A community hybrid energy storage configuration optimization method and system considering electric vehicles

Abstract

The present invention discloses a method and system for optimizing community hybrid energy storage configuration taking into account electric vehicles. The method comprises: constructing an upper-layer long-time-scale scheduling model and a lower-layer short-time-scale optimization model; optimizing the capacity configuration of the energy storage system based on the upper-layer long-time-scale scheduling model, optimizing the configuration parameters of the energy storage system based on the lower-layer short-time-scale optimization model, and iteratively generating an optimal plan. The system comprises: a model construction module and an iterative planning module. By using the present invention, electric vehicles are regarded as dynamic energy storage resources over long time scales, thereby reducing the community energy storage configuration cost and annual operating cost. The present invention can be widely applied to the field of energy storage configuration optimization.

Description

Technical Field

[0001] The present invention relates to the field of energy storage optimization, and in particular to a method and system for optimizing community hybrid energy storage configuration considering electric vehicles. Background Art

[0002] With the development of energy storage technology, community hybrid energy storage, as an important component of community energy operation, can store excess energy, thereby effectively responding to emergencies such as energy shortages caused by extreme weather, while also reducing the community's electricity purchase costs. However, with the increasing popularity of electric vehicles, more and more electric vehicles are connected to community microgrids, which increases the grid load, reduces grid reliability, and significantly increases the operation, maintenance, and configuration costs of the grid and energy storage facilities. At the same time, electric vehicles can use vehicle-to-grid (V2G) technology to transmit excess energy back to the grid. Therefore, it is necessary to design a community hybrid energy storage configuration optimization strategy that takes electric vehicles into consideration to reduce the total cost of community grid operation and enhance grid reliability.

[0003] However, existing technologies only use electric vehicles in short-term emergency situations, such as post-disaster power supply and community power outages, and fail to fully explore the role of electric vehicles in community energy on a long-term scale. Summary of the Invention

[0004] In view of this, in order to solve the technical problem that existing energy storage configuration optimization methods do not consider the role of electric vehicles in community energy over long time scales, thereby resulting in low application universality, the present invention proposes a community hybrid energy storage configuration optimization method considering electric vehicles, the method comprising the following steps:

[0005] Construct an upper-layer long-time-scale scheduling model and a lower-layer short-time-scale optimization model;

[0006] The capacity configuration of the energy storage system is optimized based on the upper-layer long-time-scale scheduling model, and the configuration parameters of the energy storage system are optimized based on the lower-layer short-time-scale optimization model, and an optimal plan is iteratively generated.

[0007] In some embodiments, the objective function of the upper-level long-time-scale scheduling model is as follows:

[0008] minC E,plan =C o +C m

[0009]

[0010] Among them, C o is the average annual initial investment cost; C mC is the equipment maintenance cost; o,SC 、C o,B They are the unit capacity prices of supercapacitors and lithium-ion batteries respectively; are the rated capacities of supercapacitor and lithium-ion battery respectively; ρ is the depreciation coefficient; r is the depreciation rate; k is the rated service life of the equipment; C s,Bat 、C s,SC The unit power operation and maintenance costs of lithium-ion batteries and supercapacitors are respectively; They are the rated configuration power of lithium-ion batteries and supercapacitors respectively.

[0011] In some embodiments, the constraints of the upper-level long-time-scale scheduling model include:

[0012] Rated capacity constraints of energy storage system configuration:

[0013]

[0014] in, Configure the upper and lower limits of rated capacity for lithium-ion batteries respectively; Configure upper and lower limits of rated capacity for supercapacitors respectively;

[0015] Rated power constraints of energy storage system configuration:

[0016]

[0017] in, Respectively represent the upper and lower limits of the supercapacitor configuration rated power; They represent the upper and lower limits of the rated power of the lithium-ion battery configuration respectively.

