each other through the network to realize the dispatching of the distribution network. The client, also known as the user end, refers to the program corresponding to the server and providing local services to the customer. The client can be installed on, but not limited to, various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0055] In one embodiment, if Figure 2 As shown, a distribution network optimization scheduling method based on electric vehicles participating in peak load shaving and valley filling is provided. Figure 1 The server in the example is used as an example to illustrate the process, including the following steps:

[0056] S201, constructing a power flow model, a demand response model and an electric vehicle cluster dispatchable model for distribution network equipment; the power flow model of the distribution network equipment includes: a distributed energy model, the distributed energy model includes active power constraints and reactive power constraints of distributed energy; the demand response model includes: demand response power constraints of each node in the distribution network and user satisfaction constraints of demand response; the electric vehicle cluster dispatchable model includes: power constraints for purchasing electricity from electric vehicle charging stations, power constraints for selling electricity to electric vehicle charging stations, and state of charge constraints of electric vehicle charging stations;

[0057] S202, constructing a relationship function between node branch currents of the distribution network and distribution network losses, a relationship function between the active power of distributed energy and the abandoned power loss of distributed energy, a relationship function between the power purchased from electric vehicle charging stations and the power purchased loss of electric vehicles, and a functional relationship between the actual load demand power of each node in the distribution network, the load power of nodes participating in demand response, the power purchased from electric vehicle charging stations and the load peak-valley difference loss;

[0058] S203, taking the minimization of the sum of the distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as the objective function, combining the power flow model of the distribution network equipment, the demand response model and the electric vehicle cluster dispatchable model, and solving to obtain the distribution network optimization scheduling result, including: the branch current scheduling value of each node, the active power scheduling value of distributed energy, the actual load demand power scheduling value of each node in the distribution network, the node load power scheduling value participating in the demand response and the power scheduling value of power purchase from the electric vehicle charging station.

[0059] Among them, when only the modeling of the active distributed energy model is considered, the distributed energy model includes wind turbine and photovoltaic models, and the expression is as follows:

[0060]

[0061] In the formula, B DG is a set of nodes containing wind turbines and photovoltaics; is the predicted active power value of distributed energy at node j of the distribution network, is the active power of distributed energy at node j of the distribution network.

[0062] When considering the modeling of active and reactive distributed energy models, the distributed energy model includes wind turbine and photovoltaic models, and the expression is as follows:

[0063]

[0064] In the formula, is the predicted active power value of distributed energy at node j of the distribution network, is the active power of distributed energy at node j of the distribution network; It is an adjustable ratio, which is converted from the power factor ratio of active power and reactive power; is the reactive power of distributed energy at node j of the distribution network.

[0065] In addition, the power flow model of the distribution network equipment can also include: on-load tap-changing transformer module, the expression is as follows:

[0066]

[0067] In the formula, B OLTC A node including an on-load tap-changing transformer device; is the voltage value of the node; is the upper and lower limits of the node voltage; r j,t is the adjustable ratio of the on-load tap-changing transformer; The upper and lower limits of the on-load tap-changing transformer ratio.

[0068] The adjustable ratio of the above-mentioned load-changing transformer can be further expressed as:

[0069]

[0070] In the formula, r j,s It represents the difference between the square of the ratio of the OLTC gear s and gear s-1. The gear range is 500-1000 turns, indicating the adjacent adjustment increment; is a 0-1 variable; t is time.

[0071] The distribution network optimization and scheduling method of this embodiment constructs a power flow model that is adjusted by an on-load tap-changing transformer, etc., in response to the influence of voltage fluctuations and power imbalance caused by distributed power sources; by setting demand response and electric vehicles to participate in the equipment output and load scheduling of the distribution network, and sharing energy storage with distributed power sources and electric vehicle clusters, the system network loss can be greatly reduced; in order to solve the distribution network optimization scheduling result, the demand response model is taken into account, and the load difference of the active distribution network during peak and valley periods is reduced, and the load peak and valley difference is reduced by 22%; and in order to solve the distribution network optimization scheduling result, the electric vehicle cluster dispatchable model is also taken into account, which can not only reduce the initial construction cost of the energy storage equipment of the active distribution network and improve the energy storage utilization rate, but also can actively absorb the excess active load in the system while meeting the electricity demand of electric vehicle users, so as to better achieve the goal of peak shaving and valley filling; so that even if the active distribution network contains a large number of distributed power sources, the scheduling capacity of the distribution network can be improved.

