A typical charging station comprehensive energy collaborative interactive optimization configuration method and system

Abstract

The present invention provides a typical charging station comprehensive energy coordinated interactive optimization configuration method and system, constructs a multi-objective optimization configuration model of the annual comprehensive investment cost of wind, solar and storage and the proportion of wind and solar energy output, fully utilizes the complementary characteristics of wind, solar and load, and effectively reduces the operating cost of the charging station. The method includes the following processes: based on the electric vehicle and distributed energy and power grid coordinated integration model, the wind, solar and storage power generation information is obtained; the electric vehicle charging load demand is obtained; according to the wind, solar and storage power generation information and the electric vehicle charging load demand, combined with the charging station multi-energy complementary optimization configuration model, the quantity configuration plan of wind, solar and storage is calculated; the charging station multi-energy complementary optimization configuration model includes two objectives and constraints; the objectives include economic objectives and new energy output proportion objectives.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging stations, and specifically relates to a typical charging station comprehensive energy collaborative interactive optimization configuration method, and also relates to a typical charging station comprehensive energy collaborative interactive optimization configuration system. Background Art

[0002] Faced with increasingly severe energy and environmental pressures, replacing fossil energy with new energy has become an inevitable trend for sustainable development. Among them, the clean, safe and inexhaustible advantages of wind and solar resources are particularly valued. Electric vehicles are gradually attracting attention from the automotive industry reform due to their low energy consumption and intelligent features. Their large-scale application can effectively reduce automobile carbon emissions and dependence on oil resources.

[0003] In order to promote the transformation of society to a low-carbon energy economy and achieve sustainable economic and environmental development, the following two ways can be used to adjust the way energy is used: First, change the long-term development pattern of my country's energy from the overall perspective, vigorously develop wind and solar energy, increase the penetration rate of wind and solar energy in the existing power grid, and achieve local energy independence and energy self-sufficiency; second, coordinate the use and development of traditional energy and new energy, establish a connection between traditional electric vehicle charging stations and new energy power generation, and realize the local use of new energy. From the current energy consumption structure in China, traditional energy, especially coal and oil, accounts for an absolute proportion. Among the secondary energy, coal-based thermal power generation is basically the dominant energy. Therefore, it is a long-term and arduous task to achieve low-carbon economic transformation by adjusting the primary energy structure. Therefore, it is necessary to start from the second way, combining new energy with electric vehicle charging systems, and constructing electric vehicle charging stations in the form of distributed power generation by introducing wind and solar new energy. Its daytime power generation can effectively supply electric vehicles in the station for charging, realizing energy consumption on the spot. This indirect way of reducing carbon emissions is also an effective means to achieve energy conservation and emission reduction in the future.

[0004] However, although electric vehicles are powered by electricity, the electricity from the power grid is mainly generated by fossil fuels. Compared with traditional fuel vehicles, the indirect carbon emissions are not low, and the current situation of dependence on traditional fuels cannot be changed. In order to respond to the environmental protection concept of clean electricity, enhance the market competitiveness of the electric vehicle industry, and promote the construction of distributed, low-carbon, and intelligent electric vehicle charging stations, it is important to achieve sustainable social development.

[0005] The present invention designs a typical charging station comprehensive energy collaborative interactive optimization configuration method to improve the optimized operation capability of the charging station and realize the aggregate management of electric vehicles and distributed energy jointly participating in demand-side response and grid ancillary services. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system for the coordinated interactive optimization configuration of comprehensive energy sources for a typical charging station, construct a multi-objective optimization configuration model of the annual comprehensive investment cost of wind, solar and storage and the proportion of wind and solar energy output, make full use of the complementary characteristics of wind, solar and load, and effectively reduce the operating cost of the charging station.

[0007] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0008] In a first aspect, the present invention provides a typical charging station comprehensive energy collaborative interactive optimization configuration method, including the following process:

[0009] Obtaining wind, solar and storage power generation information based on the coordinated integration model of electric vehicles, distributed energy and power grid;

[0010] Obtain electric vehicle charging load demand;

[0011] According to the power generation information of wind, solar and storage and the charging load demand of electric vehicles, combined with the multi-energy complementary optimization configuration model of charging stations, the quantity configuration plan of wind, solar and storage is calculated; the multi-energy complementary optimization configuration model of charging stations includes two objectives and constraints;

[0012] The goals include economic goals and new energy output share targets.

[0013] Furthermore, the multi-energy complementary optimization configuration model of the charging station includes an economic objective function and a new energy output ratio objective function;

[0014] The economic objective function is expressed as follows:

[0015] F1=minC Σ =min(C deploy +C Grid )

[0016] In the formula, C Σ is the average annual investment cost; C deploy is the equivalent investment cost of the equipment; C Grid The annual electricity purchase cost.

[0017] C deploy =C inst +C mer +C loss

[0018]

[0019] C mer =δ mer C inst C loss =δ loss C inst

[0020] In the formula, C inst , C mer , C loss They are system infrastructure cost, operation and maintenance cost, and loss cost; N PV 、N W 、N B and p PV 、p W 、p B are the purchase quantity and unit price of photovoltaic panels, wind turbines, and energy storage batteries; C ob , C install are fixed investment cost and installation cost respectively; δ mer , δ loss are the ratio coefficients of system operation and maintenance cost and management personnel salary cost to annual construction cost and the ratio coefficient of loss cost to annual construction cost respectively; r is the discount rate; τ is the planning period;

[0021] Annual electricity purchase cost of the power grid C Grid It can be expressed as:

[0022]

[0023] Where P grid (t) represents the power grid output at time t; R g (t) represents the unit price of electricity purchased from the power grid at time t;

[0024] The target of renewable energy output ratio is based on the load ratio RAT of conventional centralized energy system supplied by large power grid. grid To reflect the output level of new energy RAT rep , the goal of optimizing the configuration of multi-energy complementarity of charging stations is to minimize the proportion of load supplied by the large power grid;

[0025]

[0026]

[0027] In the formula, Q rep is the total power generation of renewable energy in one year, Q EV The total demand load for charging electric vehicles in one year, Q grid is the total output power of the large power grid in one year, P grid (t) is the output power of the large power grid at time t, P EV (t) is the charging demand load of the electric vehicle at time t.

[0028] Furthermore, the constraints include: power balance constraints, system safety operation constraints, wind, solar and storage equipment quantity constraints, wind and solar power generation equipment output power constraints, energy storage battery output power constraints and energy storage battery capacity constraints;

[0029] The power balance constraint means that in order to ensure the stable operation of the charging station, the power balance between the power source and the load in the charging station must be met at any time throughout the year. The expression is as follows:

[0030] P EV (t) = P B (t)+P PV (t)+P W (t)+P grid (t)

[0031] Where P PV (t), P W (t), P B (t) represents the output of photovoltaic, wind turbine and energy storage at time t, where P B (t) Charging is negative and discharging is positive.

[0032] The system safety operation constraint means that once the external power grid fails, in order to ensure that the local important loads can still operate normally in this state, the new energy power generation load ratio is required during grid-connected operation, and its expression is as follows:

[0033] P rep (t)≥αP total (t)

[0034] P rep (t) = P PV (t)+P W (t)

[0035] P total (t) = P EV (t)+P 站内负荷 (t)

[0036] Where P rep (t) is the power generated by renewable energy at time t; P total (t) is the total demand load of the charging station at time t; P 站内负荷 (t) is the other load demand in the charging station at time t; α is the minimum self-generation rate of the charging station system, which is initialized to 0.3 and can be modified according to the actual operating status of the charging station.

[0037] The number of wind, solar and storage devices is constrained, and its expression is as follows:

[0038]

[0039] Where N W.max、N PV.max 、N B.max This is the maximum number of wind, solar and storage equipment to be installed, which is determined based on the actual scale and operating conditions of the charging station.

[0040] The output power constraint of the wind and photovoltaic power generation equipment means that the output power of the wind and photovoltaic power generation equipment at any time must meet the range constraint determined by the wind speed or solar radiation intensity at that time, and its expression is as follows:

[0041]

[0042] Where P PV,min , P PV.max is the minimum and maximum output power of the photovoltaic array; P W,min , P W.max is the minimum and maximum output power of the wind turbine.

[0043] The energy storage battery output power constraint expression is as follows:

[0044] P B.min ≤P B (t)≤P B.max

[0045] Where P B,min is the minimum charging and discharging power of the energy storage battery; P B,max It is the maximum charging and discharging power of the energy storage battery.

[0046] The energy storage battery capacity constraint expression is as follows:

[0047] According to the rated capacity and discharge depth of the energy storage battery, the energy storage capacity variation range must meet the following requirements:

[0048] E B.min ≤E B (t)≤E B.max

[0049] In the formula, E B.max 、E B.min are the upper and lower limits of the energy storage battery respectively; let the rated capacity of the energy storage battery be E B.max , while E B.min Depends on the maximum discharge depth of the energy storage battery.

