Optical storage and charging integrated charging station operation planning method and device

Abstract

The invention provides an optical storage and charging integrated charging station operation planning method, which comprises the following steps that a double-layer model of optical storage and charging integrated charging station operation planning driven by dynamic pricing is established, and the upper-layer model takes minimization of user charging total cost as a target function; the lower-layer model takes the minimization of the annual total investment cost of the charging station as a target function; integrating the dynamic pricing and the total charging power of the electric vehicle, and solving a target function of the upper-layer model; and solving the lower-layer model by using a particle swarm algorithm according to the working mode of in-station energy balance to obtain a photovoltaic and energy storage configuration quantity result when the total equivalent annual investment cost of the charging station is lowest. Therefore, the double-layer model is established, the influence of the electricity price change on the user charging behavior is considered, the double-layer model is solved to realize the operation planning of the charging station, the charging cost can be reduced, the configuration result of the charging station is optimal, and the capacity redundancy of the charging station can be avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging station control, and particularly relates to a photovoltaic energy storage and charging integrated charging station operation planning method and device. BACKGROUND

[0002] The rapid growth of the number of electric vehicles has led to an increasing demand for electric vehicle charging stations. At present, due to the continuous development and improvement of photovoltaic power generation technology and energy storage technology, comprehensive charging stations considering photovoltaic energy storage have attracted much attention. However, the volatility and randomness of photovoltaic power generation and electric vehicle charging load also increase the burden of the power grid. This uncertainty may cause the charging station to produce insufficient photovoltaic power supply at a high electricity price and adopt the purchase of electricity to meet the charging demand of electric vehicle load, thereby increasing the power cost. Therefore, reasonable prediction of photovoltaic power generation and charging load, and combination of energy storage system and photovoltaic to suppress the impact of uncertainty and improve photovoltaic energy utilization are the mainstream trend of current electric vehicle charging station construction.

[0003] In the existing research, there are many methods to optimize the equipment capacity of photovoltaic energy storage and charging integrated charging stations. In order to solve the problem of inaccurate and suboptimal solution results of the solving algorithm, many studies consider the algorithm from the perspective of the solving process, and propose different optimization methods such as K-means clustering algorithm, hierarchical clustering algorithm and fuzzy logic algorithm. In addition, many studies consider different optimization indicators from the perspective of the objective function: economic performance indicators are the most commonly used objectives, such as construction and maintenance costs, distribution network losses, investment return rates, user-side economic losses, user numbers, user-charging station distances, driving ranges and user charging demand changes; for technical indicators, some studies consider power loss and electric vehicle service rate; for the environment, energy self-consumption rate and carbon emissions are usually the main indicators considered in the study.

[0004] The above studies widely apply various objectives and optimization algorithms in the capacity configuration stage, but the random characteristics of photovoltaic / load behavior in the actual operation process may lead to infeasible and suboptimal capacity configuration results.

[0005] In addition, the current research considers adjusting the electricity price in the actual operation process, i.e., after the charging station capacity planning is determined and the charging station is constructed and operated, to coordinate the electric vehicle charging load, so as to reduce the charging pressure and charging cost. However, this behavior may lead to charging station capacity redundancy. SUMMARY

[0006] To solve one of the above technical problems, the present application proposes the following technical solution.

[0007] The first aspect of the present application proposes a photovoltaic energy storage and charging integrated charging station operation planning method, which comprises the following steps:

[0008] A bi-level model of operation planning of the integrated charging station of photovoltaic storage and charging driven by dynamic pricing is established, wherein the upper model takes the minimization of total charging cost of users as an objective function, and the lower model takes the minimization of total investment cost of the charging station in equal annual value as an objective function;

[0009] A time-by-time photovoltaic power generation output prediction model is established according to meteorological factors, and the photovoltaic power generation prediction result is obtained by solving the model; the fuzzy membership function is used to divide the membership value of the hourly power generation, and the peak and valley periods of the annual photovoltaic power generation are classified; and the dynamic pricing of the charging station is calculated according to the membership value of the hourly power generation.

[0010] The charging load of the electric vehicle is modeled to describe the charging start time of the electric vehicle, the daily driving mileage of the electric vehicle and the charging duration, the user demand side model is combined to describe the influence degree of the charging price on the charging behavior of the user, and the total charging power of the electric vehicle is extracted by using the Monte Carlo algorithm according to the dynamic pricing of the charging station.

[0011] The objective function of the upper model is solved by comprehensively considering the dynamic pricing of the charging station and the total charging power of the electric vehicle.

[0012] An energy management model of the energy storage system is established, and the working mode of the station energy balance is formed by combining the photovoltaic power generation prediction result and the total charging power of the electric vehicle under the constraint that the charging station purchases power from the power distribution network.

[0013] The lower model is solved by using the particle swarm algorithm according to the working mode of the station energy balance, and the results of the photovoltaic and energy storage configuration quantity when the total investment cost of the charging station in equal annual value is the lowest are obtained.

[0014] In addition, the operation planning method of the integrated charging station of photovoltaic storage and charging according to the above-mentioned embodiments of the application can also have the following additional technical features.

[0015] According to one embodiment of the application, the objective function of the upper model is:

[0016]

[0017] In the formula, f evch is the total charging cost of users, p evn (t) is the charging power of the nth electric vehicle entering the charging station at t time, c1(t) is the dynamic pricing of the charging station at t time, N evt is the number of electric vehicles entering the charging station at t time, and Hy is the number of hours in a year.

[0018] The constraint conditions of the upper model include the charging power constraint of the electric vehicle and the constraint of the number of electric vehicles parallel charging in the integrated charging station of photovoltaic storage and charging.

[0019] According to one embodiment of the present application, the objective function of the lower layer model is:

[0020] min C initial =C invest +C mt +C aba +C pbuy (21)

[0021] In the formula, C initial is the annual total investment cost of the charging station; C invest is the annual investment cost of the charging station; C mt is the annual operation and maintenance cost of the charging station; C aba is the annual light abandonment benefit loss of the charging station; C pbuy is the annual electricity purchase cost of the charging station.

