Power distribution network collaborative planning method, device, equipment, storage medium and program product

Abstract

The invention relates to a power distribution network collaborative planning method and device, equipment, a storage medium and a program product. The method comprises the following steps: constructing an electric vehicle behavior model by adopting different charging state scenes generated based on historical electric vehicle travel data; according to electric vehicle behavior data obtained by the electric vehicle behavior model, constructing an electric vehicle parking lot demand model of each region in the power distribution network; constructing a cost calculation model based on the electric vehicle behavior data and electric vehicle charging scheduling information determined through each electric vehicle parking lot demand model; constructing an electric vehicle parking lot construction model of each region in the power distribution network by adopting the electric vehicle charging scheduling information; and combining the cost calculation model and each electric vehicle parking lot construction model to obtain a collaborative planning model, and solving the collaborative planning model to obtain a power distribution network collaborative planning result. By adopting the method, continuously increasing load and electric vehicle requirements can be met at the same time, and the flexibility of the power distribution network is effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network planning, and in particular to a distribution network collaborative planning method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] As the reform of the new power system continues to deepen, the penetration rate of PEV (pure electric vehicles) and RES (Renewable Energy Sources) continues to increase. The uncertainty of renewable energy generation also brings challenges to the power system, such as increased congestion of transformer lines and feeders, and steep net load ramps.

[0003] In order to meet the challenge of large-scale access of renewable energy to new distribution networks, how to use plug-in electric vehicles to build a transportation-power coupling network to determine the optimal planning strategy for the transportation network and distribution network is an urgent problem to be solved. Summary of the invention

[0004] Based on this, it is necessary to provide a distribution network collaborative planning method, device, computer equipment, computer-readable storage medium and computer program product that can improve the effect of distribution network collaborative planning in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a distribution network collaborative planning method, comprising:

[0006] Build an EV behavior model using different charging state scenarios generated based on historical EV trip data;

[0007] constructing an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model;

[0008] Building a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models;

[0009] Using the electric vehicle charging scheduling information, constructing an electric vehicle parking lot construction model in each area of ​​the distribution network;

[0010] The cost calculation model and the electric vehicle parking lot construction models are combined to obtain a collaborative planning model, and the distribution network collaborative planning results are obtained by solving the collaborative planning model; the distribution network collaborative planning results are used to characterize the planning scheme based on distribution network planning and electric vehicle parking lot construction locations.

[0011] In one embodiment, the electric vehicle behavior model is constructed by using different charging state scenarios generated based on historical electric vehicle travel data, including:

[0012] Determining, based on the historical electric vehicle travel data, an electric vehicle behavior probability distribution for characterizing arrival times and departure times of the electric vehicle;

[0013] Using different scenario generators to generate different charging state scenarios based on the electric vehicle behavior probability distribution;

[0014] Combining electric vehicle behavior constraints and different charging state scenarios, constructing the electric vehicle behavior model;

[0015] Among them, the different charging status scenarios include the electric vehicle arrival time scenario, the electric vehicle departure time scenario, the electric vehicle initial power scenario, and the electric vehicle required power scenario.

[0016] In one embodiment, the electric vehicle behavior data obtained from the electric vehicle behavior model is used to construct an electric vehicle parking lot demand model for each area in the distribution network, including:

[0017] Based on the historical electric vehicle travel data, construct an electric vehicle charging control model;

[0018] The electric vehicle behavior data is used in combination with the electric vehicle behavior model and the electric vehicle charging control model to construct an electric vehicle parking lot demand model for each area in the distribution network.

[0019] In one embodiment, the cost calculation model is constructed based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each electric vehicle parking lot demand model, including:

[0020] According to each of the electric vehicle parking lot demand models, obtaining electric vehicle parking lot charging demand data in each area of ​​the distribution network;

[0021] Determining the electric vehicle charging scheduling information for each of the areas based on the electric vehicle parking lot charging demand data for each of the areas;

[0022] The cost calculation model is constructed by using the electric vehicle behavior data and the electric vehicle charging scheduling information of each area; the cost calculation model is a model for performing full life cycle cost calculation processing based on electric vehicle parking lot demand and distribution network planning.

[0023] In one embodiment, the electric vehicle charging scheduling information is used to construct an electric vehicle parking lot construction model in each area of ​​the distribution network, including:

[0024] Combining different control information for electric vehicle parking lot construction and the electric vehicle charging scheduling information, constructing an electric vehicle parking lot construction model for each area in the distribution network;

[0025] Among them, the different control information includes parking lot demand satisfaction information, parking lot maximum power capacity control information, demand satisfaction information after rescheduling charging scheduling, charging time change control information, and parking lot operation control information.

