Distribution network optimization scheduling method and equipment based on electric vehicle participation in peak shaving and valley filling

Abstract

This invention discloses a method and device for optimal scheduling of distribution networks based on peak shaving and valley filling by electric vehicles. The method includes constructing a power flow model, a demand response model, and a schedulable model for electric vehicle clusters within the distribution network. It also involves constructing functional relationships between node branch currents and distribution network losses, active power of distributed energy sources and their curtailment losses, power purchased from electric vehicle charging stations and their power purchase losses, as well as functional relationships 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. The optimal scheduling result of the distribution network is obtained by solving the problem using the power flow model, demand response model, and schedulable model of the distribution network equipment, with the objective function being the minimum sum of distribution network loss cost, curtailment loss, electric vehicle power purchase loss, and load peak-valley difference loss.

Description

Technical Field

[0001] This invention relates to the field of power distribution network dispatching, and in particular to a method and device for optimizing power distribution network dispatching based on electric vehicles participating in peak shaving and valley filling. Background Technology

[0002] With the continuous development of distributed generation (DG), its participation as a power supply end in active distribution networks (ADNs) is constantly increasing. The increasing electricity consumption of active distribution networks leads to a widening peak-to-valley load difference at each node. Simultaneously, the uncertainty of distributed generation output and the volatility of each node increase the risks and challenges of optimizing the scheduling of active distribution networks, specifically, the reduced scheduling capacity of the distribution network when it contains a large number of distributed sources. Summary of the Invention

[0003] This invention provides a method and device for optimizing the scheduling of a distribution network based on the participation of electric vehicles in peak shaving and valley filling, in order to solve the problem of reduced scheduling capability of the distribution network when there are a large number of distributed power sources in the active distribution network.

[0004] A method for optimizing power distribution network scheduling based on electric vehicles participating in peak shaving and valley filling includes:

[0005] A power flow model, a demand response model, and a dispatchable model for electric vehicle clusters are constructed for the distribution network equipment. The power flow model for the distribution network equipment includes a distributed energy model, which includes active power constraints and reactive power constraints for the distributed energy. The demand response model includes demand response power constraints and user satisfaction constraints for each node in the distribution network. The dispatchable model for electric vehicle clusters includes power purchase constraints from electric vehicle charging stations, power sales constraints to electric vehicle charging stations, and state of charge constraints for electric vehicle charging stations.

[0006] Construct the relationship functions between the node branch current and distribution network loss of the distribution network, the relationship functions between the active power of distributed energy and the curtailment loss of distributed energy, the relationship functions between the power purchased from electric vehicle charging stations and the power purchase loss of electric vehicles, and the functional relationships between the actual load demand power of each node of the distribution network, the load power of the nodes participating in demand response, the power purchased from electric vehicle charging stations and the load peak-valley difference loss.

[0007] Using 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 combining the power flow model, demand response model, and electric vehicle cluster dispatchable model of the distribution network equipment, the optimal dispatching result of the distribution network is obtained, including: the branch current dispatching value of each node, the active power dispatching value of distributed energy, the actual load demand power dispatching value of each node of the distribution network, the load power dispatching value of the nodes participating in demand response, and the power purchase power dispatching value from the electric vehicle charging station.

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

[0009]

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

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

[0012]

[0013] In the formula, denoted as user satisfaction with the response to the demand; T represents the sampled value at sampling time t.

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

[0015]

[0016] In the formula, The amount of electricity sold at electric vehicle charging stations. The power purchased from electric vehicle charging stations. These are the upper limits for the amount of electricity that microgrid operators can purchase from and sell to electric vehicles, respectively. These are the actual and expected values ​​of the state of charge (SOC) of an electric vehicle when it leaves the charging station. Let t be the state of charge value of the electric vehicle charging station at time t. These are the preset upper and lower limits for the state of charge; Let be the state of charge (SOC) value of the electric vehicle charging station at time t-1; T be the number of electric vehicles participating in peak shaving and valley filling; N be the type of electric vehicle participating in peak shaving and valley filling; η be the electric vehicle charging station's state of charge (SOC) value at time t-1; T be the number of electric vehicles participating in peak shaving and valley filling; N be the type of electric vehicle participating in peak shaving and valley filling; η be the electric vehicle charging station' EV For the charging and discharging efficiency of electric vehicles; This represents the charging / discharging state of the electric vehicle; i and n are the vehicle's serial numbers.

