Multi-stage optimal configuration method and system for optical storage and charging integrated charging station considering demand growth

Abstract

The invention discloses a multi-stage optimization configuration method and system for an optical storage and charging integrated charging station considering demand growth, belongs to the field of optical storage and charging integrated charging station equipment configuration, and relates to a configuration method and system for performing multi-stage optimization according to an electric vehicle load growth rule. The method comprises the following steps: acquiring inventory information of a current electric vehicle; predicting the electric vehicle ownership at a future time node; simulating and calculating the charging scale and capacity of the service electric vehicle at different time nodes based on the user trip chain; establishing a light storage and charging integrated charging station multi-stage optimization configuration model considering the construction time sequence; and solving the model to obtain the optimal investment and construction year of each device. Facilities are gradually increased according to the electric vehicle load increasing rule, and photovoltaic power generation and energy storage systems are effectively integrated through multi-stage optimal time sequence optimization configuration; charging equipment and power resource configuration are dynamically adjusted, the utilization efficiency of infrastructures is improved, investment is more accurate, operation cost is reduced, and economic benefits are enhanced.

Description

Technical Field

[0001] The present invention discloses a multi-stage optimization configuration method and system for a photovoltaic storage and charging integrated charging station taking into account demand growth, belonging to the field of photovoltaic storage and charging integrated charging station equipment configuration, and in particular relates to a configuration method and a configuration system for performing multi-stage optimization according to the growth law of electric vehicle load. Background Art

[0002] As electric vehicles become more popular around the world, the demand for charging infrastructure is also growing significantly. As a core part of the electric vehicle support system, the construction and operation of electric vehicle charging stations face a series of technical and economic challenges. These challenges include the rapid growth of charging demand, high initial investment costs, and load management of charging infrastructure.

[0003] The integrated photovoltaic, energy storage and charging station integrates solar power generation, energy storage and electric vehicle charging functions. It has the advantages of cleanliness, efficiency, intelligence and integration. It is suitable for cities, communities, enterprises and other scenarios, and helps promote the application of clean energy and the development of electric transportation.

[0004] The initial investment cost of establishing charging infrastructure is high, including the cost of purchasing charging equipment, installation, network construction, land use, and grid transformation. Due to the uncertainty and variability of charging demand, the payback period of investment costs has also become difficult to predict. Especially in the early stages of charging station construction, when demand has not yet fully emerged, how to reasonably allocate resources and reduce initial investment costs is a key issue.

[0005] Although there are currently a variety of technologies and strategies for the construction and optimization of charging infrastructure, existing investment strategies usually require large investments in the initial stage and fail to effectively reduce initial investment costs. Traditional load forecasting methods may not accurately reflect the growth pattern of electric vehicle loads, resulting in excessive or insufficient investment. Existing technical solutions lack adaptability to fluctuations in charging demand and are difficult to dynamically adjust resource allocation according to actual demand. Summary of the invention

[0006] The purpose of the present invention is to address the problems mentioned in the background technology and to provide a multi-stage optimization configuration method and system for a photovoltaic storage and charging integrated charging station taking into account the growth in demand. This is a multi-stage optimal timing optimization configuration method for a photovoltaic storage and charging integrated charging station taking into account the growth law of electric vehicles. It can adjust the load forecasting model in combination with the actual charging demand of electric vehicles to achieve the best economic benefits. It will help to improve the rate of return on infrastructure while ensuring the quality of charging services and better respond to changes in the charging demand of electric vehicles.

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

[0008] The present invention provides a multi-stage optimization configuration method for a photovoltaic storage and charging integrated charging station taking into account demand growth, the method comprising:

[0009] Get the current electric vehicle ownership information;

[0010] Predict the number of electric vehicles in the future;

[0011] Based on the user travel chain simulation, calculate the charging scale and capacity of electric vehicles served at different time nodes;

[0012] Establish a multi-stage optimization configuration model for integrated photovoltaic storage and charging stations taking into account the construction sequence;

[0013] The model is solved to obtain the optimal investment and construction year for each equipment.

