Regulation and control method for multi-element energy storage system comprising electric vehicle and 5G base station

Abstract

The invention belongs to the technical field of power grid peak regulation and control, and particularly relates to a multi-element energy storage system regulation and control method comprising an electric vehicle and a 5G base station, and the method comprises the steps: constructing a multi-element energy storage system model, analyzing the energy utilization rule of energy storage equipment, and calculating the schedulable potential of energy storage; a multivariate energy storage optimal scheduling model with peak load shifting as the target is constructed, the charging and discharging power of energy storage equipment in the multivariate energy storage system model serves as decision variables, power balance and equipment characteristics serve as constraint conditions, and a commercial solver is used for solving an optimal scheduling scheme of the multivariate energy storage optimal scheduling model; and performing simulation verification on the optimal scheduling scheme through an example to realize effective regulation and control of the multi-element energy storage system. The energy storage potential of the electric vehicle and the 5G base station can be cooperatively excavated to participate in power grid regulation and control operation, and the purposes of promoting full consumption of distributed power generation and improving power utilization safety and stability of a transformer area are achieved by optimizing photovoltaic output and energy storage charging and discharging operation.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid peak regulation and control, and in particular relates to a control method for a multi-energy storage system including electric vehicles and 5G base stations. Background Art

[0002] In recent years, with the continuous breakthroughs in energy storage technology, energy storage applications have gradually become diversified. On the one hand, the load-side equipment has also become more diversified. The large-scale access of resources such as electric vehicles and 5G base stations has led to a surge in power load, increased operating costs, and increased environmental pollution in the power system. On the other hand, the above resources themselves have good energy storage characteristics. Their wide application enhances the flexibility and controllability of the load side and provides more possibilities for grid dispatching. Distributed photovoltaics, as an important form of new energy power generation, has been quite large on the user side in recent years. With the increasing number of distributed energy access in different substations, traditional centralized grid dispatching is difficult to effectively integrate the various flexible resources that are too dispersed in the substation. At present, with the help of Internet of Things technology, data collection, transmission and feedback between different devices in the substation can be realized, and connected with the upper power grid, so as to better integrate and dispatch distributed energy, integrate it into the grid operation and dispatching system, and provide new solutions for the stable operation of the power system. Due to the mismatch between the power generation characteristics of distributed photovoltaics and the load characteristics, the problem of "reverse power transmission" from the substation to the distribution network often exists.

[0003] Therefore, there is an urgent need for a multi-energy storage system control method including electric vehicles and 5G base stations to coordinate the exploration of the energy storage potential of electric vehicles and 5G base stations to participate in the control and operation of the power grid, and to achieve the goal of promoting the full absorption of distributed power generation and improving the safety and stability of electricity consumption in the substation area by optimizing photovoltaic output and energy storage charging and discharging operations. Summary of the invention

[0004] The object of the present invention is to provide a method for controlling a multi-energy storage system including an electric vehicle and a 5G base station, which is characterized by comprising the following steps:

[0005] Step S1: construct a multi-element energy storage system model, analyze the energy usage law of the energy storage equipment and calculate the dispatchable potential of the energy storage; the energy storage equipment includes: electric vehicles, 5G base stations and distributed energy storage; the multi-element energy storage system model includes: a dispatchable potential model of electric vehicles, a dispatchable potential model of 5G base stations and a dispatchable model of distributed energy storage;

[0006] Step S2: construct a multi-element energy storage optimization scheduling model with the goal of peak shaving and valley filling, wherein the multi-element energy storage optimization scheduling model uses the charging and discharging power of the energy storage equipment in the multi-element energy storage system model as a decision variable, and uses power balance and equipment characteristics as constraints, and uses a commercial solver to solve the optimal scheduling plan of the multi-element energy storage optimization scheduling model;

[0007] Step S3: The optimal scheduling scheme is simulated and verified through examples to achieve effective regulation of the multi-element energy storage system.

[0008] The dispatchable potential model of the electric vehicle includes: an electric vehicle charging and discharging model and an electric vehicle behavior model.

[0009] The electric vehicle charging and discharging model includes: battery state of charge, charging model and discharging model;

[0010] The battery state of charge is:

[0011]

[0012] In the formula, SOC ev (t) is the battery charge state of the electric vehicle at time t; C ev (t) is the battery capacity of the electric vehicle at time t; is the rated capacity of the electric vehicle battery;

[0013] The charging model is:

[0014]

[0015] In the formula, SOC ev (t+1) is the battery charge state of the electric vehicle at time t+1; η chr charging efficiency for electric vehicles; is the charging power of the electric vehicle at time t;

[0016] The discharge model is:

[0017]

[0018] Where η dis Discharge efficiency of electric vehicles; is the discharge power of the electric vehicle at time t.

[0019] The electric vehicle behavior model includes: a probability density function of the charging time at the station, a probability density function of the parking time of the electric vehicle, a probability density function of the daily mileage, and a driving state of charge model of the electric vehicle;

[0020] The probability density function of the charging time at the station is expressed as:

[0021]

[0022] In the formula, t start Represents the expected value of the normal distribution of the network access time and the station entry time μ s is equal to 17.6, standard deviation σ s =3.4;

[0023] The electric vehicle parking time probability density function is expressed as:

[0024]

[0025] t end =t start +t Z (6)

[0026] Where, t z is the parking time, t start represents the pit stop time, t end Represents the normal distribution expected value of the departure time and parking time μ Z is 2.74; standard deviation σ Z is 1.21;

[0027] The probability density function of the daily mileage is:

[0028]

[0029] Where L represents the off-grid time, and the normal distribution expected value of parking time is μ L is 3.2; standard deviation σ L is 0.88;

[0030] The electric vehicle driving state of charge model is:

[0031]

[0032] In the formula, The state of charge of the electric vehicle when it returns to the start of charging after the last trip; The initial state of charge of the electric vehicle before driving; d r is the daily mileage of electric vehicles; W 100 The amount of electricity consumed by an electric car traveling 100 km; is the rated capacity of the electric vehicle battery.