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

[0018]

[0019] In the formula, Let j be the predicted active power of distributed energy sources at node j in the distribution network. Let be the active power of the distributed energy source at node j in the distribution network; Let be the reactive power of the distributed energy source at node j in the distribution network; The adjustable turns ratio value for a distribution network equipped with an on-load tap-changing transformer; These are the upper and lower limits of the adjustable ratio; Let be the upper and lower limits of the reactive power of the distributed energy source 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 demand response, the power purchased from electric vehicle charging stations, and the load peak-valley difference loss is expressed as follows:

[0021]

[0022] In the formula, F p-v For load peak-valley difference loss, c p-v Cost per unit load peak-to-valley difference; P represents the sum of the peak and valley values ​​of the load's active power, respectively. t sum N is the sum of the load values ​​of all nodes at time t; Bus For the number of nodes, This refers to the active power within the actual load demand of each node. The active power in the node load power participating in demand response; The charging power of the input node is equal to the power purchased from the electric vehicle charging station.

[0023] Optionally, the expression for 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 For the electricity purchase loss of electric vehicles, c buy N represents the unit power purchase loss; Bus For the set of substation nodes; t represents the power purchased from the electric vehicle charging station; t is the sampling time; T is the sampling period; and i is a node in the substation node set.

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

[0027]

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

[0029] The expression for the relationship between the active power of the distributed energy source and the power curtailment loss of the distributed energy source is as follows:

[0030]

[0031] In the formula, F DG The power curtailment loss of distributed energy resources, where t is the sampling time, T is the sampling period, and c is the power loss. DG The cost per unit of abandoned electricity; N DG A collection of nodes equipped with distributed energy resources; Let j be the predicted active power of distributed energy sources at node j in the distribution network. Let be the active power of the distributed energy source at node j in the distribution network.

[0032] Optionally, it also includes constructing a reactive power compensation device model for the distribution network, wherein the reactive power compensation device model includes reactive power compensation constraints for switching capacitor banks.

[0033] The objective function is to minimize 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. The optimal scheduling result of the distribution network is obtained by combining the power flow model, demand response model, electric vehicle cluster dispatchable model, and reactive power compensation device model of the distribution network equipment.

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

[0035]

[0036] In the formula, Let t be the reactive power compensation of a group of switched capacitors, t be the sampling time, and j be the node. B is the compensation power of a switched capacitor; CB The set of nodes in a distribution network where capacitors are switched on and off; This represents the total number of switched capacitors in operation at sampling time t; This represents the upper limit for the number of generating units; This represents the total number of switched capacitors in operation at sampling time t-1. This is the maximum number of times the capacitor can be switched on and off.

[0037] A power distribution network optimization scheduling device based on electric vehicles participating in peak shaving and valley filling includes:

[0038] The constraint construction module is used to construct the power flow model, demand response model, and electric vehicle cluster dispatchable model of the distribution network equipment. The power flow model of the distribution network equipment includes: a distributed energy model, which includes active power constraints and reactive power constraints of the distributed energy; the demand response model includes: demand response power constraints and user satisfaction constraints of each node in the distribution network; 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] The objective function construction module is used to construct the relationship function between the node branch current and the distribution network loss, the relationship function between the active power of distributed energy and the curtailment loss of distributed energy, the relationship function between the power purchased from electric vehicle charging stations and the power purchase loss of electric vehicles, as well as the functional relationship between the actual load demand power of each node in the distribution network, the load power of the nodes participating in demand response, the power purchased from electric vehicle charging stations and the load peak-valley difference loss.

[0040] The scheduling value calculation module is used to minimize 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. It combines the power flow model, demand response model, and electric vehicle cluster schedulable model of the distribution network equipment to obtain the optimized scheduling result of the distribution network. The result includes: 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 of the distribution network, the load power scheduling value of the nodes participating in demand response, and the power purchase power scheduling value from the electric vehicle charging station.

[0041] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described power distribution network optimization scheduling method.

[0042] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described power distribution network optimization scheduling method.