[0026] In one embodiment, obtaining the distribution network collaborative planning result by solving the collaborative planning model includes:

[0027] Get the preset model optimality gap parameters;

[0028] The collaborative planning model is solved according to the optimality gap parameter of the model by adopting a solution method for mixed integer linear programming to obtain the distribution network collaborative planning result.

[0029] In a second aspect, the present application also provides a distribution network collaborative planning device, comprising:

[0030] An electric vehicle behavior model building module for building an electric vehicle behavior model using different charging state scenarios generated based on historical electric vehicle trip data;

[0031] A parking lot demand model building module, used to build an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model;

[0032] A cost calculation model building module, used to build a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models;

[0033] A parking lot construction model building module, used to construct an electric vehicle parking lot construction model for each area in the distribution network using the electric vehicle charging scheduling information;

[0034] The collaborative planning model solving module is used to combine the cost calculation model and the electric vehicle parking lot construction models to obtain a collaborative planning model, and obtain the distribution network collaborative planning results by solving the collaborative planning model; the distribution network collaborative planning results are used to characterize the planning scheme based on the distribution network planning and the electric vehicle parking lot construction location.

[0035] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the distribution network collaborative planning method as described in the first aspect are implemented.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the distribution network collaborative planning method as described in the first aspect are implemented.

[0037] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the distribution network collaborative planning method as described in the first aspect.

[0038] The above-mentioned distribution network collaborative planning method, device, computer equipment, computer-readable storage medium and computer program product construct an electric vehicle behavior model by adopting different charging status scenarios generated based on historical electric vehicle travel data, and construct an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model. Then, based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each electric vehicle parking lot demand model, a cost calculation model is constructed, and the electric vehicle charging scheduling information is used to construct an electric vehicle parking lot construction model for each area in the distribution network. Then, the cost calculation model and the electric vehicle parking lot construction model are combined to obtain a collaborative planning model. By solving the collaborative planning model, the distribution network collaborative planning result is obtained. The distribution network collaborative planning result is used to characterize the planning scheme based on the distribution network planning and the electric vehicle parking lot construction location, which can simultaneously reduce the distribution network expansion and electric vehicle parking lot installation costs, meet the growing load and electric vehicle demand, and effectively improve the flexibility of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A schematic diagram of a flow chart of a distribution network collaborative planning method in one embodiment;

[0041] Figure 2 A schematic diagram of a model building process in one embodiment;

[0042] Figure 3 A schematic diagram of a collaborative planning model building process in one embodiment;

[0043] Figure 4 A schematic diagram of a flow chart of a distribution network collaborative planning method in another embodiment;

[0044] Figure 5 It is a structural block diagram of a distribution network collaborative planning device in one embodiment;

[0045] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] As the proportion of high-proportion renewable energy access to the distribution network continues to increase, intermittent power generation has brought a series of problems to the grid, such as increased congestion of transformers and feeders, and steep net load ramps. At present, considering the integration of PEVs (electric vehicles), the main considerations for distribution network expansion planning are to reduce planning costs, consider future uncertainties in traditional load levels, and the impact of PEV penetration on distribution network planning, so as to minimize the operating costs of the distribution network.

[0048] However, the above studies did not consider how to improve the flexibility of the distribution network, and some studies ignored the fact that PEV demand in the distribution network should be used as an input to the planning model, which resulted in the load effect of PEV on specific feeders in the network being masked, which easily led to a more optimistic planning scheme.

[0049] In response to the problems of the above-mentioned traditional methods, this application proposes a new distribution network collaborative planning method that takes into account the uncertainty of renewable energy and the flexibility of electric vehicles. By simultaneously integrating a new multi-level expansion planning model of the distribution network and electric vehicle parking lots, an accurate PEV demand model is constructed. This can simultaneously reduce the cost of distribution network expansion and electric vehicle parking lot installation, meet the growing load and PEV demand, and effectively improve the flexibility of the distribution network.

[0050] In an exemplary embodiment, Figure 1 As shown, a distribution network collaborative planning method is provided. This embodiment uses the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps 101 to 104. Among them:

[0051] Step 101 , constructing an electric vehicle behavior model using different charging state scenarios generated based on historical electric vehicle travel data.

[0052] As an example, different charging status scenarios may include an electric vehicle arrival time scenario, an electric vehicle departure time scenario, an electric vehicle initial power scenario, and an electric vehicle required power scenario.