[0033] The schedulable potential model of the 5G base station includes:

[0034] Base station load factor index λ load 、Energy storage charge and discharge SOC lower limit S min 、Energy storage charge and discharge SOC upper limit S max , the final charge and discharge SOC upper and lower limits of base station i, the upward chargeable capacity and downward dischargeable capacity of energy storage,

[0035] Maximum charging power of base station i energy storage within Δt The maximum charging power of the charging energy storage group at time t and the maximum discharge power of the base station energy storage i within Δt The maximum discharge power of the energy storage group at time t, the charging model of the base station energy storage battery, and the discharge model of the base station energy storage battery;

[0036] The base station load factor index λ load for:

[0037]

[0038] In the formula, L represents the number of users currently connected to the base station, L all Indicates the maximum number of users that the base station can handle; load ∈[0,1];

[0039] The energy storage charge and discharge SOC lower limit S min for:

[0040] S min = kλ load (10)

[0041] Where k is the correlation coefficient and k∈[0,1], λ load It is the base station communication load status indicator;

[0042] The energy storage charge and discharge SOC upper limit S max for:

[0043] S max =S up

[0044] In the formula, S up The SOC upper limit value set for the energy storage battery to avoid overcharge and over discharge;

[0045] The final charge and discharge SOC upper and lower limits of the base station i are:

[0046] S i,min,t =max{S down ,S min}(11)

[0047] S i,max,t =S max =S up (12)

[0048] In the formula, S i,min,t is the lower limit of the SOC of the base station energy storage i at time t; S i,max,t is the upper limit of the charging SOC of the base station energy storage i at time t; S down The SOC lower limit value set for the energy storage battery to avoid overcharge and over discharge;

[0049] The upward chargeable capacity and downward dischargeable capacity of energy storage:

[0050]

[0051] In the formula, The rechargeable capacity for base station energy storage i upwards; S is the downward discharge capacity of the base station energy storage i; i (t) is the SOC value of the energy storage of base station i at time t, and E is the rated capacity of the energy storage;

[0052] Maximum charging power of base station i energy storage within Δt for:

[0053]

[0054] In the formula, is the maximum charging power limit of the energy storage battery, Δt is the time interval;

[0055] The maximum charging power of the energy storage group at time t is:

[0056]

[0057] In the formula, is the maximum charging power of the charging base station energy storage group at time t;

[0058] Maximum discharge power of base station energy storage i within Δt for:

[0059]

[0060] In the formula, is the maximum discharge power limit of the energy storage battery;

[0061] The maximum discharge power of the energy storage group at time t is:

[0062]

[0063] In the formula, is the maximum discharge power of the discharge base station energy storage group at time t;

[0064] The charging model of the base station energy storage battery is:

[0065]

[0066] In the formula, SOC G (t+1) is the battery charge state of the base station at time t+1; η Gchr Charging efficiency of base station energy storage batteries; is the charging power of the battery energy storage at time t; C G Rated capacity of energy storage for base stations;

[0067] The discharge model of the base station energy storage battery is:

[0068]

[0069] Where η Gdis is the discharge efficiency of the energy storage base station; is the discharge power of the battery at time t.

[0070] The dispatchable model of the distributed energy storage includes:

[0071] Distributed energy storage battery charging model and distributed energy storage battery discharging model;

[0072] The distributed energy storage battery charging model is:

[0073]

[0074] In the formula, SOC ess (t+1) is the battery charge state at the energy storage time t+1; η esschr Charging efficiency of energy storage batteries; is the charging power of the battery energy storage at time t; C ess is the rated capacity of the distributed energy storage battery.

[0075] The distributed energy storage battery discharge model is:

[0076]

[0077] Where η essdis is the discharge efficiency of the energy storage base station; is the discharge power of the battery at time t.

[0078] When dispatching energy storage, the available capacity should meet the following constraints:

[0079]

[0080] In the formula, E ess is the charge of the energy storage system; Refers to the upper and lower limits of the energy storage system charge;

[0081] The available power meets the following constraints:

[0082]

[0083] In the formula, Refers to the upper and lower limits of the charging and discharging power of the energy storage system; is the charging and discharging power of the energy storage system; P ess is the energy storage system power; The charging and discharging efficiency of the energy storage system;

[0084] According to the real-time SOC state of the energy storage and the upper and lower limits of the charge and discharge SOC, the upward chargeable capacity and downward dischargeable capacity of the energy storage can be calculated:

[0085]

[0086] In the formula, E ess,c 、E ess,d Refers to the remaining available capacity of the energy storage system for charging and discharging.

[0087] The objective function of the multivariate energy storage optimization scheduling model is:

[0088]

[0089] In the formula, T is 24 hours, R is Load (t) is the variance of the power load curve, P av is the average net load of the power consumption in the substation area; P Load (t) is the net power load, P 1 (t) is the power load, P pv (t) is the distributed photovoltaic power generation power; P G (t) is the power consumed by the 5G base station battery energy storage system, P ev (t) is the power of the electric vehicle energy storage system, P ess (t) is the power of the distributed energy storage system, P imp (t) is the fixed electric load, The charging and discharging power of the 5G base station battery energy storage system, The charging and discharging power of the electric vehicle energy storage system, The charging and discharging power of the electric vehicle energy storage system, It is the charging flag of the base station energy storage system, 1 means charging, 0 means not charging, It is the discharge flag of the battery energy storage system, 1 means discharge, 0 means no discharge, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, It is the discharge mark of electric vehicle, 1 means discharge, 0 means no discharge, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, It is the discharge flag of electric vehicle, 1 means discharge, 0 means no discharge.

