Method and device for configuring optical storage and charging capacity of power distribution network area in compensation and aggregation mode

Abstract

The invention discloses a power distribution network area optical storage and charging capacity configuration method and equipment in a compensation and aggregation mode, and the method comprises the steps: enabling an electric vehicle to participate in peak regulation and voltage regulation of a power distribution network through reducing the charging power of the electric vehicle when necessary through employing a V2G technology under the control strategy of the compensation and aggregation mode; the adjustable capacity of the transformer area is improved, and the adjustment requirement of a power distribution network is met; optical storage and charging of all power distribution network areas are scheduled in a unified mode, the redundancy of photovoltaic and energy storage capacity configuration of the areas can be reduced, and the comprehensive utilization rate of optical storage and charging equipment of the power distribution network areas is improved. By means of the configuration mode, optimal configuration of optical storage and charging of the distribution network area can be achieved, and the maximum benefit and the lowest investment cost of optical storage and charging of the distribution network area are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic storage and charging capacity configuration in distribution network substations, and in particular to a method and device for photovoltaic storage and charging capacity configuration in distribution network substations under compensation and aggregation modes. Background Art

[0002] With the gradual promotion of integrated photovoltaic, storage and charging technology, compensation and aggregation modes have become important means to improve the efficiency of photovoltaic, storage and charging in distribution network substations. However, the traditional photovoltaic, storage and charging operation mode in distribution network substations is difficult to cope with the dynamic changes of diversified power sources and loads. Load fluctuations in the distribution network may lead to problems such as voltage limit exceeding and power imbalance. In order to solve these problems, some have proposed that the photovoltaic, storage and charging in the distribution network substation adopt a compensation mode (self-generation and self-use), and increase the adjustable capacity in the substation by increasing the configuration capacity of photovoltaic and energy storage, thereby reducing dependence on the power grid. Other studies have proposed that the photovoltaic, storage and charging in the distribution network substation should adopt a grid-connected mode, that is, when the photovoltaic power generation and energy storage system can be connected to the power grid, through the optimization control of the dispatching system, it can choose to sell electricity to the power grid when the photovoltaic power generation is sufficient, or to send electricity to the power grid when the energy storage is sufficient, so as to increase revenue.

[0003] Although these control modes have improved the economic benefits of the substation to a certain extent, they have not fully utilized their advantages in the regulation of the distribution network. The distribution network substation photovoltaic storage and charging that adopts compensation and aggregation modes can not only improve its own economic benefits, but also use its V2G (Vehicle-to-Grid) technology to significantly increase the adjustable capacity of the substation. Through unified scheduling through the aggregation mode, the distribution network substation photovoltaic storage and charging can provide services to the power grid and participate in market transactions, realize joint regulation with the distribution network, and further improve economic benefits.

[0004] However, the current configuration of photovoltaic storage and charging in distribution network areas still has the following shortcomings:

[0005] 1) It mainly focuses on self-use within the substation area, lacks flexibility, leads to redundant configuration of photovoltaic and energy storage capacity, and is difficult to achieve optimal scheduling under variable load and power generation environments;

[0006] 2) The regulation capacity of the substation was not fully considered. When participating in the regulation of the distribution network, the substation reduced the charging power of the charging piles and did not use V2G technology to improve the regulation capacity of the substation;

[0007] 3) The impact of the photovoltaic storage and charging system at different locations in the distribution network was ignored, and the coordinated optimization of the location and capacity of the photovoltaic storage and charging in the distribution network substation area was not achieved. Summary of the invention

[0008] Based on the problems raised by the above background technology, the purpose of the present invention is to provide a method and equipment for configuring the photovoltaic storage and charging capacity of distribution network substations under compensation and aggregation modes. Through an improved particle swarm algorithm, while ensuring that the distribution network operates with optimal indicators and lowest cost under compensation and aggregation modes, the optimal photovoltaic and energy storage capacity configuration of the photovoltaic storage and charging in the distribution network substation is obtained, which solves the problem that the current photovoltaic storage and charging configuration of the distribution network substation is difficult to achieve optimal scheduling under variable load and power generation environments.

[0009] The present invention is achieved through the following technical solutions:

[0010] The first aspect of the present invention provides a method for configuring the photovoltaic storage and charging capacity of a distribution network area under compensation and aggregation modes, comprising the following steps:

[0011] Step S1, obtaining historical record data of electric vehicle charging stations in different regions, analyzing the historical record data, and obtaining the characteristics of changes in the number of electric vehicles;

[0012] Step S2, using the Monte Carlo method to simulate the change characteristics of the number of electric vehicles to obtain the change of the number of electric vehicles in the photovoltaic storage and charging area of ​​the distribution network;

[0013] Step S3, analyzing the change in the number of electric vehicles in the photovoltaic storage and charging system in the distribution network area, and obtaining the initial capacity configuration of the photovoltaic storage and charging system in the distribution network area in the compensation mode and the aggregation mode;

[0014] Step S4: establish an objective function, and optimize and analyze the initial capacity configuration using an improved particle swarm algorithm based on the objective function to obtain the photovoltaic and energy storage capacity configuration of the distribution network area.

