A power distribution network carrying capacity improvement method, device and medium

Abstract

The application discloses a power distribution network carrying capacity improving method and device and medium, and belongs to the technical field of power systems, which comprises the following steps: acquiring a dynamic electricity price adjustment coefficient, calculating the electricity price according to the dynamic electricity price adjustment coefficient, coordinating photovoltaic output and electric vehicle charging and discharging behavior; acquiring the adjustable and controllable margin of the charging and discharging power of an electric vehicle cluster, classifying the electric vehicle cluster according to the adjustable and controllable margin of the charging and discharging power, and calculating the total charging and discharging power margin of the electric vehicle cluster at each node; constructing a multi-objective real-time optimization scheduling model according to the total charging and discharging power margin of each node; solving the multi-objective real-time optimization scheduling model by using a multi-objective optimization algorithm to obtain an optimal scheduling strategy, and realizing the improvement of the carrying capacity of the power distribution network, which significantly improves the carrying capacity and safety and stability of the power distribution network for high-proportion photovoltaic and electric vehicles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a power distribution network carrying capacity improvement method, device and medium. BACKGROUND

[0002] To promote energy transformation, the penetration rate of distributed photovoltaic and electric vehicles in the power distribution network continues to increase. However, the volatility and intermittency of photovoltaic output, and the spatio-temporal uncertainty of electric vehicle charging behavior, bring serious challenges to the operation of the power distribution network, mainly manifested in that photovoltaic output peaks are prone to cause power flow reverse transmission and local voltage over-limit; electric vehicle charging load peaks are prone to cause line overload and local voltage under-limit. Node voltage constraints and line load constraints become the main bottlenecks restricting the carrying capacity of the power distribution network to photovoltaic and electric vehicles. In the prior art, the fixed time-of-use electricity price strategy is widely used, which divides the valley, flat and peak periods according to the day-ahead forecast and formulates fixed electricity prices. However, this method is difficult to track the dynamic changes of photovoltaic output and load in real time, and is difficult to effectively guide the charging and discharging behavior of electric vehicles to smooth fluctuations and promote photovoltaic consumption. In addition, the traditional method does not sufficiently tap the regulation potential of electric vehicles, and lacks a controllable margin quantification model based on real-time state. Although reasonable light rejection and orderly charging and discharging of electric vehicles are considered effective means to improve carrying capacity, how to establish a precise dynamic electricity price mechanism, quantify the regulation capacity of electric vehicle clusters, and build a multi-objective optimization model considering economy and carrying capacity improvement, to realize collaborative optimization scheduling, is still a problem to be solved.

[0003] The existing fixed time-of-use electricity price is single in the same period, and cannot be finely guided when photovoltaic output suddenly changes or load fluctuates. At the same time, there is a lack of real-time controllable capacity evaluation of electric vehicles at each node of the power distribution network, resulting in that the optimization scheduling potential is not fully tapped. The existing optimization method also focuses on a single target, and it is difficult to maximize the system carrying capacity while ensuring economy. Therefore, there is an urgent need for a power distribution network carrying capacity improvement method that can dynamically respond to system state, deeply tap the regulation potential, and collaboratively optimize economy and safety. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is to solve the problems that the prior art cannot dynamically respond to photovoltaic output and load changes, the regulation potential of electric vehicles is not fully tapped, and economy and system carrying capacity cannot be collaboratively optimized.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a power distribution network carrying capacity improvement method, comprising:

[0007] The dynamic electricity price adjustment coefficient is obtained, the electricity price is calculated according to the dynamic electricity price adjustment coefficient, and the photovoltaic output and the charging and discharging behavior of the electric vehicle are coordinated.

[0008] The charging and discharging power adjustable margin of the electric vehicle cluster is obtained, the electric vehicle cluster is classified according to the charging and discharging power adjustable margin, and the total charging and discharging power margin of the electric vehicle cluster at each node is calculated.

[0009] The multi-objective real-time optimization scheduling model is constructed according to the total charging and discharging power margin of each node.

[0010] The multi-objective real-time optimization scheduling model is solved by using a multi-objective optimization algorithm to obtain an optimal scheduling strategy, and the carrying capacity of the distribution network is improved.

[0011] As a preferred scheme of the power distribution network carrying capacity improvement method, the electricity price is calculated according to the dynamic electricity price adjustment coefficient, which comprises:

[0012] The electricity price is divided into charging electricity price and discharging electricity price.

[0013] For the charging electricity price, when the valley, flat and peak periods, the charging electricity price is respectively the time-of-use electricity price coefficient multiplied by the electricity price of the corresponding period, and then multiplied by the dynamic adjustment factor.

[0014] For the discharging electricity price, when the valley and flat periods, the discharging electricity price is respectively the time-of-use electricity price coefficient multiplied by the electricity price of the corresponding period, and then multiplied by the dynamic adjustment factor.