[0018] In some embodiments, the objective function of the lower-level short-time-scale optimization model is as follows:

[0019] min C run =C w +C grid +C d +C EV

[0020]

[0021] Among them, C w Penalty cost for energy waste; K waste is the energy waste penalty coefficient, P waste (t) is the energy surplus at time t; C grid is the grid interaction cost, c but,t 、C sell,t They represent the price of electricity purchased from the public grid and the price of electricity sold to the public grid at time t; C d is the degradation cost of energy storage facilities; CEV is the cost of dispatching EVs; p is the unit electricity subsidy price; is the total energy delivered to the microgrid by participating EVs; C Bat,EV Subsidize the price for battery degradation.

[0022] In some embodiments, the constraints of the underlying short-time-scale optimization model are as follows:

[0023] Power balance constraints:

[0024]

[0025] Where, are the number of EVs discharged and charged respectively; P PV 、 P buy They represent photovoltaic output, lithium-ion battery discharge power, supercapacitor discharge power and grid-purchased power respectively; P NL is the conventional load power excluding EV; u buy is a state variable;

[0026] Energy storage system charge state constraints:

[0027]

[0028] Among them, SOC i,t Indicates the state of charge of device i at time t; SOC i,max , SOC i,min are the upper and lower bounds of energy storage device i; is the rated capacity of equipment i; η cha,i is the charging efficiency of device i, η dis,i is the discharge efficiency of device i.

[0029] Energy storage equipment charging and discharging power constraints:

[0030]

[0031] Where, P i,c (t), P i,dis (t) are the charging power and discharging power of device i; u i (t) is the state variable of the energy storage device;

[0032] Grid interaction power constraints:

[0033]

[0034] Where u grid (t) is the electricity purchasing state variable; P buy,max 、P sell,maxare the maximum power of electricity purchased and sold respectively;

[0035] Energy storage equipment climbing power constraints:

[0036]

[0037] Where, P i,omax is the maximum climbing power of device i;

[0038] Supercapacitors to smooth out photovoltaic output constraints:

[0039]

[0040] Where, P P ' V (t) is the photovoltaic power after stabilization, ζ1 is the stabilization index;

[0041] Lithium-ion battery load constraints:

[0042]

[0043] Where ζ2 is the power fluctuation index of the tie line with the public grid;

[0044] EV participation scheduling constraints:

[0045]

[0046] Where, is the EV rated power; SOC EV,t is the state of charge of EV at time t; SOC EV,max , SOC EV,min are the upper and lower limits of the EV state of charge.

[0047] In some embodiments, the step of optimizing the capacity configuration of the energy storage system based on the upper-layer long-time-scale scheduling model, optimizing the configuration parameters of the energy storage system based on the lower-layer short-time-scale optimization model, and iteratively generating the optimal plan specifically includes:

[0048] Initialize energy storage system parameters;

[0049] Calculating the total cost of capacity configuration and configuration planning for the energy storage system based on the objective function and constraints of the upper-level long-time-scale scheduling model;

[0050] transmitting the capacity configuration to the underlying short-timescale scheduling model;

[0051] Calculating configuration parameters and total daily operating costs of the energy storage system based on the objective function and constraints of the lower-level short-time-scale scheduling model;

[0052] Transmitting the configuration parameters and daily operating total cost of the energy storage system to the upper-level long-time-scale scheduling model;

[0053] The optimization of the upper-layer long-time-scale scheduling model and the lower-layer short-time-scale scheduling model is iterated until the total cost of the configuration plan reaches a preset condition, thereby generating an optimal plan.

[0054] In some embodiments, it further includes:

[0055] Predicting electric vehicle charging load based on Monte Carlo and user behavior characteristics.

[0056] Since the present invention is aimed at hybrid energy storage communities that include electric vehicles, the real-time load of electric vehicles in a region is often not directly available. In order to ensure the stability of the power grid (or the power balance constraint in the lower-level optimization model), it is necessary to predict the load of electric vehicles in advance based on a probabilistic model for the lower-level model optimization calculation.

[0057] In some embodiments, the step of predicting the electric vehicle charging load based on Monte Carlo and user behavior characteristics specifically includes:

[0058] Based on the historical traffic flow data of the community, a probability distribution model is established;

[0059] Based on the probability distribution model, the traffic flow is simulated using the Monte Carlo method, and the average traffic flow index is calculated;

[0060] The total EV charging load power is calculated by combining the average vehicle flow index, the EV reserve capacity, and the initial state of charge.