[0090] The constraints of the multivariate energy storage optimization scheduling model include:

[0091] 1) Electric power balance constraints:

[0092] P pv (t)+P gird (t) = P G (t)+P EV (t)+Pess (t)+P imp (t) (30)

[0093] Where P gird (t) is the grid interaction power;

[0094] 2) Electric vehicle charging and discharging constraints:

[0095]

[0096] In the formula, The upper limit of charging power for electric vehicles, The lower limit of charging power for electric vehicles, The upper limit of the electric vehicle discharge power, The lower limit of electric vehicle discharge power; SOC ev (t) is the battery charge state of the electric vehicle at time t; are the upper and lower limits of the battery state of charge of the electric vehicle at time t;

[0097] 3) Constraints on charging and discharging of 5G base station energy storage:

[0098]

[0099] In the formula, Set a power cap for charging 5G base stations; The lower limit of charging power for 5G base stations; The upper limit of the discharge power of 5G base stations; The lower limit of the discharge power of 5G base stations; SOC G (t) is the battery charge state of the base station at time t; are the upper and lower limits of the battery state of charge of the base station at time t;

[0100] 4) Distributed energy storage charging and discharging constraints:

[0101]

[0102] In the formula, Provides a maximum charging power limit for distributed energy storage; The lower limit of charging power for distributed energy storage; The upper limit of the discharge power of distributed energy storage; is the lower limit of the distributed energy storage discharge power;

[0103] SOC ess (t) is the battery charge state at the energy storage time t; are the upper and lower limits of the battery state of charge at time t;

[0104] 5) Photovoltaic output constraints:

[0105]

[0106] In the formula, is the upper limit of photovoltaic power generation power;

[0107] 6) Grid interaction power constraints:

[0108]

[0109] In the formula, is the upper limit of grid interaction power; is the lower limit of grid interaction power;

[0110] 7) Device uniqueness constraint:

[0111]

[0112] The step of simulating and verifying the optimal scheduling scheme through a calculation example comprises:

[0113] Step S31: using Monte Carlo simulation of electric vehicle data, and predicting the upper and lower limits of the dispatchable power of the electric vehicle energy storage system, using the Minkowski method to obtain the total dispatchable potential of the electric vehicles participating in the dispatch, and obtaining the dispatch potential of the electric vehicle cluster;

[0114] Step S32: Calculate the minimum backup capacity of the 5G base station according to the differences in the communication load states of the 5G base station, and then obtain the adjustable potential of the base station energy storage; in order to ensure that the base station as energy storage participating in the grid interaction does not affect its reliability as a backup power source, the energy storage of the base station in a heavy load state is not considered to be regulated;

[0115] Step S33: Combining the dispatchable potential model of electric vehicles, the dispatchable potential model of 5G base stations, and the dispatchable model of distributed energy storage, the Cplex solver in MATLAB is used to solve the load changes before and after the multivariate energy storage participates in the optimization and regulation.

[0116] The beneficial effects of the present invention are:

[0117] With the widespread popularization of distributed photovoltaics, charging piles, 5G base stations and other new infrastructure, residential areas are prone to problems such as large peak-to-valley differences and reverse power transmission. In view of the above problems, the present invention proposes a multi-energy storage collaborative scheduling method containing electric vehicles and 5G base stations. First, a multi-energy storage system model is established to analyze the energy consumption rules of electric vehicles, 5G base stations and distributed energy storage and calculate the dispatchable potential of energy storage; secondly, the charging and discharging power of the three energy storage devices is used as the decision variable, and the constraints such as power balance and equipment characteristics are considered. An optimization scheduling model with the goal of peak shaving and valley filling is established, and the Cplex commercial solver in MATLAB is used to solve the optimal scheduling plan; finally, the proposed optimization scheduling strategy is simulated and verified through examples. The multi-energy storage collaborative scheduling method containing electric vehicles and 5G base stations disclosed in the present invention is applied to give full play to the role of the energy storage system, improve the ability of the entire power system to access and absorb new energy power generation, and achieve the purpose of improving the system's photovoltaic absorption rate and safe operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0118] Figure 1 It is a flow chart of a method for controlling a multi-energy storage system including an electric vehicle and a 5G base station according to the present invention;

[0119] Figure 2 It is a specific flow chart of a method for controlling a multi-energy storage system including electric vehicles and 5G base stations in an embodiment of the present invention;

[0120] Figure 3 Schematic diagram of the system structure of the multi-energy storage power supply system in an embodiment of the present invention;

[0121] Figure 4 Schematic diagram comparing load changes before and after the multi-element energy storage system participates in the optimization and regulation in an embodiment of the present invention. DETAILED DESCRIPTION

[0122] The present invention provides a control method for a multi-energy storage system including an electric vehicle and a 5G base station. The present invention is further described in detail below in conjunction with the accompanying drawings.

[0123] like Figure 1 The embodiment of the present invention shown discloses a control method for a multi-energy storage system including an electric vehicle and a 5G base station, comprising:

[0124] Step S1: construct a multi-element energy storage system model, analyze the energy usage law of the energy storage equipment and calculate the dispatchable potential of the energy storage; the energy storage equipment includes: electric vehicles, 5G base stations and distributed energy storage; the multi-element energy storage system model includes: a dispatchable potential model of electric vehicles, a dispatchable potential model of 5G base stations and a dispatchable model of distributed energy storage;

[0125] Step S2: construct a multi-element energy storage optimization scheduling model with the goal of peak shaving and valley filling, wherein the multi-element energy storage optimization scheduling model uses the charging and discharging power of the energy storage equipment in the multi-element energy storage system model as a decision variable, and uses power balance and equipment characteristics as constraints, and uses a commercial solver to solve the optimal scheduling plan of the multi-element energy storage optimization scheduling model;

[0126] Step S3: The optimal scheduling scheme is simulated and verified through examples to achieve effective regulation of the multi-element energy storage system.

[0127] The present invention discloses a method for regulating and controlling a multi-energy storage system including electric vehicles and 5G base stations, which can give full play to the role of the energy storage system, improve the ability of the entire power system to access and absorb new energy power generation, and achieve the purpose of improving the system's photovoltaic absorption rate and safe operation. Figure 2 As shown, the implementation process of each step is described in detail below.