[0015] When the peak period, the discharging electricity price is the time-of-use electricity price coefficient multiplied by the electricity price of the corresponding period, and then multiplied by the dynamic adjustment factor and the incentive coefficient.

[0016] The dynamic adjustment factor is composed of the equivalent load coefficient and the equivalent load rate.

[0017] The beneficial effects of the preferred technical scheme are that the charging and discharging electricity prices are calculated by the dynamic electricity price adjustment coefficient, the electric vehicles are guided to charge and discharge at appropriate periods, the power grid load is optimized, and the system operation efficiency and stability are improved.

[0018] As a preferred scheme of the power distribution network carrying capacity improvement method, the charging and discharging power adjustable margin of the electric vehicle cluster is obtained, which comprises:

[0019] The state of charge margin and the response time margin of a single electric vehicle are calculated according to the real-time state of charge, the next trip mileage, the network access time and the parking time and the charging demand power of the single electric vehicle.

[0020] The state of charge margin includes a maximum discharging margin and a maximum charging margin, and the response time margin includes a discharging time margin, a charging time margin and a delayed charging margin.

[0021] The preferred technical scheme has the beneficial effect that the electric vehicle regulation and control capability can be quantified to provide accurate data for optimized scheduling and improve power grid regulation flexibility and resource utilization efficiency.

[0022] As a preferred scheme of the power distribution network carrying capacity improvement method, the classification of the electric vehicle cluster includes:

[0023] When the maximum discharging margin and the maximum charging margin of the electric vehicle are both greater than zero, and the discharging time margin and the charging time margin are both greater than zero, the electric vehicle is in the charging and discharging cluster;

[0024] When the maximum discharging margin of the electric vehicle is zero and the delayed charging margin is greater than zero, the electric vehicle is in the delayed charging cluster;

[0025] When the maximum discharging margin and the delayed charging margin of the electric vehicle are both zero, the electric vehicle is in the immediate charging cluster.

[0026] The preferred technical scheme has the beneficial effect that the electric vehicle can be accurately classified to improve scheduling flexibility and optimize power grid operation efficiency.

[0027] As a preferred scheme of the power distribution network carrying capacity improvement method, the calculation of the total charging and discharging power margin of the electric vehicle cluster at each node includes:

[0028] The total charging power margin of each node is the sum of the charging power margin of all charging and discharging clusters of electric vehicles and the charging power margin of all delayed charging clusters of electric vehicles;

[0029] The total discharging power margin of each node is the sum of the discharging power margin of all charging and discharging clusters of electric vehicles.

[0030] As a preferred scheme of the power distribution network carrying capacity improvement method, the construction of the multi-objective real-time optimization scheduling model includes:

[0031] The minimum power distribution network optimization scheduling cost and the maximum power distribution network carrying capacity are taken as objective functions, respectively;

[0032] The maximum power distribution network carrying capacity is realized by minimizing the power distribution network node voltage deviation and line load deviation;

[0033] The constraint conditions include power distribution network operation constraints, distributed photovoltaic output constraints and electric vehicle charging and discharging power constraints;

[0034] The power distribution network operation constraints include power balance constraints of each node of the power distribution network, node voltage constraints, voltage fluctuation constraints, line transmission capacity constraints and transformer load constraints.

[0035] As a preferred scheme of the power distribution network carrying capacity improvement method, solving the multi-objective real-time optimization scheduling model comprises:

[0036] The multi-objective real-time optimization scheduling model is solved by using the improved non-dominated sorting genetic algorithm to obtain an optimal solution set.

[0037] The single-objective function value of each solution in the optimal solution set is simulated and processed.

[0038] When the single-objective function value of each solution is less than the minimum value of the objective function value of the corresponding solution, the fuzzy single-objective function value is 1.

[0039] When the single-objective function value of each solution is between the maximum value and the minimum value of the objective function value of the corresponding solution, the fuzzy single-objective function value is the difference between the maximum value of the objective function value of the corresponding solution and the single-objective function value divided by the difference between the maximum value and the minimum value of the objective function value of the corresponding solution.

[0040] When the single-objective function value of each solution is greater than the maximum value of the objective function value of the corresponding solution, the fuzzy single-objective function value is 0.

[0041] As a preferred scheme of the power distribution network carrying capacity improvement method, the method further comprises:

[0042] The weighted sum of the fuzzy single-objective function values is obtained according to the set target weight preference.

[0043] The weighted sum value is sorted to obtain an optimal solution as a final scheduling strategy, so as to improve the carrying capacity of the power distribution network.

[0044] The application provides a computer device, which comprises a memory and a processor, and the memory stores a computer program.

[0045] The application provides a computer readable storage medium, which stores a computer program.