[0015] In the above technical scheme, firstly, based on the historical record data of electric vehicle charging stations in different regions, the number change law of electric vehicles in different seasons and at various costs is analyzed. By classifying and processing the historical record data, the characteristics of the change in the number of electric vehicles, including the average load and standard deviation, are extracted according to the season, electricity cost and day type (working day or rest day), and it is assumed that the load change obeys the normal distribution. Then, the average value and standard deviation of the historical record data are used to determine the normal distribution parameters of the load under each condition, and a large number of random samples are generated to simulate the charging number of electric vehicles in the distribution network area. Finally, the Monte Carlo method is used to generate 500 samples, and a stable prediction result of the change in the number of electric vehicles in different regions under seasons, day types and electricity costs is obtained.

[0016] The operation status of the photovoltaic, storage and charging in the distribution network substation is analyzed according to the prediction results, and the initial capacity configuration of the photovoltaic, storage and charging in the distribution network substation in the compensation mode and the aggregation mode is obtained. Using an improved particle swarm algorithm, the photovoltaic and energy storage capacity configuration of the photovoltaic, storage and charging in the distribution network substation is optimized and determined under the premise of ensuring the distribution network has the best benefits and the lowest investment cost in the compensation and aggregation modes. The present invention solves the problem of photovoltaic and energy storage capacity configuration of photovoltaic, storage and charging in the distribution network substation, with the purpose of reducing the investment cost of photovoltaic and energy storage and increasing the operating income of photovoltaic and energy storage, thereby improving the overall operating efficiency of the substation and the comprehensive efficiency of photovoltaic power generation and energy storage.

[0017] In an optional embodiment, establishing an objective function includes:

[0018] Obtain cost and revenue data of PV storage and charging under various conditions in distribution network areas;

[0019] Calculate the cost data and the revenue data to obtain the comprehensive cost of photovoltaic energy storage and the photovoltaic storage and charging revenue of the distribution network area;

[0020] The comprehensive cost of photovoltaic energy storage and the photovoltaic storage and charging benefits of the distribution network area are calculated using a linear weight method to obtain an optimization target;

[0021] The optimization objective is constrained by energy storage charging and discharging power constraints, energy storage operating state constraints, voltage constraints and photovoltaic constraints, electric vehicle charging V2G constraints, distribution network power balance constraints, and distribution network flow constraints to obtain an objective function.

[0022] In an optional embodiment, the expression of the energy storage charging and discharging power constraint is as follows:

[0023]

[0024] In the above formula, B is the rated power of the energy storage for the p-th distribution network area; ch,p and B dis,p It is the charging and discharging state of the photovoltaic energy storage in the p-th distribution network area.

[0025] In an optional embodiment, the expressions of voltage constraint and photovoltaic constraint are as follows:

[0026]

[0027] In the above formula: [V min ,V max ] are the upper and lower limits of node voltage; Q is the active power generated by the photovoltaic storage and charging system in the p-th distribution network under the maximum power point tracking (MPPT);pv,p and S pv,p The reactive power and capacity output by the photovoltaic inverter of the p-th distribution network area at a certain moment; is the power factor angle of the photovoltaic inverter; It is the minimum power factor of the photovoltaic inverter.

[0028] In an optional embodiment, the expression of the electric vehicle charging V2G constraint is as follows:

[0029]

[0030] In the above formula, P ev,j,p (t) and are the power, discharge power and charging power of the jth electric vehicle charging pile of the pth distribution network area at time t; N ev is the total number of electric vehicle charging piles; t0 and t d The start and end times of charging the electric vehicle; and is the capacity at the start time of charging the electric vehicle, the capacity at the end time and the rated capacity of the electric vehicle; η is the charging efficiency of the electric vehicle; SOC ev,j,p (t) is the state of charge of the jth electric vehicle charged by photovoltaic storage in the pth distribution network area at time t; SOC ev,j,p,min and SOC ev,j,p,max The minimum and maximum state of charge of the j-th electric vehicle charged by solar energy storage in the p-th distribution network area.

[0031] In an optional embodiment, the expression of the power balance constraint of the distribution network is as follows:

[0032]

[0033] In the above formula, P s is the total power input to the distribution network; P loadn is the load power of n nodes at a certain moment; P esi is the active power output of the i-th energy storage at a certain moment; P pvj is the active power output of the jth photovoltaic at a certain moment; n bat is the number of distribution network nodes.

[0034] In an optional embodiment, the power flow constraint of the distribution network is:

[0035]

[0036] In the above formula, P SI (t), P pvI (t), P esI (t), PLI (t) are the active power flowing into node I at time t, the active output of photovoltaic power, the active output of full energy storage, and the active power consumed by node I; Q SI (t), Q pvI (t), Q LI (t) are the reactive power flowing into node I, the PV reactive output, and the reactive power consumed by node I at time t; V I (t), V J (t) are the voltages of nodes I and J at time t; matrix R IJ and X IJ are the resistance and reactance between node I and node J respectively; δ IJ (t) is the phase difference between nodes I and J at time t.

[0037] In an optional embodiment, the initial capacity configuration is optimized and analyzed using an improved particle swarm algorithm based on the objective function, including the following steps:

[0038] Step S41, setting the particle swarm algorithm parameters, making the initial capacity configuration correspond to the particles in the particle swarm algorithm parameters one by one, and generating an initial population;

[0039] Step S42: using a dynamic power flow algorithm and a linear quadratic programming algorithm to calculate the initial population to obtain the power required by the distribution network;

[0040] Step S43: Based on the objective function, the power required by the distribution network is analyzed by using compensation and aggregation modes, and the power is adjusted according to the demand analysis result to obtain an optimized capacity configuration;

[0041] Step S44, calculating the fitness value of the particle corresponding to the optimized capacity configuration, performing optimization based on the fitness value, and updating the initial population according to the optimization result to obtain an updated population;

[0042] Step S45: Repeat steps S42 to S45 using the updated population as the initial population until the termination condition is reached.