[0061] The present invention also proposes a community hybrid energy storage configuration optimization system considering electric vehicles, the system comprising:

[0062] Model building module, used to build upper-level long-time-scale scheduling models and lower-level short-time-scale optimization models;

[0063] An iterative planning module optimizes the capacity configuration of the energy storage system based on the upper-level long-time-scale scheduling model, optimizes the configuration parameters of the energy storage system based on the lower-level short-time-scale optimization model, and iteratively generates an optimal plan.

[0064] Based on the above scheme, the present invention provides a community hybrid energy storage configuration optimization method and system considering electric vehicles. By scheduling electric vehicles as dynamic energy storage resources, it is possible to share the grid load during peak power demand periods. In the short term, when responding to sudden power outages caused by public power grid emergencies, by scheduling electric vehicles and combining them with the community hybrid energy storage system, the community power grid can be temporarily disconnected from the main grid and operate independently, thereby improving the stability of the power grid. In the long term, electric vehicles are regarded as dynamic energy storage resources, thereby reducing the community energy storage configuration cost and annual operating cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flowchart of the steps of a method for optimizing community hybrid energy storage configuration considering electric vehicles according to the present invention;

[0066] Figure 2 It is a schematic diagram of the double-layer optimization process of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0068] It should be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0069] It should be understood that the terms "system," "device," "unit," and / or "module" used in this application are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0070] As used in this application and the claims, unless the context clearly indicates an exception, the terms "a," "an," "an," and / or "the" are not intended to refer to the singular and may include the plural, unless the context clearly indicates otherwise. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements. The phrase "comprises a..." does not preclude the presence of additional identical elements in the process, method, product, or apparatus that includes the elements.

[0071] In the description of the embodiments of this application, "plurality" refers to two or more than two. The terms "first" and "second" below are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include one or more of the features.

[0072] In addition, flow charts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0073] Reference Figure 1 , which is a flow chart of an optional example of a community hybrid energy storage configuration optimization method considering electric vehicles proposed by the present invention. This method can be applied to computer devices. The energy storage configuration optimization method proposed in this embodiment may include but is not limited to the following steps:

[0074] Step S1, constructing an upper-layer long-time-scale scheduling model and a lower-layer short-time-scale optimization model;

[0075] Step S2: Optimize the capacity configuration of the energy storage system based on the upper-layer long-time-scale scheduling model, optimize the configuration parameters of the energy storage system based on the lower-layer short-time-scale optimization model, and iteratively generate an optimal plan.

[0076] This paper employs a two-tiered optimization approach for energy storage configuration, providing strategies for community energy storage configuration and EV scheduling, including those for electric vehicles. The upper-tier optimization considers the supply constraints of the energy storage system over a long timeframe, aiming to minimize the annual initial investment and ongoing maintenance costs of the energy storage system. The lower-tier optimization, based on a shorter timeframe, considers various grid emergencies and aims to minimize the typical daily operating cost of the energy storage system, including the cost of scheduling electric vehicles.

[0077] In some feasible embodiments, the step S1 specifically includes:

[0078] Upper-level long-time-scale scheduling model:

[0079] This paper focuses on the energy storage system configuration planning on a long time scale and constructs an upper-level optimization model whose goal is to minimize the total cost C of energy storage system configuration planning. E,plan The model uses the rated configuration capacity of the energy storage system as the decision variable and breaks down the configuration planning cost into the annual initial investment cost and the ongoing maintenance cost of the equipment. The specific cost calculation formula is as follows:

[0080] minCE,plan =C o +C m

[0081]

[0082] Among them, C o is the average annual initial investment cost; C m C is the equipment maintenance cost; o,SC 、C o,B They are the unit capacity prices of supercapacitors and lithium-ion batteries respectively; are the rated capacities of supercapacitor and lithium-ion battery respectively; ρ is the depreciation coefficient; r is the depreciation rate; k is the rated service life of the equipment; C s,Bat 、C s,SC The unit power operation and maintenance costs of lithium-ion batteries and supercapacitors are respectively; They are the rated configuration power of lithium-ion batteries and supercapacitors respectively.