[0128] 1. Construct a multi-element energy storage system model and analyze the dispatchable potential of the multi-element energy storage system

[0129] Step S1: construct a multi-element energy storage system model, analyze the energy usage law of the energy storage equipment and calculate the dispatchable potential of the energy storage; the energy storage equipment includes: electric vehicles, 5G base stations and distributed energy storage; the multi-element energy storage system model includes: a dispatchable potential model of electric vehicles, a dispatchable potential model of 5G base stations and a dispatchable model of distributed energy storage;

[0130] In this embodiment, the multi-energy storage system adopts a combination of multiple energy storage technologies in the energy system to improve energy utilization efficiency, enhance the grid regulation capability, and promote the consumption of new energy. By combining different types of energy storage technologies, the multi-energy storage system can give full play to the advantages of each technology and form complementarity to adapt to different application scenarios and needs.

[0131] The system structure of the multi-energy storage power supply system involved in this embodiment is as follows: Figure 3 As shown, the multi-energy storage power supply system includes: an upper-level transmission network and a photovoltaic power generation system to meet the power demand of conventional power loads, electric vehicles and 5G base stations. Distributed energy storage has strong flexibility, high energy density, high charging and discharging efficiency, good economy, mature technology, and large installation capacity. It is suitable for participating in power grid dispatching, absorbing new energy, and ensuring power supply. As flexible adjustment resources, electric vehicles and 5G base stations have good energy storage potential, can accept dispatching instructions, and help improve the operation quality of the power grid.

[0132] The energy storage equipment includes: electric vehicles, 5G base stations and distributed energy storage; the multi-energy storage system model includes: a dispatchable potential model of electric vehicles, a dispatchable potential model of 5G base stations and a dispatchable model of distributed energy storage; the multi-energy storage system model analyzes the dispatchable potential of the multi-energy storage system, analyzes the energy consumption rules of the energy storage equipment and calculates the dispatchable potential of the energy storage.

[0133] In this embodiment, the analysis of the dispatchable potential of the multi-element energy storage system includes:

[0134] 1.1. Construct a dispatchable potential model for electric vehicles;

[0135] 1.2. Construct a schedulable potential model for 5G base stations;

[0136] 1.3. Construct a dispatchable model for distributed energy storage;

[0137] The following describes the implementation process of each step:

[0138] 1.1. Constructing a dispatchable potential model for electric vehicles

[0139] In this embodiment, the electric vehicle's dispatchable potential model includes: an electric vehicle charging and discharging model and an electric vehicle behavior model. Considering that electric vehicles themselves have good energy storage characteristics and contain dispatching potential, an electric vehicle's adjustable potential model is established by studying the electric vehicle's charging and discharging model and user behavior rules.

[0140] 1) Electric vehicle charging and discharging model:

[0141] The relevant parameters affecting the charging and discharging of electric vehicles mainly include: the battery state of charge of the electric vehicle, the battery capacity, the battery charging and discharging power, and the battery charging and discharging efficiency. In this embodiment, the battery state of charge, charging model, and discharging model of the electric vehicle correspond to Formulas 1 to 3 respectively.

[0142] The battery state of charge is:

[0143]

[0144] In the formula, SOC ev (t) is the battery charge state of the electric vehicle at time t; C ev (t) is the battery capacity of the electric vehicle at time t; is the rated capacity of the electric vehicle battery;

[0145] The charging model is:

[0146]

[0147] In the formula, SOCev (t+1) is the battery charge state of the electric vehicle at time t+1; η chr charging efficiency for electric vehicles; is the charging power of the electric vehicle at time t;

[0148] The discharge model is:

[0149]

[0150] Where η dis Discharge efficiency of electric vehicles; is the discharge power of the electric vehicle at time t;

[0151] 2) Electric vehicle behavior model

[0152] The electric vehicle user behaviors involved in this embodiment mainly include the following aspects: daily mileage, driving state of charge, charging time at the station, and travel time from the station. Taking electric private cars with strong flexibility as an example, according to the survey results of household vehicle travel in relevant literature, the time distribution of vehicle behavior can be approximated as a normal distribution through maximum likelihood estimation.

[0153] The starting time of charging an electric vehicle determines the distribution of the charging load of the electric vehicle. The charging time of an electric vehicle is usually a probability function of the starting time of the trip satisfying the formula after the last trip home.

[0154] Assuming that the electric vehicle starts charging immediately after entering the station, the probability density function of the charging time at the station is expressed as:

[0155]

[0156] Where, t start Represents the expected value of the normal distribution of the network access time and the station entry time μ s is equal to 17.6, standard deviation σ s Equal to 3.4.

[0157] Electric vehicle departure time t end and pit stop time t start and parking time t z The parking time follows a log-normal distribution.

[0158] The probability density function of electric vehicle parking time is expressed as:

[0159]

[0160] t end =t start +t Z (6)

[0161] Where, t z is the parking time, t start represents the pit stop time, t end Represents the normal distribution expected value of the departure time and parking time μ Z is 2.74; standard deviation σ Z is 1.21;

[0162] The daily mileage of electric vehicles affects the time of electric vehicles joining the grid, charging requirements and off-grid time. For electric private cars, their travel patterns are relatively random, and the probability density function of the daily mileage is obtained after fitting.

[0163] The probability density function of the daily mileage is:

[0164]

[0165] Where L represents the off-grid time, and the normal distribution expected value of parking time is μ L is 3.2; standard deviation σ L is 0.88;

[0166] The state of charge of an electric vehicle will also change with the mileage of the electric vehicle. The state of charge model of an electric vehicle is:

[0167]

[0168] In the formula, The state of charge of the electric vehicle when it returns to the start of charging after the last trip; The initial state of charge of the electric vehicle before driving; d r is the daily mileage of electric vehicles; W 100 The amount of electricity consumed by an electric car traveling 100 km; is the rated capacity of the electric vehicle battery.

