A power distribution network collaborative regulation method and system considering electric vehicle access

Abstract

The application relates to the technical field of power distribution network resource regulation, and discloses a power distribution network collaborative regulation method and system considering electric vehicle access, which comprises the following steps: collecting regional network frame information, regional real-time load data and historical load data; constructing a regional load curve prediction model according to the regional real-time load data and the historical load data; constructing an optimization control model based on source-side photovoltaic regulation; constructing a power flow calculation topological network frame according to the regional network frame information; inputting predicted load values output by the regional load curve prediction model and optimal regulation values output by the optimization control model into the power flow calculation topological network frame to output a power distribution network collaborative regulation scheme. The application fully considers the reasonable distribution of resources among charging piles, photovoltaics and energy storage, and formulates a real-time regulation scheme for regional charging stations and energy storage devices in combination with real-time data of the day, so that the economic benefits are maintained while the safety and reliability of the system are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network resource regulation, and more particularly to a power distribution network collaborative regulation method and system considering electric vehicle access. BACKGROUND

[0002] With the adjustment of energy structure and the concept of energy internet, the importance of power system optimal dispatching is increasingly highlighted. As the end of the power system, the power distribution network has power sources including power supply from the upper grid and various distributed power sources, and power consumption including conventional load and various flexible load. Among them, the charging load as a new type of flexible load is increasingly influenced with the vigorous promotion of electric vehicles. The system also includes energy storage resources with both power supply and use functions. With the gradual increase of the penetration rate of the above controllable distributed power sources, charging loads and energy storage in the distribution network, the development of smart grid and ubiquitous power internet, the boundaries of the roles of "source-storage-load" tend to be blurred, showing a diversified state. The interaction of resources of the power distribution network is stronger, and the active control of "source-storage-load" will be more conducive to the consumption of new energy and the optimal operation of the power distribution network.

[0003] Currently, a multi-load direct regulation system is proposed to realize the participation of large-scale dispatchable loads such as electric vehicles and energy storage at the customer side in multi-level power grid dynamic balance optimization. It is proposed that the provincial dispatching center can connect the micro longitudinal isolation device through the bidirectional communication network, and further connect the electric vehicle charging station for real-time parallel control. However, the regulation process lacks consideration of the reasonable allocation of resources among charging piles, photovoltaic and energy storage, which has certain influence on the safety, economy and environmental friendliness of regional power grid operation. SUMMARY

[0004] In order to overcome the defects of the prior art that lacks consideration of the reasonable allocation of resources among charging piles, photovoltaic and energy storage, the present application provides a power distribution network collaborative regulation method considering electric vehicle access, and a power distribution network collaborative regulation system considering electric vehicle access.

[0005] To solve the above technical problems, the technical solutions of the present application are as follows:

[0006] A power distribution network collaborative regulation method considering electric vehicle access, comprising the following steps:

[0007] Collecting regional network frame information, regional real-time load data and historical load data;

[0008] According to the regional real-time load data and the historical load data, a regional load curve prediction model is constructed;

[0009] An optimal control model is constructed based on source-side photovoltaic regulation;

[0010] According to the regional network frame information, a power flow calculation topological network frame is constructed;

[0011] The predicted load value output by the regional load curve prediction model and the optimal regulation value output by the optimization control model are input into the power flow calculation topological network frame, and a power distribution network coordinated regulation scheme is output.

[0012] In the technical solution, on the basis of day-ahead regional load conditions, charging load, energy storage information and other data, a real-time regulation scheme for regional charging stations and energy storage devices is formulated in combination with real-time data of the day, an optimization control model of source-grid-load-storage is established, and the regulation of regional source-grid-load-storage is effectively improved.

[0013] As a preferred solution, the regional network frame information includes a regional power distribution network topological structure, line impedance and length of each main branch.

[0014] As a preferred solution, the regional load curve prediction model includes a load growth rate calculation sub-model, an average load growth rate calculation sub-model and a real-time load prediction sub-model, wherein:

[0015] The load growth rate calculation sub-model calculates the load growth rate of each time point of each day according to load data of the previous n days, and the expression formula is as follows:

[0016]

[0017] In the formula, ρ i (t+1) represents the load growth rate of the t+1 time point of the i-th day in history, P i (t) represents the regional load value of the t time point of the i-th day in history.

[0018] The average load growth rate calculation sub-model calculates the average load growth rate of each time point of the previous n days in history, and the expression formula is as follows:

[0019]

[0020] In the formula, is the average load growth rate of each time point of the previous n days in history.