[0043] The aforementioned distribution network optimization scheduling method, device, computer equipment, and storage medium utilize demand response and electric vehicle participation in the distribution network's output and load scheduling, and significantly reduce system network losses through distributed power sources and shared energy storage with electric vehicle clusters. The solution for optimal distribution network scheduling considers the demand response model, reducing the load difference between peak and valley periods in the active distribution network. Furthermore, the solution also considers the dispatchability model of electric vehicle clusters, which not only reduces the initial construction cost of energy storage equipment in the active distribution network and improves energy storage utilization, but also actively absorbs excess active load in the system while meeting the electricity demand of electric vehicle users, better achieving the goal of peak shaving and valley filling. This ensures that even when the active distribution network contains a large number of distributed power sources, it can still improve the network's scheduling capability. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[0046] Figure 2 This is a flowchart of a power distribution network optimization scheduling method according to an embodiment of the present invention;

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

[0048] Figure 4a , Figure 4b These are schematic diagrams of the node voltage curves for scenarios 1 and 4 in an embodiment of the present invention, respectively.

[0049] Figure 5a , Figure 5b , Figure 5c , Figure 5dThese are schematic diagrams of the 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 This is a schematic diagram of the total capacity curve of an electric vehicle in one embodiment of the present invention;

[0051] Figure 7 This is a schematic diagram of a power distribution network optimization scheduling device according to an embodiment of the present invention;

[0052] Figure 8 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort 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 this invention can be applied to, for example... Figure 1 The application environment is shown. Specifically, this distribution network optimization scheduling method is applied in a distribution network scheduling system, which includes, for example, the following: Figure 1 The diagram illustrates a client and server that communicate over a network to implement power distribution network dispatching. The client, also known as the user terminal, is the program that provides local services to the client, corresponding to the server. The client can be installed on, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0055] In one embodiment, such as Figure 2 As shown, a distribution network optimization scheduling method based on electric vehicles participating in peak shaving and valley filling is provided, and this method is applied to... Figure 1 Taking the server in the example, the following steps are included:

[0056] S201, construct a power flow model, a demand response model, and a dispatchable model for electric vehicle clusters for distribution network equipment; the power flow model for distribution network equipment includes: a distributed energy model, which includes active power constraints and reactive power constraints for distributed energy; the demand response model includes: demand response power constraints and user satisfaction constraints for each node in the distribution network; the dispatchable model for electric vehicle clusters includes: power purchase constraints from electric vehicle charging stations, power sales constraints to electric vehicle charging stations, and state of charge constraints for electric vehicle charging stations;

[0057] S202, construct the relationship function between the node branch current and the distribution network loss, the relationship function between the active power of distributed energy and the curtailment loss of distributed energy, the relationship function between the power purchased from electric vehicle charging stations and the power purchase loss of electric vehicles, and the functional relationship between the actual load demand power of each node of the distribution network, the load power of the nodes participating in demand response, the power purchased from electric vehicle charging stations and the load peak-valley difference loss.

[0058] S203, with the objective function being 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, and combining the power flow model, demand response model, and electric vehicle cluster schedulable model of the distribution network equipment, the optimal scheduling result of the distribution network is obtained, 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 of the distribution network, the load power scheduling value of the nodes participating in demand response, and the power purchase power scheduling value from the electric vehicle charging station.

[0059] When considering only the modeling of active distributed energy resources, this model includes wind turbine and photovoltaic models, as shown in the following expression:

[0060]

[0061] In the formula, B DG It is a set of nodes containing both wind turbines and photovoltaics; Let j be the predicted active power of distributed energy sources at node j in the distribution network. Let be the active power of the distributed energy source at node j in the distribution network.

[0062] When considering the modeling of distributed energy resources that include both active and reactive power, the model includes wind turbines and photovoltaics, as expressed below:

[0063]

[0064] In the formula, Let j be the predicted active power of distributed energy sources at node j in the distribution network. Let be the active power of the distributed energy source at node j in the distribution network; This is the adjustable transformer ratio, which is derived from the power factor ratio of active to reactive power. Let be the reactive power of the distributed energy source at node j in the distribution network.

[0065] Furthermore, the power flow model for distribution network equipment can also include: an on-load tap-changing transformer module, expressed as follows:

[0066]

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

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

[0069]

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

[0071] The distribution network optimization scheduling method in this embodiment addresses the impacts of voltage fluctuations and power imbalances caused by distributed generation by constructing a power flow model that uses on-load tap-changing transformers for regulation. It utilizes demand response and electric vehicle (EV) participation in the distribution network's output and load scheduling, and significantly reduces system network losses by sharing energy storage between distributed generation and EV clusters. The optimal scheduling solution considers the demand response model, reducing the load difference between peak and valley periods in the active distribution network, achieving a 22% reduction in peak-valley load difference. Furthermore, the optimal scheduling solution also considers the dispatchable model of EV clusters, which not only reduces the initial construction cost of energy storage equipment in the active distribution network and improves energy storage utilization, but also actively absorbs excess active load in the system while meeting the electricity demand of EV users, better achieving the goal of peak shaving and valley filling. This ensures that even when the active distribution network contains a large number of distributed generation sources, it can still improve the network's scheduling capability.