[0169] 1.2. Constructing a schedulable potential model for 5G base stations

[0170] In order to prevent the outage of the city power supply from affecting the communication of the base station, energy storage batteries are needed as backup power supply to ensure the reliability of the power supply of the base station. In the current base station construction process, most of them are designed according to the 4-hour backup power of the energy storage battery. In the actual operation process, the load of the base station will vary greatly due to the different temporal and spatial distribution of the base station. The installed capacity of its energy storage battery is redundant in certain periods of time, which contains certain scheduling potential.

[0171] The base station communication load indicates the amount of business undertaken by the base station, which can be represented by the number of users accessing the base station, the occupied bandwidth ratio, the number of occupied sub-bandwidths, etc. The present invention represents the base station load by the number of users accessing the base station, and introduces a base station load rate indicator.

[0172] The base station load factor index λ load for:

[0173]

[0174] In the formula, L represents the number of users currently connected to the base station, L all Indicates the maximum number of users that the base station can handle; load ∈[0,1];

[0175] Base station load factor index λ load It is defined as the ratio of the current number of users accessing the base station to the maximum number of users accessing the base station, which reflects the current load level of the base station. Since users are not allowed to overload when accessing the base station, λ load ∈[0,1];

[0176] In order to improve the rationality of base station energy storage regulation, it is considered to associate the dispatchable capacity of base station energy storage with the load state of the base station by limiting the charge state range of base station energy storage charging and discharging, so as to minimize the impact of energy storage regulation on the backup power function.

[0177] In this embodiment, S up The SOC upper limit value set for the energy storage battery to avoid overcharge and over discharge; S down It is the lower limit of SOC set by the energy storage battery to avoid overcharge and over discharge. min The lower limit of energy storage charging and discharging SOC is set to take into account the base station communication load state; S max The upper limit of energy storage charging and discharging SOC is set to take into account the base station communication load state;

[0178] Energy storage charge and discharge SOC lower limit S min for:

[0179] S min = kλ load (10)

[0180] Where k is the correlation coefficient and k∈[0,1], λ load It is the base station communication load status indicator;

[0181] Formula (10) associates the lower limit of the discharge SOC of the base station energy storage with the base station communication load state, k is the correlation coefficient and k∈[0,1]. load When the value increases, the lower limit of the discharge SOC of the energy storage min As it increases, the energy storage stops discharging when it reaches the restricted SOC value, thereby maximizing the backup power reliability of the energy storage.

[0182] The upper limit constraint of base station energy storage charging is not as strict as the lower limit constraint of discharging. Since the base station energy storage is in a state of sufficient capacity, it is beneficial to the reliable backup power of the base station backup energy storage. Therefore, S max The default setting is: S max =S up .

[0183] Energy storage charge and discharge SOC upper limit S max for:

[0184] S max =S up

[0185] In the formula, S up The SOC upper limit value set for the energy storage battery to avoid overcharge and over discharge;

[0186] Taking the two constraints of energy storage into consideration, the upper and lower limits of the final charging and discharging SOC of base station i are obtained.

[0187] The final charge and discharge SOC upper and lower limits of the base station i are:

[0188] S i,min,t =max{S down ,S min}(11)

[0189] S i,max,t =S max =S up (12)

[0190] In the formula, S i,min,t is the lower limit of the SOC of the base station energy storage i at time t; S i,max,t is the upper limit of the charging SOC of the base station energy storage i at time t; S down The SOC lower limit value set for the energy storage battery to avoid overcharge or over discharge.

[0191] According to the real-time SOC status of the base station energy storage i and the upper and lower limits of the charge and discharge SOC, the upward chargeable capacity and downward dischargeable capacity of the energy storage can be calculated:

[0192]

[0193] In the formula, The rechargeable capacity for base station energy storage i upwards; S is the downward discharge capacity of the base station energy storage i; i (t) is the SOC value of the energy storage of base station i at time t, and E is the rated capacity of the energy storage;

[0194] According to the rechargeable capacity and dischargeable capacity of energy storage, the maximum charging power and maximum discharging power of energy storage of base station i within a scheduling time interval Δt are calculated:

[0195] Maximum charging power of base station i energy storage within Δt for:

[0196]

[0197] In the formula, is the maximum charging power limit of the energy storage battery, Δt is the time interval;

[0198] Assuming that the number of base stations in the charging base station energy storage group is M, the maximum charging power of the charging energy storage group at time t is:

[0199]

[0200] In the formula, is the maximum charging power of the charging base station energy storage group at time t;

[0201] Similarly, when the energy storage is discharged:

[0202] Maximum discharge power of base station energy storage i within Δt for:

[0203]

[0204] In the formula, is the maximum discharge power limit of the energy storage battery;

[0205] Assuming that the number of base stations in the discharge base station energy storage group is N, the maximum discharge power of the discharge energy storage group at time t is:

[0206]

[0207] In the formula, is the maximum discharge power of the discharge base station energy storage group at time t;

[0208] In this embodiment, the base station energy storage battery charging model is:

[0209]

[0210] In the formula, SOC G (t+1) is the battery charge state of the base station at time t+1; η Gchr Charging efficiency of base station energy storage batteries; is the charging power of the battery energy storage at time t; C G The rated capacity of the base station energy storage.

[0211] The discharge model of the base station energy storage battery is:

[0212]

[0213] Where ηGdis is the discharge efficiency of the energy storage base station; is the discharge power of the battery at time t.

[0214] 1.3. Constructing a dispatchable model for distributed energy storage

[0215] Distributed energy storage has flexible charging and discharging capabilities and can be charged and discharged under dispatch control. By simulating available capacity and available power, the charging and discharging scheduling potential of the energy storage system can be obtained.