[0046] The application has the beneficial effects that the application guides the charging and discharging behavior of the electric vehicle through the dynamic real-time electricity price mechanism; the cluster adjustable margin model constructed based on the real-time state of the vehicle deeply excavates the flexible adjustment potential of each node of the distribution network; the multi-objective optimization model constructed with the minimum economic cost and the maximum bearing capacity as the target realizes the collaborative optimization of photovoltaic abandonment and electric vehicle charging and discharging, effectively solves the voltage out-of-limit and line overload problems under high proportion of new energy access, significantly improves the safe and stable operation level of the distribution network and the bearing capacity of photovoltaic and electric vehicle, reduces the system peak-valley difference, and optimizes the resource allocation. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 The overall flowchart of a distribution network bearing capacity improvement method provided for an embodiment of the application;

[0049] Figure 2 The test scene 33-node distribution system of a distribution network bearing capacity improvement method provided for an embodiment of the application;

[0050] Figure 3 The node voltage situation chart of a distribution network bearing capacity improvement method provided for an embodiment of the application applied to photovoltaic output peak time;

[0051] Figure 4 The node photovoltaic bearing capacity situation chart of a distribution network bearing capacity improvement method provided for an embodiment of the application applied to photovoltaic output peak time;

[0052] Figure 5 The node voltage and line load situation chart of a distribution network bearing capacity improvement method provided for an embodiment of the application applied to charging load peak time;

[0053] Figure 6 The charging load bearing capacity situation chart of a distribution network bearing capacity improvement method provided for an embodiment of the application applied to charging load peak time. DETAILED DESCRIPTION

[0054] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0055] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a power distribution network carrying capacity improvement method, comprising:

[0056] S100: Obtain a dynamic electricity price adjustment coefficient, calculate the electricity price according to the dynamic electricity price adjustment coefficient, coordinate the photovoltaic output and the electric vehicle charging and discharging behavior;

[0057] S200: Obtain the adjustable and controllable margin of the electric vehicle cluster, classify the electric vehicle cluster according to the adjustable and controllable margin, and calculate the total charging and discharging power margin of the electric vehicle cluster at each node;

[0058] S300: Construct a multi-objective real-time optimization scheduling model according to the total charging and discharging power margin of each node;

[0059] S400: Solve the multi-objective real-time optimization scheduling model by using a multi-objective optimization algorithm to obtain an optimal scheduling strategy, and realize the improvement of the carrying capacity of the power distribution network.

[0060] It should be noted that by adjusting the photovoltaic output and the electric vehicle charging and discharging behavior through dynamic electricity price, accurately quantifying and classifying the adjustable and controllable margin of the electric vehicle cluster, constructing and solving the multi-objective optimization scheduling model, the carrying capacity of the power distribution network is significantly improved, the problems of voltage out-of-limit and line overload caused by photovoltaic output fluctuation and electric vehicle disordered charging are effectively solved, the system peak-valley difference is reduced, the resource allocation is optimized, and the safe and stable operation level of the power distribution network is enhanced.

[0061] In the embodiment of the present application, the above step S100 comprises the following sub-steps A1-A5;

[0062] In A1: the electricity price is divided into charging electricity price and discharging electricity price;

[0063] In A2: for the charging electricity price, when the valley, flat and peak periods, the charging electricity price is respectively the time-of-use electricity price coefficient multiplied by the electricity price of the corresponding period, and then multiplied by the dynamic adjustment factor;

[0064] In A3: for the discharging electricity price, when the valley and flat periods, the discharging electricity price is respectively the time-of-use electricity price coefficient multiplied by the electricity price of the corresponding period, and then multiplied by the dynamic adjustment factor;

[0065] In A4: when the peak period, the discharge price is the time-of-use price coefficient multiplied by the corresponding period price, multiplied by the dynamic adjustment factor and the incentive coefficient;

[0066] In A5: wherein the dynamic adjustment factor is composed of the equivalent load coefficient and the equivalent load rate.

[0067] Specifically, the equivalent load rate is represented as:

[0068]

[0069] The dynamic adjustment factor is represented as:

[0070]

[0071] The charging price is represented as:

[0072]

[0073] The discharge price is represented as:

[0074]

[0075] wherein, is the power distribution network the equivalent load rate at the moment, is the power distribution network the regular load at the moment, is the power distribution network the photovoltaic output at the moment, is the maximum regular load of the power distribution network, is the dynamic adjustment factor, is the equivalent load coefficient, is the electric vehicle charging price at the moment, is the electric vehicle discharge price at the moment, is the time-of-use price coefficient, , , are the valley price, flat price and peak price of the fixed time-of-use price respectively, , , are the valley period, flat period and peak period in the fixed time-of-use price setting strategy respectively, is the time point, and the incentive coefficient is set to 1.2 according to the principle of widening the peak-valley price difference to encourage electric vehicles to discharge to the grid during the peak period.

[0076] It should be noted that by adjusting the charging and discharging behavior of electric vehicles through dynamic pricing, the load fluctuation is effectively suppressed, the photovoltaic consumption is promoted, the operation efficiency and stability of the power distribution network are improved, the system peak-valley difference is reduced, and the resource allocation is optimized.