[0053] In practical applications, such as Figure 2 As shown, in the process of coordinated planning of distribution networks, an electric vehicle behavior model can be constructed based on historical electric vehicle travel data. For example, historical data (i.e., historical electric vehicle travel data) can be used to estimate the probability distribution of PEV arrival and departure times, and then charging state scenarios (i.e., different charging state scenarios) in PEV can be generated based on the truncated Gaussian distribution function and the truncated exponential distribution function, and then a PEV behavior uncertainty model (i.e., electric vehicle behavior model) can be constructed.

[0054] Step 102: constructing an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model.

[0055] After constructing the electric vehicle behavior model, PEV behavior data (i.e., electric vehicle behavior data) can be constructed based on the electric vehicle behavior model, and then the constructed PEV behavior data can be used to estimate the EVPL (Electric Vehicle Parking Lot) demand in each area of ​​the distribution network, such as Figure 2 As shown, EVPL demand models for different regions (i.e., electric vehicle parking lot demand models for each region) can be constructed.

[0056] In an optional embodiment, the PEV behavior uncertainty model may be used to generate a PEV uncertainty scenario, and the EVPL demand model of different regions may be used to generate an EVPL demand scenario.

[0057] Step 103: construct a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models.

[0058] In one example, if Figure 3 As shown in the figure, in the process of constructing the coordinated planning model of distribution network and EVPL, the full life cycle cost model (i.e., cost calculation model) considering EVPL demand and distribution network planning can be constructed by inputting PEV behavior uncertainty data (i.e., electric vehicle behavior data) and PEV preliminary charging scheduling plan (i.e., electric vehicle charging scheduling information).

[0059] Step 104: Use the electric vehicle charging scheduling information to construct an electric vehicle parking lot construction model for each area in the distribution network.

[0060] Alternatively, if Figure 3As shown, the EVPL investment and construction model for each region (i.e., the electric vehicle parking lot construction model for each region) can be constructed in combination with the PEV charging scheduling plan (i.e., the electric vehicle charging scheduling information).

[0061] Step 105, combining the cost calculation model and the electric vehicle parking lot construction models to obtain a collaborative planning model, and obtaining a distribution network collaborative planning result by solving the collaborative planning model.

[0062] Among them, the distribution network collaborative planning results can be used to characterize the planning scheme based on distribution network planning and the location of electric vehicle parking lot construction.

[0063] In practical applications, for the collaborative planning model combining PDN (distribution network) and EVPL, that is, combining the cost calculation model and the construction model of each electric vehicle parking lot, a collaborative planning model is obtained, such as Figure 3 As shown, by solving the MILP (mixed integer linear programming) form of the distribution network and EVPL collaborative planning model, the optimal plan for distribution network planning and EVPL construction (i.e., the distribution network collaborative planning result) can be obtained. Thus, by integrating the multi-level planning model of the distribution network and the electric vehicle parking lot, and considering the accurate electric vehicle demand, this embodiment can be based on a new distribution network collaborative planning method that considers the uncertainty of renewable energy and the flexibility of electric vehicles, to minimize the cost of the distribution network and improve the flexibility of the distribution network, and can simultaneously reduce the distribution network expansion planning and electric vehicle charging costs, and at the same time meet the growing load and plug-in electric vehicle demand.

[0064] In the above-mentioned distribution network collaborative planning method, an electric vehicle behavior model is constructed by adopting different charging status scenarios generated based on historical electric vehicle travel data. According to the electric vehicle behavior data obtained from the electric vehicle behavior model, an electric vehicle parking lot demand model for each area in the distribution network is constructed. Then, based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each electric vehicle parking lot demand model, a cost calculation model is constructed. The electric vehicle charging scheduling information is used to construct an electric vehicle parking lot construction model for each area in the distribution network. Then, the cost calculation model and the electric vehicle parking lot construction model are combined to obtain a collaborative planning model. By solving the collaborative planning model, the distribution network collaborative planning result is obtained. The distribution network collaborative planning result is used to characterize the planning scheme based on the distribution network planning and the electric vehicle parking lot construction location, which can simultaneously reduce the distribution network expansion and electric vehicle parking lot installation costs, meet the growing load and electric vehicle demand, and effectively improve the flexibility of the distribution network.

[0065] In an exemplary embodiment, the construction of an electric vehicle behavior model using different charging state scenarios generated based on historical electric vehicle travel data may include the following steps:

[0066] According to the historical electric vehicle travel data, an electric vehicle behavior probability distribution for characterizing the arrival time and departure time of the electric vehicle is determined; different scenario generators are used to generate different charging state scenarios based on the electric vehicle behavior probability distribution; and the electric vehicle behavior model is constructed by combining electric vehicle behavior constraints and different charging state scenarios.