[0050] Furthermore, the electric vehicle and distributed energy and power grid coordinated integration model includes a photovoltaic power generation system model, a wind power generation system model and an energy storage battery system model;

[0051] The input of the photovoltaic power generation system model is the rated output power P under standard test conditions. STC , Solar radiation intensity under standard test conditions G STC, component surface temperature T under standard test conditions STC , Real-time solar radiation intensity G C , Real-time photovoltaic module surface temperature T C , the output is the actual output power P of the photovoltaic panel PV , which is expressed as follows:

[0052]

[0053]

[0054] Where P PV is the actual output power of the photovoltaic panel; k is the power temperature coefficient; G C , T C is the real-time solar radiation intensity and the surface temperature of the photovoltaic module, T a is the ambient temperature;

[0055] The wind power generation system model input is the real-time wind speed v, the rated wind speed v r , are the cut-in wind speed v of the fan respectively in , cut-off wind speed v out , fan rated power P r , the output is the actual output power P of the fan at time t W (t), which is expressed as follows:

[0056]

[0057] Where P W (t) is the actual output power of the wind turbine at time t; v is the real-time wind speed; v r is the rated wind speed; v in 、v out are the cut-in wind speed and cut-off wind speed of the fan respectively; P r is the rated power of the fan.

[0058] The energy storage battery system model input is the temperature T of the energy storage battery at the operating point bat , Rated capacity under standard test conditions E STC and temperature T STC , Capacity temperature coefficient σ B , the output is the capacity E of the energy storage battery at the working point bot , which is expressed as follows:

[0059] E bat =E STC [1+σ B (T bat -T STC )] (8)

[0060] Where, Tbat is the temperature of the energy storage battery at the working point; E STC , T STC is the rated capacity and temperature under standard test conditions; σ B is the capacity temperature coefficient.

[0061] Furthermore, the method for obtaining wind, solar and storage power generation information based on the electric vehicle and distributed energy and power grid coordinated integrated model includes the following steps:

[0062] Obtain the annual wind and solar resource data of the target area through data collection and software simulation; the wind and solar resource data includes annual solar radiation intensity data and annual wind speed data;

[0063] According to the annual solar radiation intensity data of the target area, combined with the photovoltaic power generation system model, the annual power generation power of the photovoltaic power generation unit is fitted and the equipment parameters of the photovoltaic panels in the capacity configuration are determined; the equipment parameters of the photovoltaic panels include rated power, open circuit voltage, short circuit current, system voltage, working current, specifications, and price;

[0064] According to the annual wind speed data of the area, the equipment parameters of the wind power generation equipment in the capacity configuration are determined; the equipment parameters of the wind power generation equipment include the cut-in wind speed v of the wind turbine in , cut-off wind speed v out , fan rated power P r and price, and obtain the real-time wind speed v at the same time, and combine with the wind power generation system model to obtain the actual output power P of the wind turbine at time t W (t);

[0065] Determine the equipment parameters of the energy storage battery; the equipment parameters of the energy storage battery include the temperature T of the energy storage battery at the working point bat , Rated capacity under standard test conditions E STC and temperature T STC , Capacity temperature coefficient σ B , combined with the energy storage system model, the capacity E of the energy storage battery working point is obtained bot .

[0066] Furthermore, the method of obtaining the annual wind and solar resource data of the target area through data collection and software simulation includes:

[0067] Obtain the local meteorological data of the project, use the renewable energy complementary power generation optimization modeling software to simulate the annual solar radiation intensity curve, and obtain the annual solar radiation intensity data of the area;

[0068] The historical data of monthly average wind speed in recent years were obtained from the China Meteorological Data Network. The renewable energy complementary power generation optimization modeling software was used for simulation. The parameters required by the Weibull distribution model and the average wind speed data of each month throughout the year were input to obtain the wind speed change data throughout the year.

[0069] Furthermore, the method for obtaining the electric vehicle charging load demand includes:

[0070] Obtain the number and penetration rate of electric vehicles, and simulate the charging demand of electric vehicles entering the charging station based on the Monte Carlo simulation-based electric vehicle charging demand prediction method to obtain the typical daily charging load demand of the charging station;

[0071] The annual charging load data of the charging station is calculated on a yearly basis.

[0072] Furthermore, according to the power generation information of wind, solar and storage and the charging load demand of electric vehicles, combined with the multi-energy complementary optimization configuration model of charging stations, the method for calculating the quantity configuration scheme of wind, solar and storage includes:

[0073] Obtaining other data, including photovoltaic installation cost, wind turbine installation cost, energy storage battery installation cost and fixed investment cost;

[0074] The annual wind speed data, solar radiation intensity data, electric vehicle charging power and equipment parameters of the selected wind power generation equipment and photovoltaic power generation units in the target area are used as input conditions for the wind-solar charging station capacity optimization configuration model;

[0075] The energy exchange strategy for economic operation of charging stations based on time-of-use electricity prices is used as the operating mechanism of charging stations;

[0076] The solution is based on the economic goals and the proportion of new energy output. According to the constraints, the multi-objective problem is handled in combination with the Pareto optimal solution theory. The solution is solved using an improved multi-objective group search algorithm to obtain the model iteration results and the optimal solution set distribution. The optimal solution set is the optimal installation quantity distribution of wind, solar and storage equipment while satisfying the two goals as much as possible; the iteration result is the objective function result under multiple optimal solutions, that is, the comprehensive investment cost and grid power supply ratio corresponding to each optimal solution.

[0077] In a second aspect, the present invention provides a typical charging station comprehensive energy collaborative interactive optimization configuration system, including:

[0078] Power generation information acquisition module: used to obtain wind, solar and storage power generation information based on the coordinated integration model of electric vehicles, distributed energy and power grid;

[0079] Load demand acquisition module: used to obtain the charging load demand of electric vehicles;

[0080] Configuration calculation module: used to calculate the quantity configuration plan of wind, solar and storage according to the power generation information of wind, solar and storage and the charging load demand of electric vehicles, combined with the multi-energy complementary optimization configuration model of charging stations; the multi-energy complementary optimization configuration model of charging stations includes two objectives and constraints; the objectives include economic objectives and new energy output ratio objectives.

[0081] In a third aspect, the present invention provides a typical charging station comprehensive energy collaborative interactive optimization configuration system, including a processor and a storage medium;

[0082] The storage medium is used to store instructions;

[0083] The processor is used to operate according to the instructions to execute the steps of the method according to the first aspect.

[0084] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention takes into account the integrated interaction of electric vehicle charging behavior, multiple types of new energy, multiple types of energy storage and power grids, and optimizes the configuration of multi-energy complementarity of electric vehicle charging stations. A multi-objective optimization configuration model that takes into account the annual comprehensive investment cost of wind, solar and storage and the proportion of wind and solar energy output is constructed. The proposed capacity configuration method can make full use of the complementary characteristics of wind, solar and load, effectively reduce the operating cost of charging stations, realize the aggregate management of electric vehicles and distributed energy jointly participating in demand-side response and power grid ancillary services, and further promote the implementation of a V2G market operation mechanism that satisfies the interest sharing of multiple participating entities. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments and in conjunction with the accompanying drawings, wherein:

[0086] Figure 1 It is a flow chart of the comprehensive energy synergy interaction model of a typical charging station;

[0087] Figure 2 is the solar radiation intensity curve throughout the year;

[0088] Figure 3 is the solar radiation intensity curve for a typical day throughout the year;

[0089] Figure 4 It is the hourly wind speed curve throughout the year;

[0090] Figure 5 is the wind speed curve for a typical day throughout the year;

[0091] Figure 6 It is the structural model of battery energy storage system;

[0092] Figure 7 This is a typical microgrid structure diagram including wind, solar and storage;

[0093] Figure 8 It is a structural model of electric vehicle charging station taking into account multi-energy complementation;

[0094] Fig. 9 It is the design of multi-energy complementary optimization configuration scheme for electric vehicle charging stations;

[0095] Fig.10 This is the annual power curve of 290W photovoltaic panels;

[0096] Fig.11 It is the typical daily power generated by 290W photovoltaic panels throughout the year;

[0097] Fig.12 This is the annual power curve of a 10kW wind turbine;

[0098] Fig.13 is the typical daily power generation of a 10kW wind turbine throughout the year;

[0099] Fig.14 is the daily charging load demand of the charging station;

[0100] Fig.15 is the distribution of model iteration results and optimal solution set;

[0101] Fig.16 It is a wind, solar and storage configuration solution;

[0102] Fig.17 is the change in the new energy consumption rate under different proportions of new energy output;

[0103] Fig.18 The comparison of the equipment output distribution under the energy exchange strategy proposed in the present invention and the traditional energy exchange strategy; wherein (a) scenario 1: energy exchange strategy considering the economic operation of the charging station, (b) scenario 2: traditional energy exchange strategy;

[0104] Fig.19 is the power distribution of typical days in summer and winter of the optimization scheme; (a) is the power distribution of typical days in summer, (b) is the power distribution of typical days in winter;

[0105] Fig. 20 It is the distribution of electricity consumption in different months. DETAILED DESCRIPTION

[0106] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0107] The technical concept of the present invention is: first find out the potential subsynchronous oscillation mode frequency in the power grid, calculate the risk of subsynchronous oscillation occurring in each potential mode, and screen out the plant-level broadband oscillation detection device corresponding to the potential subsynchronous oscillation mode with higher risk.