[0021] The real-time load prediction sub-model performs real-time load prediction of the next time point, and the expression formula is as follows:

[0022]

[0023] In the formula, P next (t+1) represents the predicted load value of the next time point of the region, P now (t) represents the real-time load value of the region.

[0024] As a preferred solution, a collaborative regulation optimization objective function is set in the optimization control model, and the expression formula is as follows:

[0025]

[0026]

[0027] In the formula, f A represents the objective function; ΔP loss is a power distribution network loss index, T is the total number of operation periods of the power distribution network; P PV_pre (t) and P PV (t) are respectively the predicted value and the scheduled value of the photovoltaic output, wherein the predicted value of the photovoltaic output is obtained by the regional load curve prediction model; is a light abandonment penalty term; E is a line set, (i,j) represents a line l ij ; r ij is the resistance of the line l ij ; the line l t,ij is the square of the current amplitude of the line l ij in the period t.

[0028] As a preferred solution, the constraint condition further includes one or more of a power balance constraint, a node voltage constraint, a line current constraint, a reactive power operation constraint of the distributed photovoltaic access power distribution network, a network end reactive power compensation device operation constraint, and a storage end constraint.

[0029] As a preferred solution, in the constraint condition, the expression formula of the power balance constraint is as follows:

[0030]

[0031] In the formula, P j,t represents the active power of the node j in the period t; P jk,t represents the active power of the line between the node j and the node k in the period t; is the current amplitude of the line flowing from the node i to the node j in the period t; g j represents the conductance of the node j; v j,t is the square of the voltage amplitude of the node j in the period t; and are respectively the injected active power of the generator, the load, and the photovoltaic power supply; and are respectively the injected reactive power of the generator, the load, and the photovoltaic power supply; Q j,t represents the reactive power of the node j in the period t; x ij is the admittance of the line between the node i and the node j; b j is the susceptance of the node j;

[0032] The expression formula of the node voltage constraint is as follows:

[0033]

[0034] In the formula, N is a node set, V i,min and V i,max are the lower and upper limits of the voltage amplitude of node i respectively;

[0035] The expression formula of the line current constraint is as follows:

[0036]

[0037] In the formula, I ij,max is the upper limit of the current amplitude passing through line l ij ;

[0038] The expression formula of the reactive power operation constraint of the distributed photovoltaic access power distribution network is as follows:

[0039]

[0040] In the formula, S N is the rated capacity of the photovoltaic inverter; Q PV,t is the reactive power emitted by the photovoltaic at time t, P PV,t is the active power emitted by the photovoltaic at time t;

[0041] The expression formula of the operation constraint of the grid-side reactive power compensation device is as follows:

[0042] Q min,i ≤ Q svg,i ≤ Q max,i , i ∈ Ω CB

[0043] In the formula, Q svg,i is the reactive power emitted by the reactive power compensation device SVG when operating; Ω CB is a node set containing the reactive power compensation device SVG, Q max,i and Q min,i are the upper and lower limits of the reactive power when the reactive power compensation device SVG operates;

[0044] The expression formula of the load-side electric vehicle constraint is as follows:

[0045]

[0046]

[0047]

[0048] In the formula, and respectively are the lower and upper limits of the charging power of the electric vehicle i running in EV mode; is the energy storage capacity of the electric vehicle i in time period t; is the charging efficiency coefficient of the electric vehicle i; and respectively are the minimum and maximum energy storage capacities of the electric vehicle i; is the charge-discharge capacity requirement of the electric vehicle i in the entire scheduling period; Δt is the measurement time interval;

[0049] The expression formula of the energy storage constraint is as follows:

[0050]

[0051]

[0052]

[0053] In the formula, and respectively are the charging state and discharging state of the energy storage device i in time period t, and take values of 0 or 1; and respectively are the lower and upper limits of the charging power of the energy storage device i; and respectively are the lower and upper limits of the discharging power of the energy storage device i; is the energy storage capacity of the energy storage device i in time period t; is the charging efficiency coefficient of the energy storage device i; is the discharging efficiency coefficient of the energy storage device i; and respectively are the minimum and maximum energy storage capacities of the energy storage device i.

[0054] As a preferred solution, the step of generating the power distribution network collaborative regulation scheme comprises:

[0055] The regional network frame information is input into the regional load curve prediction model to obtain the predicted load values of the regional electric vehicles, photovoltaic, energy storage, and reactive power compensation;

[0056] The predicted load values are input into the optimization control model, the optimization control model optimizes and solves with the scheduling values as decision variables, and outputs the optimal scheduling values;

[0057] The optimal scheduling values are input into the power flow calculation topology network frame to perform power flow calculation, to obtain the energy storage charge-discharge power and time, and the electric vehicle charging pile charge-discharge power and time of each compensation point in the next 24 hours, and the power flow calculation result of the first hour is taken as the power distribution network collaborative regulation scheme output.