[0022] Annual total investment cost of the charging station:

[0023]

[0024] In the formula, r is the discount rate; n k (k = 1, 2, 3) are the service lives of the photovoltaic array, the energy storage system, and the charging station respectively; P pv , P bat are the rated output powers of the photovoltaic array and the energy storage system; U pv , U bat are the unit rated power investment costs of the photovoltaic array and the energy storage system; N pv , N bat , N ch are the installation quantities of the photovoltaic array, the energy storage system, and the charging pile respectively; and U ch is the unit price of the charging pile.

[0025] The maximum charging demand of the electric vehicle per unit time is effectively estimated for the installation quantity of the charging pile:

[0026]

[0027] In the formula, C is the maximum charging demand of the electric vehicle per unit time; p ev is the charging power of each electric vehicle, η ev is the battery charging efficiency of each electric vehicle, T t is the unit charging time length, and β is the parallel charging probability.

[0028] Annual operation and maintenance cost of the charging station:

[0029]

[0030] In the formula, C mtThe annual operation and maintenance cost of the charging station; The annual operation and maintenance cost of the photovoltaic array and the energy storage system per unit rated power; pv (t), P bat (t) is the rated output power of the photovoltaic array and the power of the energy storage system at time t, respectively; C is the operation and maintenance proportion coefficient of the charging pile, ch N is the operation and maintenance cost of a single charging pile, ch Hy is the number of charging piles installed, and Hy is the number of hours in a year;

[0031] The annual light abandonment income loss of the charging station is:

[0032]

[0033] In the formula, C aba is the annual light abandonment income loss of the charging station; φ aba is the light abandonment income loss coefficient, E aba (t) is the light abandonment power of the charging station at time t.

[0034] The constraint conditions of the lower layer model include: photovoltaic output constraint of the light storage and charging integrated charging station, energy storage system charge and discharge state, state of charge, charge and discharge power constraint, power balance constraint in the light storage and charging integrated charging station, and light abandonment rate constraint.

[0035] According to one embodiment of the application, the photovoltaic power generation output prediction model is:

[0036]

[0037] In the formula, P pv (t) is the unit photovoltaic array rated output power at time t; N pv is the number of photovoltaic arrays installed; is the latitude of the photovoltaic area to be built is the hourly average value of the surface solar radiation intensity at the mth month, the dth day and the hth hour at the latitude; G0 is the standard radiation intensity; θ T is the power temperature coefficient of the photovoltaic array; T is the temperature of the running photovoltaic array; T r is the reference temperature of the photovoltaic array;

[0038] The fuzzy membership function is as follows:

[0039]

[0040] In the formula, g f (t) and g g (t) respectively represent the membership degrees of the photovoltaic power at time t under the peak and valley model calculations, respectively; Q tis the photovoltaic power at time t; a and b are the minimum and maximum values of the photovoltaic power prediction result;

[0041] The peak-valley period is divided according to the expression:

[0042]

[0043] In the expression, T f is the peak period; T p is the flat period; T g is the valley period; θ f and θ g are the membership threshold values for dividing the peak-valley period; g f (t) and g g (t) are the peak and valley membership values of the photovoltaic power at time t, respectively.

[0044] The calculation formula of the dynamic pricing of the charging station is:

[0045] c1(t) = c0(t) + k·g(t) (30)

[0046] In the expression, c1(t) represents the dynamic pricing of the charging station at time t; c0(t) represents the original charging price of the charging station at time t; g(t) represents the membership value of the corresponding peak or valley period to which the photovoltaic power at time t belongs, and g(t) takes the value of g f (t) or g g (t) determined according to the expression (10) after the period is determined; if it is the flat period, the value is 0; k represents the dynamic pricing coefficient.

[0047] According to one embodiment of the present application, the charging start time of the electric vehicle is subject to a normal distribution, and the probability density function is:

[0048]

[0049] In the expression, T st is the charging start time of the electric vehicle, μ s1 and μ s2 are the mean values of the charging start time, σ s1 and σ s2 are the standard deviations of the charging start time.

[0050] The daily driving mileage of the electric vehicle is subject to a lognormal distribution, and the probability density function is:

[0051]

[0052] In the expression, s day is the daily driving mileage of the electric vehicle, μ d and σ drespectively are the expectation and standard deviation of the daily driving mileage probability density function of the electric vehicle;

[0053] The function relationship of the charging duration of the electric vehicle is expressed as:

[0054]

[0055] In the formula, T ev is the charging duration of the electric vehicle, η ev is the charging efficiency of the battery of each electric vehicle, p ev is the charging power of each electric vehicle, E h is the power consumption per 100 kilometers of each electric vehicle.

[0056] According to one embodiment of the present application, the user demand side model is:

[0057]

[0058] In the formula, λ is the rate of the electric vehicle user changing the charging behavior under the influence of dynamic pricing; Δc fg is the peak-valley price difference of the electricity price obtained by dynamic pricing; λ max is the maximum response rate; c a is the response price difference starting value; c b is the response price difference saturation value.

[0059] According to one embodiment of the present application, the calculation formula of the total charging power of the electric vehicle is:

[0060]

[0061] In the formula, P ev (t) is the total charging power of the electric vehicle at t, p evn (t) is the charging power of the nth electric vehicle entering the charging station at t, N evt is the number of electric vehicles entering the charging station within t, and 24 is the number of hours in a day.

[0062] According to one embodiment of the present application, the energy management model of the energy storage system is:

[0063]

[0064] In the formula, SOC bat (t) is the state of charge of the energy storage system at t, SOC bat (t0) is the state of charge of the energy storage system at initial t0, β bat is the self-discharge rate of the energy storage system, γ cbat , γ dbat are the charging and discharging state variables of the energy storage system, and Δt is the unit scheduling time scale.cbat (t) is the charging power of the energy storage system at time t, taking a positive value; P dbat (t) is the discharging power of the energy storage system at time t, taking a negative value; η cbat , η dbat is the charging and discharging efficiency of the energy storage system, V bat is the total capacity of the energy storage system.