[0072] In one embodiment, the expression of the demand response power constraint condition of each node in the distribution network is as follows:

[0073]

[0074] In the formula, j is the node, t is the sampling time, are respectively the active power and reactive power in the node load power participating in demand response; They are the upper and lower limits of the node load power participating in demand response, respectively; are respectively the active power and reactive power in the actual load demand power of each node; B DR It is the set of nodes participating in demand response in the active distribution network;

[0075] The expression of the user satisfaction constraint condition of the demand response is as follows:

[0076]

[0077] In the formula, is the user satisfaction of demand response; T is the sampling value at sampling time t.

[0078] In this embodiment, the set electric vehicle cluster dispatchable model can not only reduce the initial construction cost of the energy storage equipment of the active distribution network and improve the utilization rate of energy storage, but also actively absorb the excess active load in the system while meeting the electricity demand of electric vehicle users, thereby better achieving the goal of peak shaving and valley filling.

[0079] In one embodiment, the power purchase constraint condition from the electric vehicle charging station, the power sale constraint condition to the electric vehicle charging station, and the state of charge constraint condition of the electric vehicle charging station are expressed as follows:

[0080]

[0081] In the formula, To sell electricity to electric vehicle charging stations, is the power purchased from the electric vehicle charging station, They are the upper limits of the power that microgrid operators can purchase and sell from electric vehicles. They are the actual state of charge value and the expected state of charge value of the electric vehicle when it leaves the charging station; is the state of charge value of the electric vehicle charging station at time t, They are respectively the preset upper limit and lower limit of the state of charge; is the state of charge value of the electric vehicle charging station at time t-1; T is the number of electric vehicles participating in peak load shifting; N is the type of electric vehicles participating in peak load shifting; η EVThe charging and discharging efficiency of electric vehicles; is the charging and discharging status of the electric vehicle; i and n are the numbers of the electric vehicle.

[0082] In one embodiment, the distributed energy model includes expressions of active power constraint conditions and reactive power constraint conditions of distributed energy as follows:

[0083]

[0084] In the formula, is the predicted active power value of distributed energy at node j of the distribution network, is the active power of distributed energy at node j of the distribution network; is the reactive power of distributed energy at node j of the distribution network; The adjustable ratio of the on-load tap-changing transformer in the distribution network is set; are the upper and lower limits of the adjustable ratio; are the upper and lower limits of the reactive power of distributed energy at node j in the distribution network.

[0085] In one embodiment, the functional relationship between the actual load demand power of each node of the distribution network, the load power of the node participating in the demand response, the power purchased from the electric vehicle charging station and the load peak-valley difference loss is expressed as follows:

[0086]

[0087] In the formula, F p-v is the load peak-to-valley loss, c p-v is the unit load peak-valley difference cost; are the sum of the peak and valley values ​​of the load active power, P t sum is the sum of all node load values ​​at time t; N Bus is the number of nodes, is the active power in the actual load demand power of each node, is the active power of the node load power participating in demand response; is the charging power of the input node, which is equal to the power purchased from the electric vehicle charging station.

[0088] In one embodiment, the expression of the relationship function between the power purchased from the electric vehicle charging station and the power purchase loss of the electric vehicle is as follows:

[0089]

[0090] In the formula, F buy is the power purchase loss of electric vehicles, c buyis the unit power purchase loss; N Bus is a set of substation nodes; is the power purchased from the electric vehicle charging station; t is the sampling time, T is the sampling period, and i is the node in the substation node set.

[0091] In one embodiment, the expression of the relationship function between the node branch current of the distribution network and the distribution network loss is as follows:

[0092]

[0093] In the formula, F loss is the power distribution network loss, c loss is the unit network loss; t is the sampling time, T is the sampling period, E line is the branch set in the distribution network; I ij,t is the branch current; r ij is the branch resistance; ij is the node number.

[0094] The expression of the relationship function between the active power of the distributed energy and the abandoned power loss of the distributed energy is as follows:

[0095]

[0096] In the formula, F DG is the power loss of distributed energy, t is the sampling time, T is the sampling period, c DG is the unit power abandonment cost; N DG A collection of nodes equipped with distributed energy; is the predicted active power value of distributed energy at node j of the distribution network, is the active power of distributed energy at node j of the distribution network.

[0097] In one embodiment, it also includes constructing a reactive compensation device model of the distribution network, wherein the reactive compensation device model includes reactive compensation power constraints of the switching capacitor bank;

[0098] Taking the minimization of the sum of the distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as the objective function, combined with the power flow model of the distribution network equipment, the demand response model, the electric vehicle cluster dispatchable model and the reactive compensation device model, the distribution network optimization scheduling result is solved.