[0083] Constraints include:

[0084] Energy storage system configuration rated capacity constraints:

[0085] The rated capacity of the energy storage system configuration is not only a key variable in the upper-level optimization model, but also plays a crucial role in parameter transmission in the lower-level optimization model. In view of the actual situation, the following constraints need to be imposed on the rated capacity of the energy storage system:

[0086]

[0087] in, Configure the upper and lower limits of rated capacity for lithium-ion batteries respectively; Configure upper and lower limits of rated capacity for supercapacitors respectively;

[0088] Rated power constraints of energy storage system configuration:

[0089] To ensure the efficiency and reliability of the energy storage system, its rated power must be maintained within a reasonable range:

[0090]

[0091] in, Respectively represent the upper and lower limits of the supercapacitor configuration rated power; They represent the upper and lower limits of the rated power of the lithium-ion battery configuration respectively.

[0092] Lower-level short-time-scale optimization model:

[0093] Designing EV dispatch responsive incentives:

[0094] According to consumer psychology theory, there is a specific threshold for consumers' sensitivity to price fluctuations. When price changes do not exceed this threshold, consumers' willingness to buy is generally unaffected. However, once price fluctuations exceed the threshold, consumers' willingness to buy will gradually increase. The present invention uses a logistic function to simulate the relationship between incentive levels and EV user response rates. The logistic function, with its S-shaped growth curve, can effectively simulate a variety of natural and social phenomena, including population growth and disease spread. Its growth characteristics at different stages enable it to well fit the user response rates to various incentive levels.

[0095] When the subsidized electricity price at time t is p, the response rate calculation expression of EV users is as follows:

[0096]

[0097] Where δ(t) is defined as the incentive level at time t; ο is the willingness threshold; f(δ(t)) is the EV response rate when the incentive level is δ(t); L represents the upper limit of the function, that is, the expected maximum user participation response rate, which is set to 1 in this paper; k is the slope of the function curve, which is related to the user's sensitivity to the incentive level; ω is the fitting coefficient.

[0098] Combining the above formula, we can get the number of EVs that can be dispatched at time t, and further calculate the EV dispatch cost:

[0099] C EV =pΔE+C Bat,EV

[0100]

[0101] Where C EV is the total scheduling cost, is the number of EVs participating in the dispatch at time t; N EV (t) is the total number of EVs that can participate in scheduling in the current area at time t, is the total energy transmitted to the microgrid in this dispatch; t0 is the starting time, t end is the scheduling end time; ΔE t,i is the energy released by the i-th vehicle at time t.

[0102] Energy storage facility degradation cost modeling:

[0103] Battery degradation cost modeling:

[0104] The depth of discharge (DOD) of a battery is defined as the percentage of discharged energy to the total battery capacity. The more frequently a battery is charged and discharged, the shorter its lifespan. Battery manufacturers specify recommended DOD for optimal battery performance. The DOD of a battery within the Vt interval can be defined as follows:

[0105]

[0106] In the above formula, P B (t) represents the charge and discharge power of the battery, C B (t) represents the battery capacity.

[0107] The number of charge and discharge cycles of a battery depends on the battery capacity used and the depth of charge and discharge during use. In general, the battery life is best matched to its DOD. The relationship between battery life and DOD can be expressed as follows:

[0108]

[0109] In the above formula, N life represents the number of cycles, μ0, μ1, μ2 are fitting coefficients, B DOD It represents the depth of discharge of the battery. Without loss of generality, this expression is applicable to batteries with different parameters. In the present invention, the parameter values ​​are μ0=-0.52, μ1=1.67, and μ2=2052.

[0110] After the battery is charged and discharged at time t, the calculation formula for the actual capacity loss of the battery at time t+Vt is as follows:

[0111]

[0112] In the above formula, C rate Indicates the rated capacity of the battery.