[0014] Furthermore, obtaining the current electric vehicle ownership information is to obtain data on the current electric vehicle ownership for several consecutive years. In order to make the predicted data more accurate, the number of years obtained is generally not less than 5 years, and the best number of years is 10 years.

[0015] Furthermore, the number of electric vehicles in use at different time points in the future is predicted, including:

[0016] According to the information on the current number of electric vehicles, considering the impact of pollution reduction and popularization of new energy, the optimal grey model algorithm is used to obtain the number of electric vehicles at future time nodes.

[0017] Furthermore, based on the user travel chain simulation, the charging scale and capacity of electric vehicles served at different time nodes are calculated. The specific process includes:

[0018] Obtaining a user's travel chain, which is related driving data, including but not limited to cruising range, ideal battery capacity, charging power in living areas, charging power in other functional areas, power consumption per 100 kilometers, average speed of electric vehicles, battery relative capacity percentage, and charging efficiency;

[0019] Combined with the obtained user travel chain and the forecast data of electric vehicle ownership, the Monte Carlo sampling method is used to predict the load of electric vehicles at each moment; and the growth rate of the electric vehicle load is calculated; the load refers to the charging scale and capacity.

[0020] Furthermore, a multi-stage optimization configuration model of integrated photovoltaic storage and charging stations is established considering the construction sequence, including:

[0021] Obtain known physical quantities included in the objective function, including but not limited to the service life of the charging pile, energy storage self-discharge efficiency, energy storage unit power investment cost, charging pile unit power investment cost, energy storage battery discharge depth, energy storage unit power maintenance cost, charging pile unit power maintenance cost, residual value rate and equipment discount rate;

[0022] Form an expression with minimum cost as the objective function;

[0023] Existing constraints are listed, including but not limited to energy storage constraints, electric power balance constraints, and photovoltaic output constraints.

[0024] Furthermore, the model solving process includes:

[0025] Writing the corresponding calculation model can be written using MATLAB (Matrix Laboratory) software program;

[0026] Considering the minimum cost and various constraints, the Gurobi solver is used to solve the model and obtain the optimal construction year for each equipment.

[0027] The present invention provides a multi-stage optimization configuration system for a photovoltaic storage and charging integrated charging station taking into account the demand growth, the system comprising:

[0028] The current information acquisition module is used to obtain the current electric vehicle ownership information;

[0029] Future information prediction module, used to predict the number of electric vehicles at future time nodes;

[0030] A simulation calculation module is used to simulate and calculate the charging scale and capacity of electric vehicles served at different time nodes based on the user travel chain;

[0031] Modeling module, used to establish a multi-stage optimization configuration model for integrated photovoltaic storage and charging stations taking into account the construction sequence;

[0032] The model solving module is used to solve the multi-stage optimization configuration model of the above-mentioned integrated photovoltaic storage and charging station to obtain the optimal investment and construction year for each equipment.

[0033] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the multi-stage optimization configuration of the integrated photovoltaic storage and charging station taking into account the demand growth is realized.

[0034] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the multi-stage optimization configuration method of a photovoltaic storage and charging integrated charging station taking into account the growth in demand is implemented.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. Gradually increase facilities according to the growth law of electric vehicle load, avoiding over-investment and waste of resources;

[0037] 2. The multi-stage investment strategy makes investment more accurate, reduces operating costs and enhances economic benefits;

[0038] 3. Through multi-stage optimal timing optimization configuration, charging stations can effectively integrate photovoltaic power generation and energy storage systems, maximize the use of solar energy, and improve overall energy efficiency;

[0039] 4. Dynamically adjust the configuration of charging equipment and power resources to improve the utilization efficiency of infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0041] Figure 1 It is a logical structure diagram of the configuration method of the present invention;

[0042] Figure 2 This is a flow chart of a configuration method according to Embodiment 1 of the present invention;

[0043] Figure 3 This is a flow chart of a configuration method according to Embodiment 2 of the present invention;