[0065] According to one embodiment of the present application, the constraint of the charging station purchasing power from the power grid is as follows:

[0066]

[0067] In the formula, P buy (t) is the power purchased by the charging station from the power grid, is the upper limit of the power provided by the power grid to the charging station at time t.

[0068] The expression of the working mode of the energy balance in the station is:

[0069] P pv (t) - P dbat (t) + P buy (t) = P ev (t) + P cbat (t) (38)

[0070] In the formula, P pv (t) is the rated output power of a unit photovoltaic array at time t; P cbat (t) is the charging power of the energy storage system at time t, taking a positive value; P dbat (t) is the discharging power of the energy storage system at time t, taking a negative value; P ev (t) is the total charging power of the electric vehicle at time t; P buy (t) is the power purchased by the charging station from the power grid.

[0071] The second aspect embodiment of the present application proposes a photovoltaic energy storage and charging integrated charging station operation planning device, comprising:

[0072] A first establishing module is configured to establish a double-layer model of a dynamic pricing driven photovoltaic energy storage and charging integrated charging station operation planning, wherein the upper-layer model takes the minimization of the total charging cost of users as an objective function, and the lower-layer model takes the minimization of the equal annual value total investment cost of the charging station as an objective function.

[0073] a second establishing module, configured to establish a time-by-time photovoltaic power generation output prediction model according to meteorological factors, and to solve the photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction result, to divide an hourly power generation membership value of the photovoltaic power generation prediction result by using a fuzzy membership function, and to classify to obtain a peak-valley period of annual photovoltaic power generation, and to calculate a dynamic pricing of the charging station according to the hourly power generation membership value;

[0074] a third establishing module, configured to model a charging load of the electric vehicle, to describe a charging start time of the electric vehicle, a daily driving distance of the electric vehicle, and a charging duration, to combine a user demand side model describing an influence degree of the charging price on the charging behavior of the user, and to extract a total charging power of the electric vehicle according to the dynamic pricing of the charging station by using a Monte Carlo algorithm;

[0075] a first solving module, configured to solve a target function of the upper model by comprehensively considering the dynamic pricing of the charging station and the total charging power of the electric vehicle;

[0076] a fourth establishing module, configured to establish an energy management model of an energy storage system, to form a station energy balance working mode according to the photovoltaic power generation prediction result and the total charging power of the electric vehicle under the constraint of a power purchase of the charging station from a power distribution network;

[0077] a second solving module, configured to solve the lower model by using a particle swarm optimization algorithm according to the station energy balance working mode, to obtain a result of a photovoltaic and energy storage configuration quantity when a total investment cost of the charging station is lowest.

[0078] The technical scheme of the embodiment of the application establishes an upper model with a minimum total charging cost of the user as a target and a lower model with a minimum total investment cost of the charging station as a target, considers an influence of a price change on the charging behavior of the user, solves the double-layer model to realize operation planning of the charging station, optimizes the charging station configuration result while reducing the charging cost, and avoids charging station capacity redundancy. BRIEF DESCRIPTION OF DRAWINGS

[0079] Figure 1 A flowchart of a photovoltaic energy storage and charging integrated charging station operation planning method according to an embodiment of the application.

[0080] Figure 2A A particle swarm optimization algorithm iteration process diagram of a specific example of the application.

[0081] Figure 2B A particle swarm optimization algorithm convergence process iteration diagram of a specific example of the application.

[0082] Figure 2C A power purchase cost iteration diagram of a particle swarm optimization algorithm of a specific example of the application.

[0083] Figure 3A A typical daily operation state diagram in spring for one specific example of the present application.

[0084] Figure 3B A typical daily operation state diagram in summer for one specific example of the present application.

[0085] Figure 4A A typical daily operation state diagram in spring for another specific example of the present application.

[0086] Figure 4B A typical daily operation state diagram in summer for another specific example of the present application.

[0087] Figure 4C A typical daily operation state diagram in autumn for another specific example of the present application.

[0088] Figure 4D A typical daily operation state diagram in winter for another specific example of the present application.

[0089] Figure 5 A comparison diagram of user charging price per hour before and after compensation price for one specific example of the present application.

[0090] Figure 6 A comparison diagram of electric vehicle load before and after guidance for one specific example of the present application.

[0091] Figure 7 A structure block diagram of an operation planning device of a light storage and charging integrated charging station according to an embodiment of the present application. DETAILED DESCRIPTION

[0092] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application.

[0093] The present application provides a light storage and charging integrated charging station operation planning method, which considers dynamic pricing driving, accurately models operation characteristics, and considers the change of user charging behavior in actual process affected by price fluctuation, to obtain the best design of the light storage and charging integrated charging station.

[0094] Figure 1 A flowchart of the light storage and charging integrated charging station operation planning method according to an embodiment of the present application.

[0095] As shown in Figure 1 , the light storage and charging integrated charging station operation planning method includes the following steps S1 to S6.

[0096] S1, a double-layer model of the operation planning of the integrated charging station is established, wherein the upper-layer model takes the minimization of the total charging cost of users as an objective function, and the lower-layer model takes the minimization of the total equivalent annual investment cost of the charging station as an objective function.

[0097] Specifically, the optimization subject of the upper-layer model is the electric vehicle user, the price guide is considered, and the total charging cost of the user is minimized, and the objective function of the upper-layer model is:

[0098]

[0099] In the formula, f evch is the total charging cost of the user, p evn (t) is the charging power of the nth electric vehicle entering the charging station at t, c1(t) is the dynamic pricing of the charging station at t, N evt is the number of electric vehicles entering the charging station at t, and Hy is the number of hours in a year, for example, when the year is 365 days, Hy = 365*24 = 8765.

[0100] The constraint conditions of the upper-layer model include: the charging power constraint of the electric vehicle and the parallel charging quantity constraint of the electric vehicle in the integrated charging station of photovoltaic energy storage and charging.

[0101] The optimization subject of the upper-layer model is the integrated charging station of photovoltaic energy storage and charging, the optimal load characteristics are simulated, the total cost of the charging station planning is considered to be the lowest, and the objective function of the lower-layer model is:

[0102] min C initial = C invest +C mt +C aba +C pbuy (40)

[0103] In the formula, C initial is the total equivalent annual investment cost of the charging station; C invest is the equivalent annual investment cost of the charging station; C mt is the annual operation and maintenance cost of the charging station; C aba is the annual light loss benefit loss of the charging station; C pbuy is the annual electricity purchase cost of the charging station.