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

[0073]

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

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

[0076]

[0077] In the formula, denoted as user satisfaction with the response to the demand; T represents the sampled value at sampling time t.

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

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

[0080]

[0081] In the formula, The amount of electricity sold at electric vehicle charging stations. The power purchased from electric vehicle charging stations. These are the upper limits for the amount of electricity that microgrid operators can purchase from and sell to electric vehicles, respectively. These are the actual and expected values ​​of the state of charge (SOC) of an electric vehicle when it leaves the charging station. Let t be the state of charge value of the electric vehicle charging station at time t. These are the preset upper and lower limits for the state of charge; Let be the state of charge (SOC) value of the electric vehicle charging station at time t-1; T be the number of electric vehicles participating in peak shaving and valley filling; N be the type of electric vehicle participating in peak shaving and valley filling; η be the electric vehicle charging station's state of charge (SOC) value at time t-1; T be the number of electric vehicles participating in peak shaving and valley filling; N be the type of electric vehicle participating in peak shaving and valley filling; η be the electric vehicle charging station' EVFor the charging and discharging efficiency of electric vehicles; This represents the charging / discharging state of the electric vehicle; i and n are the vehicle's serial numbers.

[0082] In one embodiment, the distributed energy model includes the following expressions for the active power constraints and reactive power constraints of the distributed energy:

[0083]

[0084] In the formula, Let j be the predicted active power of distributed energy sources at node j in the distribution network. Let be the active power of the distributed energy source at node j in the distribution network; Let be the reactive power of the distributed energy source at node j in the distribution network; The adjustable turns ratio value for a distribution network equipped with an on-load tap-changing transformer; These are the upper and lower limits of the adjustable ratio; Let be the upper and lower limits of the reactive power of the distributed energy source at node j in the distribution network.

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

[0086]

[0087] In the formula, F p-v For load peak-valley difference loss, c p-v Cost per unit load peak-to-valley difference; P represents the sum of the peak and valley values ​​of the load's active power, respectively. t sum N is the sum of the load values ​​of all nodes at time t; Bus For the number of nodes, This refers to the active power within the actual load demand of each node. The active power in the node load power participating in demand response; The charging power of the input node is equal to the power purchased from the electric vehicle charging station.

[0088] In one embodiment, the expression for 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 For the electricity purchase loss of electric vehicles, c buyN represents the unit power purchase loss; Bus For the set of substation nodes; t represents the power purchased from the electric vehicle charging station; t is the sampling time; T is the sampling period; and i is a node in the substation node set.

[0091] In one embodiment, the expression for the relationship between the node branch currents and distribution network losses in the distribution network is as follows:

[0092]

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

[0094] The expression for the relationship between the active power of the distributed energy source and the power curtailment loss of the distributed energy source is as follows:

[0095]

[0096] In the formula, F DG The power curtailment loss of distributed energy resources, where t is the sampling time, T is the sampling period, and c is the power loss. DG The cost per unit of abandoned electricity; N DG A collection of nodes equipped with distributed energy resources; Let j be the predicted active power of distributed energy sources at node j in the distribution network. Let be the active power of the distributed energy source at node j in the distribution network.

[0097] In one embodiment, the system further includes constructing a reactive power compensation device model for the power distribution network, the reactive power compensation device model including reactive power compensation constraints for switching capacitor banks.

[0098] The objective function is to minimize 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. The optimal scheduling result of the distribution network is obtained by combining the power flow model, demand response model, electric vehicle cluster dispatchable model, and reactive power compensation device model of the distribution network equipment.

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

[0100]

[0101] In the formula, Let t be the reactive power compensation of a group of switched capacitors, t be the sampling time, and j be the node. B is the compensation power of a switched capacitor; CB The set of nodes in a distribution network where capacitors are switched on and off; This represents the total number of switched capacitors in operation at sampling time t; This represents the upper limit for the number of generating units; This represents the total number of switched capacitors in operation at sampling time t-1. This is the maximum number of times the capacitor can be switched on and off.