[0044] Figure 4 This is a flow chart of electric vehicle charging load prediction in Embodiment 3 of the present invention;

[0045] Figure 5 This is a daily charging load prediction diagram in the fourth embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of multi-stage planning in Embodiment 5 of the present invention;

[0047] Figure 7 This is a multi-stage cost comparison diagram in Example 6 of the present invention;

[0048] Figure 8 is a structural block diagram of a configuration system according to Embodiment 7 of the present invention;

[0049] Fig. 9 It is a schematic diagram of the structure of the computer device in the eighth embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, and the embodiments in the present invention and all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] See attached Figure 1 The present invention provides a multi-stage optimization configuration method for a photovoltaic storage and charging integrated charging station taking into account demand growth, the method comprising:

[0052] Get the current electric vehicle ownership information;

[0053] Predict the number of electric vehicles in the future;

[0054] Based on the user travel chain simulation, calculate the charging scale and capacity of electric vehicles served at different time nodes;

[0055] Establish a multi-stage optimization configuration model for integrated photovoltaic storage and charging stations taking into account the construction sequence;

[0056] The model is solved to obtain the optimal investment and construction year for each equipment.

[0057] The future time nodes can be adjusted according to demand. For example, if you want to understand the forecast under a certain market condition, you may choose a shorter or longer time frame to make a forecast; the forecasted future time nodes need to match the years of current data to ensure that the basis of the forecast is up to date and relevant. For example, if the forecast is based on data from 2012-2022, then its most accurate forecast range is 2023-2033. Forecasts beyond this time range may require more assumptions and uncertainties.

[0058] Embodiment 1:

[0059] like Figure 2 As shown, obtain the data of electric vehicle ownership for several consecutive years (2018-2022);

[0060] According to the data of electric vehicle ownership from 2018 to 2022, considering the impact of factors such as reducing pollution and popularizing new energy, the optimal grey model algorithm is used to take the actual annual pure electric vehicle ownership from 2018 to 2020 as the basic data, and the pure electric vehicle ownership data from 2021 to 2022 is predicted, which verifies the feasibility of the algorithm. The method is continued to be used to obtain the electric vehicle ownership data from 2023 to 2027.

[0061] Then, according to the user travel chain, the Monte Carlo sampling method is used to predict the load of electric vehicles at each moment, and the growth rate of electric vehicle load is calculated;

[0062] Finally, according to the given multi-stage optimal timing optimization configuration method model, it is solved to obtain the optimal investment and construction year for each equipment.

[0063] Embodiment 2:

[0064] like Figure 3 As shown, the data of electric vehicle ownership for several consecutive years (2012-2022) is obtained;

[0065] According to the data of electric vehicle ownership from 2012 to 2022, considering the impact of factors such as reducing pollution and popularizing new energy, the optimal grey model algorithm is used to take the actual annual pure electric vehicle ownership from 2012 to 2017 as the basic data, and the pure electric vehicle ownership data from 2018 to 2022 is predicted, which verifies the feasibility of the algorithm. The method is continued to be used to obtain the electric vehicle ownership data from 2023 to 2033.

[0066] Then, according to the user travel chain, the Monte Carlo sampling method is used to predict the load of electric vehicles at each moment, and the growth rate of electric vehicle load is calculated;

[0067] Finally, according to the given multi-stage optimal timing optimization configuration method model, it is solved to obtain the optimal investment and construction year for each equipment.

[0068] Embodiment three:

[0069] Taking the electric vehicle ownership data of a certain region in a certain province from 2012 to 2022 as a sample, the proposed optimal grey model is used for forecasting analysis.

[0070] The firefly algorithm parameters are set as follows: the number of fireflies is 50, the maximum number of iterations is 100, the maximum attraction coefficient is 1, and the light absorption coefficient is 0.5. The data of pure electric vehicle ownership from 2012 to 2022 is selected as a sample. Taking the pure electric vehicle ownership in this region from 2012 to 2022 as an example, this method is used to take the actual annual pure electric vehicle ownership from 2012 to 2017 as the basic data, and the pure electric vehicle ownership data from 2018 to 2022 is predicted to verify the feasibility of this method, and then the electric vehicle ownership in this region of Jiangsu in the next ten years is predicted.