[0216] The charging model of distributed energy storage battery is:

[0217]

[0218] In the formula, SOC ess (t+1) is the battery charge state at the energy storage time t+1; η esschr Charging efficiency of energy storage batteries; is the charging power of the battery energy storage at time t; C ess is the rated capacity of the distributed energy storage battery.

[0219] The discharge model of distributed energy storage batteries is:

[0220]

[0221] Where η essdis is the discharge efficiency of the energy storage base station; is the discharge power of the battery at time t.

[0222] When dispatching energy storage, the available capacity should meet the following constraints:

[0223]

[0224] In the formula, E ess is the charge of the energy storage system; Refers to the upper and lower limits of the energy storage system charge;

[0225] The available power meets the following constraints:

[0226]

[0227] In the formula, Refers to the upper and lower limits of the charging and discharging power of the energy storage system; is the charging and discharging power of the energy storage system; P ess is the energy storage system power; It is the charging and discharging efficiency of the energy storage system.

[0228] According to the real-time SOC state of the energy storage and the upper and lower limits of the charge and discharge SOC, the upward chargeable capacity and downward dischargeable capacity of the energy storage can be calculated:

[0229]

[0230] In the formula, E ess,c 、E ess,d Refers to the remaining available capacity of the energy storage system for charging and discharging.

[0231] 2. Constructing a multi-dimensional energy storage optimization dispatch model

[0232] Step S2: construct a multi-element energy storage optimization scheduling model with the goal of peak shaving and valley filling, wherein the multi-element energy storage optimization scheduling model uses the charging and discharging power of the energy storage equipment in the multi-element energy storage system model as a decision variable, and uses power balance and equipment characteristics as constraints, and uses a commercial solver to solve the optimal scheduling plan of the multi-element energy storage optimization scheduling model;

[0233] 2.1. Taking peak load shaving and valley filling as the optimization goal, the objective function of the multi-element energy storage optimization scheduling model is constructed:

[0234] The essence of "peak shaving and valley filling" is to reduce the superposition of power load during peak power consumption and appropriately increase the power load during low power consumption through scheduling strategies, so as to achieve the effect of reducing the peak-to-valley difference of load, and establish the objective function by minimizing the variance of the power load curve.

[0235] The objective function of the multivariate energy storage optimization scheduling model is:

[0236]

[0237] In the formula, T is 24 hours, R is Load (t) is the variance of the power load curve, P av is the average net load of the power consumption in the substation area; P Load (t) is the net power load, P 1 (t) is the power load, P pv (t) is the distributed photovoltaic power generation power; P G (t) is the power consumed by the 5G base station battery energy storage system, P ev (t) is the power of the electric vehicle energy storage system, P ess (t) is the power of the distributed energy storage system, P imp (t) is the fixed electric load, The charging and discharging power of the 5G base station battery energy storage system, The charging and discharging power of the electric vehicle energy storage system, The charging and discharging power of the electric vehicle energy storage system, It is the charging flag of the base station energy storage system, 1 means charging, 0 means not charging, It is the discharge flag of the battery energy storage system, 1 means discharge, 0 means no discharge, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, It is the discharge mark of electric vehicle, 1 means discharge, 0 means no discharge, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, It is the discharge flag of electric vehicle, 1 means discharge, 0 means no discharge.

[0238] 2.2. Constructing Constraints

[0239] 1) Electric power balance constraints:

[0240] P pv (t)+P gird (t) = P G (t)+P EV (t)+P ess (t)+P imp (t) (30)

[0241] Where P gird (t) is the grid interaction power;

[0242] 2) Electric vehicle charging and discharging constraints:

[0243]

[0244] In the formula, The upper limit of charging power for electric vehicles, The lower limit of charging power for electric vehicles, The upper limit of the electric vehicle discharge power, The lower limit of electric vehicle discharge power; SOC ev (t) is the battery charge state of the electric vehicle at time t; are the upper and lower limits of the battery state of charge of the electric vehicle at time t.

[0245] 3) Constraints on charging and discharging of 5G base station energy storage:

[0246]

[0247] In the formula, Set a power cap for charging 5G base stations; The lower limit of charging power for 5G base stations; The upper limit of the discharge power of 5G base stations; The lower limit of the discharge power of 5G base stations; SOC G (t) is the battery charge state of the base station at time t; are the upper and lower limits of the battery state of charge of the base station at time t.

[0248] 4) Distributed energy storage charging and discharging constraints:

[0249]

[0250] In the formula, Provides a maximum charging power limit for distributed energy storage; The lower limit of the charging power for distributed energy storage; The upper limit of the discharge power of distributed energy storage; is the lower limit of the distributed energy storage discharge power;

[0251] SOC ess (t) is the battery charge state at the energy storage time t; are the upper and lower limits of the battery state of charge at time t.

[0252] 5) Photovoltaic output constraints:

[0253]

[0254] In the formula, is the upper limit of photovoltaic power generation power;

[0255] 6) Grid interaction power constraints:

[0256]

[0257] In the formula, is the upper limit of grid interaction power; is the lower limit of grid interaction power;

[0258] 7) Device uniqueness constraint:

[0259]

[0260] 3. Solving the proposed multi-element energy storage participation regulation model

[0261] Step S3: The optimal scheduling scheme is simulated and verified through examples to achieve effective regulation of the multi-element energy storage system.

[0262] In order to verify the effectiveness of the control method of a multi-energy storage system containing electric vehicles and 5G base stations disclosed in the present invention, the optimal scheduling scheme is simulated and verified through a calculation example.