[0108] The application principle of the present invention is described in detail below in conjunction with the accompanying drawings.

[0109] Embodiment 1:

[0110] A typical charging station comprehensive energy collaborative interactive optimization configuration method of the present invention, see Figure 1 As shown, including:

[0111] Step 1: Establish a coordinated integration model of electric vehicles, distributed energy and power grids.

[0112] Specifically: Considering the random volatility of wind and solar resource output, establish photovoltaic power generation system model, wind power generation system model and energy storage battery system model respectively, use HOMER software to simulate the wind speed and solar radiation intensity characteristic curve, analyze the change characteristics of wind speed and light data throughout the year, and provide data reference for the selection of photovoltaic panels and wind turbines in the example of step three.

[0113] (1) Photovoltaic power generation system modeling

[0114] Photovoltaic power generation is the conversion of solar energy into electrical energy based on the photovoltaic effect. Photovoltaic cells have a small capacity. According to the needs of different scenarios, dozens or hundreds of individual photovoltaic cells can be combined in series and parallel to form photovoltaic cell modules. The modules are packaged into solar panels of different powers, and then connected in series and parallel to form photovoltaic arrays. Photovoltaic arrays use the photovoltaic effect of light to convert solar energy into direct current, and then connect to the grid after a series of convergence, inversion and boosting operations.

[0115] The actual output power of the photovoltaic array is determined by the rated output power P under standard test conditions. STC , solar radiation intensity G STC and component surface temperature T STC Waiting for decision.

[0116]

[0117] Where P PV is the actual output power of the photovoltaic panel; k is the power temperature coefficient, generally -0.3% / ℃; G C , T C It is the real-time solar radiation intensity and photovoltaic module surface temperature.

[0118] The operating temperature of photovoltaic cells is one of the factors that affect the capacity of photovoltaic power generation systems. Under normal circumstances, when solar panels are exposed to sunlight, most of it is converted into heat energy that heats the cells. Therefore, the temperature of photovoltaic modules is generally higher than the ambient temperature. Real-time photovoltaic module temperature T C The real-time solar radiation intensity and ambient temperature T a Estimation, as shown in formula (2):

[0119]

[0120] In order to obtain the annual solar radiation intensity curve of the planned area, the Hybrid Optimization Model for Electric Renewable (HOMER) software is used to obtain the local meteorological data of the project and simulate the annual solar radiation intensity curve. The fitting module of the solar energy resource is selected for simulation, and the annual solar radiation intensity curve of the area is obtained discretely as follows: Figure 2 As shown in the figure, the solar radiation intensity curve of the typical day in the planning area throughout the year is as follows Figure 3 shown.

[0121] It can be seen from the obtained annual light intensity data that the solar energy resources in this area are relatively abundant, with the strongest radiation in summer, followed by spring and autumn. The obtained annual light intensity data provides data reference for the selection of photovoltaic panels.

[0122] (2) Wind power generation system modeling

[0123] Wind speed simulation is required before simulating wind turbine output. Usually, due to the many restrictions on field wind speed data collection, it is difficult to obtain long-term reliable data. Weibull distribution is a wind speed simulation mathematical model used to study the dynamic impact between wind power generation systems and power grids. In the absence of measured data, the Weibull distribution curve is used to simulate wind speed data for field wind speed sampling. The simulated wind speed change curve is closest to the actual wind speed and is a wind speed probability distribution density function established based on shape parameters and scale parameters, as shown in formula (3):

[0124]

[0125] In the formula, v is the input wind speed value; k is the shape parameter; c is the Weibull scale coefficient. k and c can be calculated from the average wind speed μ and the standard deviation σ. The distribution function of wind speed is expressed as follows:

[0126]

[0127]

[0128] Where Γ is the Gamma function. The predicted wind speed at the hub height of the wind turbine is obtained using formula (6):

[0129]

[0130] Where v is the wind speed at hub height; h is the hub height of the wind turbine; v0 is the simulated wind speed on the ground; h0 is the ground height; and a is the wind shear index.

[0131] In the ideal wind energy conversion model, the wind turbine power generation is affected by air density, blade radius and wind energy conversion efficiency. The output power of the wind turbine changes with the wind speed. Based on the wind speed simulation model, the linear interpolation method can be used to obtain the functional expression of the wind turbine output as shown in formula (7):

[0132]

[0133] Where P W (t) is the actual output power of the wind turbine at time t; v is the real-time wind speed; v r is the rated wind speed; v in 、v out are the cut-in wind speed and cut-off wind speed of the fan respectively; P r is the rated power of the fan.

[0134] In order to obtain the annual wind speed variation curve of the region, we first collected the historical data of the monthly average wind speed in recent years from the China Meteorological Data Network, used the HOMER software wind turbine module for simulation, input the parameters required by the Weibull distribution model and the average wind speed data of each month throughout the year, and obtained the annual wind speed variation curve as shown in the figure. Figure 4 As shown in the figure, the wind speed curves of typical days throughout the year are extracted by season as follows Figure 5 shown.

[0135] Depend on Figure 4 , 5 It can be seen that the wind speed throughout the year is basically small in summer and large in winter and spring, and the wind speed in 24 hours a day is basically small during the day and large at night. The obtained wind speed data throughout the year provides data reference for the selection of wind turbines.

[0136] (3) Energy storage system modeling

[0137] In addition to smoothing out the randomness and intermittent power fluctuations of wind and solar power generation, the configuration of energy storage systems can also alleviate the contradiction between energy supply and demand to a certain extent, ensure the safe operation of the power grid, and achieve more flexible energy coordination and optimization. In distributed microgrid systems, the charging and discharging characteristics of energy storage systems will play a significant role in improving system reliability.

[0138] The typical structure of a single energy storage battery is as follows: Figure 6 As shown. In combination with the wind and solar power generation system, the energy storage battery pack STeq 、Converter CNV eq 、Transformer T req and impedance Z eq The battery energy storage system is connected to each other, outputs electric energy, passes through the power control system and is connected to the transformer TR0 and the impedance Z0, and finally is connected to the wind and solar power generation system; when the wind and solar power generation system outputs insufficient power, it transmits electric energy to the load. Based on the above model, an effective working mode for reliable operation of the system is achieved.

[0139] The performance of energy storage batteries is usually described by battery capacity and state of charge. The capacity E of the energy storage battery at the working point is bat It can be expressed by formula (8):

[0140] E bat =E STC [1+σ B (T bat -T STC )] (8)

[0141] Where, T bat is the temperature of the energy storage battery at the working point; E STC , T STC is the rated capacity and temperature under standard test conditions; σ B It is the capacity temperature coefficient, which is generally taken as 0.6%.

[0142] In order to coordinate the power balance between power source and load, the energy storage control system interacts with external equipment to achieve coordinated operation; under the action of the monitoring and dispatching system, the power regulation execution equipment in the energy storage battery performs safe and effective charge and discharge management. Its charge and discharge status is as follows:

[0143] Charging process:

[0144] E ba (λt)=E ba [(λ-1)t]+P ba (λt)tη c (9)

[0145] Discharge process:

[0146] E ba (λt)=E ba [(λ-1)t]-P ba (λt)t / η d (10)

[0147] In the formula, E ba (λt) and E ba [(λ-1)t]) are the energy storage capacity of the energy storage battery at λt and (λ-1)t respectively; P ba(λt) is the charge and discharge power of the battery at time λt; η c , η d They are the charging and discharging efficiency of the energy storage battery respectively.

[0148] During the charging and discharging process of the energy storage battery, the energy storage controller regulates the charging and discharging state of the energy storage battery according to the battery's state of charge (SOC), which is related to the charge in the (t-1) period, the charge and discharge amount of the energy storage battery in the (t-1, t) period, and the amount of power attenuation per hour.

[0149] When the energy storage battery is charged, the SOC during period t can be expressed as:

[0150] SOC(λt)=SOC[(λ-1)t]+|ΔE ba (λt)| / E B (11)

[0151] When the energy storage battery is discharged, the SOC in period t can be expressed as:

[0152] SOC(λt)=SOC[(λ-1)t]-|ΔE ba (λt)| / E B (12)

[0153] In the formula, SOC(λt) and SOC[(λ-1)t] are the charge states of the energy storage battery at λt and (λ-1)t respectively; E B is the rated capacity of the energy storage battery; △E ba (λt) is the energy change of the energy storage battery at time λt.

[0154] Step 2: Establish the structural model and optimization configuration model of the electric vehicle multi-energy complementary charging station.

[0155] Specifically: Based on the typical structure of a multi-energy complementary microgrid integrating wind, solar and storage, a structural model of an electric vehicle charging station taking into account multi-energy complementarity is constructed. For electric vehicle charging stations containing wind, solar and storage, in order to achieve multi-energy complementarity of charging stations, a wind, solar and storage capacity optimization configuration model is constructed that considers the annual comprehensive investment cost of the charging station and the proportion of new energy output.