[0104] The equivalent annual investment cost of the charging station is:

[0105]

[0106] In the formula, r is the discount rate; n k (k = 1, 2, 3) are the service lives of the photovoltaic array, the energy storage system and the charging station respectively; P pv , P bat are the rated output powers of the photovoltaic array and the energy storage system.pv , U bat is the unit rated power investment cost of photovoltaic array and energy storage system; N pv , N bat , N ch is the installation quantity of photovoltaic array, energy storage system and charging pile; U ch is the unit price of charging pile;

[0107] The maximum charging demand of electric vehicles in unit time is effectively estimated for the installation quantity of charging piles:

[0108]

[0109] In the formula, is the maximum charging demand of electric vehicles in unit time; p ev is the charging power of each electric vehicle, η ev is the battery charging efficiency of each electric vehicle, T t is the unit charging time; β is the parallel charging probability;

[0110] The annual operation and maintenance cost of charging station:

[0111]

[0112] In the formula, C mt is the annual operation and maintenance cost of charging station; is the unit rated power operation and maintenance cost of photovoltaic array and energy storage system; P pv (t), P bat (t) are the rated output power of photovoltaic array and the power of energy storage system at t moment respectively; is the operation and maintenance proportion coefficient of charging pile, C ch is the operation and maintenance cost of single charging pile, N ch is the installation quantity of charging pile, and Hy is the annual hours;

[0113] The annual loss of abandoned light income of charging station:

[0114]

[0115] In the formula, C aba is the annual loss of abandoned light income of charging station; φ aba is the loss coefficient of abandoned light income, E aba (t) is the abandoned light power of charging station at t moment.

[0116] The constraint conditions of the lower model include: photovoltaic output constraint of light storage and charging integrated charging station, power balance constraint, power balance constraint, and abandoned light rate constraint.

[0117] S2, a time-based photovoltaic power generation output prediction model is established according to meteorological factors, and a photovoltaic power generation prediction is obtained by solving the model, a fuzzy membership function is used to divide the hourly power generation membership value of the photovoltaic power generation prediction result, and the peak and valley periods of the annual photovoltaic power generation are classified, and the dynamic pricing of the charging station is calculated according to the hourly power generation membership value.

[0118] S3, the electric vehicle charging load is modeled, the electric vehicle charging start time, the electric vehicle daily driving distance and the charging time are described, the user demand side model describing the influence degree of the charging price on the user charging behavior is combined, and the total charging power of the electric vehicle is extracted by using the Monte Carlo algorithm according to the dynamic pricing of the charging station.

[0119] S4, the dynamic pricing of the charging station and the total charging power of the electric vehicle are integrated, and the objective function of the upper model is solved.

[0120] Specifically, in the upper model, first, a photovoltaic power generation output prediction model (7) is established to obtain a photovoltaic power generation prediction result, a fuzzy membership function (8) (9) is used to divide the hourly power generation membership value of the photovoltaic power generation prediction result, and the peak and valley periods of the annual photovoltaic power generation are classified (10). According to the hourly power generation membership value, the dynamic pricing of the charging pile is calculated (11).

[0121] Then, the electric vehicle charging load is modeled, the electric vehicle charging start time (12), the electric vehicle daily driving distance (13) and the charging time (14) are described. Combined with the user demand side model (15), that is, the influence degree of the charging price on the user charging behavior, the dynamic pricing (11) obtained in the above steps is used to extract the electric vehicle charging load (16) by using the Monte Carlo algorithm.

[0122] Finally, the dynamic pricing (11) and the total charging power (16) of the electric vehicle charging are integrated, and the objective function (1) of the upper model is solved.

[0123] S5, an energy management model of the energy storage system is established, and under the constraint of the power purchase of the charging station from the power distribution network, the photovoltaic power generation prediction result and the total charging power of the electric vehicle are combined to form a station energy balance working mode.

[0124] S6, the station energy balance working mode is integrated, and the lower model is solved by using the particle swarm algorithm to obtain the photovoltaic and energy storage configuration quantity result when the total investment cost of the charging station is the lowest.

[0125] Specifically, in the lower layer model, the energy management mode of the energy storage system is modeled (17) and solved. Under the constraint of the power purchased from the power distribution network at the charging station (18), combined with the above-mentioned photovoltaic power generation prediction result and the prediction result of the electric vehicle charging load, the working mode of the station energy balance is formed (19). The particle swarm algorithm is used to solve the lower layer model, and the photovoltaic and energy storage configuration quantity result when the equal annual total investment cost of the charging station is the lowest is obtained.

[0126] The optimization subject is the photovoltaic energy storage capacity configuration in the photovoltaic energy storage integrated charging station, based on the time scale analysis of the whole year, the particle swarm algorithm is used to solve the photovoltaic array N pv and the energy storage unit N bat configuration result, the equal annual total investment cost of the charging station is calculated according to formula (2), and the iteration is continuously carried out until the convergence condition is met or the iteration number requirement is met.

[0127] The embodiment of the present application analyzes the influence of dynamic pricing on the charging behavior of users from the operation process, studies the capacity configuration problem of the charging station operation planning from the perspective of the photovoltaic energy storage integrated charging station operation planning:

[0128] From the perspective of electric vehicle users, the charging demand under the driving of dynamic pricing is analyzed, the charging cost of users is minimized, and the user satisfaction is improved. The optimized charging load distribution is beneficial to the simulation of the operation analysis of the charging station.

[0129] In order to consider the influence of the charging load change under the driving of dynamic pricing on the capacity planning result of the photovoltaic energy storage integrated charging station, a double-layer model is proposed. The upper layer of the model takes the electric vehicle user as the subject, optimizes the load distribution, and minimizes the charging cost. The lower layer of the model, based on the load distribution obtained by the upper layer, solves the optimal result of the photovoltaic energy storage integrated charging station configuration by simulating the operation of the charging station.