[0058] As a preferred solution, the following steps are further included: re-collecting the regional grid information, the regional real-time load data and the historical load data every hour, inputting the regional load curve prediction model to obtain a predicted load value, re-solving the optimal scheduling value according to the predicted load value, and re-performing the power flow calculation to output the distribution network collaborative regulation scheme after the circulation optimization.

[0059] Further, the application also proposes a distribution network collaborative regulation system considering the access of electric vehicles, which is applied to the distribution network collaborative regulation method considering the access of electric vehicles proposed in any of the above technical solutions. The distribution network collaborative regulation system includes a collection module, a prediction module, an optimization module, a power flow calculation module and a regulation scheme generation module.

[0060] The collection module is used to collect the regional grid information, the regional real-time load data and the historical load data; the prediction module includes a regional load curve prediction model constructed according to the regional real-time load data and the historical load data; the optimization module includes an optimization control model constructed based on source-side photovoltaic regulation; the power flow calculation module includes a power flow calculation topology grid constructed according to the regional grid information; the predicted load value output by the prediction module and the optimal regulation value output by the optimization module are respectively input into the power flow calculation module, and the power flow calculation module outputs the energy storage charge-discharge power and time and the electric vehicle charging pile charge-discharge power and time of each compensation point in the next 24 hours; the regulation scheme generation module is used to select the power flow calculation result of the first hour as the distribution network collaborative regulation scheme output according to the power flow calculation result output by the power flow calculation module.

[0061] As a preferred solution, the prediction module includes: a load growth rate calculation unit for calculating the historical load growth rate; an average load growth rate calculation unit for calculating the historical load growth rate; and a real-time load prediction sub-model for performing real-time next-time load prediction and outputting a regional next-time predicted load value.

[0062] Compared with the prior art, the beneficial effects of the technical solution of the application are: the application performs centralized optimization control by coordinating the resources on the "source-grid-load" sides, fully considers the reasonable allocation of resources among the charging piles, photovoltaics and energy storage, performs coordinated control on the electric vehicles, energy storage and reactive power compensation devices, and thus guarantees the safety and reliability of the system while maintaining the economic benefits. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 The flowchart of the distribution network collaborative regulation method considering the access of electric vehicles of embodiment 1.

[0064] Figure 2 The flowchart of the distribution network collaborative regulation scheme generation of embodiment 2.

[0065] Figure 3 Scenario and resource distribution diagram for the 33-node power distribution network system of Example 3.

[0066] Figure 4 Load curve and photovoltaic output curve diagram for Example 3.

[0067] Figure 5 Active load curve diagram for Example 3.

[0068] Figure 6 Line end node 33 voltage condition diagram for Example 3.

[0069] Figure 7 Architecture diagram of the power distribution network collaborative regulation system considering electric vehicle access of Example 4. DETAILED DESCRIPTION

[0070] The accompanying drawings are only intended to illustrate, and cannot be understood as a limitation to the patent;

[0071] It is understandable for those skilled in the art that some well-known structures and their descriptions in the drawings can be omitted.

[0072] The technical solutions of the present application will be further described below in combination with the drawings and examples.

[0073] Example 1

[0074] The present embodiment proposes a power distribution network collaborative regulation method considering electric vehicle access, as shown in the flowchart of the power distribution network collaborative regulation method considering electric vehicle access of the present embodiment. Figure 1

[0075] The power distribution network collaborative regulation method considering electric vehicle access proposed in the present embodiment includes the following steps:

[0076] S1, collect regional network architecture information, regional real-time load data and historical load data.

[0077] In the present embodiment, the collected regional network architecture information includes regional power distribution network network topology, line impedance and the length of each main branch.

[0078] S2, according to the regional real-time load data and the historical load data, construct a regional load curve prediction model.

[0079] The regional load curve prediction model in the present embodiment is used to predict the load of the next moment in real time according to the regional real-time load data and the historical load data for the actual operation of the target region, and generate a prediction value.

[0080] ​S3, constructing an optimal control model based on photovoltaic regulation; the optimal control model has a constraint condition, and the constraint condition includes a load-end electric vehicle constraint.

[0081] The optimal control model in the embodiment considers photovoltaic regulation on the source side, and considers economy and environmental friendliness as key indexes, and considers the load-end electric vehicle constraint generated when the electric vehicle is accessed, sets an appropriate optimization target, and generates an optimal scheduling value.

[0082] S4, constructing a power flow calculation topology network based on regional network frame information.