[0156] (1) Typical microgrid structure with multi-energy complementarity

[0157] Multi-energy complementarity refers to the complementary use of different types of energy or the complementary use of different forms of the same type of energy. The multi-energy complementary microgrid system has great development potential because it integrates a variety of distributed power sources, loads, and energy storage devices, can flexibly realize off-grid or grid-connected operation, improve the utilization rate of distributed power sources and the reliability of system power supply. The typical multi-energy complementary microgrid system structure with wind, solar and storage is as follows: Figure 7 shown.

[0158] Figure 7 The typical wind-solar-storage microgrid structure shown adopts the currently more common wind-solar complementarity + energy storage multi-energy complementary power generation mode. As an important application form of distributed power generation, compared with separate wind power generation or photovoltaic power generation, the power output is more stable and the reliability is higher. This is due to the flexible charging and discharging characteristics of energy storage in the microgrid, which can smooth the wind and solar output, continuously optimize the power generation curve, and continuously reduce the variance of the power generation curve, thereby achieving the effect of multi-energy complementarity.

[0159] (2) Electric vehicle charging station structure considering multi-energy complementarity

[0160] The electric vehicle charging station that takes into account multi-energy complementarity is a microgrid form that integrates the wind, solar and storage microgrid system with the electric vehicle charging station. Before building the charging station structure model, it is necessary to first clarify the distributed power interface type and the microgrid system connection form.

[0161] Different distributed power sources have different grid-connected interfaces. Photovoltaic power generation units and energy storage battery units emit direct current, which can be converted into a usable form through a DC / AC inverter or a DC / DC converter; wind power generation units emit alternating current, which can be connected to the AC bus through an AC / DC / AC converter or directly connected to the upper-level DC bus through an AC / DC rectifier.

[0162] Since the DC bus is easy to control and expand, it can meet the capacity expansion requirements of power consumption and power generation equipment. At the same time, the rectification part is omitted when the distributed power supply is connected to the grid, and the system construction cost is low; therefore, the present invention selects a system structure based on the DC bus, and each unit is connected to the DC bus through a corresponding power electronic converter. The structural model of the multi-energy complementary electric vehicle charging station with wind, solar and storage constructed by the present invention is as follows Figure 8 shown.

[0163] The charging station includes AC distribution network, wind turbines (wind power generation system), photovoltaic battery array (photovoltaic power generation system), energy storage device (energy storage system), central control unit, electric vehicle charger and other equipment. The energy storage system in the charging station cooperates with the wind power generation system and the photovoltaic power generation system to provide electric energy for electric vehicles and ensure the continuity of electric vehicle charging. The functions of each part are as follows:

[0164] 1) AC distribution network: Receives electrical energy from the transmission grid or regional power plant and connects to the DC bus through a step-down transformer and AC / DC rectifier module.

[0165] 2) Photovoltaic cell array (photovoltaic power generation system): It is composed of solar panels connected in series and parallel. The photovoltaic array converts solar energy into electrical energy and connects the DC / DC converter module to the DC bus.

[0166] 3) Wind turbine generator set (wind power generation system): It consists of two parts: wind turbine and generator. The wind turbine generator set converts wind energy into electrical energy and connects the AC / DC rectifier module to the DC bus.

[0167] 4) Energy storage battery pack (energy storage system): Under the control of the dispatching system, it cooperates with wind and solar power output to provide electricity for electric vehicles, playing a role in energy storage and regulation.

[0168] 5) DC / DC converter module: The converter unit that connects the energy storage battery pack and the photovoltaic power generation system to the power grid. The energy storage battery pack uses bidirectional energy control to achieve charging and discharging functions.

[0169] 6) AC / DC rectifier module: It is a rectifier unit that connects the DC bus of the wind power generation system to the AC side distribution network.

[0170] 7) Central Control Unit: Responsible for monitoring the dynamic changes of energy, voltage and current within the system, and coordinating the normal operation of each unit in the system.

[0171] 8) Station load: including lighting load, air conditioning load and other AC power loads within the station.

[0172] 9) Electric vehicle charger: Similar to the gas station’s gas pump, it can charge different models of electric vehicles in parking spaces according to different voltage levels.

[0173] (3) Charging station multi-energy complementary optimization configuration model

[0174] Aiming at the problem of optimizing the configuration of renewable energy sources such as wind and solar power connected to electric vehicle charging stations in the form of distributed power sources, in the planning of such charging stations, the optimal configuration of distributed power sources in charging stations to achieve multi-energy complementarity is studied while taking into account the investment economy and the increase in the output ratio of new energy on the basis of meeting the charging needs of electric vehicles in the station. The optimal configuration scheme of wind turbines, photovoltaic generators and energy storage battery capacity in multi-energy complementary electric vehicle charging stations is solved, such as Fig. 9 shown.

[0175] 1) Objective function

[0176] Economic goals

[0177] The economic goal mainly considers the minimum annual average investment cost of the system, including the investment cost of distributed power sources (including energy storage) and the power purchase cost of the power grid. The objective function is expressed as follows:

[0178] F1=minC Σ =min(C deploy +C Grid ) (13)

[0179] In the formula, C Σ is the average annual investment cost; C deploy is the equivalent investment cost of the equipment; C Grid The annual electricity purchase cost.

[0180] C deploy =C inst +C mer +C loss (14)

[0181]

[0182] C mer =δ mer C inst C loss =δ loss C inst (16)

[0183] In the formula, C inst , C mer , C loss They are system infrastructure cost, operation and maintenance cost, and loss cost; N PV 、N W 、N B and p PV 、p W 、p B are the purchase quantity and unit price of photovoltaic panels, wind turbines, and energy storage batteries; C ob , C install are fixed investment cost and installation cost respectively; δ mer , δ loss are the proportion coefficients of system operation and maintenance cost and management personnel salary cost in the annual construction cost, as well as the proportion coefficient of loss cost in the annual construction cost; r is the discount rate; τ is the planning period.

[0184] Define the difference between the charging load and the output of new energy at each moment as the energy absorbed from the grid, then the annual power purchase cost of the grid is C Grid It can be expressed as:

[0185]

[0186] Where P grid (t) represents the power grid output at time t; R g (t) represents the unit price of electricity purchased from the power grid at time t.

[0187] Renewable energy output ratio target

[0188] The use of new energy is of great significance to adjusting the urban energy structure and promoting the sustainable development of cities. For electric vehicle charging stations that take into account multi-energy complementarity, the ideal operation mode is to achieve local consumption of new energy power generation and charging load, while achieving effective matching with conventional energy systems. The proportion of new energy output is expressed by the ratio of the load that new energy can provide to the total load demand of electric vehicle charging within a certain period, which also reflects the self-balancing ability of the system.

[0189] The present invention uses a conventional centralized energy system to supply power through a large power grid load ratio RAT grid To reflect the output level of new energy RAT rep , taking the minimum proportion of load supplied by the large power grid as one of the goals of the multi-energy complementary optimization configuration of charging stations.

[0190]

[0191]

[0192] In the formula, Q rep is the total power generation of renewable energy in one year, Q EV The total demand load for charging electric vehicles in one year, Q grid is the total output power of the large power grid in one year, P grid (t) is the output power of the large power grid at time t, P EV (t) is the charging demand load of the electric vehicle at time t.

[0193] For the charging station system, the smaller the proportion of load that relies on power from the large power grid, the higher the new energy output level of the configuration plan, and the stronger the self-balancing ability of the charging station system.

[0194] 2) Constraints

[0195] For the problem of optimizing the configuration of multi-energy complementary capacity in electric vehicle charging stations, the constraints are set as follows:

[0196] Power balance constraints

[0197] To ensure the stable operation of the charging station, the power balance between the power source and the load in the charging station must be met at any time throughout the year.

[0198] P EV (t) = P B (t)+P PV (t)+P W (t)+P grid (t) (20)

[0199] Where P PV (t), P W (t), P B(t) represents the output of photovoltaic, wind turbine and energy storage at time t, where P B (t) Charging is negative and discharging is positive.

[0200] System safety operation constraints

[0201] Once the external power grid fails, the charging station system will enter an isolated operation state. In order to ensure that the local important load can still operate normally in this state, it is necessary to put forward requirements for the new energy power generation load ratio of the charging station when it is connected to the grid. It is required that the ratio of new energy power generation to the total demand load of the charging station in any hour should not be lower than a certain value, which is the minimum self-generation rate of the charging station system. The multi-energy complementary optimization configuration model of the charging station is set at intervals of one hour to study the load demand of new energy power generation throughout the year.

[0202] P rep (t)≥αP total (t) (21)

[0203] P rep (t) = P PV (t)+P W (t) (22)

[0204] P total (t) = P EV (t)+P 站内负荷 (t) (23)

[0205] Where P rep (t) is the power generated by renewable energy at time t; P total (t) is the total demand load of the charging station at time t; P 站内负荷 (t) is the other load demand in the charging station at time t; α is the minimum self-generation rate of the charging station system, which is initialized to 0.3 and can be modified according to the actual operating status of the charging station.