[0130] Therefore, the photovoltaic energy storage integrated charging station operation planning method of the embodiment of the present application establishes the upper layer model with the minimum total charging cost of users as the target and the lower layer model with the minimum equal annual total investment cost of the charging station, considers the influence of the price change on the charging behavior of users, solves the double-layer model to realize the operation planning of the charging station, can optimize the charging station configuration result while reducing the charging cost, and can avoid the capacity redundancy of the charging station.

[0131] In one embodiment, the photovoltaic power generation output prediction model is:

[0132]

[0133] In the formula, P pv (t) is the unit photovoltaic array rated output power at t moment; N pv is the installation number of the photovoltaic array. Gm,d,h is the hourly average of the ground solar radiation intensity of the mth month, dth day and hth hour in the photovoltaic region (latitude is ) where the photovoltaic array is to be built; G0 is the standard radiation intensity; θ T is the power temperature coefficient of the photovoltaic array; T is the temperature of the running photovoltaic array; T r is the reference temperature of the photovoltaic array.

[0134] In one embodiment, the fuzzy membership function is as follows:

[0135]

[0136] In the formula, g f (t) and g g (t) respectively represent the membership of the photovoltaic power at time t under the calculation of the peak and valley model; Q t is the photovoltaic power at time t; a and b are the minimum and maximum values of the photovoltaic power prediction result.

[0137] The expression for dividing the "peak-valley" period is as follows:

[0138]

[0139] In the formula, T f is the peak period; T p is the flat period; T g is the valley period; θ f and θ g are the membership threshold values for dividing the peak-valley period; g f (t) and g g (t) are respectively the peak and valley membership values of the photovoltaic power at time t;

[0140] The calculation formula of the dynamic pricing of the charging station is as follows:

[0141] c1(t) = c0(t) + k·g(t) (49)

[0142] In the formula, c1(t) represents the dynamic pricing of the charging station at time t; c0(t) represents the original charging price of the charging station at time t; g(t) represents the membership value of the corresponding peak or valley period to which the photovoltaic power at time t belongs (g(t) takes the value of g f (t) or g g (t) after the period is determined according to formula (10), and if it is a flat period, the value is 0); k represents the dynamic pricing coefficient.

[0143] In one embodiment, the starting time of the electric vehicle charging obeys the normal distribution, and the probability density function is as follows:

[0144]

[0145] where T st is the charging start time, μ s1 is the mean of charging start time, σ s2 is the standard deviation of charging start time. s1 s2

[0146] The daily driving distance of the electric vehicle obeys a lognormal distribution, and the probability density function is:

[0147]

[0148] where s day is the daily driving distance of the electric vehicle, μ d and σ d are the mean and standard deviation of the probability density function of the daily driving distance of the electric vehicle, respectively.

[0149] The charging duration of the electric vehicle is related to the daily driving distance s day of the electric vehicle, the battery charging efficiency η ev , and the charging power p ev , and the function relationship of the charging duration can be expressed as:

[0150]

[0151] where T ev is the charging duration of the electric vehicle, η ev is the battery charging efficiency of each electric vehicle, p ev is the charging power of each electric vehicle, and E h is the power consumption per 100 kilometers of each electric vehicle.

[0152] In an embodiment, the user demand side model is:

[0153]

[0154] where λ is the rate of electric vehicle users changing charging behavior under the influence of dynamic pricing; Δc fg is the peak-valley price difference of the electricity price obtained by dynamic pricing; λ max is the maximum response rate; c a is the response price difference starting value; c b is the response price difference saturation value.

[0155] The Monte Carlo method is used to calculate the charging load of the electric vehicle, and the charging time, the initial state of charge, the number of charging vehicles, the charging power p ev , the daily driving distance, and other factors are comprehensively considered. The probability density distribution function is randomly sampled to obtain the charging load of all electric vehicles. ​​

[0156] The total charging power of the electric vehicle is calculated by the following formula:

[0157]

[0158] where P ev (t) is the total charging power of the electric vehicle at time t, P evn (t) is the charging power of the nth electric vehicle entering the charging station at time t, N evt is the number of electric vehicles entering the charging station within time t, and 24 is the number of hours in a day.

[0159] In one embodiment, the energy management model of the energy storage system is:

[0160]

[0161] where SOC bat (t) is the state of charge of the energy storage system at time t, SOC bat (t0) is the initial state of charge of the energy storage system at time t0, β bat is the self-discharge rate of the energy storage system, γ cbat , γ dbat are the charging and discharging state variables of the energy storage system, Δt is the unit scheduling time scale, P cbat (t) is the charging power of the energy storage system at time t, taking a positive value, P dbat (t) is the discharging power of the energy storage system at time t, taking a negative value, η cbat , η dbat are the charging and discharging efficiencies of the energy storage system, V bat is the total capacity of the energy storage system.

[0162] In one embodiment, the constraints of the charging station purchasing power from the power distribution network are as follows:

[0163]

[0164] where P buy (t) is the power purchased by the charging station from the power distribution network at time t, P is the upper limit of the power provided by the power distribution network to the charging station at time t.

[0165] The working mode expression of the energy balance in the station is:

[0166] P pv (t) - P dbat (t) + P buy (t) = P ev (t) + P cbat (t) (57)

[0167] where P pv(t) is the unit photovoltaic array rated output power at time t; P cbat (t) is the charging power of the energy storage system at time t, taking a positive value; P dbat (t) is the discharging power of the energy storage system at time t, taking a negative value; P ev (t) is the total charging power of the electric vehicle at time t; P buy (t) is the power purchased by the charging station from the power distribution network.