[0102] In one verification example, Scenario 4 was set up to implement a distribution network optimization scheduling method that considers demand response and electric vehicle participation in peak shaving and valley filling. In contrast, Scenario 1 was set up to implement a distribution network optimization scheduling method that does not consider demand response and electric vehicle participation in peak shaving and valley filling. Figures 5a to 5d It can be seen that the addition of demand response further reduced the load differential of the active distribution network. After demand response was implemented, the peak load decreased from 4MW to 3.52MW, and the peak-to-valley load difference decreased from 2.9MW to 2.26MW, representing a 22% reduction. Analyzing at 24 different time points, the maximum active load change rate of the active distribution network decreased from 16% to 10%, and the active load change rate remained within the range of 7% to 10% for most of the time points. This is because time-of-use pricing and the actual electricity consumption of the active distribution network were taken into account, and the electricity satisfaction model was also considered in the demand response model, further constraining the change in power consumption of the active distribution network at a given time.

[0103] The node voltages of scenarios 1 and 4 are as follows: Figure 4a and Figure 4b As shown, a comparative analysis of the node voltages in the two scenarios reveals that voltage stability is significantly improved after incorporating the 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 to scenario 1, verifying the effectiveness of the proposed scheme. Furthermore, analysis of the node voltage characteristics over 24 hours shows that the overall voltage of the active distribution network in scenario 4 is lower than that in scenario 1 during the 1:00-6:00 time period. This is because the time-of-use electricity price is lower during this period, and the demand response mechanism considers transferring loads from periods with higher electricity prices to this time period, thus reducing voltage deviation.

[0104] To further analyze the role of shared energy storage in electric vehicle clusters, this paper analyzes the charging and discharging power versus energy storage capacity curves of the shared energy storage device at node 32 in scenario 4. Specific details are as follows: Figure 5a , Figure 5b , Figure 5c , Figure 5d and Figure 6 As shown.

[0105] Depend on Figures 5a to 6Data analysis shows that electric vehicle charging stations choose to charge during the lower time-of-use (TOU) electricity price period (1:00-6:00) and discharge during the higher TOU electricity price period (7:00-11:00). This not only reduces their own energy costs but also absorbs wind and solar power curtailment from the system, avoiding resource waste. Furthermore, combined with... Figures 5a to 5d As can be seen from the active power curve in Scenario 4, during the period from 18:00 to 22:00 when the active power of the active distribution network is relatively high, the charging operation of electric vehicle charging stations not only meets the power demand of electric vehicles, but also reduces the active load of the system to achieve peak shaving and valley filling, thus achieving a win-win situation for both.

[0106] Table 1 shows the network losses and electricity purchase costs for the four scenarios. The data in the table shows that as the model complexity increases, the corresponding solution time also increases. The solution times of the above models all meet the scheduling and real-time optimization requirements of the active distribution network in active operation. Comparing the network loss costs of scenarios 1 and 3 with scenarios 2 and 4, it can be seen that adding demand response can significantly reduce the network loss costs of the active distribution network, indicating that demand response can reduce the peak-to-valley difference at nodes and simultaneously reduce the system's energy costs. Analysis of scenarios 2 and 4 shows that after considering the shared energy storage characteristics of electric vehicle clusters, the network losses and electricity purchase costs of the active distribution network decreased by 17.8% and 34.3%, respectively, solving the problems of high initial investment and low utilization rate of energy storage devices.

[0107] Table 1. Economic Benefit Indicators and Carbon Emission Analysis for Different Scenarios

[0108]

[0109]

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

[0111] It should be understood that the sequence number of each step in the above embodiments does not imply 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 embodiments of the present invention.

[0112] In one embodiment, a power distribution network optimization scheduling device based on electric vehicles participating in peak shaving and valley filling is provided. This power distribution network optimization scheduling device corresponds one-to-one with the power distribution network optimization scheduling method in the above embodiments. For example... Figure 7As shown, the power distribution network optimization scheduling device includes a constraint construction module 71, an objective function construction module 72, and a scheduling value calculation module 73. Detailed descriptions of each functional module are as follows:

[0113] The constraint construction module is used to construct the power flow model, demand response model, and electric vehicle cluster dispatchable model of the distribution network equipment. The power flow model of the distribution network equipment includes: a distributed energy model, which includes active power constraints and reactive power constraints of the distributed energy; the demand response model includes: demand response power constraints and user satisfaction constraints of each node in the distribution network; 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] The objective function construction module is used to construct the relationship function between the node branch current and the distribution network loss, the relationship function between the active power of distributed energy and the curtailment loss of distributed energy, the relationship function between the power purchased from electric vehicle charging stations and the power purchase loss of electric vehicles, as well as the functional relationship between the actual load demand power of each node in the distribution network, the load power of the nodes participating in demand response, the power purchased from electric vehicle charging stations and the load peak-valley difference loss.