[0099] In one embodiment, the expression of the reactive compensation power constraint condition is as follows:

[0100]

[0101] In the formula, is the reactive compensation power of a group of switched capacitors, t is the sampling time, and j is the node; is the compensation power of a switched capacitor; B CB A node set for setting switching capacitors in the distribution network; is the total number of switched capacitors put into operation at sampling time t; is the upper limit of the number of units; is the total number of switched capacitors put into operation at sampling time t-1, It is the upper limit of the number of operations of switching capacitors.

[0102] In a verification example, scenario 4 is set to realize the distribution network optimization dispatching method with demand response and electric vehicles participating in peak shaving and valley filling. For comparison, scenario 1 is set to realize the distribution network optimization dispatching method without considering demand response and electric vehicles participating in peak shaving and valley filling. Figure 5a to Figure 5d It can be seen that the system further reduces the load difference of the active distribution network after adding demand response. After the participation of demand response, the load peak value dropped from 4MW to 3.52MW, and the load peak-to-valley difference dropped from 2.9MW to 2.26MW, with a peak-to-valley difference reduction of 22%; from the analysis of 24 moments, the maximum active load change rate of the active distribution network dropped from 16% to 10%, and the active load change rate at most moments was in the range of 7% to 10%. This is because the actual situation of time-of-use electricity prices and active distribution network electricity consumption is taken into account. At the same time, the electricity satisfaction model is considered in the demand response model, which further constrains the change of power consumption of the active distribution network at a certain moment.

[0103] The node voltages in scenario 1 and scenario 4 are as follows: Figure 4a and Figure 4b As shown in the figure, by comparing the node voltages of the two scenarios, it can be seen that the voltage stability has been significantly improved after adding demand response and electric vehicle cluster energy storage model. Taking node 7 as an example, the voltage deviation of node 7 in scenario 4 is reduced by 25% compared with that in scenario 1, which verifies the effectiveness of the proposed scheme. At the same time, the analysis of the 24-hour node voltage characteristics shows that in the time period of 1:00-6:00, the overall voltage of the active distribution network in scenario 4 is lower than that in scenario 1. This is because the time-of-use electricity price is lower at this time, and the demand response considers transferring the load to this time period when the electricity price is higher, which reduces the voltage deviation.

[0104] In order to further analyze the role of electric vehicle cluster shared energy storage, this paper analyzes the charging and discharging power and energy storage capacity curves of the electric vehicle cluster shared energy storage device at node 32 in scenario 4. The specific contents are as follows: Figure 5a , Figure 5b , Figure 5c , Figure 5d and Figure 6 shown.

[0105] Depend on Figures 5a to 6According to the data analysis, the electric vehicle cluster charging station chooses to charge in the time period of 1:00-6:00 when the time-of-use electricity price is lower, and discharges in the time period of 7:00-11:00 when the time-of-use electricity price is higher. This can not only reduce its own energy consumption costs, but also absorb the abandoned wind and solar power in the system to avoid waste of resources. At the same time, combined with Figure 5a to Figure 5d From the active power curve of scenario 4, it can be seen that during the time period of 18:00-22:00 when the active power of the active distribution network is relatively high, the charging operation of the electric vehicle charging station not only meets the electricity demand of the electric vehicles, but also reduces the active load of the system to achieve peak shaving and valley filling, achieving a win-win situation for both parties.

[0106] The network loss and power purchase loss of the four scenarios are shown in Table 1. From the data in the table, it can be seen that as the complexity of the model continues to increase, the corresponding solution time also increases. The solution time of the above models meets the scheduling and real-time optimization requirements of the active distribution network in active operation. At the same time, by comparing the network loss costs of scenarios 1 and 3 with scenarios 2 and 4, it can be seen that the network loss cost of the active distribution network can be greatly reduced after adding demand response, indicating that the need for response can reduce the peak-to-valley difference of the node and reduce the energy cost of the system. By analyzing scenarios 2 and 4, it can be seen that after considering the shared energy storage characteristics of electric vehicle clusters, the network loss and power purchase cost of the active distribution network decreased by 17.8% and 34.3% respectively, solving the problems of high initial investment and low utilization of energy storage devices.

[0107] Table 1 Economic benefit indicators and carbon emission analysis in different scenarios

[0108]

[0109]

[0110] In summary, the distribution network economic optimization scheduling method proposed in the present invention, which takes into account demand response and the participation of electric vehicle clusters in peak shaving and valley filling, has better economy, fully considers the ability of the active distribution network to absorb the output of distributed power sources, and reduces the energy cost of the system while improving the overall economy of the active distribution network.