[0168] In general, the embodiment of the present application considers the change of the user charging behavior caused by the change of the electricity price, that is, the change of the electric vehicle charging load, takes the integrated photovoltaic storage and charging station as the optimization subject, minimizes the equal annual value investment cost as the optimization target, and fully models the energy management of the energy storage system in the integrated photovoltaic storage and charging station in consideration of the energy storage regulation characteristics. In the operation planning method of the integrated photovoltaic storage and charging station driven by dynamic pricing, on the basis of the original planning method of the integrated photovoltaic storage and charging station (the configuration results of the photovoltaic array, the energy storage unit and the charging pile are obtained through the photovoltaic power generation and the electric vehicle load prediction results, and the simulation operation), the demand side response is considered in the load prediction stage, that is, the influence of the dynamic pricing on the user charging behavior, which plays a role in optimizing the load distribution and reducing the redundancy of the capacity configuration of the charging station. The influence of the load optimization result on the planning of the charging station is comprehensively considered, and a double-layer planning model is established. In the upper-layer model, the optimal load distribution is solved, and the result is input into the lower-layer model. The particle swarm optimization algorithm is used to simulate the operation of the charging station, and the optimal result of the capacity configuration of the charging station is obtained through continuous iteration.

[0169] The simulation results of the operation planning method of the integrated photovoltaic storage and charging station of the present application are described below in combination with the drawings, and specifically as follows:

[0170] The configuration results of the integrated photovoltaic storage and charging station without the constraint of no light waste and without considering dynamic pricing are simulated and analyzed. The iteration process of the particle swarm optimization algorithm is shown in Figure 2A , 2B and 2C.

[0171] The simulation configuration result is that the equal annual value total investment cost is 4781500 yuan, the number of photovoltaic array configurations is 966, the number of energy storage unit configurations is 1730, the equal annual value investment cost of the charging station is 2213200 yuan, the annual operation and maintenance cost of the charging station is 293200 yuan, the light waste income loss of the charging station is 97274 yuan, and the annual power purchase cost is 2177900 yuan. Through calculation, the annual photovoltaic utilization rate of the station at this time reaches 98.79%.

[0172] Typical days in typical spring and summer seasons are selected for analysis, and the single-day hourly station operation state is shown in Figure 3A , 3B .

[0173] Under the condition of the electric vehicle charging demand load and the environmental photovoltaic condition being constant, combined with the fluctuation of real-time electricity price, on the basis of the above analysis of the charging and discharging of the energy storage system, reasonable electricity purchase for storage is considered at the electricity price low valley to ensure the user demand condition, so as to realize the reduction of the total annual investment cost.

[0174] The operation conditions of the station in a typical year of four seasons are analyzed. The operation results are shown in Figure 4A 、 4B , 4C and 4D.

[0175] The simulation configuration results are as follows: the total investment cost of equal annual value is 4306400 yuan, the configuration number of photovoltaic array is 1229, the configuration number of energy storage unit is 1700, the equal annual value investment cost of the charging station is 2670700 yuan, the annual operation and maintenance cost of the charging station is 338580 yuan, the abandoned light revenue loss of the charging station is 130410 yuan, and the annual electricity purchase cost is 1166700 yuan. Through calculation, the abandoned light rate of the station is 25.74%.

[0176] Considering the rationality of the light-storage capacity configuration under different abandoned light rate constraints, the economic and reasonable configuration mode is selected by analyzing and comparing the light-storage capacity configuration, the equal annual value investment and operation and maintenance cost of the charging station, the annual abandoned light revenue loss and the abandoned light rate. The optimal light-storage capacity configuration under different abandoned light rate constraints is shown in Table 1.

[0177] Table 1 Comparison of optimal light-storage capacity configuration results under different abandoned light rate constraints

[0178]

[0179]

[0180] The results in Table 1 show that under the reasonable condition, the charging of the energy storage system from the power grid can effectively reduce the total annual investment cost by at least 9.9%. In addition, the running of a certain degree of abandoned light phenomenon exists, which can further improve the economic benefit.

[0181] After considering the dynamic pricing, the user charging price and the electric vehicle charging load are shown in Figure 5 and Figure 6 .

[0182] Under the action of the electricity price compensation mechanism, the annual investment cost of the light-storage-charging integrated charging station is reduced from 4306400 yuan to 4124800 yuan. This shows that the method plays a good role in optimization configuration.

[0183] The total annual cost of the configuration of the light storage and charging station is 4124800 yuan under the comprehensive consideration of the economic benefits of the charging users. Compared with the configuration requirement of 4306400 yuan mentioned above, the result is obviously better (charging cost after electric vehicle guidance, 3361949 yuan; charging cost before electric vehicle guidance, 10294270 yuan).

[0184] In summary, the operation planning of the light storage and charging integrated charging station considering dynamic pricing is proposed based on the size of photovoltaic output, and appropriate pricing strategies are proposed. Through user demand side response, the electric vehicle charging load characteristics are reasonably changed, on the one hand, the user charging cost is reduced, and on the other hand, the contradiction between low photovoltaic power and large charging load demand or high photovoltaic power and small charging load demand can be alleviated, the capacity redundancy of the charging station is reduced, and the total annual cost of the station is reduced.

[0185] The energy management of the energy storage system in the station is fully modeled, which can effectively suppress the uncertainty of photovoltaic output and improve the consumption capacity of the charging station to photovoltaic; at the same time, considering the coordination of "power purchase-energy storage", the power can be purchased to store energy when the grid price is appropriate, to meet the demand, and further improve the operation economy of the light storage and charging integrated charging station.

[0186] Corresponding to the operation planning method of the light storage and charging integrated charging station of the above embodiment, the application also proposes a light storage and charging integrated charging station operation planning device.

[0187] Figure 7 The structure block diagram of the light storage and charging integrated charging station operation planning device of the embodiment of the application is shown.

[0188] As shown in Figure 7 , the light storage and charging integrated charging station operation planning device comprises:

[0189] The first establishment module 10 is used for establishing a double-layer model of the light storage and charging integrated charging station operation planning driven by dynamic pricing, wherein the upper-layer model takes the minimization of the total charging cost of the user as the objective function, and the lower-layer model takes the minimization of the annual value total investment cost of the charging station as the objective function;

[0190] The second establishment module 20 is used for establishing a time-by-time photovoltaic power generation output prediction model according to meteorological factors and solving the model to obtain a photovoltaic power generation prediction value, using a fuzzy membership function to divide the membership value of the hourly power generation, classifying to obtain the peak and valley period of the annual photovoltaic power, and calculating the dynamic pricing of the charging station according to the membership value of the hourly power generation;

[0191] The third establishing module 30 is configured to model the electric vehicle charging load, describe the electric vehicle charging starting time, the daily driving mileage of the electric vehicle and the charging duration, combine the user demand side model describing the influence degree of the charging price on the user charging behavior, and extract the total charging power of the electric vehicle according to the dynamic pricing of the charging station by using the Monte Carlo algorithm;

[0192] The first solving module 40 is configured to solve the objective function of the upper model by comprehensively considering the dynamic pricing of the charging station and the total charging power of the electric vehicle.