[0206] Constraints on the number of wind, solar and storage devices

[0207]

[0208] Where N W.max 、N PV.max 、N B.max This is the maximum number of wind, solar and storage equipment to be installed, which is determined based on the actual scale and operating conditions of the charging station.

[0209] Output power constraints of wind and solar power generation equipment

[0210] The output power of wind and solar power generation equipment at any time must meet the range constraints determined by the wind speed or solar radiation intensity at that time.

[0211]

[0212] Where P PV,min , P PV.max is the minimum and maximum output power of the photovoltaic array; P W,min , P W.max is the minimum and maximum output power of the wind turbine.

[0213] Energy storage battery output power constraints

[0214] P B.min ≤P B (t)≤P B.max (26)

[0215] Where P B,min is the minimum charging and discharging power of the energy storage battery; P B,max It is the maximum charging and discharging power of the energy storage battery.

[0216] Energy storage battery capacity constraints

[0217] According to the rated capacity and discharge depth of the energy storage battery, the energy storage capacity variation range must meet the following requirements:

[0218] E B.min ≤E B (t)≤E B.max (27)

[0219] In the formula, E B.max 、E B.min are the upper and lower limits of the energy storage battery respectively; let the rated capacity of the energy storage battery be E B.max , while E B.min Depends on the maximum discharge depth of the energy storage battery.

[0220] Step 3, finally verify the feasibility of the proposed method through different configuration examples, and verify the feasibility and correctness of the comprehensive energy collaborative interactive optimization configuration method of the typical charging station.

[0221] (1) Example

[0222] Assuming the planned construction is 1400m 2 For an electric vehicle charging station with wind, solar and storage, the configuration parameters are determined according to the decision variables and objective function requirements of the system model as follows:

[0223] 1) Wind and solar power output data

[0224] By collecting data and simulating the wind and solar resource data of a certain place for one year, the average wind speed, solar radiation intensity and other wind and solar data of the area are obtained as described in the first step. According to the selected location and application scenario, a common photovoltaic panel monomer with a rated power of 290W can be selected as the photovoltaic power generation unit in the capacity configuration. Its performance parameters are shown in Table 1:

[0225] Table 1 Equipment parameters of photovoltaic panels

[0226]

[0227] Combining the photovoltaic power generation system model and the annual solar radiation intensity data, the annual power generation of a single photovoltaic cell is fitted. The annual power generation curve of a 290W photovoltaic panel and the annual typical daily power generation are shown as follows: Fig.10 and 11 shown.

[0228] According to the average wind speed data of the area, considering the flexibility and operability of the equipment, a wind turbine with a rated power of 10kW is selected as the wind power generation equipment in the capacity configuration. Its performance parameters are shown in Table 2.

[0229] Table 2 Wind turbine equipment parameters

[0230]

[0231] Combining the wind power generation system model and the annual wind speed data, we can obtain the annual power generation curve of a 10kW wind turbine and the annual typical daily power generation of a 10kW wind turbine, as shown in the following figure: Fig.12 and Fig.13 shown.

[0232] Combining the energy storage system model and the annual charging station system load requirements, the main parameters of the selected energy storage batteries are shown in Table 3.

[0233] Table 3 Energy storage battery equipment parameters

[0234]

[0235] 2) Electric vehicle charging demand

[0236] Assume that the number of cars in the service area of ​​a certain electric vehicle charging station is about 10,000 and the penetration rate of electric vehicles is 5%. According to the Monte Carlo simulation-based electric vehicle charging demand prediction method, the charging demand of electric vehicles entering the charging station is simulated and calculated, and the typical daily charging load demand of the charging station is obtained as follows: Fig.14 The annual charging load data of the charging station is calculated on a yearly basis so that the multi-energy complementary optimization configuration of the charging station can be carried out in combination with the wind, solar and storage output characteristics, that is, to meet the aforementioned economic goals and new energy output ratio goals.

[0237] 3) Other system parameters

[0238] The above is a brief description of the annual wind speed data, solar radiation intensity data, electric vehicle charging power and the selected wind turbine and photovoltaic cell specifications in the planning area. These data will serve as the basic input conditions for the capacity optimization configuration problem of wind and solar charging stations. The method for obtaining electric vehicle charging power is to make assumptions about the number of cars and electric vehicle penetration rate in the service area of ​​the electric vehicle charging station, and then simulate the charging demand of electric vehicles entering the charging station based on the existing electric vehicle charging demand prediction method based on the travel probability matrix.

[0239] Other parameters of the charging station are set as follows:

[0240] Table 4 Charging station equipment related parameters

[0241]

[0242] In the capacity configuration stage, the energy exchange strategy for economic operation of charging stations is used as the charging station operation mechanism based on the time-of-use electricity price, and the time-of-use electricity price is set as shown in Table 5.

[0243] Table 5 Time-of-use electricity price parameters

[0244]

[0245] (2) Simulation results and analysis

[0246] The optimization configuration model of the present invention, i.e., the above-mentioned economic objectives and the proportion of new energy output, is solved, and the multi-objective problem is processed in combination with the Pareto optimal solution theory. The improved multi-objective group search algorithm is used for solving the problem. The specific settings of the algorithm are: the population size is set to 50, the maximum number of iterations is 200, and the control parameters are the number of photovoltaic cells X PV 、Number of wind turbines X W 、Number of energy storage batteries X B Due to the limitation of configuration space, it is assumed that the upper limit of the number of photovoltaic cells and wind turbines is 1800 and 15 respectively, and the maximum installed capacity of energy storage is 1600kWh, which is about 133333Ah, that is, the upper limit of energy storage battery configuration is about 533, and the energy storage system is set to charge and discharge power of 200kW per hour. Solve the multi-energy complementary optimization configuration model, and the multi-objective configuration results are as follows Fig.15 and Fig.16 shown. Fig.15 It is the distribution of the model iteration result and the optimal solution set. The optimal solution set is the optimal installation quantity allocation of wind, solar and storage equipment while satisfying the two objectives as much as possible. The iteration result is the objective function result under multiple optimal solutions, that is, the comprehensive investment cost and grid power supply ratio corresponding to each optimal solution. Fig.15Describe the relationship between the two to facilitate further analysis and find a more reasonable optimal solution; Fig.16 It is the wind, solar and storage configuration scheme, and the result of solving target 1 and target 2.

[0247] It can be seen from the figure that the Pareto solution set obtained by the algorithm search is distributed in a relatively regular manner. Based on the obtained Pareto solution set, the photovoltaic and wind turbine power and power generation are calculated at the same time, and the wind and solar power generation power is compared with the electric vehicle charging load. According to the energy exchange strategy, the grid output power and the energy storage system charging and discharging power are calculated, and the energy storage system charging and discharging amount, grid output power and electric vehicle charging power are calculated. The upper and lower limits of the energy storage battery charging power are added, and finally the annual comprehensive investment cost and the power supply ratio of the grid are calculated. Pareto optimal solution theory. Here, the Pareto solution set is a series of optimal solutions for solving the above multi-objective model.

[0248] from Fig.15 It can be seen that the two goals of comprehensive investment cost of charging stations and the proportion of power supply from the power grid conflict with each other. As the investment cost increases, the proportion of power supply from the power grid gradually decreases, and the proportion of new energy output increases, thus showing a trend of increasing investment cost as the proportion of new energy output increases. The proportion of new energy output is jointly determined by wind, solar and storage; the same energy utilization rate may correspond to different configuration schemes, and the capacity of the energy storage battery is closely related to the operation and control strategy of the charging station system, resulting in different corresponding station construction costs and electricity purchase costs.

[0249] By solving and analyzing the examples, a series of optimal solutions that meet the two objective functions are obtained. In order to illustrate the rationality of the optimization model, several groups of schemes are selected to analyze the annual comprehensive investment cost and new energy output of the charging station under different wind, solar and storage capacity configurations.

[0250] Table 6 Annual comprehensive investment cost of charging stations and power supply ratio of power grid under the optimal configuration scheme

[0251]

[0252] It can be seen from the configuration schemes in Table 6 that as the power configuration capacity increases, the proportion of reliance on grid power supply decreases, that is, the higher the proportion of new energy output, the higher the investment cost. Due to the large volatility of photovoltaic array output, in order to increase the proportion of new energy utilization in the system, the number of energy storage configurations is coordinated with the changes in the number of photovoltaic and wind turbine configurations. Comparing Schemes 1 and 6 in the table, Scheme 1 has 177 more energy storage batteries than Scheme 6. At this time, the proportion of grid power supply is reduced by 2.3%, while the investment cost increases by 47,900 yuan. This shows that simply increasing the energy storage battery is not obvious and uneconomical to increase the proportion of new energy output; comparing Schemes 5 and 6, it is obvious that as the main energy supply source for charging stations, adjusting the number of wind and solar configurations directly affects the output proportion and investment cost of new energy.