[0043] A second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for configuring the photovoltaic storage and charging capacity of a distribution network substation under compensation and aggregation modes is implemented.

[0044] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for configuring the photovoltaic storage and charging capacity of a distribution network substation under compensation and aggregation modes.

[0045] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0046] 1. Under the control strategy of compensation and aggregation mode, the photovoltaic storage charging in the distribution network substation can reduce the charging power of electric vehicles and adopt V2G technology when necessary to enable electric vehicles to participate in the peak load regulation and voltage regulation of the distribution network, thereby increasing the adjustable capacity of the substation and meeting the regulation needs of the distribution network;

[0047] 2. Unified scheduling of photovoltaic storage and charging in each distribution network area can reduce the redundancy of photovoltaic and energy storage capacity configuration in the area and improve the comprehensive utilization rate of photovoltaic storage and charging equipment in the distribution network area;

[0048] 3. This configuration method can achieve the optimal configuration of photovoltaic storage and charging in the distribution network area, and realize the maximum benefit and lowest investment cost of photovoltaic storage and charging in the distribution network area. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0050] Figure 1 A flow chart for solving the photovoltaic and energy storage configuration capacity provided in Example 1 of the present invention;

[0051] Figure 2 The IEEE34 node test system provided in Example 1 of the present invention;

[0052] Figure 3 The changes in active load and reactive load of each node in the distribution network provided in Embodiment 1 of the present invention;

[0053] Figure 4 A photovoltaic power generation variation trend diagram provided in Example 1 of the present invention;

[0054] Figure 5 The changes of photovoltaic storage and charging electric vehicles in each distribution network area provided in Example 1 of the present invention;

[0055] Figure 6 A comparison diagram of voltage changes at each node of the distribution network under the two strategies provided in Example 1 of the present invention;

[0056] Figure 7 The photovoltaic power generation situation of the photovoltaic storage and charging in each distribution network area provided in Example 1 of the present invention;

[0057] Figure 8A comparison diagram of the photovoltaic storage and charging energy storage power in the distribution network area under the two strategies provided in Example 1 of the present invention;

[0058] Fig. 9 A comparison chart of the photovoltaic storage and charging energy storage capacity of the distribution network area under the two strategies provided in Example 1 of the present invention;

[0059] Fig.10 A comparison diagram of the total power of photovoltaic storage charging piles in the distribution network area under the two strategies provided in Example 1 of the present invention;

[0060] Fig.11 A comparison chart of the total capacity of photovoltaic storage and charging electric vehicles in the distribution network area under the two strategies provided in Example 1 of the present invention;

[0061] Fig.12 A schematic diagram of the structure of an electronic device provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0062] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0063] Example 1

[0064] Embodiment 1 of the present invention provides a method for configuring the photovoltaic storage and charging capacity of a distribution network area under compensation and aggregation modes, the method comprising the following steps:

[0065] Step S1, obtaining historical record data of electric vehicle charging stations in different regions, analyzing the historical record data, and obtaining the characteristics of changes in the number of electric vehicles;

[0066] Step S2, using the Monte Carlo method to simulate the change characteristics of the number of electric vehicles to obtain the change of the number of electric vehicles in the photovoltaic storage and charging area of ​​the distribution network;

[0067] Step S3, analyzing the change in the number of electric vehicles in the photovoltaic storage and charging system in the distribution network area, and obtaining the initial capacity configuration of the photovoltaic storage and charging system in the distribution network area in the compensation mode and the aggregation mode;

[0068] Step S4: establish an objective function, and optimize and analyze the initial capacity configuration using an improved particle swarm algorithm based on the objective function to obtain the photovoltaic and energy storage capacity configuration of the distribution network area.

[0069] It should be noted that, firstly, based on the historical record data of electric vehicle charging stations in different regions, the number change law of electric vehicles in different seasons and at various costs is analyzed. By classifying and processing the historical record data, the characteristics of the change in the number of electric vehicles, including the average load and standard deviation, are extracted according to the season, electricity cost and day type (working day or rest day), and it is assumed that the load change obeys the normal distribution. Then, the average value and standard deviation of the historical record data are used to determine the normal distribution parameters of the load under each condition, and a large number of random samples are generated to simulate the charging number of electric vehicles in the photovoltaic storage and charging of the distribution network station area. Finally, the Monte Carlo method is used to generate 500 samples, and the stable prediction results of the change in the number of electric vehicles in different regions under seasons, day types and electricity costs are obtained.

[0070] The operation status of photovoltaic storage and charging in the distribution network substation is analyzed according to the prediction results, and the initial capacity configuration of photovoltaic storage and charging in the distribution network substation in the compensation mode and aggregation mode is obtained. The improved particle swarm algorithm is used to optimize the photovoltaic and energy storage capacity configuration of photovoltaic storage and charging in the distribution network substation, while ensuring the distribution network in the compensation and aggregation modes with the best benefits and the lowest investment cost. The present invention solves the problem of photovoltaic and energy storage capacity configuration of photovoltaic storage and charging in the distribution network substation, with the purpose of reducing the investment cost of photovoltaic and energy storage and increasing the operating income of photovoltaic and energy storage, thereby improving the overall operating efficiency of the substation and the comprehensive efficiency of photovoltaic power generation and energy storage.