[0111] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0112] In one embodiment, a distribution network optimization dispatching device based on electric vehicles participating in peak load shaving and valley filling is provided, and the distribution network optimization dispatching device corresponds one-to-one to the distribution network optimization dispatching method in the above embodiment. Figure 7As shown, the distribution network optimization dispatching device includes a constraint condition construction module 71, an objective function construction module 72, and a dispatch value solving module 73. The detailed description of each functional module is as follows:

[0113] A constraint condition construction module is used to construct a power flow model of distribution network equipment, a demand response model and an electric vehicle cluster dispatchable model; the power flow model of the distribution network equipment includes: a distributed energy model, and the distributed energy model includes active power constraints and reactive power constraints of distributed energy; the demand response model includes: demand response power constraints of each node in the distribution network and user satisfaction constraints of demand response; the electric vehicle cluster dispatchable model includes: power purchase constraints from electric vehicle charging stations, power sales constraints to electric vehicle charging stations, and state of charge constraints of electric vehicle charging stations;

[0114] An objective function construction module is used to construct a relationship function between the node branch current of the distribution network and the distribution network loss, a relationship function between the active power of distributed energy and the abandoned power loss of distributed energy, a relationship function between the power purchased from the electric vehicle charging station and the power purchased loss of the electric vehicle, and a functional relationship between the actual load demand power of each node in the distribution network, the load power of the nodes participating in the demand response, the power purchased from the electric vehicle charging station and the load peak-valley difference loss;

[0115] The scheduling value solving module is used to take the minimum sum of the distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as the objective function, and combine the power flow model of the distribution network equipment, the demand response model and the electric vehicle cluster dispatchable model to solve the distribution network optimization scheduling result, including: the branch current scheduling value of each node, the active power scheduling value of distributed energy, the actual load demand power scheduling value of each node in the distribution network, the node load power scheduling value participating in the demand response and the power scheduling value of power purchase from the electric vehicle charging station.

[0116] For the specific definition of the distribution network optimization and dispatching device, please refer to the definition of the distribution network optimization and dispatching method above, which will not be repeated here. Each module in the above-mentioned distribution network optimization and dispatching device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0117] In one embodiment, Figure 8 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 8As shown, the computer device of this embodiment includes: at least one processor ( Figure 8 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned distribution network optimization and scheduling method embodiments are implemented.

[0118] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 8 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0119] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0120] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may also be an external storage device of a computer device, for example, 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), etc. equipped on a computer device. Further, the memory may also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of computer programs, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0121] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit 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 such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. 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. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0122] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0123] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for optimizing the dispatching of the distribution network in the above embodiment is implemented. For example, Figure 2 S201-S203 are shown, and will not be described here to avoid repetition. Alternatively, when the computer program is executed by the processor, the functions of each module / unit in the above-mentioned distribution network optimization and dispatching device are realized, for example Figure 7 The distribution network optimization dispatching function shown is not repeated here to avoid repetition.

[0125] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0126] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0127] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0128] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0129] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0130] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A distribution network optimization dispatching method based on electric vehicles participating in peak load shaving and valley filling, characterized in that: The following steps are involved: Constructing a power flow model, a demand response model and an electric vehicle cluster dispatchable model for distribution network equipment; the power flow model of the distribution network equipment includes: a distributed energy model, the distributed energy model includes active power constraints and reactive power constraints of distributed energy; the demand response model includes: demand response power constraints of each node in the distribution network and user satisfaction constraints of demand response; the electric vehicle cluster dispatchable model includes: power constraints for purchasing electricity from electric vehicle charging stations, power constraints for selling electricity to electric vehicle charging stations, and state of charge constraints of electric vehicle charging stations; Construct the relationship function between the node branch current of the distribution network and the distribution network loss, the relationship function between the active power of distributed energy and the abandoned power loss of distributed energy, the relationship function between the power purchased from the electric vehicle charging station and the power purchased by the electric vehicle loss, and the functional relationship between the actual load demand power of each node in the distribution network, the load power of the nodes participating in the demand response, the power purchased from the electric vehicle charging station and the load peak-valley difference loss; Taking the minimization of the sum of the distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as the objective function, combined with the power flow model of the distribution network equipment, the demand response model and the electric vehicle cluster dispatchable model, the distribution network optimization scheduling result is solved, including: the branch current scheduling value of each node, the active power scheduling value of distributed energy, the actual load demand power scheduling value of each node in the distribution network, the node load power scheduling value participating in the demand response and the power scheduling value of power purchase from the electric vehicle charging station.

2. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: The expression of the demand response power constraint condition of each node in the distribution network is as follows: In the formula, j is the node, t is the sampling time, are respectively the active power and reactive power in the node load power participating in demand response; They are the upper and lower limits of the node load power participating in demand response, respectively; are respectively the active power and reactive power in the actual load demand power of each node; B DR It is the set of nodes participating in demand response in the active distribution network; The expression of the user satisfaction constraint condition of the demand response is as follows: In the formula, is the user satisfaction of demand response; T is the sampling value at sampling time t.

3. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: The expressions of the power purchase constraint condition from the electric vehicle charging station, the power sale constraint condition to the electric vehicle charging station, and the state of charge constraint condition of the electric vehicle charging station are as follows: In the formula, To sell electricity to electric vehicle charging stations, is the power purchased from the electric vehicle charging station, They are the upper limits of the power that microgrid operators can purchase and sell from electric vehicles. They are EVS when the electric vehicle leaves the charging station. The actual state of charge and the expected state of charge; S t is the state of charge value of the electric vehicle charging station at time t, They are respectively the preset upper limit and lower limit of the state of charge; is the state of charge value of the electric vehicle charging station at time t-1; T is the number of electric vehicles participating in peak load shifting; N is the type of electric vehicles participating in peak load shifting; η EV The charging and discharging efficiency of electric vehicles; is the charging and discharging status of the electric vehicle; i and n are the numbers of the electric vehicle.

4. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: The distributed energy model includes the expressions of active power constraint conditions and reactive power constraint conditions of distributed energy as follows: In the formula, is the predicted active power value of distributed energy at node j of the distribution network, is the active power of distributed energy at node j of the distribution network; is the reactive power of distributed energy at node j of the distribution network; The adjustable ratio of the on-load tap-changing transformer in the distribution network is set; are the upper and lower limits of the adjustable ratio; are the upper and lower limits of the reactive power of distributed energy at node j in the distribution network.

5. The distribution network optimization dispatching method according to claim 1, characterized in that: The functional relationship between the actual load demand power of each node in the distribution network, the load power of the nodes participating in the demand response, the power purchased from the electric vehicle charging station and the load peak-valley difference loss is expressed as follows: In the formula, F p-v is the load peak-to-valley loss, c p-v is the unit load peak-valley difference cost; are the sum of the peak and valley values ​​of the load active power, P t sum is the sum of all node load values ​​at time t; N Bus is the number of nodes, is the active power in the actual load demand power of each node, is the active power of the node load power participating in demand response; is the charging power of the input node, which is equal to the power purchased from the electric vehicle charging station.

6. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: The expression of the relationship function between the power purchased from the electric vehicle charging station and the power purchase loss of the electric vehicle is as follows: In the formula, F buy is the power purchase loss of electric vehicles, c buy is the unit power purchase loss; N Bus is a set of substation nodes; is the power purchased from the electric vehicle charging station; t is the sampling time, T is the sampling period, and i is the node in the substation node set.

7. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: The expression of the relationship function between the node branch current of the distribution network and the distribution network loss is as follows: In the formula, F loss is the power distribution network loss, c loss is the unit network loss; t is the sampling time, T is the sampling period, E line is the branch set in the distribution network; I ij,t is the branch current; r ij is the branch resistance; ij is the node number. The expression of the relationship function between the active power of the distributed energy and the abandoned power loss of the distributed energy is as follows: In the formula, F DG is the power loss of distributed energy, t is the sampling time, T is the sampling period, c DG is the unit power abandonment cost; N DG A collection of nodes equipped with distributed energy; is the predicted active power value of distributed energy at node j of the distribution network, is the active power of distributed energy at node j of the distribution network.

8. The method for optimizing and dispatching a distribution network according to claim 1, characterized in that: It also includes constructing a reactive compensation device model of the distribution network, wherein the reactive compensation device model includes reactive compensation power constraints of the switching capacitor bank; Taking the minimization of the sum of the distribution network loss cost, the abandoned power loss, the electric vehicle power purchase loss and the load peak-valley difference loss as the objective function, combined with the power flow model of the distribution network equipment, the demand response model, the electric vehicle cluster dispatchable model and the reactive compensation device model, the distribution network optimization scheduling result is solved.

9. The method for optimizing and dispatching a distribution network according to claim 8, characterized in that: The expression of the reactive compensation power constraint condition is as follows: In the formula, is the reactive compensation power of a group of switched capacitors, t is the sampling time, and j is the node; is the compensation power of a switched capacitor; B CB A node set for setting switching capacitors in the distribution network; is the total number of switched capacitors put into operation at sampling time t; is the upper limit of the number of units; is the total number of switched capacitors put into operation at sampling time t-1, It is the upper limit of the number of operations of switching capacitors.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the distribution network optimization scheduling method according to any one of claims 1 to 9 is implemented.

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