[0253] It can be seen from the multi-objective configuration results that if the output of new energy in the charging station is given priority, the investment cost of the charging station will increase greatly; if the investment economy is given priority, the output ratio of new energy cannot be increased. Therefore, the optimal decision of the station construction plan must be combined with the different construction needs of different charging stations to select the most reasonable wind, solar and storage capacity configuration plan for electric vehicle charging stations. In practical applications, although the configuration quantity of wind and solar energy is increased, a high proportion of wind and solar resources is achieved. However, due to the operating constraints related to the charging station system and the intermittent and unstable characteristics of new energy power generation, the configured energy storage system can only completely smooth out the wind and solar power generation in some periods of the year, and a certain amount of energy abandonment will inevitably occur. In order to achieve a high proportion of new energy consumption, improve the output level of energy, and enable decision makers to choose configuration plans more reasonably, it is proposed to use the new energy consumption rate as an indicator for selecting the optimal configuration plan.

[0254] The proportion of new energy consumption under different proportions of new energy output is studied, and the difference between the combined output of wind and solar power and the sum of the grid output and the total charging load is used to represent the part of new energy that is not consumed. The new energy consumption rate RAT csp and renewable energy output ratio RAT rep It can be expressed as:

[0255]

[0256] The RAT under the optimized configuration model is calculated according to the optimization solution rep With RAT csp The relationship is as follows Fig.17 Shown by: Fig.17 It can be seen that when the proportion of renewable energy output is 77.4%, that is, the proportion of power supply by the power grid is 22.6%, the new energy consumption rate reaches its maximum value. At this time, the multi-energy complementary optimization configuration results in the electric vehicle charging station meet the decision-makers' requirements for economy and the proportion of renewable energy output, while taking into account the consumption rate of renewable energy, reaching a relatively optimal situation. Under this configuration, the energy exchange strategy in the charging station can be better implemented, and the optimized operation of the multi-energy complementary charging station can be realized.

[0257] The optimal configuration scheme when the new energy consumption rate is maximized is:

[0258] Table 7 Optimal configuration scheme

[0259]

[0260] The optimization configuration scheme is analyzed to illustrate the rationality of the multi-energy complementary optimization configuration model.

[0261] 1) Analysis of charging station equipment output and economic efficiency under different energy exchange strategies

[0262] According to the above optimization configuration results, the distribution of equipment output under the two scenarios of charging station system operation controlled by the improved energy exchange strategy proposed by the present invention and the traditional energy exchange strategy is as follows: Fig.18 (In order to visually display the changes in the output power of photovoltaic and wind turbine power generation at the charging station, the output power of the power grid, and the charging and discharging of energy storage equipment, several curves are integrated into one graph, and the energy storage charging is set to the negative direction and the discharging is set to the positive direction.)

[0263] When the wind and solar outputs are the same, the energy storage system performs charging and discharging according to different operation strategies, and the large power grid cooperates to output the missing load. Fig.18 (a) Analyze the output status of each device in the system under the energy exchange strategy proposed in this invention.

[0264] Depend on Fig.18 (a) It can be seen that from 0:00 to 7:00 the next day, the power grid implements the off-peak electricity price; due to the low electricity purchase cost during this period, the charging station purchases electricity from the power grid to provide electric vehicles with electricity, and the wind turbine output and the charging station purchase electricity from the power grid to supplement the energy storage equipment. The energy storage system reaches the full battery state at 5 o'clock. 7:00 to 12:00 is the peak period of electricity consumption. At this time, the power grid implements the peak electricity price; wind and solar output give priority to powering electric vehicles. If the wind and solar output is greater than the charging demand, the excess electricity is used to charge the energy storage equipment that is not fully charged according to the charge state of the energy storage equipment; if the wind and solar output does not meet the charging demand, the energy storage equipment is discharged; from 12:00 to 17:00, this period implements the normal electricity price, and the electricity purchase cost is lower. If the wind and solar output cannot meet the charging demand at this time, the corresponding demand shortfall will be supplemented by the power grid. From 17:00 to 22:00, the power grid implements the peak electricity price policy. If the wind and solar power output is less than the charging demand at this time, the energy storage equipment will make up the shortfall to reduce the peak electricity purchase cost; if the capacity of the energy storage equipment is insufficient, the power grid will supplement the electricity required by electric vehicles during this period; from 22:00 to 24:00, the remaining energy storage power is released, and the shortfall is provided by the wind turbine output and the power grid.

[0265] contrast Fig.18 (a) and Fig.18 (b) It can be seen that Fig.18 (a) The relative energy storage charging capacity and grid output Fig.18 (b) more, and Fig.18 (a) The power grid output time is concentrated in the valley and normal times of electricity prices. Fig.18 (b) The power grid output time is mostly concentrated during the peak electricity price period. The annual power purchase cost and construction cost under the two scenarios are calculated, as shown in Table 8.

[0266] Table 8 Annual investment costs under two scenarios

[0267]

[0268] It can be seen from Table 8 that when the wind and solar energy storage capacity is used in the electric vehicle charging station, the annual electricity purchase cost of the charging station under the energy exchange strategy scenario proposed by the present invention is relatively small. This strategy encourages the charging station to purchase electricity from the power grid during flat and valley periods, which is beneficial to reducing the peak electricity purchase cost of the charging station and improving the economy of the charging station operation.

[0269] 2) Power balance analysis of multi-energy complementary charging station system

[0270] Wind and solar power are affected by weather changes, so their output is relatively random. Based on the configuration scheme results, the distribution of electric vehicle charging power, wind and solar power generation power, energy storage charging and discharging power, and grid output power on typical days in summer and winter is studied.

[0271] contrast Fig.19 (a) and (b) show that, due to seasonal influences, the photovoltaic output in summer is greater than that in winter, while the wind turbine output is less than that in winter. In general, the amount of electricity purchased from the power grid in winter is greater than that in summer. Fig.19 (a) It can be seen that during the period from 9:00 to 12:00, the wind and solar power output is higher than the charging demand, and the energy storage battery is fully charged, so the phenomenon of solar power abandonment occurs at this time; Fig.19 In (b), during the period from 9:00 to 10:00, the wind and solar power output does not meet the charging load, and the energy storage battery discharges to make up for the shortfall; a comprehensive analysis of the output of each device on a typical summer day and a typical winter day shows that the charging power of electric vehicles under the optimized configuration scheme meets the power balance conditions between the grid output power, the wind and solar power generation power, and the energy storage charging and discharging power.

[0272] 3) Power balance analysis of multi-energy complementary charging station system

[0273] Conduct a power balance analysis on the optimized configuration plan, and count the distribution of electric vehicle charging demand, total wind and solar combined output, and total power purchased by the distribution network in the next month under the optimal configuration plan.

[0274] Depend on Fig. 20 It can be seen that the total amount of wind-solar combined power generation in each month of the year and the sum of the power provided by the power grid in each month of the year basically meet the charging needs of electric vehicles. From June to October, it can be found that the combined power generation of wind and solar is relatively large, and some energy has not been consumed; this is mainly due to the difference in photovoltaic output due to changes in solar radiation intensity in different seasons. At the same time, considering the planning cost, the configured energy storage battery can only smooth the photovoltaic power generation in some periods of the year. This is a normal phenomenon of general distributed power generation, especially photovoltaic power stations. From the above figure, it can be seen that the power balance basically meets the operating characteristics.

[0275] Embodiment 2:

[0276] This embodiment provides a typical charging station comprehensive energy collaborative interactive optimization configuration system, including:

[0277] Power generation information acquisition module: used to obtain wind, solar and storage power generation information based on the coordinated integration model of electric vehicles, distributed energy and power grid;

[0278] Load demand acquisition module: used to obtain the charging load demand of electric vehicles;

[0279] Configuration calculation module: used to calculate the quantity configuration plan of wind, solar and storage according to the power generation information of wind, solar and storage and the charging load demand of electric vehicles, combined with the multi-energy complementary optimization configuration model of charging stations; the multi-energy complementary optimization configuration model of charging stations includes two objectives and constraints; the objectives include economic objectives and new energy output ratio objectives.

[0280] The system described in this embodiment can be used to implement the method described in the first embodiment.

[0281] Embodiment three:

[0282] The embodiment of the present invention also provides a typical charging station comprehensive energy collaborative interactive optimization configuration system, including a processor and a storage medium;

[0283] The storage medium is used to store instructions;

[0284] The processor is used to operate according to the instructions to execute the steps of the method described in embodiment 1.

[0285] The specific implementation scheme of each module of the device of the present invention refers to the specific implementation process of the above method.