[0067] Among them, different charging status scenarios may include electric vehicle arrival time scenario, electric vehicle departure time scenario, electric vehicle initial power scenario, and electric vehicle required power scenario.

[0068] In practical applications, the behavior of PEVs can be modeled by estimating the probability distribution of EV arrival and departure times (i.e., EV behavior probability distribution) using historical EV trip data. Alternatively, in the absence of sufficient PEV trip history data, generation scenarios of PEV characteristics, such as arrival time, departure time, EV initial SOC (state of charge), and EV required SOC, can be used to model the uncertainty associated with PEV behavior, i.e., to build an EV behavior model.

[0069] In one example, the Gaussian distribution function can be used to generate the arrival time and departure time scenarios. Since the initial state of charge SOC of the PEV is random, the truncated exponential distribution function can be used to generate the required PEV random SOC scenario, and the electric vehicle behavior model can be constructed using the following formula:

[0070] (1-1)

[0071] (1-2)

[0072] (1-3)

[0073] (1-4)

[0074] (1-5)

[0075] (1-6)

[0076] in, and is the arrival and departure time of the nth PEV; and is the initial and required SOC capacity of the nth PEV; They represent scene generators based on truncated Gaussian function, truncated exponential function and uniform probability distribution function respectively (i.e. different scene generators); Shows the mean and standard deviation of the Gaussian distribution function; represents the rate parameter of the exponential distribution, (a, b) represents the cutoff region; P CH,max Represents the battery capacity and maximum charging rate of the nth PEV.

[0077] Specifically, the above formulas (1-1) to (1-5) can be used to generate each PEV arrival and departure time scenario, the electric vehicle initial SOC scenario, and the electric vehicle required SOC scenario respectively; formula (1-6) is a logical constraint (i.e., electric vehicle behavior constraint), which can be used to ensure that the PEV battery can be charged to the required SOC when staying at the EVPL, the arrival time is earlier than the departure time, and the required SOC is higher than the initial SOC of each PEV.

[0078] In an exemplary embodiment, constructing an electric vehicle parking lot demand model for each area in a distribution network based on the electric vehicle behavior data obtained from the electric vehicle behavior model may include the following steps:

[0079] Based on the historical electric vehicle travel data, an electric vehicle charging control model is constructed; using the electric vehicle behavior data, combined with the electric vehicle behavior model and the electric vehicle charging control model, an electric vehicle parking lot demand model for each area in the distribution network is constructed.

[0080] In the specific implementation, the historical electric vehicle travel data can be used to construct a PEV charging control model (i.e., an electric vehicle charging control model). For example, based on the PEV behavior data (i.e., electric vehicle behavior data) constructed from the electric vehicle behavior model, the EVPL demand in each region can be estimated; assuming that the EVPL load in the distribution network does not affect the retail electricity price, the EVPL can be regarded as an electricity price recipient in the electricity market, and the electricity price can be considered constant. In order to obtain a preliminary scheduling model for PEV charging, the EVPL demand in each region can be estimated, and the electric vehicle charging control model can be constructed using the following formula:

[0081] (1-7)

[0082] (1-8)

[0083] (1-9)

[0084] (1-10)

[0085] (1-11)

[0086] (1-12)

[0087] (1-13)

[0088] (1-14)

[0089] Where n is the index set of PEV; h is the index set of hours in a day; Z is the index of the network zone; is the charging power of the nth PEV in hour h; is a binary variable indicating whether the PEV battery is being charged; SOC n,h and is the SOC and charging efficiency of the PEV battery, RP h is the retail electricity price in hours; Represents the estimated EVPL charging demand in Zone Z.

[0090] Specifically, the objective function (1-7) is to minimize the charging cost of all PEVs parked on the EVPL in each area during the day; the constraint formula (1-8) limits the charging power of PEVs; equation formula (1-9) is the unschedulable time interval constraint; formula (1-10) is the charging balance constraint; formula (1-11) is the safety constraint of the PEV battery; formulas (1-12) and (1-13) assign the initial and required SOC generation values ​​to each PEV battery. Finally, the estimated demand for EVPL in a specific area (i.e., the electric vehicle parking lot demand model for each area) is calculated by formula (1-14).

[0091] By solving the above optimization model, the estimated EVPL demand in each area can be obtained, which can be used as the input parameter of the collaborative planning model. Based on the PEV behavior uncertainty model and the PEV charging control model, a multi-level expansion planning model can be constructed in combination with PEV demand, providing data support for determining the solution considering the distribution network planning and the optimal location of EVPL construction.