[0077] In this embodiment of the invention, step S200 includes the following sub-steps B1-B2;

[0078] In B1: The state of charge margin and response time margin of a single electric vehicle are calculated based on the real-time state of charge, the mileage of the next trip, the network access time, the parking time, and the charging demand.

[0079] In B2: The state-of-charge margin includes the maximum discharge margin and the maximum charge margin, and the response time margin includes the discharge time margin, the charge time margin, and the delayed charge margin.

[0080] Specifically, the maximum discharge margin is expressed as:

[0081]

[0082] Maximum charging margin is expressed as:

[0083]

[0084] in, , They are respectively The maximum discharge margin and maximum charging margin of electric vehicles at any given time. for The state of charge of the electric vehicle at all times. For the battery capacity of electric vehicles, This refers to the driving range of an electric vehicle for the next leg of its journey. Electricity consumption per unit distance traveled by an electric vehicle.

[0085] The discharge time margin is expressed as:

[0086]

[0087] The charging time margin is expressed as:

[0088]

[0089] The delayed charging margin is expressed as:

[0090]

[0091] in, For discharge time margin, for The state of charge of the electric vehicle at all times. For the battery capacity of electric vehicles, This refers to the driving range of an electric vehicle for the next leg of its journey. Electricity consumption per unit distance traveled by an electric vehicle. For electric vehicles to reach their destination The duration of the stay For electric vehicles to reach their destination The time of network access, Electric vehicles at their destinations Charging demand, For charging time margin, To allow for extended charging margin, The charging power for electric vehicles, For the discharge power of electric vehicles, For a point in time, This refers to the destination location.

[0092] It should be noted that by accurately calculating the charging and discharging margins and time margins of electric vehicles, their control capabilities can be quantified, providing data support for optimized scheduling and effectively improving the flexibility and carrying capacity of the power distribution network.

[0093] In this embodiment of the invention, after completing steps B1-B2, step S200 also includes steps B3-B7.

[0094] In B3: When the maximum discharge margin and maximum charging margin of an electric vehicle are both greater than zero, and the discharge time margin and charging time margin are both greater than zero, the electric vehicle is in a cluster that balances charging and discharging.

[0095] In B4: When the maximum discharge margin of an electric vehicle is zero and the delayed charging margin is greater than zero, the electric vehicle is in a delayed charging cluster.

[0096] In B5: When both the maximum discharge margin and the delayed charging margin of an electric vehicle are zero, the electric vehicle is in an immediate charging cluster.

[0097] In B6: The total charging power margin of each node is the sum of the charging power margins of all electric vehicles in the charging and discharging cluster and the charging power margins of all electric vehicles in the delayed charging cluster.

[0098] In B7: The total discharge power margin of each node is the sum of the discharge power margins of all electric vehicles in the charging and discharging cluster.

[0099] Specifically, based on the state of charge margin and response time margin, electric vehicles connected to the grid are divided into three clusters, taking into account charging and discharging clusters, delayed charging clusters, and immediate charging clusters.

[0100] When both the vehicle's maximum discharge margin and maximum charge margin are greater than zero, and both the discharge time margin and charge time margin are greater than zero, the vehicle... It is constantly in a state of balancing charging and discharging clusters;

[0101] When the vehicle's maximum discharge margin is zero and the delayed charging margin is greater than zero, the vehicle is in Always in the delayed charging cluster;

[0102] When both the vehicle's maximum discharge margin and delayed charging margin are zero, the vehicle is in Always in the instant charging cluster.

[0103] Specifically, The total charging power margin of a distribution network node at any given time is expressed as:

[0104]

[0105] The total discharge power margin of a distribution network node at any given time is expressed as:

[0106]

[0107] in, for Distribution network nodes at all times Total charging power margin, for Distribution network nodes at all times Total discharge power margin, For electric vehicles Always maintain charging power margin. For electric vehicles Discharge power margin at all times For electric vehicles Time delay charging power margin, , They are respectively Always connected to the distribution network node The number of electric vehicles in both the charging / discharging cluster and the delayed charging cluster should be considered. For distribution network nodes, For the number of electric vehicles;

[0108] Electric vehicles Electric vehicles that can both charge and discharge to the power grid are designed to balance charging and discharging power margins. Before the charging or discharging time margin is exceeded, their charging or discharging power margin is equal to the vehicle's rated charging and discharging power.

[0109] Electric vehicles The power margin of delayed charging is such that electric vehicles in a delayed charging cluster do not have the ability to discharge. When the delayed charging margin is not exceeded, the charging power margin is equal to the vehicle's rated charging power.

[0110] It should be noted that by accurately classifying electric vehicles and calculating the total charging and discharging power margin of each node, the flexible adjustment potential of the distribution network has been deeply explored, providing accurate data support for optimized scheduling and significantly improving the carrying capacity and operational flexibility of the distribution network.