[0083] The power flow calculation topology network in the embodiment is used for power flow calculation in combination with the current predicted value and the scheduling value, so as to facilitate generation of a corresponding scheduling scheme.

[0084] S5, inputting the predicted load value output by the regional load curve prediction model and the optimal regulation value output by the optimal control model into the power flow calculation topology network, and outputting a power distribution network collaborative regulation scheme.

[0085] In the embodiment, the access of the electric vehicle is fully considered, the optimal control model of the source-network-load-storage is constructed, the reasonable allocation of resources among the charging pile, the photovoltaic, and the energy storage is fully considered, the actual situation of the regional electric vehicle is further combined, economy and environmental friendliness are taken as key indexes, the output power distribution network collaborative regulation scheme takes into account the conditions of each resource side, and the safety and economy of the power grid operation are ensured to the greatest extent, the effects of peak load shifting, improving the power grid operation environment, and reasonably consuming energy storage are achieved.

[0086] Embodiment 2

[0087] The embodiment further limits the regional load curve prediction model and the optimal control model on the basis of the power distribution network collaborative regulation method considering the access of the electric vehicle proposed in embodiment 1.

[0088] The regional load curve prediction model in the embodiment includes a load growth rate calculation submodel, an average load growth rate calculation submodel, and a real-time load prediction submodel.

[0089] The load growth rate calculation submodel is used for calculating the load growth rate of each time point of each day according to the load data of the previous n days. In the embodiment, the load data of the previous 3 days is taken for calculation, and the calculation formula is as follows:

[0090]

[0091]

[0092]

[0093] wherein ρ i (t+1) represents the load growth rate at time t+1 on the i-th day in history, P i (t) represents the regional load value at time t on the i-th day in history, and in the present embodiment, i takes the values 1, 2, and 3.

[0094] The average load growth rate calculation sub-model is used to calculate the average load growth rate at each time in the previous n days. The calculation formula is as follows:

[0095]

[0096] wherein, is the average load growth rate at each time in the previous 3 days in history.

[0097] For the actual operation of the region, the real-time load prediction sub-model performs load prediction at the next time. The expression formula is as follows:

[0098]

[0099] wherein P next (t+1) represents the predicted load value of the region at the next time, P now (t) represents the real-time load value of the region.

[0100] Further, in the optimization control model in the present embodiment, a coordinated regulation optimization objective function is set up, and the expression formula is as follows:

[0101]

[0102]

[0103] wherein f A represents the objective function; ΔP loss is the power distribution network loss index, T is the total number of operation periods of the power distribution network; P PV_pre (t) and P PV (t) are respectively the predicted value and the scheduled value of the photovoltaic output, wherein the predicted value of the photovoltaic output is obtained by the regional load curve prediction model; is the light rejection penalty term; E is a line set, (i,j) represents line l ij ; r ij is the resistance of line l ij ; and P t,ij is the square of the current amplitude of line l ij in period t.

[0104] The optimization control model in the embodiment is a source-grid-load-storage centralized optimization control model, which comprehensively considers the regulation resource capabilities and constraints of photovoltaic, charging pile, reactive power compensation device and energy storage, and fully taps the potential of load regulation resources.

[0105] Specifically, the constraint conditions of the optimization control model include one or more of the following: electric vehicle constraints at the load end, power balance constraints, node voltage constraints, line current constraints, reactive power operation constraints of the distributed photovoltaic access distribution network, reactive power compensation device operation constraints at the grid end, and storage end constraints.

[0106] The expression formula of the power balance constraint is as follows:

[0107]

[0108] In the formula, P j,t represents the active power of node j at time period t; P jk,t represents the active power of the line between node j and node k at time period t; is the current amplitude of the line from node i to node j at time period t; g j represents the conductance of node j; v j,t is the square of the voltage amplitude of node j at time period t; and are the injected active power of the generator, load and photovoltaic power source, respectively; and are the injected reactive power of the generator, load and photovoltaic power source, respectively; Q j,t represents the reactive power of node j at time period t; x ij is the admittance of the line between node i and node j; b j is the susceptance of node j.

[0109] The expression formula of the node voltage constraint is as follows:

[0110]

[0111] In the formula, N is a node set, V i,min and V i,max are the lower and upper limits of the voltage amplitude of node i, respectively.

[0112] The expression formula of the line current constraint is as follows:

[0113]

[0114] In the formula, I ij,max is the current amplitude through the line l ij .

[0115] The expression formula of the reactive power operation constraint of the distributed photovoltaic access power distribution network is as follows:

[0116]

[0117] In the formula, S N is the rated capacity of the photovoltaic inverter; Q PV,t is the reactive power generated by the photovoltaic at time t, P PV,t is the active power generated by the photovoltaic at time t.