[0113] The operating cost function of the battery cycle life can be obtained:

[0114]

[0115] In the above formula, G B is the investment cost of the battery, P B (t) is the battery charging and discharging power, N life is the number of cycles, η B η' B are the charge and discharge efficiencies of the battery respectively.

[0116] Supercapacitor degradation cost:

[0117] Generally speaking, supercapacitors, under normal operating conditions, can be cycled for long periods of time within their designed service life. Unlike traditional batteries, charge and discharge rate and depth of discharge have little impact on the degradation cost of supercapacitors. Therefore, the degradation cost of supercapacitors can be primarily viewed as linearly related to time.

[0118] The degradation cost of a supercapacitor at any time interval Δt can be expressed as:

[0119]

[0120] In practical applications, the degradation cost of a supercapacitor is often replaced by a constant that is independent of the number of cycles, which is also the case in this embodiment.

[0121] Objective function:

[0122] In the lower-level model planning, the focus is on optimizing the output of the energy storage system to minimize the total cost of typical daily operation, including EV dispatch costs, C run For the goal:

[0123] min C run =C w +C grid +C d +C EV

[0124]

[0125] Among them, C w Penalty cost for energy waste; K waste is the energy waste penalty coefficient, P waste (t) is the energy surplus at time t; C grid is the grid interaction cost, c but,t 、C sell,t They represent the price of electricity purchased from the public grid and the price of electricity sold to the public grid at time t; C d is the degradation cost of energy storage facilities; C EV is the cost of dispatching EVs; p is the unit electricity subsidy price, and the willingness of EV users to participate in dispatching is positively correlated with p; is the total energy delivered to the microgrid by participating EVs; C Bat,EV Subsidize the price for battery degradation.

[0126] N0 is the number of cycles at which the battery reaches the end of its life at 100% depth of discharge, C s,day is the daily degradation cost of lithium batteries. Since the cycle life of supercapacitors is much longer than that of lithium-ion batteries, the degradation cost of supercapacitors is no longer considered here. The degradation cost of the system energy storage facilities can be approximated by the degradation cost of batteries.

[0127] Constraints:

[0128] Power balance constraints:

[0129]

[0130] Where, are the number of EVs discharged and charged respectively; P PV 、 P buy They represent photovoltaic output, lithium-ion battery discharge power, supercapacitor discharge power and grid-purchased power respectively; P NL is the conventional load power excluding EV; u buy is a state variable. When the microgrid is operating normally, u buy The value of is set to 1. If the external power supply is interrupted due to extreme events such as circuit failure or natural disasters, the microgrid will switch to island operation mode. buy The value of is set to 0;

[0131] Energy storage system charge state constraints:

[0132] To mitigate the performance degradation of energy storage devices and EV batteries and extend their lifespan, real-time control constraints on battery state of charge and charge and discharge power must be implemented:

[0133]

[0134] Among them, SOC i,t Indicates the state of charge of device i at time t; SOC i,max , SOC i,min are the upper and lower bounds of energy storage device i; is the rated capacity of equipment i; η cha,i is the charging efficiency of device i, η dis,i is the discharge efficiency of device i.

[0135] Energy storage equipment charging and discharging power constraints:

[0136] The charging and discharging power of the energy storage device cannot exceed its rated power, and charging and discharging cannot be performed simultaneously.

[0137]

[0138] Where, P i,c (t), P i,dis (t) are the charging power and discharging power of device i; u i (t) is the state variable of the energy storage device, which is 1 when the device i is in the charging state at time t, otherwise it is 0;

[0139] Grid interaction power constraints:

[0140]

[0141] Where u grid (t) is the power purchase state variable, which is 1 when the community power grid purchases power from the public power grid, otherwise it is 0; P buy,max 、P sell,max are the maximum power of electricity purchased and sold respectively;

[0142] Energy storage equipment climbing power constraints:

[0143] Considering the stability of the power grid, the output of energy storage equipment also needs to involve the limitation of climbing power:

[0144]

[0145] Where, P i,omax is the maximum climbing power of device i;

[0146] Supercapacitors to smooth out photovoltaic output constraints:

[0147] Supercapacitors have an extremely fast response speed, which allows them to participate in smoothing photovoltaic output fluctuations in the power grid. In addition, according to the national grid standards, the smoothing index should be less than 10%.