[0111] In this embodiment of the invention, step S300 includes the following sub-steps C1-C4;

[0112] In C1: the objective functions are minimizing the cost of optimal dispatching of the distribution network and maximizing the carrying capacity of the distribution network, respectively.

[0113] In C2: The maximum carrying capacity of the distribution network is achieved by minimizing the voltage deviation at distribution network nodes and the load deviation on lines.

[0114] In C3: the constraints include distribution network operation constraints, distributed photovoltaic power output constraints, and electric vehicle charging and discharging power constraints;

[0115] In C4: Distribution network operation constraints include power balance constraints at each node of the distribution network, node voltage constraints, voltage fluctuation constraints, line transmission capacity constraints, and transformer load constraints.

[0116] Specifically, the multi-objective real-time optimization scheduling model includes objective functions and constraints;

[0117] The objective functions include minimizing the cost of optimal distribution network scheduling and maximizing the distribution network carrying capacity;

[0118] Objective function 1, which aims to minimize the cost of optimal dispatching of the distribution network, is expressed as:

[0119]

[0120]

[0121] in, for The cost of electric vehicles participating in dispatching at any time. Penalties for abandoning photovoltaic power generation for Electricity prices for electric vehicle charging at all times for Electricity price for electric vehicles at all times for The charging electricity price for the distribution network participating in dispatch at any time. for The discharge price of the distribution network participating in dispatch at any time. To optimize the scheduling time interval, The price is the penalty for curtailing solar power, expressed in yuan / kWh. for Constant power consumption.

[0122] Objective function 2, which aims to maximize the carrying capacity of the distribution network, is expressed as follows:

[0123]

[0124]

[0125] in, For the number of nodes in the distribution network, For the number of transmission lines, for Distribution network nodes at all times Voltage, This is the nominal voltage value. For distribution network nodes Voltage deviation value, For distribution network nodes Minimum voltage deviation For distribution network nodes Maximum voltage deviation for Timetable load, This is the nominal load value. For the line Load deviation value, For distribution network lines Maximum load deviation For distribution network lines Minimum load deviation For power distribution network lines.

[0126] The constraints include distribution network operation constraints, distributed photovoltaic power output constraints, and electric vehicle charging and discharging power constraints.

[0127] Distribution network operation constraints include power balance constraints at each node of the distribution network, node voltage constraints, voltage fluctuation constraints, line transmission capacity constraints, and transformer load constraints.

[0128] The power balance constraints at each node of the distribution network are expressed as follows:

[0129]

[0130] in, for Distribution network nodes at all times Injected active power, for Distribution network nodes at all times Injected reactive power, and Distribution network nodes and conductance and susceptance between, distribution network nodes and voltage phase angle difference between, distribution network nodes voltage at time , distribution network nodes voltage at time .

[0131] The node voltage constraint is expressed as:

[0132]

[0133] where, distribution network nodes voltage at time , is the voltage nominal value, is the voltage deviation rate limit.

[0134] The voltage fluctuation constraint is expressed as:

[0135]

[0136] where, is the short-time voltage variation, is the voltage fluctuation percentage limit, distribution network nodes voltage fluctuation percentage.

[0137] The line transmission capacity constraint is expressed as:

[0138]

[0139] where, distribution network nodes load at time , is the line maximum transmission power.

[0140] The transformer load constraint is expressed as:

[0141]

[0142] where, distribution network nodes equivalent output at time is the equivalent load at the same time carried by the transformer, is the transformer transmission power limit.

[0143] The distributed photovoltaic output constraint is expressed as:

[0144]

[0145] wherein, , are respectively charging and discharging power optimization variables of the electric vehicle cluster at the distribution network node upper and lower limits of distributed photovoltaic output, are respectively charging and discharging power optimization variables of the electric vehicle cluster at the distribution network node photovoltaic optimization variables.

[0146] The charging and discharging power constraints of the electric vehicle are expressed as:

[0147]

[0148] wherein, , are respectively charging and discharging power optimization variables of the electric vehicle cluster at the distribution network node charging and discharging power optimization variables of the electric vehicle cluster at the distribution network node are respectively total charging power margin and total discharging power margin of the distribution network node at the moment.

[0149] It should be noted that by constructing a multi-objective real-time optimization scheduling model, the economy and safety are comprehensively considered, the minimum cost of distribution network optimization scheduling and the maximum carrying capacity are realized, the problems of photovoltaic output fluctuation and electric vehicle disordered charging are effectively solved, and the operation efficiency and stability of the distribution network are significantly improved.

[0150] In the embodiment of the application, the above step S400 comprises the following sub-steps D1-D7.