[0118] The expression formula of the operation constraint of the network-side reactive power compensation device is as follows:

[0119] Q min,i ≤Q svg,i ≤Q max,i ,i∈Ω CB

[0120] In the formula, Q svg,i is the reactive power generated by the reactive power compensation device SVG when operating; Ω CB is the node set containing the reactive power compensation device SVG, Q max,i , Q min,i are the upper and lower limits of the reactive power when the reactive power compensation device SVG operates.

[0121] The load-side electric vehicle constraint includes an electric vehicle charging power limit constraint, a charging capacity limit constraint, and an electric vehicle capacity demand constraint, and the expression formulas are as follows:

[0122]

[0123]

[0124]

[0125] In the formula, and are the lower and upper limits of the charging power of the electric vehicle i operating in the EV mode; is the energy storage capacity of the electric vehicle i in the time period t; is the charging efficiency coefficient of the electric vehicle i; and are the minimum and maximum energy storage capacities of the electric vehicle i; is the charging and discharging capacity demand of the electric vehicle i in the entire scheduling period; and Δt is the measurement time interval.

[0126] The storage-side constraint includes a storage device charging and discharging state constraint, a charging and discharging power limit constraint, and a capacity constraint, and the expression formulas are as follows:

[0127]

[0128]

[0129]

[0130] wherein, and respectively represent the charging state and discharging state of the energy storage device i in period t, and take values of 0 or 1; and respectively represent the lower limit and upper limit of the charging power of the energy storage device i; and respectively represent the lower limit and upper limit of the discharging power of the energy storage device i; represents the energy storage capacity of the energy storage device i in period t; represents the charging efficiency coefficient of the energy storage device i; represents the discharging efficiency coefficient of the energy storage device i; and respectively represent the minimum and maximum energy storage capacity of the energy storage device i.

[0131] Further, in the embodiment, the predicted load value output by the regional load curve prediction model and the optimal regulation value output by the optimization control model are input into the power flow calculation topology network frame to output a power distribution network coordinated regulation scheme. The specific steps are as follows:

[0132] Step A: input the regional network frame information into the regional load curve prediction model to obtain the predicted load values of the regional electric vehicles, photovoltaic, energy storage, and reactive power compensation.

[0133] Step B: input the predicted load values into the optimization control model, and the optimization control model optimizes and solves with the scheduling value as the decision variable to output the optimal scheduling value.

[0134] Step C: input the optimal scheduling value into the power flow calculation topology network frame to perform power flow calculation, obtain the energy storage charging and discharging power and time and the electric vehicle charging pile charging and discharging power and time of each compensation point in the next 24 hours, and output the power flow calculation result of the first hour as the power distribution network coordinated regulation scheme.

[0135] In another embodiment, further, for more than one hour, step A is re-executed to perform cyclic optimization. Specifically, after each hour, the regional network frame information, regional real-time load data and historical load data are re-collected and input into the regional load curve prediction model to obtain the predicted load values, the optimal scheduling value is re-solved according to the predicted load values, and the power flow calculation is re-performed to output the power distribution network coordinated regulation scheme after cyclic optimization. As shown in Figure 2 , it is a flow chart of the power distribution network coordinated regulation scheme generation of the embodiment.

[0136] In this embodiment, the potential of each control resource is fully tapped by considering the control resource capabilities and constraints of photovoltaic, charging pile, reactive power compensation device and energy storage, and a power distribution network collaborative control scheme is obtained by taking into account the conditions of each resource party.

[0137] Embodiment 3

[0138] In this embodiment, the power distribution network collaborative control method considering the access of electric vehicles proposed in Embodiment 1 or Embodiment 2 is applied to a 33-node power distribution network system. As shown in FIG. 4, it is a scene and resource distribution diagram of the 33-node power distribution network system of this embodiment. Figure 2

[0139] Among them, the photovoltaic grid-connected nodes are 18 and 31, and the single installed capacity is 1 MW; the energy storage grid-connected nodes are 17 and 30, and the power limit is -0.25-0.25 MW; the electric vehicle grid-connected nodes are 15 and 33, and the number of electric vehicles is 150, and the power limit of a single electric vehicle is -7-7 kW; the SVG grid-connected nodes are 9, 16, 21 and 29, and the power limit is -0.1-0.25 MVar. The load curve and photovoltaic output curve in this embodiment are shown in FIG. 4.