[0071] Furthermore, in order to predict the changing trend of the number of electric vehicles in different regions under different seasons and electricity cost conditions, the historical charging load data of electric vehicles in different regions are first collected, and the data are classified according to the season, electricity cost level and day type (working day and rest day).

[0072] Furthermore, the Monte Carlo method is used to simulate the characteristics of the change in the number of electric vehicles to obtain the change in the number of electric vehicles in the photovoltaic storage and charging area of ​​the distribution network, including:

[0073] Through statistical analysis, the characteristics of electric vehicle load in each region under different seasons, electricity costs and day types are extracted, including average load, standard deviation, etc. It is assumed that the load changes of electric vehicles follow a normal distribution. The average value and standard deviation obtained by historical data statistics are used to determine the normal distribution parameters (mean μ and standard deviation σ) of the load under each condition. A large number of random samples are generated using the parameters (μ, σ) of the normal distribution to simulate the charging load of electric vehicles under this condition. Through the Monte Carlo method, 500 load samples are generated to obtain stable results. These samples can reflect the possible range and distribution of electric vehicle load under specific conditions. The generated load samples are summarized and statistically analyzed to calculate their expected value, variance, confidence interval, etc. The distribution characteristics of the number of electric vehicles in each region under different time periods and different conditions are analyzed. In this way, the range of changes in the number of electric vehicles in the distribution network substations of different regions under a certain season and electricity cost level is estimated.

[0074] Furthermore, according to the changes in the number of electric vehicles in the distribution network area, the operation of the distribution network area in the compensation mode and the aggregation mode is analyzed, including:

[0075] In the operation stage, the prediction results obtained by the Monte Carlo algorithm are used to analyze the operation of photovoltaic storage and charging in distribution network substations in different regions: when the load of the distribution network is small, there is no voltage over-limit problem, and the photovoltaic storage and charging in the distribution network substation adopts the compensation mode. In this mode, photovoltaic power generation is preferentially supplied to charging piles, and the remaining electric energy is stored in the energy storage device; the energy storage device is charged during the valley period when the electricity cost is lower, and discharged during the peak period when the electricity cost is higher, so as to maximize the benefits. When the load of the distribution network is large, the voltage over-limit problem may occur, and the photovoltaic storage and charging in the distribution network substation needs to switch to the compensation and aggregation mode. At this time, the energy storage equipment and photovoltaic power generation not only supply electric vehicles that are in urgent need of charging, but also the excess electric energy can be connected to the grid. Through unified scheduling, the photovoltaic storage and charging in each distribution network substation uses the linear quadratic programming algorithm to solve the problem of voltage over-limit according to the capacity demand of the distribution network, and determines the optimal power configuration of the electric vehicle charging piles in each substation to participate in V2G discharge, so as to solve the problem of voltage over-limit.

[0076] The operation control strategies of photovoltaic storage and charging in distribution network substations include compensation mode and aggregation mode. The goal of compensation mode is to maximize the benefits of photovoltaic storage and charging in distribution network substations when the voltage of the distribution network does not exceed the limit; while the goal of aggregation mode is to meet the regulation needs of the distribution network by coordinating the power of photovoltaic storage and charging in each distribution network substation when the voltage of the distribution network exceeds the limit, solve the problem of voltage exceeding the limit, and take into account the benefits of photovoltaic storage and charging in the distribution network substation.

[0077] In the compensation mode, the distribution network substation uses photovoltaic power generation and energy storage systems to reduce the cost of purchasing electricity from the power grid and achieve a local balance of power supply and demand. In this mode, photovoltaic power generation gives priority to meeting the electricity demand in the substation, especially the charging needs of electric vehicles, reducing dependence on grid power. During the peak period of photovoltaic power generation in the daytime, most charging needs can be met; the energy storage system charges during periods of lower electricity costs and discharges during periods of higher electricity costs, thereby reducing the overall storage cost. In addition, the energy storage system reserves capacity for storing excess photovoltaic power generation for use when photovoltaic power generation is insufficient, thereby ensuring the stability of power supply in the substation.

[0078] The aggregation model focuses more on the participation of the distribution network substation photovoltaic storage and charging as a whole in the electricity market transaction, and participates in the peak-shaving and voltage-regulating services of the distribution network by integrating electric vehicle loads, energy storage resources and photovoltaic power generation, and obtains additional benefits. In this mode, the distribution network substation photovoltaic storage and charging not only meets its own electricity demand, but also participates in grid dispatching through energy storage systems and load regulation. The energy storage system in the substation can participate in the auxiliary services of the power grid such as frequency regulation and peak regulation, provide flexible load response capabilities, and help the power grid cope with power fluctuations, thereby obtaining compensation benefits from regulation services. When the power grid needs to shave peaks and fill valleys, the substation can adjust the charging load according to the power cost signal: reduce charging demand during peak power consumption periods and increase charging demand during low power consumption periods, thereby reducing the electricity cost of the entire system.