[0263] The step of simulating and verifying the optimal scheduling scheme through a calculation example comprises:

[0264] Step S31: using Monte Carlo simulation of electric vehicle data, and predicting the upper and lower limits of the dispatchable power of the electric vehicle energy storage system, using the Minkowski method to obtain the total dispatchable potential of the electric vehicles participating in the dispatch, and obtaining the dispatch potential of the electric vehicle cluster;

[0265] Step S32: Calculate the minimum spare capacity of the 5G base station according to the differences in the communication load states of the 5G base station, and then obtain the adjustable potential of the base station energy storage;

[0266] In this embodiment, in order to ensure that the base station's participation in grid interaction as energy storage does not affect its reliability as a backup power source, the energy storage of the base station in a heavy load state is not considered to be regulated;

[0267] Step S33: Combining the dispatchable potential model of electric vehicles, the dispatchable potential model of 5G base stations, and the dispatchable model of distributed energy storage, the Cplex solver in MATLAB is used to solve the load changes before and after the multivariate energy storage participates in the optimization and regulation.

[0268] Load changes before and after multi-energy storage participates in optimization and regulation Figure 4 As shown in the figure. Before considering the energy storage potential of various resources, the electricity load is low during the day and high at noon and night. The peak of electricity load is from 11:00 to 13:00 and from 19:00 to 20:00, and the trough of electricity load is in the afternoon and early morning, with a large difference between peak and valley. After electric vehicles and 5G base stations participate in the optimization scheduling, the peak-to-valley difference of electricity load is significantly reduced by making full use of the adjustable potential of both.

Claims

1. A control method for a multi-element energy storage system including electric vehicles and 5G base stations, characterized in that: The steps include: Step S1: construct a multi-element energy storage system model, analyze the energy usage law of the energy storage equipment and calculate the dispatchable potential of the energy storage; the energy storage equipment includes: electric vehicles, 5G base stations and distributed energy storage; the multi-element energy storage system model includes: a dispatchable potential model of electric vehicles, a dispatchable potential model of 5G base stations and a dispatchable model of distributed energy storage; Step S2: construct a multi-element energy storage optimization scheduling model with the goal of peak shaving and valley filling, wherein the multi-element energy storage optimization scheduling model uses the charging and discharging power of the energy storage equipment in the multi-element energy storage system model as a decision variable, and uses power balance and equipment characteristics as constraints, and uses a commercial solver to solve the optimal scheduling plan of the multi-element energy storage optimization scheduling model; Step S3: The optimal scheduling scheme is simulated and verified through examples to achieve effective regulation of the multi-element energy storage system.

2. The control method of a multi-element energy storage system including electric vehicles and 5G base stations according to claim 1, characterized in that: The dispatchable potential model of the electric vehicle includes: an electric vehicle charging and discharging model and an electric vehicle behavior model.

3. The control method of a multi-element energy storage system including electric vehicles and 5G base stations according to claim 2, characterized in that: The electric vehicle charging and discharging model includes: battery state of charge, charging model and discharging model; The battery state of charge is: In the formula, SOC ev (t) is the battery charge state of the electric vehicle at time t; C ev (t) is the battery capacity of the electric vehicle at time t; is the rated capacity of the electric vehicle battery; The charging model is: In the formula, SOC ev (t+1) is the battery charge state of the electric vehicle at time t+1; η chr charging efficiency for electric vehicles; is the charging power of the electric vehicle at time t; The discharge model is: Where η dis Discharge efficiency of electric vehicles; is the discharge power of the electric vehicle at time t.

4. The control method of a multi-element energy storage system including electric vehicles and 5G base stations according to claim 2, characterized in that: The electric vehicle behavior model includes: a probability density function of the charging time at the station, a probability density function of the parking time of the electric vehicle, a probability density function of the daily mileage, and a driving state of charge model of the electric vehicle; The probability density function of the charging time at the station is expressed as: Where, t start Represents the expected value of the normal distribution of the network access time and the station entry time μ s is equal to 17.6, standard deviation σ s =3.4; The electric vehicle parking time probability density function is expressed as: t end =t start +t Z (6) Where, t z is the parking time, t start represents the pit stop time, t end Represents the normal distribution expected value of the departure time and parking time μ Z is 2.74; standard deviation σ Z is 1.21; The probability density function of the daily mileage is: Where L represents the off-grid time, and the normal distribution expected value of parking time is μ L is 3.2; standard deviation σ L is 0.88; The electric vehicle driving state of charge model is: In the formula, The state of charge of the electric vehicle when it returns to the start of charging after the last trip; The initial state of charge of the electric vehicle before driving; d r is the daily mileage of electric vehicles; W 100 The amount of electricity consumed by an electric car traveling 100 km; is the rated capacity of the electric vehicle battery.

5. The control method of a multi-element energy storage system including electric vehicles and 5G base stations according to claim 1, characterized in that: The schedulable potential model of the 5G base station includes: Base station load factor index λ load 、Energy storage charge and discharge SOC lower limit S min 、Energy storage charge and discharge SOC upper limit S max , The final charge and discharge SOC upper and lower limits of base station i, the upward chargeable capacity and downward dischargeable capacity of energy storage, Maximum charging power of base station i energy storage within Δt The maximum charging power of the charging energy storage group at time t and the maximum discharge power of the base station energy storage i within Δt The maximum discharge power of the energy storage group at time t, the charging model of the base station energy storage battery, and the discharge model of the base station energy storage battery; The base station load factor index λ load for: In the formula, L represents the number of users currently connected to the base station, L all Indicates the maximum number of users that the base station can handle; load ∈[0,1]; The energy storage charge and discharge SOC lower limit S min for: S min =kλ load (10) Where k is the correlation coefficient and k∈[0,1], λ load It is the base station communication load status indicator; The energy storage charge and discharge SOC upper limit S max for: S max =S up In the formula, S up The SOC upper limit value set for the energy storage battery to avoid overcharge and over discharge; The final charge and discharge SOC upper and lower limits of the base station i are: S i,min,t =max{S down ,S min } (11) S i,max,t =S max =S up (12) In the formula, S i,min,t is the lower limit of the SOC of the base station energy storage i at time t; S i,max,t is the upper limit of the charging SOC of the base station energy storage i at time t; S down The SOC lower limit value set for the energy storage battery to avoid overcharge and over discharge; The upward chargeable capacity and downward dischargeable capacity of energy storage: In the formula, The rechargeable capacity for base station energy storage i upwards; S is the downward discharge capacity of the base station energy storage i; i (t) is the SOC value of the energy storage of base station i at time t, and E is the rated capacity of the energy storage; Maximum charging power of base station i energy storage within Δt for: In the formula, is the maximum charging power limit of the energy storage battery, Δt is the time interval; The maximum charging power of the energy storage group at time t is: In the formula, is the maximum charging power of the charging base station energy storage group at time t; Maximum discharge power of base station energy storage i within Δt for: In the formula, is the maximum discharge power limit of the energy storage battery; The maximum discharge power of the energy storage group at time t is: In the formula, is the maximum discharge power of the discharge base station energy storage group at time t; The charging model of the base station energy storage battery is: In the formula, SOC G (t+1) is the battery charge state of the base station at time t+1; η Gchr Charging efficiency of base station energy storage batteries; is the charging power of the battery energy storage at time t; C G Rated capacity of energy storage for base stations; The discharge model of the base station energy storage battery is: Where η Gdis is the discharge efficiency of the energy storage base station; is the discharge power of the battery at time t.