[0151] In D1: the improved non-dominated sorting genetic algorithm is used to solve the multi-objective real-time optimization scheduling model to obtain an optimal solution set;

[0152] In D2: the single-objective function value of each solution in the optimal solution set is simulated;

[0153] In D3: when the single-objective function value of each solution is less than the minimum value of the objective function value of the corresponding solution, the fuzzy single-objective function value is 1;

[0154] In D4: when the single-objective function value of each solution is between the maximum value and the minimum value of the objective function value of the corresponding solution, the fuzzy single-objective function value is the difference between the maximum value of the objective function value of the corresponding solution and the single-objective function value divided by the difference between the maximum value and the minimum value of the objective function value of the corresponding solution;

[0155] In D5: when the single-objective function value of each solution is greater than the maximum value of the objective function value of the corresponding solution, the fuzzified single-objective function value is 0;

[0156] In D6: the weighted sum of the fuzzified single-objective function values is obtained according to the set target weight preference;

[0157] In D7: the weighted sum value is sorted, and the optimal solution is obtained as the final scheduling strategy to improve the carrying capacity of the power distribution network.

[0158] Specifically, the improved non-dominated sorting genetic algorithm is used to solve the multi-objective real-time optimization scheduling model to obtain a Pareto optimal solution set;

[0159] The single-objective function value of each solution in the Pareto optimal solution set is fuzzified, and the linear membership function is represented as:

[0160]

[0161] wherein, is the fuzzified single-objective function value, is the th objective function value of the th objective, , are the maximum and minimum function values of the th objective, respectively.

[0162] The weighted sum of the fuzzified single-objective function values is obtained according to the set target weight preference, and is represented as:

[0163]

[0164] wherein, is the weight preference of the th objective, is the total number of optimization objectives, is the number of objective function values corresponding to each objective, is the weighted sum value.

[0165] The weighted sum value is sorted, and the optimal solution that meets the constraints and best meets the actual demand is selected as the final scheduling strategy.

[0166] It should be noted that the improved non-dominated sorting genetic algorithm is used to solve the multi-objective optimization problem to obtain a Pareto optimal solution set, and combined with fuzzification and weighted sum, the optimal solution that best meets the actual demand is selected, effectively balancing the conflict between optimization objectives and improving the scientificity and practicality of the scheduling strategy.

[0167] The embodiment also provides a computer device suitable for the power distribution network carrying capacity improvement method, and the computer device comprises a memory and a processor.

[0168] The embodiment also provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the power distribution network carrying capacity improvement method.

[0169] The storage medium provided by the embodiment and the power distribution network carrying capacity improvement method provided by the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0170] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary universal hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, and includes a number of instructions to make a computer device (which can be a personal computer, a server or a network device) execute the methods of various embodiments of the present application.

[0171] In the embodiment 2, refer to table 1~table 2, Figures 2-6 The embodiment is different from the first embodiment, and provides a verification test of the power distribution network carrying capacity improvement method, and the technical effects used in the method are verified and explained.

[0172] The method of the present application is applied to a typical power distribution network test system for testing.

[0173] As shown in Figure 2 , the power distribution network comprises 33 nodes, accesses distributed photovoltaic and electric vehicle charging load, sets fixed time-of-use electricity price parameters: valley electricity price 0.465 yuan / kWh, flat electricity price 0.687 yuan / kWh, peak electricity price 0.869 yuan / kWh, and sets dynamic real-time electricity price parameters: , The improved non-dominated sorting genetic algorithm parameters include a population size of 100, 500 iterations, a crossover rate of 0.8, a mutation rate of 0.2, and a target weight of 0.5.

[0174] To illustrate the technical effect of the method of the application, multi-scenario simulation analysis is carried out, covering different time scales, including targeted analysis of typical moments such as photovoltaic output peak and charging load peak, and systematic testing of the operation of the distribution network throughout the day.

[0175] The optimal scheduling strategy proposed by the application can be divided into three schemes, scheme 1 focuses on the reasonable light rejection strategy, scheme 2 focuses on guiding the orderly charging and discharging of electric vehicles, and scheme 3 combines the optimization ideas of the previous two schemes, as shown in Table 1 and Table 2, the node voltage change at the typical moment before and after optimization and the line load change at the typical moment before and after optimization.

[0176] Table 1 Node voltage change at typical moment before and after optimization

[0177]

[0178] Table 2 Line load change at typical moment before and after optimization

[0179]

[0180] As shown in Table 1 and Table 2, at the photovoltaic output peak moment, when the optimization scheme is not used, the voltage of each node of the distribution network is generally maintained at a high level of 1.041~1.056, there is a serious risk of voltage exceeding the upper limit, which threatens the safe and stable operation of the system and restricts the accommodation capacity of distributed photovoltaic. Therefore, effective optimization scheduling strategy must be taken to control the system voltage level.

[0181] From Figure 3 And Figure 4 It can be seen from the three optimization schemes proposed by the application that by reasonably rejecting light and orderly charging and discharging of electric vehicles, the load balance of the power grid and the optimal allocation of power resources are realized, the system node voltage level and voltage deviation are significantly improved, and the carrying capacity of the distribution network for photovoltaic is improved. Among them, the optimization effect of scheme 3 is the best, scheme 2 is second, and scheme 1 is relatively weak. However, from the economic point of view, the optimization scheduling costs of scheme 1, scheme 2 and scheme 3 are 388 yuan, 468 yuan and 856 yuan respectively.