[0079] In an optional embodiment, establishing an objective function includes:

[0080] Obtain cost and revenue data of PV storage and charging under various conditions in distribution network areas;

[0081] Calculate the cost data and the revenue data to obtain the comprehensive cost of photovoltaic energy storage and the photovoltaic storage and charging revenue of the distribution network area;

[0082] The comprehensive cost of photovoltaic energy storage and the photovoltaic storage and charging benefits of the distribution network area are calculated using a linear weight method to obtain an optimization target;

[0083] The optimization objective is constrained by energy storage charging and discharging power constraints, energy storage operating state constraints, voltage constraints and photovoltaic constraints, electric vehicle charging V2G constraints, distribution network power balance constraints, and distribution network flow constraints to obtain an objective function.

[0084] Specifically, the expression of the optimization objective is as follows:

[0085] F1=min(αf1-βf2)

[0086] In the above formula, f1 is the comprehensive cost of photovoltaic energy storage; f2 is the photovoltaic storage and charging income in the distribution network area; α is the comprehensive cost weight coefficient of photovoltaic and energy storage; β is the photovoltaic storage and charging income weight coefficient of the distribution network area.

[0087] The linear weight method is used to unify the comprehensive cost of photovoltaic energy storage and the photovoltaic storage and charging benefits of the distribution network into the order of magnitude of f1, and satisfy α+β=1.

[0088] Furthermore, the cost data and the revenue data are calculated to obtain the comprehensive cost of photovoltaic energy storage and the photovoltaic storage and charging revenue of the distribution network area as follows:

[0089]

[0090] In the above formula, and The comprehensive investment cost and daily operation and maintenance cost of the solar-storage-charging energy storage in the p-th distribution network area; is the comprehensive photovoltaic investment cost of photovoltaic storage and charging in the p-th distribution network area; and The revenue of energy storage, photovoltaic and charging piles in the p-th distribution network area under the compensation mode; and is the revenue of energy storage, photovoltaic and charging piles in the p-th distribution network area under the compensation aggregation mode; n pee It is the total number of photovoltaic storage and charging in the distribution network area.

[0091] Furthermore, the calculation process of the comprehensive investment cost of energy storage, daily operation and maintenance cost of photovoltaic storage and charging in the p-th distribution network area and the comprehensive investment cost of photovoltaic storage and charging in the p-th distribution network area is as follows:

[0092]

[0093] In the above formula, c e and c p is the unit capacity cost and unit power cost of energy storage; E cap,p is the total energy storage capacity of the photovoltaic storage and charging in the p-th distribution network area; P cap,p is the total rated power of the photovoltaic storage and charging in the p-th distribution network area; c op is the annual operation and maintenance cost of energy storage; i is the number of years of energy storage operation; i r is the cost change rate; d r is the discount rate; c pv is the unit investment cost of photovoltaics; B cap,p is the total photovoltaic capacity of the p-th distribution network area.

[0094] Furthermore, the energy storage of the photovoltaic storage and charging system in the p-th distribution network area, the income of photovoltaic and charging piles in the compensation mode, and the income of the energy storage of the photovoltaic storage and charging system in the p-th distribution network area, photovoltaic and charging piles in the compensation aggregation mode are calculated as follows:

[0095]

[0096] In the above formula, λ(t) is the electricity cost at time t; λ ser (t) is the service cost of photovoltaic storage charging collected by the distribution network station at time t; cp (t) is the subsidy cost of the PV-storage-charging compensation aggregation mode in the distribution network area at time t; P ch,p and P dis,p is the charging power and discharging power of the energy storage of the p-th distribution network area; n t is the total time of the scheduling cycle; P pv,p is the photovoltaic power generation power of the p-th distribution network area; P ecv,p is the charging pile power of the photovoltaic storage charging in the pth distribution network area.

[0097] In an optional embodiment, the expression of the energy storage charging and discharging power constraint is as follows:

[0098]

[0099] In the above formula, B is the rated power of the energy storage for photovoltaic charging in the pth distribution network area; ch,p and B dis,p The charging and discharging states of the photovoltaic energy storage in the p-th distribution network area.

[0100] In an optional embodiment, the expression of the energy storage operation state constraint is as follows:

[0101]

[0102] In the above formula, E es,p (t) is the state of charge of the photovoltaic energy storage in the p-th distribution network area at time t; η ch,p and η dis,p is the charging and discharging efficiency of the solar energy storage in the p-th distribution network area.

[0103] In an optional embodiment, the expressions of voltage constraint and photovoltaic constraint are as follows:

[0104]

[0105] In the above formula: [V min ,V max ] are the upper and lower limits of node voltage; Q is the active power generated by the photovoltaic storage and charging system in the p-th distribution network under the maximum power point tracking (MPPT); pv,p and S pv,p The reactive power and capacity output by the photovoltaic inverter of the p-th distribution network area at a certain moment; is the power factor angle of the photovoltaic inverter; It is the minimum power factor of the photovoltaic inverter.

[0106] In an optional embodiment, the expression of the electric vehicle charging V2G constraint is as follows:

[0107]

[0108] In the above formula, P ev,j,p (t) and are the power, discharge power and charging power of the jth electric vehicle charging pile of the pth distribution network area at time t; N ev is the total number of electric vehicle charging piles; t0 and t d The start and end times of charging the electric vehicle; and is the capacity at the start time of charging the electric vehicle, the capacity at the end time and the rated capacity of the electric vehicle; η is the charging efficiency of the electric vehicle; SOC ev,j,p (t) is the state of charge of the jth electric vehicle charged by photovoltaic storage in the pth distribution network area at time t; SOC ev,j,p,min and SOC ev,j,p,max The minimum and maximum state of charge of the j-th electric vehicle charged by solar energy storage in the p-th distribution network area.