[0140] In this embodiment, the power distribution network collaborative control method considering the access of electric vehicles proposed in Embodiment 1 or Embodiment 2 is applied to the simulation of the following three scenes:

[0141] (1) Scene 1 (original scene): without considering photovoltaic grid connection, without accessing electric vehicles, energy storage as emergency power, not participating in daily power grid dispatching, and not using reactive power compensation device for optimal adjustment.

[0142] (2) Scene 2 (source-grid-load scene): considering photovoltaic grid connection, electric vehicles charging according to user demand, energy storage as emergency power, not participating in daily power grid dispatching, and not using reactive power compensation device for optimal adjustment.

[0143] (3) Scene 3 (source-grid-load-storage scene): considering photovoltaic grid connection, using centralized optimization strategy to adjust energy storage charging and discharging, electric vehicle access, and considering the reactive power adjustment of SVG and photovoltaic inverter, so as to minimize the objective function of the power distribution network.

[0144] After the simulation experiment of the above three scenes, the results are shown in FIG. 4. Figure 5 6 ​​The active load curve and the line end node 33 voltage condition diagram are shown. As can be seen from the figure, the optimization control model in the embodiment adopts a centralized optimization strategy, regulates and controls according to the distribution network loss index, coordinates the control of the electric vehicles, energy storage and reactive power compensation devices, maximizes peak shaving and valley filling while maintaining economic benefits, stabilizes the load curve, reduces the reactive load level, further reduces the network loss, and reduces the node voltage fluctuation.

[0145] The embodiment further compares the comprehensive operation indexes of the distribution network in each scenario, and obtains the distribution network comprehensive operation index results shown in Table 1.

[0146] Table 1: Distribution network comprehensive operation index results

[0147] Indicator Scenario 1 Scenario 2 Scenario 3 Active load peak-valley difference / MW 3.9 6.6 4.0 Minimum terminal voltage (p.u.) 0.91 0.86 0.96 Total network loss of distribution network / MW 1.242 4.71 1.234 Light curtailment rate / % —— 5.6％ 3.9％

[0148] As can be seen from the above table, after the access of photovoltaic and electric vehicles in scenario 2, due to the differences in time sequence of photovoltaic, basic load and electric vehicle load, the peak-valley difference of the distribution network system further increases, the network loss significantly increases, and the end voltage significantly decreases.

[0149] The embodiment can maximize the control benefit of the distribution network by coordinating the centralized optimization control of the resources on the "source-grid-load" side, guaranteeing photovoltaic consumption and reducing light abandonment, ensuring the safety and reliability of the system, and further reducing the network loss. After considering the reactive power regulation of the photovoltaic inverter, the cost of additional compensation equipment can be reduced, the peak shaving benefit of electric vehicles and energy storage is also conducive to alleviating the upgrading of the distribution network, and the economic efficiency of the distribution network is improved. The above results verify the effectiveness of the model.

[0150] Embodiment 4

[0151] The embodiment proposes a distribution network collaborative control system considering the access of electric vehicles, and applies the distribution network collaborative control method considering the access of electric vehicles proposed in embodiment 1 or embodiment 2. As shown in Figure 7 Fig. 1 is a block diagram of the distribution network collaborative control system considering the access of electric vehicles in the embodiment.

[0152] The distribution network collaborative control system considering the access of electric vehicles in the embodiment includes a collection module, a prediction module, an optimization module, a power flow calculation module and a control scheme generation module.

[0153] The collection module in the embodiment is used to collect regional network architecture information, regional real-time load data and historical load data. The collected regional network architecture information includes the regional distribution network network topology, line impedance and the length of each main branch.

[0154] The prediction module in the embodiment comprises a regional load curve prediction model constructed according to regional real-time load data and historical load data, which is used to predict the load values of regional electric vehicles, photovoltaic, energy storage and reactive power compensation according to input regional grid information.

[0155] Further, the prediction module in the embodiment comprises a load growth rate calculation unit, an average load growth rate calculation unit and a real-time load prediction sub-model. The load growth rate calculation unit is used to calculate the historical load growth rate; the average load growth rate calculation unit is used to calculate the historical load growth rate; and the real-time load prediction sub-model is used to perform real-time load prediction of the next moment and output the predicted load value of the next moment of the region.

[0156] The optimization module in the embodiment comprises an optimization control model constructed based on source-side photovoltaic regulation, which is used to perform optimization solution with the scheduling value as the decision variable according to the predicted load value output by the prediction module, and output the optimal scheduling value.

[0157] Further, the optimization module in the embodiment is provided with constraint conditions, which include power balance constraint, node voltage constraint, line current constraint, reactive power operation constraint of distributed photovoltaic access distribution network, grid-side reactive power compensation device operation constraint, load-side electric vehicle constraint and storage-side constraint, so as to realize the optimization of coordinated control of electric vehicles, energy storage and reactive power compensation devices.