[0148]

[0149] Where, P P ' V (t) is the photovoltaic power after stabilization, ζ1 is the stabilization index;

[0150] Lithium-ion battery load constraints:

[0151] When the microgrid is operating, supercapacitors are prioritized for load balancing. When the net load is greater than 0, lithium-ion batteries and the public grid are required for power compensation. The power fluctuation index ζ2 of the tie line with the public grid is defined as follows:

[0152]

[0153] Where ζ2 is the power fluctuation index of the tie line with the public grid;

[0154] EV participation scheduling constraints:

[0155] The discharge power of EVs participating in the dispatch cannot exceed a certain range, and their real-time state of charge cannot be lower than a minimum value.

[0156]

[0157] Where, is the EV rated power; SOCEV,t is the state of charge of EV at time t; SOC EV,max , SOC EV,min are the upper and lower limits of the EV state of charge.

[0158] In some feasible embodiments, step S2 specifically includes:

[0159] Initialize energy storage system parameters;

[0160] Calculating the total cost of capacity configuration and configuration planning for the energy storage system based on the objective function and constraints of the upper-level long-time-scale scheduling model;

[0161] transmitting the capacity configuration to the underlying short-timescale scheduling model;

[0162] Calculating configuration parameters and total daily operating costs of the energy storage system based on the objective function and constraints of the lower-level short-time-scale scheduling model;

[0163] Transmitting the configuration parameters and daily operating total cost of the energy storage system to the upper-level long-time-scale scheduling model;

[0164] The optimization of the upper-layer long-time-scale scheduling model and the lower-layer short-time-scale scheduling model is iterated until the total cost of the configuration plan reaches a preset condition, thereby generating an optimal plan.

[0165] Specifically, refer to Figure 2 The present invention adopts a two-level optimization method to solve the above model. The upper-level optimization starts from the long time scale, the objective function is the configuration planning cost of the energy storage system, and the optimization variable is the capacity configuration of the energy storage system; the lower-level optimization starts from the short time scale, the objective function is the microgrid operation cost including EV scheduling cost, and the optimization variable is the output configuration of the energy storage system, that is, parameters such as rated power.

[0166] In some feasible embodiments, the following further comprises:

[0167] Predicting electric vehicle charging load based on Monte Carlo and user behavior characteristics.

[0168] To ensure the power grid can consistently meet load demands, electric vehicle (EV) load forecasting is essential. Based on historical community traffic data, a probability distribution model is developed. Monte Carlo simulations are used to simulate traffic flow in a specific area, calculating average traffic flow metrics to predict future traffic flow. Combined with data on regional EV retention, the final number of EVs in the area is calculated. EV driving characteristics, such as daily mileage and initial state of charge (SOC), are then generated to calculate EV charging requirements.

[0169] The daily mileage follows a log-normal distribution, that is, Its probability density function is:

[0170]

[0171] Where S is the daily mileage in km; b and σ b They represent the mean and standard deviation of daily mileage, which are 3.3 and 0.88 respectively.

[0172] The initial state of charge (SOC) is defined as the ratio of the current battery storage energy to the total capacity, which affects the user's charging time and power demand. This paper assumes that the EV's initial SOC at time t is a random variable that follows a normal distribution, and its probability density function is:

[0173]

[0174] In the formula is the starting SOC of EV, yes The mean of is the standard deviation of SOC, set to 0.3 and 0.15 respectively.

[0175] The SOC of the EV when it reaches the charging station at time t is:

[0176]

[0177] Where E h is the battery capacity of EV; E b The power consumption of EV per 100 kilometers. Due to the characteristics of EV batteries, the EV's mileage is greatly affected by the ambient temperature, that is, E b The values ​​are different.

[0178] Assume that the EV starts charging at If it obeys the normal distribution, then its probability density function is:

[0179]

[0180] Where μ CH , σ CH They are The mean and standard deviation are 17.6 and 3.4 respectively.