[0092] In an exemplary embodiment, the cost calculation model is constructed based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each electric vehicle parking lot demand model, and may include the following steps:

[0093] According to each of the electric vehicle parking lot demand models, the electric vehicle parking lot charging demand data of each area in the distribution network is obtained; based on the electric vehicle parking lot charging demand data of each of the areas, the electric vehicle charging scheduling information of each of the areas is determined; the electric vehicle behavior data and the electric vehicle charging scheduling information of each of the areas are used to construct the cost calculation model; the cost calculation model is a model for performing full life cycle cost calculation processing based on the electric vehicle parking lot demand and distribution network planning.

[0094] In one example, for a collaborative planning model combining PDN and EVPL, the model considers improving the flexibility of a network with high renewable energy integration while meeting the growing demand for new energy flexibility and EVPL. When considering the EVPL demand, the preliminary PEV scheduling (i.e., electric vehicle charging scheduling information) obtained in each area can be determined based on the estimated EVPL demand (i.e., electric vehicle parking lot charging demand data) in each area as the input of the collaborative planning model. Assuming that the scheduling of some PEVs can be rearranged to provide greater flexibility for the distribution network, if the distribution company decides to postpone or advance the charging time of the vehicle, the difference in electricity prices at different times can be compensated.

[0095] For example, the planning scope includes several planning stages, each of which consists of several typical days; in order to capture the impact of intermittent renewable energy generation and EVPL demand on the network net load, the cost calculation model included in the constructed collaborative planning model can be expressed as follows:

[0096] (1-15)

[0097] (1-16)

[0098] (1-17)

[0099] Where t and d are the sets of planning stages and typical days, respectively; and They represent the set of load nodes and RES connection nodes respectively; is the index of the load node; kp represents the investment selection set of EVPL; C T is the total cost during the planning period, and are the investment and operating costs of each stage; r and D represent the annual interest rate and the number of years of each stage; and They are EVPL investment cost and electricity price; is the ramp cost, i.e., the penalty cost for the change in unit network static load within one hour, A binary variable indicating whether to invest in EVPL; represents the energy cut by RES; and are the non-negative variables for dispatching and re-dispatching EVPL demands in load nodes, respectively; It is the absolute value of the network static load change per hour.

[0100] In another example, in the above planning model, the objective function of formula (1-15) can minimize the total cost, which can include investment and operation costs within the planning scope; formula (1-16) is the total cost of each stage, which can include investment cost, EVPL construction cost, substation construction cost, feeder section construction cost; formula (1-17) represents the operation cost of each stage, which can include substation operation and maintenance cost, feeder section operation and maintenance cost, penalty cost due to reduced RES generation, flexibility-oriented cost (such as penalty cost due to changes in network static load) and re-dispatch cost. In order to improve network flexibility, the initial dispatch of PEV charging can be rearranged.

[0101] In an exemplary embodiment, the use of the electric vehicle charging scheduling information to construct an electric vehicle parking lot construction model in each area of ​​the distribution network may include the following steps:

[0102] In combination with different control information for electric vehicle parking lot construction and the electric vehicle charging scheduling information, an electric vehicle parking lot construction model for each area in the distribution network is constructed.

[0103] Among them, different control information may include parking lot demand satisfaction information, parking lot maximum power capacity control information, demand satisfaction information after rescheduling charging scheduling, charging time change control information, and parking lot operation control information.

[0104] For example, the electric vehicle parking lot construction model of each area included in the constructed collaborative planning model can be expressed as follows:

[0105] (1-18)

[0106] (1-19)

[0107] (1-20)

[0108] (1-21)

[0109] (1-22)

[0110] in, The set of load nodes in area Z; N is the number of distribution network areas; PLC kp is the total charging capacity of the EVPL; is the percentage of PEVs that require emergency battery charging.

[0111] In one example, the above formula (1-18) can ensure that the EVPL demand of each area is fully met by the load node EVPL installed in the corresponding area (i.e., parking lot demand satisfaction information); formula (1-19) can be used to set the maximum power capacity limit of the EVPL located at the node (i.e., parking lot maximum power capacity control information); formula (1-20) can ensure that the daily EVPL demand of each load node is completely met after the PEV charging schedule is rearranged (i.e., demand satisfaction information after the charging schedule is rearranged); formula (1-21) can ensure that only the charging schedule of PEVs that do not require emergency battery charging can be changed (i.e., charging time change control information); based on (1-22), it can be ensured that each installed EVPL is economical, that is, the present value of the income obtained during its entire life cycle is higher than the corresponding investment cost (i.e., parking lot operation control information). And it can be defined that the possibility of the variable having a non-zero value in the load node where no investment in EVPL is made is rejected by the positive continuous variable together with formulas (1-19) and (1-20).