[0071] The mathematical model of EV (electric vehicle) charging load is as follows:

[0072] The spatiotemporal distribution characteristics of EV charging load are strongly correlated with the user's charging behavior, and the user's charging behavior is often closely related to travel conditions. Travel destinations are now divided into three categories: H (home), work W (work) and other affairs O (other). Relevant surveys show that residents' travel activities generally start and end in area H. The following assumptions are made: the user's initial travel location and final location are both located in area H; the impact of other factors (such as path factors between different functional areas) on EV charging is not considered; the entire travel process is limited to the range of 00:00-24:00.

[0073] The entire travel chain is divided into two parts: the external space chain and the internal time chain. In the external space chain, d is the mileage between two destinations; in the internal time chain, t is the time, and the subscripts (x, y) represent the EV driving state and the destination location, respectively. Among them, x takes the value of 0 or 1. When the value is 0, it indicates the departure state, and when the value is 1, it indicates the arrival state. Δt is the driving time from one destination to the next destination, then:

[0074] Δt stay,i =t (0,i) -t (1,i)

[0075] Where: Δt stay,i is the user’s stay time at destination i; t (0,i) is the arrival time of destination i; t (1,i) is the departure time of destination i.

[0076] The probability of the user's trip start time approximately follows a normal distribution, and its probability density function is:

[0077]

[0078] Where: μ s is the mean of the start time of the first trip, which is 7.89; σ s is the variance of the start time of the first trip, which is 1.96.

[0079] The mileage of each trip of an electric vehicle can be approximated as a log-normal distribution, with the following formula:

[0080]

[0081] Where: d ij is the travel distance from destination i to destination j; σ ij and μ ij is the model fitting parameter, σ ij The value is 0.88, μ ij The value is 3.2.

[0082] In order to quantify the effect of daytime temperature on battery capacity during charging load simulation, a polynomial model is used to fit the relationship between temperature and battery relative capacity. The formula is:

[0083] C r (T) = ρ3T 3 +ρ2T 2 +ρ1T+ρ0

[0084] Where: C r (T) represents the relative capacity percentage of the battery; ρ0, ρ1, ρ2, ρ3 represent the model fitting coefficients; T represents temperature (in °C).

[0085] Actual battery capacity E at different temperatures r for:

[0086] E r =E exp C r (T)

[0087] Where: E exp For the ideal battery level.

[0088] The charging efficiency η of electric vehicles can be expressed as:

[0089]

[0090] Where: P grid The electric energy provided to the grid; P storage Store chemical energy in batteries.

[0091] The present invention assumes that an electric car decides to charge at a certain place. When it arrives at the destination and there is an available charging position for charging, it immediately connects to the charging facility and charges continuously until the ideal power or the maximum stay time is reached. The formula is as follows:

[0092]

[0093] Where: t ch E is the charging time; n is the remaining power after the nth trip; η is the charging efficiency; P ch is the charging power.

[0094] The length of the parking time affects the length of its charging time. stay,i It is closely related to the type of stop location. Existing studies have not yet determined the length of stay Δt. stay,i Therefore, the present invention fits the data into several common distribution forms.

[0095] The probability density of the dwell time in the visual W region follows the generalized extreme value distribution:

[0096]

[0097] The probability density of the dwell time in area H follows the Weibull distribution:

[0098]

[0099] The probability density of the dwell time in region O follows the generalized extreme value distribution:

[0100]

[0101] The regional EV ownership prediction model and EV charging load prediction model are integrated to dynamically predict the EV charging load in the next few years. The process is shown in Figure 4 The firefly algorithm is used to optimize the development coefficient of the initial grey model, and the prediction result is substituted into the grey model output, and finally the predicted value of electric vehicle ownership is obtained.