[0109] In an optional embodiment, the expression of the power balance constraint of the distribution network is as follows:

[0110]

[0111] In the above formula, P s is the total power input to the distribution network; P loadn is the load power of n nodes at a certain moment; P esi is the active power output of the i-th energy storage at a certain moment; P pvj is the active power output of the jth photovoltaic at a certain moment; n bat is the number of distribution network nodes.

[0112] In an optional embodiment, the power flow constraint of the distribution network is:

[0113]

[0114] In the above formula, P SI (t), P pvI (t), P esI (t), P LI (t) are the active power flowing into node I at time t, the active output of photovoltaic power, the active output of full energy storage, and the active power consumed by node I; QSI (t), Q pvI (t), Q LI (t) are the reactive power flowing into node I, the PV reactive output, and the reactive power consumed by node I at time t; V I (t), V J (t) are the voltages of nodes I and J at time t; matrix R IJ and X IJ are the resistance and reactance between node I and node J respectively; δ IJ (t) is the phase difference between nodes I and J at time t.

[0115] In an optional embodiment, the initial capacity configuration is optimized and analyzed using an improved particle swarm algorithm based on the objective function, including the following steps:

[0116] Step S41, setting the particle swarm algorithm parameters, making the initial capacity configuration correspond to the particles in the particle swarm algorithm parameters one by one, and generating an initial population;

[0117] Step S42: using a dynamic power flow algorithm and a linear quadratic programming algorithm to calculate the initial population to obtain the power required by the distribution network;

[0118] Step S43: Based on the objective function, the power required by the distribution network is analyzed by using compensation and aggregation modes, and the power is adjusted according to the demand analysis result to obtain an optimized capacity configuration;

[0119] Step S44, calculating the fitness value of the particle corresponding to the optimized capacity configuration, performing optimization based on the fitness value, and updating the initial population according to the optimization result to obtain an updated population;

[0120] Step S45: Repeat steps S42 to S45 using the updated population as the initial population until the termination condition is reached.

[0121] like Figure 1 As shown, the optimization analysis of the initial capacity configuration using the improved particle swarm algorithm includes:

[0122] Initialization, including the initialization of the particle swarm algorithm parameters and the initialization of the parameters related to photovoltaic storage and charging in the distribution network substation, and loading the load data in step S2 and the predicted data on the change in the number of photovoltaic storage and charging electric vehicles in each distribution network substation, generating an initial population P containing 100 particles, each particle represents a configuration capacity of photovoltaic and energy storage, and finally obtaining the initial population used for optimization.

[0123] Then, the photovoltaic and energy storage configuration capacities in the initial population are input into the operation layer, and the power required by the distribution network is calculated through dynamic power flow calculation and linear quadratic programming algorithm.

[0124] Then, based on the constructed objective function including the comprehensive cost of photovoltaic and energy storage and the income of photovoltaic storage and charging in the distribution network substation, the relationship between power and cost under the compensation and aggregation mode is calculated. Specifically, the compensation and aggregation mode is used to adjust the power of photovoltaic storage and charging in each distribution network substation so that its total power meets the required capacity of the distribution network. The configuration capacity and output power of photovoltaic and energy storage at this time are recorded, and the investment cost and the income of the substation are calculated. At this time, an optimized capacity configuration is obtained after the configuration capacity is updated.

[0125] At this time, it is necessary to calculate the fitness value of the particles corresponding to the optimized capacity configuration, and to search for the best solution based on the fitness value. The search includes the global optimal solution and the individual optimal solution. After the search, the particle swarm is updated to obtain a new population, and the inertia weight and speed parameters of the particle swarm are adjusted. At the same time, the number, location and rated capacity of the energy storage equipment are updated.

[0126] The updated particle swarm is used as the initial population of step S42, and then steps S42 to S45 are repeated until the preset termination condition is reached, and finally the optimal configuration capacity of photovoltaic and energy storage is obtained, and the photovoltaic and energy storage capacities corresponding to the optimal configuration are output, as well as the investment costs of photovoltaic and energy storage and the income of photovoltaic storage and charging in each distribution network area.

[0127] In order to verify the effectiveness of the photovoltaic storage and charging configuration technology in the distribution network area under compensation and aggregation modes, the IEEE34 node system is used as the test system for simulation verification. Figure 2 When nodes 4, 11, 20 and 28 are set as the photovoltaic storage and charging locations in the distribution network area, the power factor of each photovoltaic power generation device is 0.95. The fluctuation of active load and reactive load at each node of the distribution network is as follows Figure 3 As shown in Figure 2, the changes in photovoltaic power generation are as follows: Figure 4 Assume that charging area 1 is located in a residential area with 30 charging piles, charging area 2 is located in a commercial area with 40 charging piles, charging area 3 is located in a transportation hub with 50 charging piles, and charging area 4 is located in a highway service area with 90 charging piles. The predicted results of the change in the number of electric vehicles in each charging area are as follows: Figure 5 As shown in Figure 2, it is assumed that each electric vehicle has V2G function and the capacity of the electric vehicle is It is 100kWh.