[0115] The scheduling value calculation module is used to minimize 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. It combines the power flow model, demand response model, and electric vehicle cluster schedulable model of the distribution network equipment to obtain the optimized scheduling result of the distribution network. The result includes: 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 of the distribution network, the load power scheduling value of the nodes participating in demand response, and the power purchase power scheduling value from the electric vehicle charging station.

[0116] Specific limitations regarding the distribution network optimization dispatching device can be found in the limitations of the distribution network optimization dispatching method described above, and will not be repeated here. Each module in the aforementioned distribution network optimization dispatching device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0117] In one embodiment, Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Figure 8As shown, the computer device of this embodiment includes: at least one processor ( Figure 8 The diagram shows only one of the following: a memory and a computer program stored in the memory and capable of running on at least one processor. When the processor executes the computer program, it implements the steps in any of the above embodiments of the power distribution network optimization scheduling method.

[0118] This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 8 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. Computer devices may include more or fewer components than shown in the illustration, or combinations of certain components, or different components, such as network interfaces, displays, and input devices.

[0119] The processor referred to can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0120] Memory includes readable storage media, internal memory, etc., wherein internal memory can be the RAM of a computer device, providing an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of a computer device, or in other embodiments, it can be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal storage units and external storage devices of a computer device. Memory is used to store the operating system, applications, bootloader, data, and other programs, such as program code for computer programs. Memory can also be used to temporarily store data that has been output or will be output.

[0121] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0122] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be accomplished by a computer program product. When the computer program product is run on a computer device, the computer device executes the steps in the above method embodiments.

[0123] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer 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 executed by a processor, the computer program implements the power distribution network optimization scheduling method described in the above embodiment, for example... Figure 2 S201-S203, as shown, will not be described again here to avoid repetition. Alternatively, when this computer program is executed by a processor, it implements the functions of each module / unit in this embodiment of the power distribution network optimization and dispatching device, for example... Figure 7 The power distribution network optimization and dispatch functions shown are not described in detail here to avoid repetition.

[0125] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0126] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be accomplished by a computer program product. When the computer program product is run on a computer device, the computer device executes the steps in the above method embodiments.

[0127] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0128] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0129] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

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

[0131] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A distribution network optimization scheduling method based on electric vehicles participating in peak shaving and valley filling, characterized in that, Includes the following steps: A power flow model, a demand response model, and a dispatchable model for electric vehicle clusters are constructed for the distribution network equipment. The power flow model for the distribution network equipment includes a distributed energy model, which includes active power constraints and reactive power constraints for the distributed energy. The demand response model includes demand response power constraints and user satisfaction constraints for each node in the distribution network. The dispatchable model for electric vehicle clusters includes power purchase constraints from electric vehicle charging stations, power sales constraints to electric vehicle charging stations, and state of charge constraints for electric vehicle charging stations. Construct the relationship functions between the node branch current and distribution network loss of the distribution network, the relationship functions between the active power of distributed energy and the curtailment loss of distributed energy, the relationship functions between the power purchased from electric vehicle charging stations and the power purchase loss of electric vehicles, and the functional relationships between the actual load demand power of each node of the distribution network, the load power of the nodes participating in demand response, the power purchased from electric vehicle charging stations and the load peak-valley difference loss. Using 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 combining the power flow model, demand response model, and electric vehicle cluster dispatchable model of the distribution network equipment, the optimal dispatching result of the distribution network is obtained, including: the branch current dispatching value of each node, the active power dispatching value of distributed energy, the actual load demand power dispatching value of each node of the distribution network, the load power dispatching value of the nodes participating in demand response, and the power purchase power dispatching value from the electric vehicle charging station.