[0102] Embodiment 4:

[0103] Based on the data of electric vehicle ownership in the region involved in Example 3, the EV charging behavior is simulated and calculated. The specific process is as follows:

[0104] Step 1: The initial battery capacity is assumed to be 90% of the actual battery capacity at different temperatures, and the initial travel time of vehicle i is extracted.

[0105] Step 2: Determine whether the travel activity type is zone H. If so, the destination is the vehicle station; if not, sample the end point according to the regional transfer probability, and obtain the mileage and charging start time of this section from the start and end areas.

[0106] Step 3: Decide whether to charge here based on the owner's charging habits and the battery level after arriving at the destination. If charging is done here, update the regional charging load.

[0107] Step 4: After the last trip of the day, recharge the battery to 90% at the station.

[0108] Step 5: After completing the simulation of all electric vehicle behaviors, the daily charging load curve of each region this year can be obtained, so that the number of electric vehicles in each region in the next year can be predicted.

[0109] The present invention firstly makes a basic prediction of the number of electric vehicles by using the optimal grey model method, and verifies that its error is within the allowable range. Then, based on the Monte Carlo sampling method, physical quantities such as electric vehicle trip chains, first trip time, and dwell time are extracted to obtain the predicted value of electric vehicle charging load from 2023 to 2033. Figure 5 shown.

[0110] Embodiment five:

[0111] The multi-stage planning method is closely integrated with the actual development process of the region. Due to the long planning time, the retirement of equipment that has reached its service life is reasonably arranged according to the actual situation of different equipment. The growth rate of charging load during the planning period is predicted according to the growth trend of regional electric vehicles, and the optimal investment and construction year of each equipment is obtained by solving the optimization model. The multi-stage planning diagram of the integrated photovoltaic storage and charging station is shown in the figure. Figure 6 shown.

[0112] The multi-stage planning model is as follows:

[0113] The objective function of the multi-stage optimization configuration model is to minimize the full life cycle cost, which specifically includes the equipment investment cost, operation and maintenance cost, and equipment residual value within the planning period, and the cost is converted by the equal annual value coefficient. The investment cost is generated at the beginning of the first year of each stage, and the settlement scale of the remaining costs is 1 year, and the settlement period is the end of each year, as shown below:

[0114]

[0115] Where: C is the life cycle cost; is the equipment investment cost of stage k; R Pk is the present value coefficient of the starting year of stage k; are the operating cost and maintenance cost in the nth year respectively; R n is the present value coefficient of the nth year; F RV R is the residual value of the equipment at the end of the planning period; N is the number of years in the entire planning period. N is the present value coefficient at the end of the planning period.

[0116] Equipment investment cost at stage k It can be expressed as:

[0117]

[0118] Where: c inv =[c inv,m ] 1×M Represents the unit capacity investment cost matrix of each type of equipment, where the element c inv,m represents the unit capacity investment cost of type m equipment, It's Q k The transpose of , M represents the number of equipment types to be built.

[0119] The operating cost of the integrated photovoltaic storage and charging station in the nth year It can be expressed as:

[0120]

[0121] Where: cB (t), c S (t) are the electricity prices for purchasing electricity from the grid and selling electricity to the grid during period t; are the power purchased from the power grid and sold to the power grid during period t; T N is the number of time periods a year is divided into; Δt is the length of a single time period.

[0122] Maintenance cost of integrated solar-storage-charging charging station in the nth year It can be expressed as:

[0123]

[0124] Where: c mai =[c mai,m ] 1×M Represents the unit power maintenance cost matrix of each type of equipment, where the element c mai,m P represents the maintenance cost per unit power of type m equipment; mai (t) = [P mai,m ] 1×M Represents the power maintenance matrix of each type of equipment in this period, where the element P mai,m It indicates the power value of type m equipment in this period, and M indicates the number of equipment types to be built.

[0125] Since the life of some equipment has not been terminated at the end of the planning period, the residual value of the equipment after depreciation should be considered, which is expressed as:

[0126]

[0127] Where: M x is the number of remaining equipment at the end of the planning period; T j is the number of years of use of the jth equipment among the remaining equipment at the end of the planning period; c inv,j represents the investment cost of the jth equipment; C dep,j is the annual depreciation cost of the jth equipment.