[0158] The power flow calculation module in the embodiment comprises a power flow calculation topology grid constructed according to regional grid information, which is used to perform power flow calculation according to the predicted load value output by the prediction module and the optimal regulation value output by the optimization module, and output the energy storage charging and discharging power and time, electric vehicle charging pile charging and discharging power and time of each compensation point in the next 24 hours.

[0159] The regulation scheme generation module in the embodiment is used to select the power flow calculation result of the first hour as the distribution network collaborative regulation scheme output according to the power flow calculation result output by the power flow calculation module.

[0160] Further, the regulation scheme generation module in the embodiment feeds back a signal to the collection module every hour, the collection module re-collects regional grid information, regional real-time load data and historical load data, and inputs the new predicted load value into the prediction module, the optimization module re-solves the optimal scheduling value according to the new predicted load value, and the power flow calculation module re-performs power flow calculation to output the distribution network collaborative regulation scheme after cyclic optimization.

[0161] The terms describing the positional relationship in the drawings are only used for illustrative description, and cannot be understood as a limitation to the patent;

[0162] Obviously, the above embodiments of the present application are merely exemplary but not intended to limit the embodiments of the present application. Based on the above description, any other variations or changes can be made by those skilled in the art without departing from the spirit and principles of the present application. It is not necessary to list all the embodiments here. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall fall within the scope of the claims of the present application.

Claims

1. A power distribution network coordinated regulation method considering electric vehicle access, characterized in that, The method comprises the following steps: Collecting regional network framework information, regional real-time load data and historical load data; According to the regional real-time load data and the historical load data, a regional load curve prediction model is constructed; The regional load curve prediction model comprises a load growth rate calculation sub-model, an average load growth rate calculation sub-model and a real-time load prediction sub-model, wherein: The load growth rate calculation sub-model calculates the load growth rate of each time point per day according to the load data of the previous n days; the expression formula is as follows: In the formula, p i (t+1) represents the load growth rate at time t+1 on the i-th day in history, P i (t) represents the regional load value at time t on the i-th day in history; The average load growth rate calculation sub-model calculates the average load growth rate of each time point in the previous n days; the expression formula is as follows: In the formula, is the average load growth rate of each time point in the past n days. The real-time load prediction sub-model performs real-time load prediction of the next time point; the expression formula is as follows: In the formula, P next (t+1) represents the predicted load value of the next time of the region, P now (t) represents the real-time load value of the region; An optimal control model is constructed based on photovoltaic regulation; the optimal control model is provided with a constraint condition, and the constraint condition comprises an electric vehicle constraint at the load end; The optimal control model is provided with a collaborative regulation optimization objective function, and the expression formula is as follows: wherein f A represents the objective function; ΔP loss is the power distribution network loss index, T is the total number of operation periods of the power distribution network; P PV_pre (t) and P PV (t) are the predicted value and the scheduled value of the photovoltaic output respectively, wherein the predicted value of the photovoltaic output is obtained by the regional load curve prediction model; is the light abandonment penalty; E is a line set, (i,j) represents a line l ij ; r ij is the resistance of the line l ij ; l t,ij is the square of the current amplitude of the line l ij in the period t. According to the regional network framework information, a power flow calculation topological network framework is constructed; The predicted load value output by the regional load curve prediction model and the optimal regulation value output by the optimal control model are input into the power flow calculation topological network framework to output a power distribution network collaborative regulation scheme.

2. The power grid coordinated regulation method considering electric vehicle access according to claim 1, characterized in that, The regional network framework information comprises a regional power distribution network topological structure, line impedance and length of each main branch.

3. The power grid coordinated regulation method considering electric vehicle access according to claim 1, characterized in that, The constraint condition further comprises one or more of a power balance constraint, a node voltage constraint, a line current constraint, a distributed photovoltaic access power distribution network reactive power operation constraint, a network end reactive power compensation device operation constraint and a storage end constraint.