[0193] The first establishing module 50 is configured to establish an energy management model of the energy storage system, form a station energy balance operation mode under the constraint of the charging station purchasing power from the power distribution network, in combination with the photovoltaic power generation prediction result and the total charging power of the electric vehicle.

[0194] The second solving module 60 is configured to solve the lower model by using the particle swarm algorithm to obtain the photovoltaic and energy storage configuration quantity result when the total investment cost of the charging station is the lowest.

[0195] It should be noted that the specific implementation of the charging pile can refer to the specific implementation of the above-mentioned operation planning method of the integrated photovoltaic energy storage and charging station, and to avoid redundancy, the specific implementation of the charging pile will not be described in detail here.

[0196] The operation planning device of the integrated photovoltaic energy storage and charging station according to the embodiment of the application establishes the upper model with the minimum total charging cost of the user as the target and the lower model with the minimum total investment cost of the charging station as the target, considers the influence of the price change on the user charging behavior, solves the double-layer model to realize the operation planning of the charging station, can optimize the charging station configuration result while reducing the charging cost, and can avoid the capacity redundancy of the charging station.

[0197] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for operation planning of a photovoltaic storage and charging integrated charging station, characterized in that: The following steps are involved: A two-tier model for the operation planning of integrated solar-storage-charging charging stations driven by dynamic pricing is established. The upper-tier model minimizes the total charging cost for users, while the lower-tier model minimizes the total annual investment cost of the charging station. Based on meteorological factors, an hourly photovoltaic power generation output forecast model is established and solved to obtain the photovoltaic power generation forecast. The photovoltaic power generation forecast result is divided into hourly power generation membership values ​​using a fuzzy membership function. The peak and valley periods of photovoltaic power generation throughout the year are classified. Based on the hourly power generation membership values, the dynamic pricing of charging stations is calculated; Modeling the electric vehicle charging load, describing the electric vehicle charging start time, daily mileage, and charging duration, combined with a user demand-side model describing the degree to which user charging behavior is affected by charging electricity prices, and based on the dynamic pricing of the charging station, using a Monte Carlo algorithm to extract the total charging power of the electric vehicle; Solving the objective function of the upper model by integrating the dynamic pricing of the charging station and the total charging power of the electric vehicle; Establishing an energy management model for the energy storage system, and forming an energy balance working mode within the station by combining the photovoltaic power generation forecast results and the total charging power of the electric vehicles under the constraint that the charging station purchases electricity from the distribution network; Based on the working mode of the energy balance within the station, the particle swarm algorithm is used to solve the lower model to obtain the photovoltaic and energy storage configuration quantities when the total annual investment cost of the charging station is the lowest.

2. The operation planning method of the integrated photovoltaic storage and charging station according to claim 1 is characterized in that: The objective function of the upper model is: Where, f evch The total cost of charging for users, p evn (t) is the charging power of the nth electric vehicle entering the charging station at time t, c1(t) is the dynamic pricing of the charging station at time t, N evt is the number of electric vehicles entering the charging station at time t, and Hy is the number of hours in a year; The constraints of the upper model include: the charging power constraint of electric vehicles and the constraint on the number of electric vehicles charging in parallel in the integrated photovoltaic storage and charging station.

3. The operation planning method of the integrated photovoltaic storage and charging station according to claim 1 is characterized in that: The objective function of the lower model is: my C initial =C invest +C mt +C aba +C pbuy (2) Where C initial is the total annual investment cost of the charging station; C invest is the annual investment cost of the charging station; C mt is the annual operation and maintenance cost of the charging station; C aba C is the annual loss of solar power revenue at the charging station; pbuy Annual electricity purchase cost for the charging station; Annual investment cost of charging stations, etc.: Where r is the discount rate; n k (k=1,2,3) are the service life of photovoltaic array, energy storage system and charging station respectively; P pv 、P bat is the rated output power of the photovoltaic array and energy storage system; U pv 、U bat N is the investment cost per unit rated power of the photovoltaic array and energy storage system; pv 、N bat 、N ch The number of photovoltaic arrays, energy storage systems, and charging piles installed; U ch is the unit price of the charging pile; The maximum charging demand of electric vehicles per unit time is used to effectively estimate the number of charging piles to be installed: Where, is the maximum charging demand of electric vehicles per unit time; p ev Charging power for each electric vehicle, η ev For each electric vehicle battery charging efficiency, T t is the unit charging time; β is the parallel charging probability; Annual operation and maintenance costs of charging stations: Where C mt The annual operation and maintenance cost of the charging station; P is the operation and maintenance cost per unit rated power of the photovoltaic array and energy storage system; pv (t), P bat (t) are the rated output power of the photovoltaic array and the power of the energy storage system at time t; is the charging pile operation and maintenance ratio coefficient, C ch is the operation and maintenance cost of a single charging pile, N ch is the number of charging piles installed, and Hy is the number of hours per year; Annual loss of revenue from abandoned solar power at charging stations: Where C aba The annual loss of solar power revenue at the charging station; aba is the loss coefficient of abandoned light, E aba (t) is the abandoned optical power of the charging station at time t; The constraints of the lower-level model include: photovoltaic output constraints of the integrated photovoltaic storage and charging station, charging and discharging status, state of charge, and charging and discharging power constraints of the energy storage system, power balance constraints within the integrated photovoltaic storage and charging station, and curtailment rate constraints.