[0128] C dep,j The calculation method is as follows:

[0129] C dep,j =c inv,j (1-δ j ) / N j

[0130] Where: j is the net residual value rate of the jth equipment; N j is the life span of the jth device.

[0131] The constraints of the multi-stage planning model are as follows:

[0132] 1. Electric power balance constraints

[0133]

[0134] Where: Indicates the PV (photovoltaic) power generation in the region during this period, in kW; Indicates the load power demand of electric vehicles during this period, in kW; Respectively represent the charging and discharging power of ES in the period, in kW; N EV It represents the total number of electric vehicles in the area during this period, in units of vehicles; It represents the load demand of the i-th electric vehicle in the area during this period.

[0135] 2. Regional PV Constraints

[0136] Based on the output characteristics of photovoltaic cells and the maximum power point tracking (MPPT) control principle of photovoltaic systems, according to typical sunlight data, the PV predicted output curve is set as the maximum output curve under the maximum power point tracking mode. The actual power consumption of PV during this period cannot exceed its predicted maximum output. To simplify the calculation, the upper limit of PV output during this period can be calculated by the product of installed capacity and output coefficient, that is,

[0137]

[0138] Where: It represents the actual power consumption of the nth device of PV at time t; Indicates the PV output coefficient predicted during this period; P i PV represents the installed capacity of PV in year i, in kW; A variable indicating whether the PV equipment is retired or not. The value is 0 or 1. 0 indicates retired and 1 indicates not retired. ξ is an arbitrarily large (not infinite) auxiliary relaxation positive number, generally taken as 10 8 , X i A variable that is 0 or 1, where 0 means no new equipment will be built in that year, and 1 means that the photovoltaic storage and charging integrated charging station will be expanded in that year;

[0139] 3. Power storage constraints

[0140] The operation model of the power storage system is:

[0141]

[0142]

[0143] Where: Respectively represent the capacity of ES (electricity storage system) in this period and the previous period; δ e Represents ES self-discharge efficiency; η e,C represents the ES charging efficiency, η e,F It represents the ES discharge efficiency; is the lower limit of ES capacity; represents the new capacity of ES in year i; P i ES Indicates the new ES power in year i (usually with ), the unit is kW; It is the charging state variable of ES, and takes the value of 0 or 1, 0 means ES is not charged, and 1 means ES is charging; is the discharge state variable of ES, with a value of 0 or 1, 0 means ES is not discharged, 1 means ES is discharging; since ES cannot be charged and discharged at the same time, and The sum of the two is less than or equal to 1; A variable indicating whether the ES equipment is retired. Its value is 0 or 1. 0 indicates retired, and 1 indicates not retired.

[0144] Embodiment six:

[0145] In order to verify the superiority of the multi-stage planning method for optimizing construction sequence proposed in this study, three typical planning and construction schemes are set as follows:

[0146] Option 1: Single-stage planning of an integrated photovoltaic, storage and charging electric vehicle charging station, with all required equipment invested at one time.

[0147] Option 2 is a multi-stage plan for integrated photovoltaic, storage and charging stations with equipment to be built in the 1st, 4th and 7th years of the planning period.

[0148] Option 3: a multi-stage plan for an integrated photovoltaic, storage and charging station with equipment construction year to be determined.

[0149] The model was written in MATLAB and solved using the Gurobi solver. Taking the minimum cost and various constraints into consideration, the optimal construction year for each equipment was obtained.

[0150] Depend on Figure 7 As shown, among the three typical schemes, Scheme 1 has the highest total cost, followed by Scheme 2, and Scheme 3, which takes construction sequence into consideration, has the lowest total cost.