[0181] Combine The EV charging time can be calculated as:

[0182]

[0183] Where ηEV,C is the EV charging efficiency, P V2G The rated power of charging piles that can use V2G technology, which can be divided into fast charging and slow charging.

[0184] Charging ends at:

[0185]

[0186] From this, we can get the unit EV charging power demand at time t:

[0187]

[0188] The Monte Carlo method is used to perform multiple simulations and the average value is obtained to obtain the total regional EV charging load power:

[0189]

[0190] Where N si is the total number of Monte Carlo simulations, The total number of EVs currently charging in the area.

[0191] Based on the above scheme, the present invention constructs community hybrid energy storage models that consider electric vehicles on both long and short time scales. The model is then solved using a two-level optimization method, resulting in a simpler and faster solution. This approach helps reduce community energy storage deployment costs and annual operating costs, while also improving grid stability.

[0192] A community hybrid energy storage configuration optimization system considering electric vehicles, comprising:

[0193] Model building module, used to build upper-level long-time-scale scheduling models and lower-level short-time-scale optimization models;

[0194] An iterative planning module optimizes the capacity configuration of the energy storage system based on the upper-level long-time-scale scheduling model, optimizes the configuration parameters of the energy storage system based on the lower-level short-time-scale optimization model, and iteratively generates an optimal plan.

[0195] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0196] A community hybrid energy storage configuration optimization device considering electric vehicles:

[0197] at least one processor;

[0198] at least one memory for storing at least one program;

[0199] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for optimizing community hybrid energy storage configuration considering electric vehicles.

[0200] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0201] A storage medium stores processor-executable instructions, which, when executed by the processor, are used to implement a community hybrid energy storage configuration optimization method considering electric vehicles as described above.

[0202] The contents of the above method embodiments are all applicable to the present storage medium embodiment. The functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0203] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A community hybrid energy storage configuration optimization method considering electric vehicles, characterized in that: The following steps are involved: Construct an upper-layer long-time-scale scheduling model and a lower-layer short-time-scale optimization model; Optimizing the capacity configuration of the energy storage system based on the upper-layer long-time-scale scheduling model, optimizing the configuration parameters of the energy storage system based on the lower-layer short-time-scale optimization model, and iteratively generating an optimal plan; The objective function of the lower-level short-time-scale optimization model is as follows: my C run =C w +C grid +C d +C EV Among them, C w Penalty cost for energy waste; K waste is the energy waste penalty coefficient, P waste (t) is the energy surplus at time t; C grid is the grid interaction cost, c buy,t 、C sell,t They represent the price of electricity purchased from the public grid and the price of electricity sold to the public grid at time t; C d is the degradation cost of energy storage facilities; C EV is the cost of dispatching EVs; p is the unit electricity subsidy price; is the total energy delivered to the microgrid by participating EVs; C Bat,EV Subsidize the price for battery degradation.

2. The community hybrid energy storage configuration optimization method considering electric vehicles according to claim 1 is characterized in that: The objective function of the upper-level long-time-scale scheduling model is as follows: minC E,plan =C o +C m Among them, C o is the average annual initial investment cost; C m C is the equipment maintenance cost; o,SC 、C o,Bat They are the unit capacity prices of supercapacitors and lithium-ion batteries respectively; are the rated capacities of supercapacitor and lithium-ion battery respectively; ρ is the depreciation coefficient; r is the depreciation rate; k is the rated service life of the equipment; C s,Bat 、C s,SC The unit power operation and maintenance costs of lithium-ion batteries and supercapacitors are respectively; They are the rated configuration power of lithium-ion batteries and supercapacitors respectively.

3. A community hybrid energy storage configuration optimization method considering electric vehicles according to claim 2, characterized in that: The constraints of the upper-level long-time-scale scheduling model include: Rated capacity constraints of energy storage system configuration: in, Configure the upper and lower limits of rated capacity for lithium-ion batteries respectively; Configure upper and lower limits of rated capacity for supercapacitors respectively; Rated power constraints of energy storage system configuration: in, Respectively represent the upper and lower limits of the supercapacitor configuration rated power; They represent the upper and lower limits of the rated power of the lithium-ion battery configuration respectively.