[0286] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0287] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0288] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0289] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0290] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A typical charging station comprehensive energy collaborative interactive optimization configuration method, characterized in that: The process includes: Obtaining wind, solar and storage power generation information based on the coordinated integration model of electric vehicles, distributed energy and power grid; Obtain electric vehicle charging load demand; According to the power generation information of wind, solar and storage and the charging load demand of electric vehicles, combined with the multi-energy complementary optimization configuration model of charging stations, the quantity configuration plan of wind, solar and storage is calculated; the multi-energy complementary optimization configuration model of charging stations includes two objectives and constraints; The targets include economic targets and new energy output ratio targets; The multi-energy complementary optimization configuration model of the charging station includes an economic objective function and a new energy output ratio objective function; The economic objective function is expressed as follows: F1=minC Σ =min(C deploy +C Grid ) In the formula, C Σ is the average annual investment cost; C deploy is the equivalent investment cost of the equipment; C Grid is the annual electricity purchase cost; C deploy =C inst +C mer +C loss C mer =d mer C inst C loss =d loss C inst In the formula, C inst , C mer , C loss They are system infrastructure cost, operation and maintenance cost, and loss cost; N PV 、N W 、N B and p PV 、p W 、p B are the purchase quantity and unit price of photovoltaic panels, wind turbines, and energy storage batteries; C ob , C install are fixed investment cost and installation cost respectively; δ mer , δ loss are the ratio coefficients of system operation and maintenance cost and management personnel salary cost to annual construction cost and the ratio coefficient of loss cost to annual construction cost respectively; r is the discount rate; τ is the planning period; Annual electricity purchase cost of the power grid C Grid It can be expressed as: Where P grid (t) represents the power grid output at time t; R g (t) represents the unit price of electricity purchased from the power grid at time t; The target of renewable energy output ratio is based on the load ratio RAT of conventional centralized energy system supplied by large power grid. grid To reflect the output level of new energy RAT rep , the goal of optimizing the configuration of multi-energy complementarity of charging stations is to minimize the proportion of load supplied by the large power grid; In the formula, Q rep is the total power generation of renewable energy in one year, Q EV The total demand load for charging electric vehicles in one year, Q grid is the total output power of the large power grid in one year, P grid (t) is the output power of the large power grid at time t, P EV (t) is the charging demand load of the electric vehicle at time t; The constraints include: power balance constraints, system safety operation constraints, wind, solar and storage equipment quantity constraints, wind and solar power generation equipment output power constraints, energy storage battery output power constraints and energy storage battery capacity constraints; The power balance constraint means that in order to ensure the stable operation of the charging station, the power balance between the power source and the load in the charging station must be met at any time throughout the year. The expression is as follows: P EV (t)=P B (t)+P PV (t)+P W (t)+P grid (t) Where P PV (t), P W (t), P B (t) represents the output of photovoltaic, wind turbine and energy storage at time t, where P B (t) charging is negative and discharging is positive; The system safety operation constraint means that once the external power grid fails, in order to ensure that the local important loads can still operate normally in this state, the new energy power generation load ratio is required during grid-connected operation, and its expression is as follows: P rep (t)≥αP total (t) P rep (t)=P PV (t)+P W (t) P total (t)=P EV (t)+P 站内负荷 (t) Where P rep (t) is the power generated by renewable energy at time t; P total (t) is the total demand load of the charging station at time t; P 站内负荷 (t) is the other load demand in the charging station at time t; α is the minimum self-generation rate of the charging station system, which is initialized to 0.3 and can be modified according to the actual operating status of the charging station; The number of wind, solar and storage devices is constrained, and its expression is as follows: Where N W.max 、N PV.max 、N B.max The maximum number of wind, solar and storage equipment to be installed is determined based on the actual scale and operating conditions of the charging station; The output power constraint of the wind and photovoltaic power generation equipment means that the output power of the wind and photovoltaic power generation equipment at any time must meet the range constraint determined by the wind speed or solar radiation intensity at that time, and its expression is as follows: Where P PV,min , P PV.max is the minimum and maximum output power of the photovoltaic array; P W,min , P W.max is the minimum and maximum output power of the wind turbine generator set; The energy storage battery output power constraint expression is as follows: P B.min ≤P B (t)≤P B.max Where P B,min is the minimum charging and discharging power of the energy storage battery; P B,max The maximum charging and discharging power of the energy storage battery; The energy storage battery capacity constraint expression is as follows: According to the rated capacity and discharge depth of the energy storage battery, the energy storage capacity variation range must meet the following requirements: E B.min ≤E B (t)≤E B.max In the formula, E B.max 、E B.min are the upper and lower limits of the energy storage battery respectively; let the rated capacity of the energy storage battery be E B.max , while E B.min Depends on the maximum discharge depth of the energy storage battery; The electric vehicle and distributed energy and power grid coordinated integration model includes a photovoltaic power generation system model, a wind power generation system model and an energy storage battery system model; The input of the photovoltaic power generation system model is the rated output power P under standard test conditions. STC , Solar radiation intensity under standard test conditions G STC , component surface temperature T under standard test conditions STC , Real-time solar radiation intensity G C , Real-time photovoltaic module surface temperature T C , the output is the actual output power P of the photovoltaic panel PV , which is expressed as follows: Where P PV is the actual output power of the photovoltaic panel; k is the power temperature coefficient; G C , T C is the real-time solar radiation intensity and the surface temperature of the photovoltaic module, T a is the ambient temperature; The wind power generation system model input is the real-time wind speed v, the rated wind speed v r , are the cut-in wind speed v of the fan respectively in , cut-off wind speed v out , fan rated power P r , the output is the actual output power P of the fan at time t W (t), which is expressed as follows: Where P W (t) is the actual output power of the wind turbine at time t; v is the real-time wind speed; v r is the rated wind speed; v in 、v out are the cut-in wind speed and cut-off wind speed of the fan respectively; P r is the rated power of the fan; The energy storage battery system model input is the temperature T of the energy storage battery at the operating point bat , Rated capacity under standard test conditions E STC and temperature T STC , Capacity temperature coefficient σ B , the output is the capacity E of the energy storage battery at the working point bot , which is expressed as follows: AND bat =E STC [1+σ B (T bat -T STC )] Where, T bat is the temperature of the energy storage battery at the working point; E STC , T STC is the rated capacity and temperature under standard test conditions; σ B is the capacity temperature coefficient; The method for obtaining power generation information of wind, solar and storage based on the coordinated integration model of electric vehicles, distributed energy and power grid includes the following steps: Obtain the annual wind and solar resource data of the target area through data collection and software simulation; the wind and solar resource data includes annual solar radiation intensity data and annual wind speed data; According to the annual solar radiation intensity data of the target area, combined with the photovoltaic power generation system model, the annual power generation power of the photovoltaic power generation unit is fitted and the equipment parameters of the photovoltaic panels in the capacity configuration are determined; the equipment parameters of the photovoltaic panels include rated power, open circuit voltage, short circuit current, system voltage, working current, specifications, and price; According to the annual wind speed data of the area, the equipment parameters of the wind power generation equipment in the capacity configuration are determined; the equipment parameters of the wind power generation equipment include the cut-in wind speed v of the wind turbine in , cut-off wind speed v out , fan rated power P r and price, and obtain the real-time wind speed v at the same time, and combine with the wind power generation system model to obtain the actual output power P of the wind turbine at time t W (t); Determine the equipment parameters of the energy storage battery; the equipment parameters of the energy storage battery include the temperature T of the energy storage battery at the working point bat , Rated capacity under standard test conditions E STC and temperature T STC , Capacity temperature coefficient σ B , combined with the energy storage system model, the capacity E of the energy storage battery working point is obtained bot ; According to the power generation information of wind, solar and storage and the charging load demand of electric vehicles, combined with the multi-energy complementary optimization configuration model of charging stations, the method for calculating the quantity configuration plan of wind, solar and storage includes: Obtaining other data, including photovoltaic installation cost, wind turbine installation cost, energy storage battery installation cost and fixed investment cost; The annual wind speed data, solar radiation intensity data, electric vehicle charging load demand and the selected wind power generation equipment and photovoltaic panels are used as input conditions for the wind and solar charging station capacity optimization configuration model; The energy exchange strategy for economic operation of charging stations based on time-of-use electricity prices is used as the operating mechanism of charging stations; The solution is based on the economic goals and the proportion of new energy output. According to the constraints, the multi-objective problem is handled in combination with the Pareto optimal solution theory. The solution is solved using an improved multi-objective group search algorithm to obtain the model iteration results and the optimal solution set distribution. The optimal solution set is the optimal installation quantity distribution of wind, solar and storage equipment while satisfying the two goals as much as possible; the iteration result is the objective function result under multiple optimal solutions, that is, the comprehensive investment cost and grid power supply ratio corresponding to each optimal solution.

2. The typical charging station comprehensive energy collaborative interactive optimization configuration method according to claim 1 is characterized in that: The methods for obtaining the annual wind and solar resource data of the target area through data collection and software simulation include: Obtain the local meteorological data of the project, use the renewable energy complementary power generation optimization modeling software to simulate the annual solar radiation intensity curve, and obtain the annual solar radiation intensity data of the area; The historical data of monthly average wind speed in recent years were obtained from the China Meteorological Data Network. The renewable energy complementary power generation optimization modeling software was used for simulation. The parameters required by the Weibull distribution model and the average wind speed data of each month throughout the year were input to obtain the wind speed change data throughout the year.

3. The typical charging station comprehensive energy collaborative interactive optimization configuration method according to claim 1 is characterized in that: Methods for obtaining electric vehicle charging load demand include: Obtain the number and penetration rate of electric vehicles, and simulate the charging demand of electric vehicles entering the charging station based on the Monte Carlo simulation-based electric vehicle charging demand prediction method to obtain the typical daily charging load demand of the charging station; The annual charging load data of the charging station is calculated on a yearly basis.