2. The distribution network optimization scheduling method according to claim 1, characterized in that, The expressions for the demand response power constraints of each node in the distribution network are as follows: ， ， In the formula, Let be the node, and t be the sampling time. , These are the active power and reactive power in the node load power participating in demand response, respectively. , These are the lower and upper limits of the node load power participating in demand response, respectively. , These represent the active power and reactive power in the actual load demand of each node, respectively. It refers to the set of nodes participating in demand response in an active distribution network; The expression for the user satisfaction constraint of the demand response is as follows: ， In the formula, denoted as user satisfaction with the response to the demand; T represents the sampled value at sampling time t.

3. The distribution network optimization scheduling method according to claim 1, characterized in that, The expressions for the power purchase constraint from the electric vehicle charging station, the power sale constraint to the electric vehicle charging station, and the state of charge constraint of the electric vehicle charging station are as follows: ， In the formula, The amount of electricity sold at electric vehicle charging stations. The power purchased from electric vehicle charging stations. , These are the upper limits for the amount of electricity sold and purchased by microgrid operators to electric vehicles, respectively. , These are the actual and expected values ​​of the state of charge (SOC) of an electric vehicle when it leaves the charging station. Let t be the state of charge value of the electric vehicle charging station at time t. , These are the preset lower and upper limits for the charged state, respectively; Let t be 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 shaving and valley filling. The types of electric vehicles that participate in peak shaving and valley filling; For the charging and discharging efficiency of electric vehicles; This represents the charging / discharging state of the electric vehicle; i and n are the vehicle's serial numbers.

4. The distribution network optimization scheduling method according to claim 1, characterized in that, The distributed energy model includes the following expressions for the active power constraints and reactive power constraints of the distributed energy source: ， In the formula, For the nodes of the distribution network The predicted active power of distributed energy resources, For the nodes of the distribution network The active power of distributed energy sources; For the nodes of the distribution network The reactive power of distributed energy resources; The adjustable turns ratio value for a distribution network equipped with an on-load tap-changing transformer; , These are the lower and upper limits of the adjustable ratio; , For the nodes of the distribution network The lower and upper limits of reactive power of distributed energy sources.

5. The distribution network optimization scheduling 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 nodes participating in demand response, the power purchased from electric vehicle charging stations, and the load peak-valley difference loss is expressed as follows: ， ， ， In the formula, F p-v This is due to the peak-to-valley load difference loss. Cost per unit load peak-to-valley difference; , These are the sum of the peak and valley values ​​of the load's active power, respectively. This is the sum of the load values ​​of all nodes at time t; For the number of nodes, This refers to the active power within the actual load demand of each node. The active power in the node load power participating in demand response; The charging power of the input node is equal to the power purchased from the electric vehicle charging station.

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

7. The distribution network optimization scheduling method according to claim 1, characterized in that, The expression for the relationship between the node branch current and the distribution network loss in the distribution network is as follows: ， In the formula, F loss For power distribution network losses, per unit network loss; t is the sampling time, and T is the sampling period. A set of branches in a power distribution network; Branch current; Here, represents the branch resistance; ij represents the node number. The expression for the relationship between the active power of the distributed energy source and the power curtailment loss of the distributed energy source is as follows: ， In the formula, F DG The power curtailment loss of distributed energy resources, where t is the sampling time and T is the sampling period. Cost per unit of abandoned electricity; A collection of nodes equipped with distributed energy resources; For the nodes of the distribution network The predicted active power of distributed energy resources, For the nodes of the distribution network The active power of distributed energy sources.

8. The distribution network optimization scheduling method according to claim 1, characterized in that, It also includes constructing a reactive power compensation device model for the power distribution network, wherein the reactive power compensation device model includes reactive power compensation constraints for switching capacitor banks. The objective function is to minimize 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. The optimal scheduling result of the distribution network is obtained by combining the power flow model, demand response model, electric vehicle cluster dispatchable model, and reactive power compensation device model of the distribution network equipment.

9. The distribution network optimization scheduling method according to claim 8, characterized in that, The expression for the reactive power compensation constraint is as follows: ， In the formula, Let t be the reactive power compensation of a group of switched capacitors, t be the sampling time, and j be the node. This is the compensation power for a switched capacitor; The set of nodes in a distribution network where capacitors are switched on and off; This represents the total number of switched capacitors in operation at sampling time t; This represents the upper limit for the number of generating units; This represents the total number of switched capacitors in operation at sampling time t-1. This is the maximum number of times the capacitor can be switched on and off.

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, it implements the power distribution network optimization scheduling method according to any one of claims 1 to 9.

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