[0151] Among the various costs (investment cost, operating cost, maintenance cost), after considering the growth rate of electric vehicle load, the phased construction time obtained according to the decision-making method is lower than the cost of directly taking uniform phased construction. The results obtained by optimization intuitively show the superiority of this method. It can be seen that in the scenario where the construction year can be changed, the configuration of each device is more flexible and the economy is further improved.

[0152] Embodiment seven:

[0153] The present invention also provides a computer device, including a memory and a processor, such as Figure 8 As shown, the memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned embodiment of the multi-stage optimization configuration method of the integrated photovoltaic storage and charging charging station taking into account the growth in demand or the technical solution in the above-mentioned embodiment of the multi-stage optimization configuration method of the integrated photovoltaic storage and charging charging station taking into account the growth in demand. The implementation principles and technical effects are similar and will not be repeated here.

[0154] Embodiment eight:

[0155] See attached Fig. 9 According to an embodiment of the present invention, there is also provided a computer-readable storage medium corresponding to a method for multi-stage optimization configuration of a photovoltaic storage and charging integrated charging station taking into account demand growth, wherein the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the above-mentioned multi-stage optimization configuration of the photovoltaic storage and charging integrated charging station taking into account demand growth.

[0156] Since the processing and functions implemented by the storage medium of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned method, for the details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments, and no further elaboration will be made here.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A multi-stage optimization configuration method for integrated photovoltaic storage and charging stations taking into account demand growth, characterized in that: The method includes: Get the current electric vehicle ownership information; Predict the number of electric vehicles in the future; Based on the user travel chain simulation, calculate the charging scale and capacity of electric vehicles served at different time nodes; Establish a multi-stage optimization configuration model for integrated photovoltaic storage and charging stations taking into account the construction sequence; The model is solved to obtain the optimal investment and construction year for each equipment.

2. The multi-stage optimization configuration method of the photovoltaic storage and charging integrated charging station taking into account the demand growth according to claim 1 is characterized in that: Acquiring the current electric vehicle ownership information is to obtain data on the current electric vehicle ownership for several consecutive years.

3. The multi-stage optimization configuration method of the photovoltaic storage and charging integrated charging station taking into account the demand growth according to claim 3 is characterized in that: The term of acquisition shall not be less than 5 years.

4. The multi-stage optimization configuration method for integrated photovoltaic storage and charging stations taking into account demand growth according to claim 1 or 2, characterized in that: Predict the number of electric vehicles in use at different time points in the future, including: According to the information on the current number of electric vehicles, considering the impact of pollution reduction and popularization of new energy, the optimal grey model algorithm is used to obtain the number of electric vehicles at future time nodes.

5. The multi-stage optimization configuration method of the photovoltaic storage and charging integrated charging station taking into account the demand growth according to claim 1 or 2 is characterized in that: Based on the user travel chain simulation, the charging scale and capacity of electric vehicles served at different time nodes are calculated. The specific process includes: Obtaining a user's travel chain, which is related driving data, including cruising range, ideal battery capacity, charging power in living areas, charging power in other functional areas, power consumption per 100 kilometers, average speed of electric vehicles, battery relative capacity percentage, and charging efficiency; Combined with the obtained user travel chain and the forecast data of electric vehicle ownership, the Monte Carlo sampling method is used to predict the load of electric vehicles at each moment; and the growth rate of the electric vehicle load is calculated; the load refers to the charging scale and capacity.

6. The multi-stage optimization configuration method of the photovoltaic storage and charging integrated charging station taking into account the demand growth according to claim 1 or 2 is characterized in that: A multi-stage optimization configuration model for integrated photovoltaic storage and charging stations considering the construction sequence is established, including: Obtain known physical quantities included in the objective function, wherein the known physical quantities include the service life of the charging pile, the energy storage self-discharge efficiency, the energy storage unit power investment cost, the charging pile unit power investment cost, the energy storage battery discharge depth, the energy storage unit power maintenance cost, the charging pile unit power maintenance cost, the residual value rate and the equipment discount rate; Form an expression with minimum cost as the objective function; Existing constraints are listed, including energy storage constraints, electric power balance constraints, and photovoltaic output constraints.