4. The operation planning method of the integrated photovoltaic storage and charging station according to claim 1 is characterized in that: The photovoltaic power generation output prediction model is: Where, P pv (t) is the rated output power of the unit PV array at time t; N pv The number of photovoltaic arrays installed; Latitude of the area where photovoltaic power is to be built The hourly average of the surface solar radiation intensity at hour h on day d in month m; G0 is the standard radiation intensity; θ T is the power temperature coefficient of the photovoltaic array; T is the operating temperature of the photovoltaic array; T r is the reference temperature of the PV array; The fuzzy membership function is as follows: Where g f (t) and g g (t) represents the membership degree of photovoltaic power generation at time t under the peak and valley models respectively; Q t is the photovoltaic power generation at time t; a and b are the minimum and maximum values ​​of the photovoltaic power generation prediction results; The peak and valley periods are divided according to the expression: Where, T f is the peak period; T p Normal period; T g is the valley period; θ f and θ g are the membership thresholds for dividing peak and valley periods; g f (t), g g (t) are the peak and valley membership values ​​of photovoltaic power generation at time t; The calculation formula for the dynamic pricing of the charging station is: c1(t)=c0(t)+k·g(t) (11) In the formula, c1(t) represents the dynamic pricing of the charging station at time t; c0(t) represents the original charging electricity price of the charging station at time t; g(t) represents the membership value of the photovoltaic power generation at time t to the corresponding peak or valley period, and g(t) is the g after the period is determined according to formula (10). f (t) or g g The value of (t) is 0 if it is a normal period; k represents the dynamic pricing coefficient.

5. The operation planning method of the integrated photovoltaic storage and charging station according to claim 1 is characterized in that: The electric vehicle charging start time obeys the normal distribution, and the probability density function is: Where, T st is the charging start time of electric vehicles, μ s1 、μ s2 is the mean value of the charging start time, σ s1 , σ s2 is the standard deviation of the charging start time; The daily mileage of the electric vehicle obeys the log-normal distribution, and the probability density function is: Where s day is the daily mileage of electric vehicles, μ d and σ d are the expectation and standard deviation of the probability density function of daily mileage of electric vehicles; The functional relationship of the electric vehicle charging time is expressed as: Where, T ev Charging time for electric vehicles, η ev For each electric vehicle battery charging efficiency, p ev Charging power for each electric vehicle, E h The electricity consumption of each electric vehicle per 100 kilometers.

6. The operation planning method of the integrated photovoltaic storage and charging station according to claim 1 is characterized in that: The user demand side model is: Where λ is the rate of electric vehicle users who change their charging behavior due to dynamic pricing; Δc fg is the peak-valley price difference of electricity price obtained by dynamic pricing; max is the maximum response rate; c a is the starting value in response to the electricity price difference; c b is the saturation value of the response electricity price difference.

7. The operation planning method of the integrated photovoltaic storage and charging station according to claim 1 is characterized in that: The calculation formula for the total charging power of the electric vehicle is: Where, P ev (t) is the total charging power of the electric vehicle at time t, p evn (t) is the charging power of the nth electric vehicle entering the charging station at time t, N evt is the number of electric vehicles entering the charging station at time t, and 24 is the number of hours in a day.

8. The operation planning method of the integrated photovoltaic storage and charging station according to claim 1 is characterized in that: The energy management model of the energy storage system: Where, SOC bat (t) is the state of charge of the energy storage system at time t; SOC bat (t0) is the charge state of the energy storage system at the initial time t0; β bat is the self-discharge rate of the energy storage system; γ cbat , γ dbat is the charging and discharging state variable of the energy storage system; Δt is the unit scheduling time scale; P cbat (t) is the charging power of the energy storage system at time t, which takes a positive value; P dbat (t) is the discharge power of the energy storage system at time t, which takes a negative value; η cbat ,η dbat is the charging and discharging efficiency of the energy storage system, V bat is the total capacity of the energy storage system.

9. The operation planning method of the integrated photovoltaic storage and charging station according to claim 1 is characterized in that: The constraints on the power purchased by the charging station from the distribution network are as follows: Where, P buy (t) is the power purchased by the charging station from the distribution network, The upper limit of the power provided by the distribution network to the charging station at time t; The working mode expression of the energy balance within the station is: P pv (t)-P dbat (t)+P buy (t)=P ev (t)+P cbat (t) (19) Where, P pv (t) is the rated output power of the unit photovoltaic array at time t; P cbat (t) is the charging power of the energy storage system at time t, which takes a positive value; P dbat (t) is the discharge power of the energy storage system at time t, which takes a negative value; P ev (t) is the total charging power of the electric vehicle at time t; P buy (t) is the power purchased by the charging station from the distribution network.

10. A photovoltaic storage and charging integrated charging station operation planning device, characterized in that: include: The first module is used to establish a two-layer model for the operation planning of integrated photovoltaic storage and charging stations driven by dynamic pricing. The upper layer model minimizes the total charging cost of users, while the lower layer model minimizes the total annual investment cost of the charging station. The second establishment module is used to establish an hourly photovoltaic power generation output forecast model based on meteorological factors and solve it to obtain the photovoltaic power generation forecast. The fuzzy membership function is used to divide the photovoltaic power generation forecast results into membership values ​​of hourly power generation, classify the peak and valley periods of photovoltaic power generation throughout the year, and calculate the dynamic pricing of charging stations based on the membership values ​​of hourly power generation. The third module is used to model the electric vehicle charging load, describing the electric vehicle charging start time, the electric vehicle daily mileage and the charging time, combined with the user demand side model that describes the degree to which the user charging behavior is affected by the charging electricity price, and based on the dynamic pricing of the charging station, using the Monte Carlo algorithm to extract the total charging power of the electric vehicle; A first solving module is used to solve the objective function of the upper model by combining the dynamic pricing of the charging station and the total charging power of the electric vehicle; A fourth establishment module is used to establish an energy management model for the energy storage system. Under the constraint that the charging station purchases electricity from the distribution network, the model combines the photovoltaic power generation prediction result and the total charging power of the electric vehicles to form an energy balance working mode within the station. The second solving module is used to integrate the working mode of the energy balance in the station, use the particle swarm algorithm to solve the lower model, and obtain the photovoltaic and energy storage configuration quantity results when the total annual investment cost of the charging station is the lowest.

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