[0128] The electricity fee collection method of the distribution network adopts the time-of-use electricity cost mechanism, which is specifically divided into three periods: off-peak period, flat period and peak period. The electricity cost of each period is different to encourage more electricity use in off-peak period and reduce electricity use in peak period. The specific electricity cost of time-of-use electricity cost is shown in Table 1. The service cost charged by the photovoltaic storage charging of the distribution network substation to electric vehicle users is in the form of time-of-use billing. During the peak power consumption period of the distribution network, in order to encourage the photovoltaic storage charging of the distribution network substation to participate in the regulation, a compensation electricity fee mechanism is adopted, and the daily scheduling time of the operation scenario is 24h. The relevant parameters of the photovoltaic storage charging of the distribution network substation are shown in Table 2.

[0129] Table 1 Related electricity cost parameters

[0130]

[0131] Table 2 Parameters related to photovoltaic storage and charging in distribution network area

[0132]

[0133] Simulation case: By comparing and analyzing the simulation configuration results of the distribution network area photovoltaic storage charging using only the compensation mode and the compensation aggregation mode, the advantages of the present invention are analyzed. The optimal configuration results of photovoltaic storage charging in each distribution network area are obtained by using the improved particle swarm algorithm as shown in Table 3.

[0134] Table 3 Distribution network area photovoltaic storage and charging configuration results

[0135]

[0136] It can be analyzed from Table 3 that the overall income of the distribution network area using compensation and aggregation mode is 1432.49 million yuan higher than that of the distribution network area using compensation mode. It can be concluded that on the basis of the compensation mode, unified scheduling of PV storage and charging in each distribution network area can significantly increase the income of each area and reduce the redundancy of photovoltaic and energy storage capacity configuration.

[0137] from Figure 6 It can be analyzed from the voltage changes of each node in the distribution network with the two control methods that the distribution network in the left figure did not adopt the compensation and aggregation joint control strategy, but only adopted the compensation mode control strategy, and the voltage over-limit problem occurred during the period from 12:00 to 16:00. The distribution network in the right figure that adopted the compensation and aggregation mode solved the voltage over-limit problem of the distribution network through the distribution network station area photovoltaic storage and charging compensation and aggregation control strategy. Figure 7 The photovoltaic power generation situation of solar storage and charging in different distribution network areas.

[0138] pass Figure 8 and Fig. 9It can be analyzed that under the compensation mode, the energy storage charging and discharging power of photovoltaic storage in different distribution network substations is affected by the time-of-use electricity cost and photovoltaic power generation, and does not participate in the allocation when the distribution network power generation voltage exceeds the limit. However, the energy storage of photovoltaic storage in the distribution network substation under compensation and aggregation, on the one hand, adjusts the charging and discharging power according to the time-of-use electricity cost, and on the other hand, participates in the regulation when the distribution network voltage exceeds the limit.

[0139] from Fig.10 and Fig.11 It can be analyzed that in the compensation mode, the photovoltaic storage and charging in the distribution network substation only reduces the overall power of the electric vehicle charging piles without fully utilizing the V2G function of electric vehicle charging. In the compensation and aggregation mode, by coordinating the V2G of the electric vehicle charging piles with photovoltaic storage and charging in different distribution network substations, electric vehicles with a capacity of more than 50% can participate in V2G regulation, thereby increasing the adjustable capacity of the photovoltaic storage and charging substation, and meeting the adjustable capacity requirements of the distribution network when the voltage exceeds the limit problem in the distribution network.

[0140] Example 2

[0141] Fig.12 A schematic diagram of the structure of an electronic device provided in Embodiment 2 of the present invention, such as Fig.12 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the computer device can be one or more. Fig.12 A processor 21 is taken as an example; the processor 21, the memory 22, the input device 23 and the output device 24 in the electronic device can be connected by a bus or other means. Fig.12 The example of connecting through bus is taken in the following.

[0142] The memory 22 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules. The processor 21 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 22, that is, the method for configuring the photovoltaic storage and charging capacity of the distribution network area under the compensation and aggregation mode of Example 1 is implemented.

[0143] The memory 22 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 22 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 22 may further include a memory remotely arranged relative to the processor 21, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0144] The input device 23 can be used to receive the ID and password input by the user, etc. The output device 24 is used to output the network configuration page.

[0145] Example 3

[0146] Embodiment 3 of the present invention further provides a computer-readable storage medium, wherein the computer-executable instructions, when executed by a computer processor, are used to implement the method for configuring the photovoltaic storage capacity of a distribution network area under the compensation and aggregation modes as provided in Embodiment 1.

[0147] An embodiment of the present invention provides a storage medium containing computer executable instructions, and its computer executable instructions are not limited to the method operations provided in Example 1, and can also execute related operations in the distribution network substation photovoltaic storage and charging capacity configuration method under the compensation and aggregation mode provided in any embodiment of the present invention.

[0148] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for configuring the photovoltaic storage and charging capacity in a distribution network area under compensation and aggregation modes, characterized in that: The steps include: Step S1, obtaining historical record data of electric vehicle charging stations in different regions, analyzing the historical record data, and obtaining the characteristics of changes in the number of electric vehicles; Step S2, using the Monte Carlo method to simulate the change characteristics of the number of electric vehicles to obtain the change of the number of electric vehicles in the photovoltaic storage and charging area of ​​the distribution network; Step S3, analyzing the change in the number of electric vehicles in the photovoltaic storage and charging system in the distribution network area, and obtaining the initial capacity configuration of the photovoltaic storage and charging system in the distribution network area in the compensation mode and the aggregation mode; Step S4, establishing an objective function, and optimizing and analyzing the initial capacity configuration using an improved particle swarm algorithm based on the objective function to obtain the photovoltaic and energy storage capacity configuration of the distribution network area.