[0182] Therefore, in actual operation, the distribution network operator can select the optimal scheduling scheme according to the system safety demand and economic constraint.

[0183] At the charging load peak time, the system distributed photovoltaic output is 0, and the system only needs to consider the carrying capacity of the charging load. When the optimization scheme is not adopted, the distribution network faces double operation risks: the minimum value of node voltage drops to 0.930, reaching the lower limit of voltage; the maximum value of line load reaches 6.67MW, approaching the line transmission capacity limit, seriously threatening the safe and stable operation of the system. Therefore, effective optimization scheduling strategies need to be taken to improve the voltage level and improve the line load condition. The optimization scheme proposed in the application realizes the balance of power load and the optimization of power resource allocation by guiding the orderly charging and discharging of electric vehicles, and significantly improves the system operation state.

[0184] From Figure 5 With Figure 6 It can be seen from the above that the system node voltage and line load are effectively controlled after optimization, and the carrying capacity of each node of the distribution network is improved by 0.432MW on average. Although the optimization scheduling cost reaches 714 yuan, considering the system operation safety and the carrying capacity improvement effect, the multi-objective optimization scheduling method has significant application value.

[0185] Based on the reasonable light rejection and orderly charging and discharging of the distribution network optimization scheduling strategy, the system is optimized and scheduled in 24 hours as a cycle and 15 minutes as a time interval.

[0186] The optimization scheduling strategy proposed in the application significantly improves the fluctuation of the system load curve and effectively reduces the peak-valley difference. When scheme 2 is adopted, through the dynamic real-time electricity price, the vehicle is effectively guided to charge at a low charging price during the peak period of photovoltaic output, and the distributed photovoltaic consumption rate is improved. And during the load peak period, the vehicle is successfully guided to discharge through the high discharge price, realizing the peak load shifting of the system. At the same time, combined with the reasonable light rejection strategy of scheme 1, the peak-valley difference of the system is reduced from 125.4% when not optimized to 76.5% when scheme 3 is adopted.

[0187] In addition, the optimal scheduling strategy proposed by the application significantly improves the safe and stable operation level of the power distribution network. For node voltage, the optimal scheduling strategy effectively suppresses the overall fluctuation of the system voltage. Before the optimal scheduling strategy is adopted, the voltage per unit of the whole network fluctuates between 0.93 and 1.056, and some nodes are on the edge of exceeding the limit, which has a large security risk. After the optimal scheduling strategy is adopted, the voltage fluctuation range is improved to 0.954-1.05, and is stabilized in a reasonable range, which not only improves the power quality of the system but also fundamentally enhances the voltage stability of the system. For line load, the optimal scheduling strategy effectively improves the time and space imbalance of the load. Before the optimal scheduling strategy is adopted, the load rate of some lines exceeds 95% in the peak period, which is not conducive to the safe and stable operation of the system. After the optimal scheduling strategy is adopted, in the peak period of photovoltaic output, by reasonably discarding light and guiding vehicles to orderly charge, the phenomenon of power reverse sending is effectively avoided. In the peak period of charging load, by guiding vehicles to orderly discharge, the line load is significantly reduced, and the load rate of each line is controlled in the safe range below 80%.