7. The multi-stage optimization configuration method of the photovoltaic storage and charging integrated charging station taking into account the demand growth according to claim 6 is characterized in that: The objective function is to minimize the total life cycle cost, which specifically includes the equipment investment cost, operation and maintenance cost and equipment residual value within the planning period, and the cost is converted using an equal annual value coefficient.

8. The multi-stage optimization configuration method of the photovoltaic storage and charging integrated charging station taking into account the demand growth according to claim 7 is characterized in that: The constraints are as follows: 1) Electric power balance constraints Where: Indicates the PV power generation in the area during this period, that is, photovoltaic power generation, in kW; Indicates the load power demand of electric vehicles during this period, in kW; Respectively represent the charging and discharging power of ES in the period, in kW; N EV It represents the total number of electric vehicles in the area during this period, in units of vehicles; represents the load demand of the i-th electric vehicle in the area during this period; 2) Regional PV Constraints Based on the output characteristics of photovoltaic cells and the maximum power point tracking control principle of photovoltaic systems, according to typical sunlight data, the PV predicted output curve is set as the maximum output curve under the maximum power point tracking mode. The actual power consumption of PV during this period cannot exceed its predicted maximum output. To simplify the calculation, the upper limit of PV output during this period can be calculated by the product of installed capacity and output coefficient, that is, Where: It represents the actual power consumption of the nth device of PV at time t; Indicates the PV output coefficient predicted during this period; P i PV represents the installed capacity of PV in year i, in kW; A variable indicating whether the PV equipment is retired or not. The value is 0 or 1. 0 indicates retired and 1 indicates not retired. ξ is an arbitrarily large positive auxiliary relaxation number. i A variable that is 0 or 1, where 0 means no new equipment will be built in that year, and 1 means that the photovoltaic storage and charging integrated charging station will be expanded in that year; 3) Power storage constraints The operation model of the power storage system is: Where: Respectively represent the capacity of the ES storage system in this period and the previous period; δ e Indicates ES self-discharge efficiency; η e,C represents the ES charging efficiency, η e,F represents the ES discharge efficiency; is the lower limit of ES capacity; represents the new capacity of ES in year i; P i ES represents the new power of ES in year i, in kW; It is the charging state variable of ES, and takes the value of 0 or 1, 0 means ES is not charged, and 1 means ES is charging; is the discharge state variable of ES, with a value of 0 or 1, 0 means ES is not discharged, 1 means ES is discharging; since ES cannot be charged and discharged at the same time, and The sum of the two is less than or equal to 1; A variable indicating whether the ES equipment is retired. Its value is 0 or 1. 0 indicates retired, and 1 indicates not retired.

9. The multi-stage optimization configuration method of the photovoltaic storage and charging integrated charging station taking into account the demand growth according to claim 6 is characterized in that: The model solving process includes: Write the corresponding computational model using MATLAB software program; Considering the minimum cost and various constraints, the Gurobi solver is used to solve the model and obtain the optimal construction year for each equipment.

10. A multi-stage optimization configuration system for integrated photovoltaic storage and charging stations taking into account demand growth, the system comprising: The current information acquisition module is used to obtain the current electric vehicle ownership information; Future information prediction module, used to predict the number of electric vehicles at future time nodes; A simulation calculation module is used to simulate and calculate the charging scale and capacity of electric vehicles served at different time nodes based on the user travel chain; Modeling module, used to establish a multi-stage optimization configuration model for integrated photovoltaic storage and charging stations taking into account the construction sequence; The model solving module is used to solve the multi-stage optimization configuration model of the above-mentioned integrated photovoltaic storage and charging station to obtain the optimal investment and construction year for each equipment.

11. A computer device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and is characterized in that the memory stores a computer program, and when the processor executes the computer program, it implements the multi-stage optimization configuration method of the integrated photovoltaic storage and charging station taking into account the demand growth as described in any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the multi-stage optimization configuration method of the integrated photovoltaic storage and charging station taking into account the demand growth as described in any one of claims 1 to 9.

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