[0112] In an exemplary embodiment, obtaining the distribution network collaborative planning result by solving the collaborative planning model may include the following steps:

[0113] Obtaining a preset model optimality gap parameter; using a solution method for mixed integer linear programming, solving the collaborative planning model according to the model optimality gap parameter, and obtaining the distribution network collaborative planning result.

[0114] In practical applications, the electric vehicle parking lot demand model for each region can be constructed through the PEV behavior uncertainty model and the PEV charging control model, and the estimated EVPL demand for each region can be obtained. Then, the estimated EVPL demand for each region can be used as the input parameter of the collaborative planning model, input into the EVPL and PDN collaborative planning model, and the optimality gap can be set to 1% (i.e., the model optimality gap parameter), and the MILP (mixed integer linear programming) model can be solved to obtain the distribution network collaborative planning results.

[0115] In an exemplary embodiment, Figure 4 As shown, a flow chart of another method for collaborative planning of distribution networks is provided. In this embodiment, the method includes the following steps:

[0116] In step 401, based on the historical electric vehicle travel data, the electric vehicle behavior probability distribution used to characterize the arrival time and departure time of the electric vehicle is determined, and different scenario generators are used to generate different charging state scenarios based on the electric vehicle behavior probability distribution. In step 402, the electric vehicle behavior model is constructed in combination with the electric vehicle behavior constraints and different charging state scenarios. In step 403, based on the historical electric vehicle travel data, the electric vehicle charging control model is constructed, and the electric vehicle parking lot demand model of each area in the distribution network is constructed by using the electric vehicle behavior data and combining the electric vehicle behavior model and the electric vehicle charging control model. In step 404, based on each electric vehicle parking lot demand model, the electric vehicle parking lot charging demand data of each area in the distribution network is obtained, and based on the electric vehicle parking lot charging demand data of each area, the electric vehicle charging scheduling information of each area is determined. In step 405, the electric vehicle behavior data and the electric vehicle charging scheduling information of each area are used to construct a cost calculation model; the cost calculation model is a model for full life cycle cost calculation based on the electric vehicle parking lot demand and the distribution network planning. In step 406, the electric vehicle parking lot construction model of each area in the distribution network is constructed in combination with different control information for electric vehicle parking lot construction and electric vehicle charging scheduling information. In step 407, the cost calculation model and the electric vehicle parking lot construction models are combined to obtain a collaborative planning model. In step 408, a preset model optimality gap parameter is obtained, and a solution method for mixed integer linear programming is adopted to solve the collaborative planning model according to the model optimality gap parameter to obtain a distribution network collaborative planning result. It should be noted that the specific limitations of the above steps can be found in the specific limitations of a distribution network collaborative planning method above, which will not be repeated here.

[0117] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0118] Based on the same inventive concept, the embodiment of the present application also provides a distribution network collaborative planning device for implementing the distribution network collaborative planning method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more distribution network collaborative planning device embodiments provided below can refer to the limitations of the distribution network collaborative planning method above, and will not be repeated here.

[0119] In an exemplary embodiment, Figure 5 As shown, a distribution network collaborative planning device is provided, comprising:

[0120] An electric vehicle behavior model building module 501 is used to build an electric vehicle behavior model using different charging state scenarios generated based on historical electric vehicle travel data;

[0121] A parking lot demand model building module 502 is used to build an electric vehicle parking lot demand model for each area in the distribution network according to the electric vehicle behavior data obtained by the electric vehicle behavior model;

[0122] A cost calculation model building module 503 is used to build a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models;

[0123] A parking lot construction model building module 504 is used to construct an electric vehicle parking lot construction model for each area in the distribution network using the electric vehicle charging scheduling information;

[0124] The collaborative planning model solving module 505 is used to combine the cost calculation model and the electric vehicle parking lot construction models to obtain a collaborative planning model, and obtain the distribution network collaborative planning results by solving the collaborative planning model; the distribution network collaborative planning results are used to characterize the planning scheme based on the distribution network planning and the electric vehicle parking lot construction location.