[0188] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for improving the carrying capacity of a power distribution network, characterized in that, include: Obtain the dynamic electricity price adjustment coefficient, calculate the electricity price based on the dynamic electricity price adjustment coefficient, and coordinate the photovoltaic power output and the charging and discharging behavior of electric vehicles; The adjustable charge and discharge power margin of the electric vehicle cluster is obtained, the electric vehicle cluster is classified according to the adjustable charge and discharge power margin, and the total charge and discharge power margin of the electric vehicle cluster at each node is calculated. A multi-objective real-time optimization scheduling model is constructed based on the total charge and discharge power margin of each node. The multi-objective real-time optimization scheduling model is solved using a multi-objective optimization algorithm to obtain the optimal scheduling strategy and improve the carrying capacity of the distribution network. Calculating the electricity price based on the dynamic electricity price adjustment coefficient includes: Electricity prices are divided into charging prices and discharging prices; For charging electricity prices, during off-peak, flat, and peak periods, the charging electricity prices are calculated by multiplying the time-of-use electricity price coefficient by the electricity price for the corresponding period, and then by the dynamic adjustment factor. For the discharge price, during off-peak and normal periods, the discharge price is calculated by multiplying the time-of-use price coefficient by the price for the corresponding period, and then by the dynamic adjustment factor. During peak hours, the discharge price is calculated by multiplying the time-of-use price coefficient by the price for the corresponding time period, and then by the dynamic adjustment factor and the incentive coefficient. The dynamic adjustment factor consists of the equivalent load coefficient and the equivalent load rate. The equivalent load factor is expressed as: ； The dynamic adjustment factor is expressed as: ； in, For distribution network Momentary equivalent load factor For distribution network Regular load at all times For distribution network Photovoltaic power output at all times This represents the maximum normal load of the distribution network. As a dynamic adjustment factor, This is the equivalent load factor; The classification of electric vehicle clusters includes: When the maximum discharge margin and maximum charging margin of an electric vehicle are both greater than zero, and the discharge time margin and charging time margin are both greater than zero, the electric vehicle is in a charging and discharging cluster that balances charging and discharging. When the maximum discharge margin of an electric vehicle is zero and the delayed charging margin is greater than zero, the electric vehicle is in a delayed charging cluster. When both the maximum discharge margin and the delayed charging margin of an electric vehicle are zero, the electric vehicle is in an immediate charging cluster. Constructing a multi-objective real-time optimization scheduling model includes: The objective functions are respectively minimizing the cost of optimal dispatching of the distribution network and maximizing the carrying capacity of the distribution network; Objective function 1, which aims to minimize the cost of optimal dispatching of the distribution network, is expressed as: ； ； in, for The cost of electric vehicles participating in dispatching at any time. Penalties for abandoning photovoltaic power generation for Electricity prices for electric vehicle charging at all times for Electricity price for electric vehicles at all times for The charging electricity price for the distribution network participating in dispatch at any time. for The discharge price of the distribution network participating in dispatch at any time. To optimize the scheduling time interval, The price is the penalty for curtailing solar power, expressed in yuan / kWh. for Constant power consumption; Objective function 2, which aims to maximize the carrying capacity of the distribution network, is expressed as follows: ； ； in, For the number of nodes in the distribution network, For the number of transmission lines, for Distribution network nodes at all times Voltage, This is the nominal voltage value. For distribution network nodes Voltage deviation value, For distribution network nodes Minimum voltage deviation For distribution network nodes Maximum voltage deviation for Timetable load, This is the nominal load value. For the line Load deviation value, For distribution network lines Maximum load deviation For distribution network lines Minimum load deviation For power distribution network lines; The maximum carrying capacity of the distribution network is achieved by minimizing the voltage deviation at the distribution network nodes and the load deviation on the lines. The constraints include distribution network operation constraints, distributed photovoltaic power output constraints, and electric vehicle charging and discharging power constraints. The constraints on the operation of the distribution network include the power balance constraints of each node, the node voltage constraints, the voltage fluctuation constraints, the line transmission capacity constraints, and the transformer load constraints.

2. The method for improving the carrying capacity of a distribution network as described in claim 1, characterized in that, Obtaining the adjustable margin of charging and discharging power for a cluster of electric vehicles includes: The state of charge margin and response time margin of a single electric vehicle are calculated based on its real-time state of charge, next trip mileage, grid access time, parking time, and charging demand. Among them, the state of charge margin includes the maximum discharge margin and the maximum charge margin, and the response time margin includes the discharge time margin, the charge time margin, and the delayed charge margin.

3. The method for improving the carrying capacity of a distribution network as described in claim 2, characterized in that, Calculating the total charge / discharge power margin of the electric vehicle cluster at each node includes: The total charging power margin of each node is the sum of the charging power margins of all electric vehicles in the charging and discharging cluster and the charging power margins of all electric vehicles in the delayed charging cluster. The total discharge power margin of each node is the sum of the discharge power margins of all electric vehicles in the charging and discharging cluster.

4. The method for improving the carrying capacity of a distribution network as described in claim 3, characterized in that, Solving the multi-objective real-time optimization scheduling model includes: An improved non-dominated sorting genetic algorithm is used to solve a multi-objective real-time optimization scheduling model to obtain the optimal solution set; The single objective function value of each solution in the optimal solution set is simulated. When the single objective function value of each solution is less than the minimum value of the objective function value of the corresponding solution, the fuzzy single objective function value is 1; When the single objective function value of each solution lies between the maximum and minimum values ​​of the objective function value of the corresponding solution, the fuzzy single objective function value is the difference between the maximum and single objective function value of the corresponding solution divided by the difference between the maximum and minimum values ​​of the objective function value of the corresponding solution. When the single objective function value of each solution is greater than the maximum value of the objective function value of the corresponding solution, the fuzzy single objective function value is 0.

5. The method for improving the carrying capacity of a distribution network as described in claim 4, characterized in that, Also includes: Based on the set target weight preferences, the fuzzy single-objective function values ​​are weighted and summed. The weighted sums are sorted to obtain the optimal solution, which is then used as the final scheduling strategy to improve the carrying capacity of the distribution network.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for improving the carrying capacity of a power distribution network as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for improving the carrying capacity of a power distribution network as described in any one of claims 1 to 5.

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