4. The power grid coordinated regulation method considering electric vehicle access according to claim 3, characterized in that, In the constraint condition, the expression formula of the power balance constraint is as follows: where P j,t represents the active power of node j at period t; P jk,t represents the active power of the line between node i and node j at period t; l ij,t is the current amplitude of the line from node i to node j at period t; g j represents the conductance of node j; v j,t is the square of the voltage amplitude of node j at period t; and are the injected active power of the generator, the load and the photovoltaic source, respectively; and are the injected reactive power of the generator, the load and the photovoltaic source, respectively; Q j,t represents the reactive power of node j at period t; x ij is the admittance of the line between node i and node j; b j is the susceptance of node j; The expression formula of the node voltage constraint is as follows: where N is the set of nodes, V i,min and V i,max are the lower and upper bounds of the voltage amplitude at node i, respectively. The expression formula of the line current constraint is as follows: wherein I ij,max is an upper bound of the current amplitude through the line l ij . The expression formula of the distributed photovoltaic access power distribution network reactive power operation constraint is as follows: In the formula, S N is the rated capacity of the photovoltaic inverter; Q PV,t is the reactive power emitted by the photovoltaic at time t, P PV,t is the active power emitted by the photovoltaic at time t; The expression formula of the network end reactive power compensation device operation constraint is as follows: Q min,i ≤Q svg,i ≤Q max,i ,i∈Ω CB In the formula, Q svg,i is the reactive power when the SVG is running; Ω CB is the set of nodes containing the SVG, Q max,i , Q min,i are the upper and lower limits of the reactive power when the SVG is running The expression formula of the electric vehicle constraint at the load end is as follows: wherein, and are the lower and upper limits of the charging power of the electric vehicle i operating in EV mode, respectively; is the energy storage capacity of the electric vehicle i in the time period t; is the charging efficiency coefficient of the electric vehicle i; and are the minimum and maximum energy storage capacities of the electric vehicle i, respectively; is the charge and discharge capacity demand of the electric vehicle i over the entire scheduling period; Δt is the measurement time interval; The expression formula of the storage end constraint is as follows: wherein, and respectively represent the charging state and discharging state of the energy storage device i in the time period t, taking values of 0 or 1; and respectively represent the lower and upper limits of the charging power of the energy storage device i; and respectively represent the lower and upper limits of the discharging power of the energy storage device i; represents the energy storage capacity of the energy storage device i in the time period t; represents the charging efficiency coefficient of the energy storage device i; represents the discharging efficiency coefficient of the energy storage device i; and respectively represent the minimum and maximum energy storage capacity of the energy storage device i.

5. The power grid coordinated regulation method considering electric vehicle access according to claim 3 or 4, characterized in that, The steps of generating the power distribution network collaborative regulation scheme comprise: The regional network framework information is input into the regional load curve prediction model to obtain predicted load values of regional electric vehicles, photovoltaics, energy storage and reactive power compensation; The predicted load values are input into the optimal control model, the optimal control model takes scheduling values as decision variables to perform optimization solving, and optimal scheduling values are output; The optimal scheduling values are input into the power flow calculation topological network framework to perform power flow calculation, and future 24-hour energy storage charging and discharging power and time, electric vehicle charging pile charging and discharging power and time of each compensation point are obtained, and the power flow calculation result of the first hour is taken as the power distribution network collaborative regulation scheme output.

6. The power grid coordinated regulation method considering electric vehicle access according to claim 5, characterized in that, Further comprising the following steps: Every time an hour elapses, the regional network framework information, the regional real-time load data and the historical load data are re-collected, the predicted load values are obtained by inputting the regional load curve prediction model, the optimal scheduling values are re-solved according to the predicted load values, and the power flow calculation is re-performed to output a power distribution network collaborative regulation scheme optimized through circulation.

7. A power distribution network coordinated control system considering electric vehicle access, characterized in that, The system is used for realizing the power distribution network collaborative regulation method considering electric vehicle access according to claim 1, comprising: A collection module is used for collecting regional network frame information, regional real-time load data and historical load data; A prediction module, the prediction module comprises a regional load curve prediction model constructed according to regional real-time load data and historical load data; An optimization module, the optimization module comprises an optimization control model constructed based on source side photovoltaic regulation; A power flow calculation module, the power flow calculation module comprises a power flow calculation topology network frame constructed according to regional network frame information; the predicted load value output by the prediction module and the optimal regulation value output by the optimization module are respectively input into the power flow calculation module, and the power flow calculation module outputs the energy storage charge-discharge power and time of each compensation point and the electric vehicle charging pile charge-discharge power and time in the future 24 hours; A regulation scheme generation module is used for selecting the power flow calculation result of the first hour as the power distribution network collaborative regulation scheme output according to the power flow calculation result output by the power flow calculation module. 8.The power grid coordinated control system considering electric vehicle access according to claim 7, wherein, The prediction module comprises: A load growth rate calculation unit is used for calculating the historical load growth rate; An average load growth rate calculation unit is used for calculating the historical load growth rate; A real-time load prediction sub-model is used for performing real-time next time load prediction and outputting the predicted load value of the next time of the region.

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