[0125] In one embodiment, the electric vehicle behavior model building module 501 includes:

[0126] A probability distribution determination submodule, for determining an electric vehicle behavior probability distribution for characterizing an arrival time and a departure time of the electric vehicle based on the historical electric vehicle travel data;

[0127] A scenario generation submodule, used to generate different charging state scenarios based on the electric vehicle behavior probability distribution using different scenario generators;

[0128] A behavior model building submodule, used to build the electric vehicle behavior model by combining the electric vehicle behavior constraints and the different charging state scenarios;

[0129] Among them, the different charging status scenarios include the electric vehicle arrival time scenario, the electric vehicle departure time scenario, the electric vehicle initial power scenario, and the electric vehicle required power scenario.

[0130] In one embodiment, the parking lot demand model building module 502 includes:

[0131] A charging control model building submodule, used to build an electric vehicle charging control model based on the historical electric vehicle travel data;

[0132] The demand model building submodule is used to use the electric vehicle behavior data, in combination with the electric vehicle behavior model and the electric vehicle charging control model, to build an electric vehicle parking lot demand model for each area in the distribution network.

[0133] In one embodiment, the cost calculation model building module 503 includes:

[0134] A charging demand data obtaining submodule is used to obtain the charging demand data of the electric vehicle parking lot in each area of ​​the distribution network according to each of the electric vehicle parking lot demand models;

[0135] A charging scheduling information determination submodule, used to determine the electric vehicle charging scheduling information of each of the areas based on the charging demand data of the electric vehicle parking lots in each of the areas;

[0136] The cost calculation model obtaining submodule is used to construct the cost calculation model by using the electric vehicle behavior data and the electric vehicle charging scheduling information of each area; the cost calculation model is a model for performing full life cycle cost calculation processing based on electric vehicle parking lot demand and distribution network planning.

[0137] In one embodiment, the parking lot construction model building module 504 includes:

[0138] A construction model building submodule, for building an electric vehicle parking lot construction model for each area in the distribution network by combining different control information for electric vehicle parking lot construction and the electric vehicle charging scheduling information;

[0139] Among them, the different control information includes parking lot demand satisfaction information, parking lot maximum power capacity control information, demand satisfaction information after rescheduling charging scheduling, charging time change control information, and parking lot operation control information.

[0140] In one embodiment, the collaborative planning model solving module 505 includes:

[0141] A parameter acquisition submodule is used to obtain preset model optimality gap parameters;

[0142] The distribution network collaborative planning result obtaining submodule is used to adopt a solution method for mixed integer linear programming, solve the collaborative planning model according to the model optimality gap parameter, and obtain the distribution network collaborative planning result.

[0143] Each module in the above-mentioned distribution network collaborative planning device can be implemented in whole or in part by software, hardware and their combination. Each of 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 each of the above modules.

[0144] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a distribution network collaborative planning method is implemented.

[0145] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0147] Build an EV behavior model using different charging state scenarios generated based on historical EV trip data;

[0148] constructing an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model;

[0149] Building a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models;

[0150] Using the electric vehicle charging scheduling information, constructing an electric vehicle parking lot construction model in each area of ​​the distribution network;

[0151] The cost calculation model and the electric vehicle parking lot construction models are combined to obtain a collaborative planning model, and the distribution network collaborative planning results are obtained by solving the collaborative planning model; the distribution network collaborative planning results are used to characterize the planning scheme based on distribution network planning and electric vehicle parking lot construction locations.

[0152] In one embodiment, when the processor executes the computer program, it also implements the steps of the distribution network collaborative planning method in the other embodiments mentioned above.

[0153] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0154] Build an EV behavior model using different charging state scenarios generated based on historical EV trip data;

[0155] constructing an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model;

[0156] Building a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models;

[0157] Using the electric vehicle charging scheduling information, constructing an electric vehicle parking lot construction model in each area of ​​the distribution network;

[0158] The cost calculation model and the electric vehicle parking lot construction models are combined to obtain a collaborative planning model, and the distribution network collaborative planning results are obtained by solving the collaborative planning model; the distribution network collaborative planning results are used to characterize the planning scheme based on distribution network planning and electric vehicle parking lot construction locations.

[0159] In one embodiment, when the computer program is executed by a processor, the steps of the distribution network collaborative planning method in the other embodiments described above are also implemented.

[0160] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0161] Build an EV behavior model using different charging state scenarios generated based on historical EV trip data;

[0162] constructing an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model;

[0163] Building a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models;

[0164] Using the electric vehicle charging scheduling information, constructing an electric vehicle parking lot construction model in each area of ​​the distribution network;

[0165] The cost calculation model and the electric vehicle parking lot construction models are combined to obtain a collaborative planning model, and the distribution network collaborative planning results are obtained by solving the collaborative planning model; the distribution network collaborative planning results are used to characterize the planning scheme based on distribution network planning and electric vehicle parking lot construction locations.