4. A community hybrid energy storage configuration optimization method considering electric vehicles according to claim 3, characterized in that: The constraints of the underlying short-time-scale optimization model are as follows: Power balance constraints: Where, are the number of EVs discharged and charged respectively; P PV 、 P buy They represent photovoltaic output, lithium-ion battery discharge power, supercapacitor discharge power and grid-purchased power respectively; P NL is the conventional load power excluding EV; u buy is a state variable; Energy storage system charge state constraints: Among them, SOC i,t Indicates the state of charge of device i at time t; SOC i,max , SOC i,min are the upper and lower bounds of energy storage device i; is the rated capacity of equipment i; η cha,i is the charging efficiency of device i, η dis,i is the discharge efficiency of device i; the charging and discharging power constraint of energy storage device is: Where, P i,c (t), P i,dis (t) are the charging power and discharging power of device i; u i (t) is the state variable of the energy storage device; Grid interaction power constraints: Where u grid (t) is the electricity purchasing state variable; P buy,max 、P sell,max are the maximum power of electricity purchased and sold respectively; Energy storage equipment climbing power constraints: Where, P i,omax is the maximum climbing power of device i; Supercapacitors to smooth out photovoltaic output constraints: Where P′ PV (t) is the photovoltaic power after stabilization, ζ1 is the stabilization index; Lithium-ion battery load constraints: Where ζ2 is the power fluctuation index of the tie line with the public grid; EV participation scheduling constraints: Where, is the EV rated power; SOC EV,t is the state of charge of EV at time t; SOC EV,max , SOC EV,min are the upper and lower limits of the EV state of charge.

5. The community hybrid energy storage configuration optimization method considering electric vehicles according to claim 1, characterized in that: The step of optimizing the capacity configuration of the energy storage system based on the upper-layer long-time-scale scheduling model, optimizing the configuration parameters of the energy storage system based on the lower-layer short-time-scale optimization model, and iteratively generating the optimal plan specifically includes: Initialize energy storage system parameters; Calculating the total cost of capacity configuration and configuration planning for the energy storage system based on the objective function and constraints of the upper-level long-time-scale scheduling model; transmitting the capacity configuration to the underlying short-timescale scheduling model; Calculating configuration parameters and total daily operating costs of the energy storage system based on the objective function and constraints of the lower-level short-time-scale scheduling model; Transmitting the configuration parameters and daily operating total cost of the energy storage system to the upper-level long-time-scale scheduling model; The optimization of the upper-layer long-time-scale scheduling model and the lower-layer short-time-scale scheduling model is iterated until the total cost of the configuration plan reaches a preset condition, thereby generating an optimal plan.

6. The community hybrid energy storage configuration optimization method considering electric vehicles according to claim 1, characterized in that: Also includes: Predicting electric vehicle charging load based on Monte Carlo and user behavior characteristics.

7. A community hybrid energy storage configuration optimization method considering electric vehicles according to claim 6, characterized in that: The step of predicting the electric vehicle charging load based on Monte Carlo and user behavior characteristics specifically includes: Based on the historical traffic flow data of the community, a probability distribution model is established; Based on the probability distribution model, the traffic flow is simulated using the Monte Carlo method, and the average traffic flow index is calculated; The total EV charging load power is calculated by combining the average vehicle flow index, the EV reserve capacity, and the initial state of charge.

8. A community hybrid energy storage configuration optimization system considering electric vehicles, characterized in that: The method for optimizing community hybrid energy storage configuration considering electric vehicles according to claim 1 comprises: Model building module, used to build upper-level long-time-scale scheduling models and lower-level short-time-scale optimization models; An iterative planning module optimizes the capacity configuration of the energy storage system based on the upper-level long-time-scale scheduling model, optimizes the configuration parameters of the energy storage system based on the lower-level short-time-scale optimization model, and iteratively generates an optimal plan.

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