2. The method for configuring the photovoltaic storage and charging capacity of the distribution network area under the compensation and aggregation mode according to claim 1 is characterized in that: Establish the objective function, including: Obtain cost and revenue data of PV storage and charging under various conditions in distribution network areas; Calculate the cost data and the revenue data to obtain the comprehensive cost of photovoltaic energy storage and the photovoltaic storage and charging revenue of the distribution network area; The comprehensive cost of photovoltaic energy storage and the photovoltaic storage and charging benefits of the distribution network area are calculated using a linear weight method to obtain an optimization target; The optimization objective is constrained by energy storage charging and discharging power constraints, energy storage operating state constraints, voltage constraints and photovoltaic constraints, electric vehicle charging V2G constraints, distribution network power balance constraints, and distribution network flow constraints to obtain an objective function.

3. The method for configuring the photovoltaic storage and charging capacity of the distribution network area under the compensation and aggregation mode according to claim 2 is characterized in that: The expression of the energy storage charging and discharging power constraint is as follows: In the above formula, B is the rated power of the energy storage for the p-th distribution network area; ch,p and B dis,p It is the charging and discharging state of the photovoltaic energy storage in the p-th distribution network area.

4. The method for configuring the photovoltaic storage and charging capacity of the distribution network area under the compensation and aggregation mode according to claim 2 is characterized in that: The expressions for voltage constraint and photovoltaic constraint are as follows: In the above formula: [V min ,V max ] are the upper and lower limits of node voltage; Q is the active power generated by the photovoltaic storage and charging system in the p-th distribution network under the maximum power point tracking (MPPT); pv,p and S pv,p The reactive power and capacity output by the photovoltaic inverter of the p-th distribution network area at a certain moment; is the power factor angle of the photovoltaic inverter; It is the minimum power factor of the photovoltaic inverter.

5. The method for configuring the photovoltaic storage and charging capacity of the distribution network area under the compensation and aggregation mode according to claim 2 is characterized in that: The expression of the V2G constraint for electric vehicle charging is as follows: In the above formula, P ev,j,p (t) and are the power, discharge power and charging power of the jth electric vehicle charging pile of the pth distribution network area at time t; N ev is the total number of electric vehicle charging piles; t0 and t d The start and end times of charging the electric vehicle; and is the capacity at the start time of charging the electric vehicle, the capacity at the end time and the rated capacity of the electric vehicle; η is the charging efficiency of the electric vehicle; SOC ev,j,p (t) is the state of charge of the jth electric vehicle charged by photovoltaic storage in the pth distribution network area at time t; SOC ev,j,p,min and SOC ev,j,p,max The minimum and maximum state of charge of the j-th electric vehicle charged by solar energy storage in the p-th distribution network area.

6. The method for configuring the photovoltaic storage and charging capacity of the distribution network area under the compensation and aggregation mode according to claim 2 is characterized in that: The expression of the power balance constraint of the distribution network is as follows: In the above formula, P s is the total power input to the distribution network; P loadn is the load power of n nodes at a certain moment; P esi is the active power output of the i-th energy storage at a certain moment; P pvj is the active power output of the jth photovoltaic at a certain moment; n bat is the number of distribution network nodes.

7. The method for configuring the photovoltaic storage and charging capacity of the distribution network area under the compensation and aggregation mode according to claim 2 is characterized in that: The distribution network flow constraints are: In the above formula, P SI (t), P pvI (t), P esI (t), P LI (t) are the active power flowing into node I at time t, the active output of photovoltaic power, the active output of energy storage, and the active power consumed by node I; Q SI (t), Q pvI (t), Q LI (t) are the reactive power flowing into node I, the PV reactive output, and the reactive power consumed by node I at time t; V I (t), V J (t) are the voltages of nodes I and J at time t; matrix R IJ and X IJ are the resistance and reactance between node I and node J respectively; δ IJ (t) is the phase angle difference between nodes I and J at time t.

8. The method for configuring the photovoltaic storage and charging capacity of distribution network substations under compensation and aggregation mode according to claim 1 is characterized in that: Based on the objective function, an improved particle swarm algorithm is used to optimize and analyze the initial capacity configuration, including the following steps: Step S41, setting the particle swarm algorithm parameters, making the initial capacity configuration correspond to the particles in the particle swarm algorithm parameters one by one, and generating an initial population; Step S42: using a dynamic power flow algorithm and a linear quadratic programming algorithm to calculate the initial population to obtain the power required by the distribution network; Step S43: Based on the objective function, the power required by the distribution network is analyzed by using compensation and aggregation modes, and the power is adjusted according to the demand analysis result to obtain an optimized capacity configuration; Step S44, calculating the fitness value of the particle corresponding to the optimized capacity configuration, performing optimization based on the fitness value, and updating the initial population according to the optimization result to obtain an updated population; Step S45: Repeat steps S42 to S45 using the updated population as the initial population until the termination condition is reached.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for configuring the photovoltaic storage and charging capacity of the distribution network area under the compensation and aggregation mode as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for configuring the photovoltaic storage and charging capacity of a distribution network area under the compensation and aggregation mode as described in any one of claims 1 to 8 is implemented.

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