[0166] In one embodiment, when the computer program is executed by a processor, the steps of the distribution network collaborative planning method in the other embodiments described above are also implemented.

[0167] It should be noted that the collection, use and processing of the relevant data involved in this application must comply with relevant regulations.

[0168] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0169] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0170] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A distribution network collaborative planning method, characterized in that: The method comprises: Build an EV behavior model using different charging state scenarios generated based on historical EV trip data; constructing an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model; Constructing a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models; Using the electric vehicle charging scheduling information, constructing an electric vehicle parking lot construction model in each area of ​​the distribution network; The cost calculation model and the electric vehicle parking lot construction models are combined to obtain a collaborative planning model, and the distribution network collaborative planning results are obtained by solving the collaborative planning model; the distribution network collaborative planning results are used to characterize the planning scheme based on distribution network planning and electric vehicle parking lot construction locations.

2. The method according to claim 1, characterized in that: The electric vehicle behavior model is constructed by using different charging state scenarios generated based on historical electric vehicle travel data, including: Determining, based on the historical electric vehicle travel data, an electric vehicle behavior probability distribution for characterizing arrival times and departure times of the electric vehicle; Using different scenario generators to generate different charging state scenarios based on the electric vehicle behavior probability distribution; Combining electric vehicle behavior constraints and different charging state scenarios, constructing the electric vehicle behavior model; Among them, the different charging status scenarios include the electric vehicle arrival time scenario, the electric vehicle departure time scenario, the electric vehicle initial power scenario, and the electric vehicle required power scenario.

3. The method according to claim 1, characterized in that The electric vehicle behavior data obtained from the electric vehicle behavior model is used to construct an electric vehicle parking lot demand model for each area in the distribution network, including: Based on the historical electric vehicle travel data, construct an electric vehicle charging control model; The electric vehicle behavior data is used in combination with the electric vehicle behavior model and the electric vehicle charging control model to construct an electric vehicle parking lot demand model for each area in the distribution network.

4. The method according to claim 1, characterized in that: The cost calculation model is constructed based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each electric vehicle parking lot demand model, including: According to each of the electric vehicle parking lot demand models, obtaining electric vehicle parking lot charging demand data in each area of ​​the distribution network; Determining the electric vehicle charging scheduling information for each of the areas based on the electric vehicle parking lot charging demand data for each of the areas; The cost calculation model is constructed by using the electric vehicle behavior data and the electric vehicle charging scheduling information of each area; the cost calculation model is a model for performing full life cycle cost calculation processing based on electric vehicle parking lot demand and distribution network planning.

5. The method according to claim 1, characterized in that The electric vehicle charging scheduling information is used to construct an electric vehicle parking lot construction model in each area of ​​the distribution network, including: Combining different control information for electric vehicle parking lot construction and the electric vehicle charging scheduling information, constructing an electric vehicle parking lot construction model for each area in the distribution network; Among them, the different control information includes parking lot demand satisfaction information, parking lot maximum power capacity control information, demand satisfaction information after rescheduling charging scheduling, charging time change control information, and parking lot operation control information.

6. The method according to any one of claims 1 to 5, characterized in that: The step of obtaining the distribution network collaborative planning result by solving the collaborative planning model includes: Get the preset model optimality gap parameters; The collaborative planning model is solved according to the optimality gap parameter of the model by adopting a solution method for mixed integer linear programming to obtain the distribution network collaborative planning result.

7. A distribution network collaborative planning device, characterized in that: The device comprises: An electric vehicle behavior model building module for building an electric vehicle behavior model using different charging state scenarios generated based on historical electric vehicle trip data; A parking lot demand model building module, used to build an electric vehicle parking lot demand model for each area in the distribution network based on the electric vehicle behavior data obtained by the electric vehicle behavior model; A cost calculation model building module, used to build a cost calculation model based on the electric vehicle behavior data and the electric vehicle charging scheduling information determined by each of the electric vehicle parking lot demand models; A parking lot construction model building module, used to construct an electric vehicle parking lot construction model for each area in the distribution network using the electric vehicle charging scheduling information; The collaborative planning model solving module is used to combine the cost calculation model and the electric vehicle parking lot construction models to obtain a collaborative planning model, and obtain the distribution network collaborative planning results by solving the collaborative planning model; the distribution network collaborative planning results are used to characterize the planning scheme based on the distribution network planning and the electric vehicle parking lot construction location.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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