4. A typical charging station comprehensive energy collaborative interactive optimization configuration system, characterized in that: include: Power generation information acquisition module: used to obtain wind, solar and storage power generation information based on the coordinated integration model of electric vehicles, distributed energy and power grid; Load demand acquisition module: used to obtain the charging load demand of electric vehicles; Configuration calculation module: used to calculate the quantity configuration scheme of wind, solar and storage according to the power generation information of wind, solar and storage and the charging load demand of electric vehicles, combined with the multi-energy complementary optimization configuration model of charging stations; the multi-energy complementary optimization configuration model of charging stations includes two goals and constraints; the goals include economic goals and new energy output ratio goals; The multi-energy complementary optimization configuration model of the charging station includes an economic objective function and a new energy output ratio objective function; The economic objective function is expressed as follows: F1=minC Σ =min(C deploy +C Grid ) In the formula, C Σ is the average annual investment cost; C deploy is the equivalent investment cost of the equipment; C Grid is the annual electricity purchase cost; C deploy =C inst +C mer +C loss C mer =d mer C inst C loss =d loss C inst In the formula, C inst , C mer , C loss They are system infrastructure cost, operation and maintenance cost, and loss cost; N PV 、N W 、N B and p PV 、p W 、p B are the purchase quantity and unit price of photovoltaic panels, wind turbines, and energy storage batteries; C ob , C install are fixed investment cost and installation cost respectively; δ mer ,δ loss are the ratio coefficients of system operation and maintenance cost and management personnel salary cost to annual construction cost and the ratio coefficient of loss cost to annual construction cost respectively; r is the discount rate; τ is the planning period; Annual electricity purchase cost of the power grid C Grid It can be expressed as: Where P grid (t) represents the power grid output at time t; R g (t) represents the unit price of electricity purchased from the power grid at time t; The target of renewable energy output ratio is based on the load ratio RAT of conventional centralized energy system supplied by large power grid. grid To reflect the output level of new energy RAT rep , the goal of optimizing the configuration of multi-energy complementarity of charging stations is to minimize the proportion of load supplied by the large power grid; In the formula, Q rep is the total power generation of renewable energy in one year, Q EV The total demand load for charging electric vehicles in one year, Q grid is the total output power of the large power grid in one year, P grid (t) is the output power of the large power grid at time t, P EV (t) is the charging demand load of the electric vehicle at time t; The constraints include: power balance constraints, system safety operation constraints, wind, solar and storage equipment quantity constraints, wind and solar power generation equipment output power constraints, energy storage battery output power constraints and energy storage battery capacity constraints; The power balance constraint means that in order to ensure the stable operation of the charging station, the power balance between the power source and the load in the charging station must be met at any time throughout the year. The expression is as follows: P EV (t)=P B (t)+P PV (t)+P W (t)+P grid (t) Where P PV (t), P W (t), P B (t) represents the output of photovoltaic, wind turbine and energy storage at time t, where P B (t) charging is negative and discharging is positive; The system safety operation constraint means that once the external power grid fails, in order to ensure that the local important loads can still operate normally in this state, the new energy power generation load ratio is required during grid-connected operation, and its expression is as follows: P rep (t)≥αP total (t) P rep (t)=P PV (t)+P W (t) P total (t)=P EV (t)+P 站内负荷 (t) Where P rep (t) is the power generated by renewable energy at time t; P total (t) is the total demand load of the charging station at time t; P 站内负荷 (t) is the other load demand in the charging station at time t; α is the minimum self-generation rate of the charging station system, which is initialized to 0.3 and can be modified according to the actual operating status of the charging station; The number of wind, solar and storage devices is constrained, and its expression is as follows: Where N W.max 、N PV.max 、N B.max The maximum number of wind, solar and storage equipment to be installed is determined based on the actual scale and operating conditions of the charging station; The output power constraint of the wind and photovoltaic power generation equipment means that the output power of the wind and photovoltaic power generation equipment at any time must meet the range constraint determined by the wind speed or solar radiation intensity at that time, and its expression is as follows: Where P PV,min , P PV.max is the minimum and maximum output power of the photovoltaic array; P W,min , P W.max is the minimum and maximum output power of the wind turbine generator set; The energy storage battery output power constraint expression is as follows: P B.min ≤P B (t)≤P B.max Where P B,min is the minimum charging and discharging power of the energy storage battery; P B,max The maximum charging and discharging power of the energy storage battery; The energy storage battery capacity constraint expression is as follows: According to the rated capacity and discharge depth of the energy storage battery, the energy storage capacity variation range must meet the following requirements: E B.min ≤E B (t)≤E B.max In the formula, E B.max 、E B.min are the upper and lower limits of the energy storage battery respectively; let the rated capacity of the energy storage battery be E B.max , while E B.min Depends on the maximum discharge depth of the energy storage battery; The electric vehicle and distributed energy and power grid coordinated integration model includes a photovoltaic power generation system model, a wind power generation system model and an energy storage battery system model; The input of the photovoltaic power generation system model is the rated output power P under standard test conditions. STC , Solar radiation intensity under standard test conditions G STC , component surface temperature T under standard test conditions STC , Real-time solar radiation intensity G C , Real-time photovoltaic module surface temperature T C , the output is the actual output power P of the photovoltaic panel PV , which is expressed as follows: Where P PV is the actual output power of the photovoltaic panel; k is the power temperature coefficient; G C 、T C is the real-time solar radiation intensity and the surface temperature of the photovoltaic module, T a is the ambient temperature; The wind power generation system model input is the real-time wind speed v, the rated wind speed v r , are the cut-in wind speed v of the fan respectively in , cut-off wind speed v out , fan rated power P r , the output is the actual output power P of the fan at time t W (t), which is expressed as follows: Where P W (t) is the actual output power of the wind turbine at time t; v is the real-time wind speed; v r is the rated wind speed; v in 、v out are the cut-in wind speed and cut-off wind speed of the fan respectively; P r is the rated power of the fan; The energy storage battery system model input is the temperature T of the energy storage battery at the operating point bat , Rated capacity under standard test conditions E STC and temperature T STC , Capacity temperature coefficient σ B , the output is the capacity E of the energy storage battery at the working point bot , which is expressed as follows: AND bat =E STC [1+σ B (T bat -T STC )] Where, T bat is the temperature of the energy storage battery at the working point; E STC 、T STC is the rated capacity and temperature under standard test conditions; σ B is the capacity temperature coefficient; The method for obtaining power generation information of wind, solar and storage based on the coordinated integration model of electric vehicles, distributed energy and power grid includes the following steps: Obtain the annual wind and solar resource data of the target area through data collection and software simulation; the wind and solar resource data includes annual solar radiation intensity data and annual wind speed data; According to the annual solar radiation intensity data of the target area, combined with the photovoltaic power generation system model, the annual power generation power of the photovoltaic power generation unit is fitted and the equipment parameters of the photovoltaic panels in the capacity configuration are determined; the equipment parameters of the photovoltaic panels include rated power, open circuit voltage, short circuit current, system voltage, working current, specifications, and price; According to the annual wind speed data of the area, the equipment parameters of the wind power generation equipment in the capacity configuration are determined; the equipment parameters of the wind power generation equipment include the cut-in wind speed v of the wind turbine in , cut-off wind speed v out , fan rated power P r and price, and obtain the real-time wind speed v at the same time, and combine with the wind power generation system model to obtain the actual output power P of the wind turbine at time t W (t); Determine the equipment parameters of the energy storage battery; the equipment parameters of the energy storage battery include the temperature T of the energy storage battery at the working point bat , Rated capacity under standard test conditions E STC and temperature T STC , Capacity temperature coefficient σ B , combined with the energy storage system model, the capacity E of the energy storage battery working point is obtained bot ; According to the power generation information of wind, solar and storage and the charging load demand of electric vehicles, combined with the multi-energy complementary optimization configuration model of charging stations, the method for calculating the quantity configuration plan of wind, solar and storage includes: Obtaining other data, including photovoltaic installation cost, wind turbine installation cost, energy storage battery installation cost and fixed investment cost; The annual wind speed data, solar radiation intensity data, electric vehicle charging load demand and the selected wind power generation equipment and photovoltaic panels are used as input conditions for the wind and solar charging station capacity optimization configuration model; The energy exchange strategy for economic operation of charging stations based on time-of-use electricity prices is used as the operating mechanism of charging stations; The solution is based on the economic goals and the proportion of new energy output. According to the constraints, the multi-objective problem is handled in combination with the Pareto optimal solution theory. The solution is solved using an improved multi-objective group search algorithm to obtain the model iteration results and the optimal solution set distribution. The optimal solution set is the optimal installation quantity distribution of wind, solar and storage equipment while satisfying the two goals as much as possible; the iteration result is the objective function result under multiple optimal solutions, that is, the comprehensive investment cost and grid power supply ratio corresponding to each optimal solution.

5. A typical charging station comprehensive energy collaborative interactive optimization configuration system, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-3.

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