6. The control method of a multi-element energy storage system including electric vehicles and 5G base stations according to claim 1, characterized in that: The dispatchable model of the distributed energy storage includes: Distributed energy storage battery charging model and distributed energy storage battery discharging model; The distributed energy storage battery charging model is: In the formula, SOC ess (t+1) is the battery charge state at the energy storage time t+1; η esschr Charging efficiency of energy storage batteries; is the charging power of the battery energy storage at time t; C ess is the rated capacity of the distributed energy storage battery; The distributed energy storage battery discharge model is: Where η essdis is the discharge efficiency of the energy storage base station; is the discharge power of the battery at time t; When dispatching energy storage, the available capacity should meet the following constraints: In the formula, E ess is the charge of the energy storage system; Refers to the upper and lower limits of the energy storage system charge; The available power meets the following constraints: In the formula, Refers to the upper and lower limits of the charging and discharging power of the energy storage system; is the charging and discharging power of the energy storage system; P ess is the energy storage system power; The charging and discharging efficiency of the energy storage system; According to the real-time SOC state of the energy storage and the upper and lower limits of the charge and discharge SOC, the upward chargeable capacity and downward dischargeable capacity of the energy storage can be calculated: In the formula, E ess,c 、E ess,d Refers to the remaining available capacity of the energy storage system for charging and discharging.

7. The control method of a multi-element energy storage system including electric vehicles and 5G base stations according to claim 1, characterized in that: The objective function of the multivariate energy storage optimization scheduling model is: In the formula, T is 24 hours, R is Load (t) is the variance of the power load curve, P av is the average net load of the power consumption in the substation area; P Load (t) is the net power load, P1(t) is the power load, P pv (t) is the distributed photovoltaic power generation power; P G (t) is the power consumed by the 5G base station battery energy storage system, P ev (t) is the power of the electric vehicle energy storage system, P ess (t) is the power of the distributed energy storage system, P imp (t) is the fixed electric load, The charging and discharging power of the 5G base station battery energy storage system, The charging and discharging power of the electric vehicle energy storage system, The charging and discharging power of the electric vehicle energy storage system, It is the charging flag of the base station energy storage system, 1 means charging, 0 means not charging, It is the discharge flag of the battery energy storage system, 1 means discharge, 0 means no discharge, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, It is the discharge mark of electric vehicle, 1 means discharge, 0 means no discharge, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, It is the discharge flag of electric vehicle, 1 means discharge, 0 means no discharge.

8. The control method of a multi-element energy storage system including electric vehicles and 5G base stations according to claim 1, characterized in that: The constraints of the multivariate energy storage optimization scheduling model include: 1) Electric power balance constraints: P pv (t)+P gird (t)=P G (t)+P EV (t)+P ess (t)+P imp (t) (30) Where P gird (t) is the grid interaction power; 2) Electric vehicle charging and discharging constraints: In the formula, The upper limit of charging power for electric vehicles, The lower limit of charging power for electric vehicles, The upper limit of the electric vehicle discharge power, The lower limit of electric vehicle discharge power; SOC ev (t) is the battery charge state of the electric vehicle at time t; are the upper and lower limits of the battery state of charge of the electric vehicle at time t; 3) Constraints on charging and discharging of 5G base station energy storage: In the formula, Set a power cap for charging 5G base stations; The lower limit of charging power for 5G base stations; The upper limit of the discharge power of 5G base stations; The lower limit of the discharge power of 5G base stations; SOC G (t) is the battery charge state of the base station at time t; are the upper and lower limits of the battery state of charge of the base station at time t; 4) Distributed energy storage charging and discharging constraints: In the formula, Provides a maximum charging power limit for distributed energy storage; The lower limit of charging power for distributed energy storage; The upper limit of the discharge power of distributed energy storage; is the lower limit of the distributed energy storage discharge power; SOC ess (t) is the battery charge state at the energy storage time t; are the upper and lower limits of the battery state of charge at time t; 5) Photovoltaic output constraints: In the formula, is the upper limit of photovoltaic power generation power; 6) Grid interaction power constraints: In the formula, is the upper limit of grid interaction power; is the lower limit of grid interaction power; 7) Device uniqueness constraint:

9. The control method of a multi-element energy storage system including electric vehicles and 5G base stations according to claim 1, characterized in that: The step of simulating and verifying the optimal scheduling scheme through a calculation example comprises: Step S31: using Monte Carlo simulation of electric vehicle data, and predicting the upper and lower limits of the dispatchable power of the electric vehicle energy storage system, using the Minkowski method to obtain the total dispatchable potential of the electric vehicles participating in the dispatch, and obtaining the dispatch potential of the electric vehicle cluster; Step S32: Calculate the minimum backup capacity of the 5G base station according to the differences in the communication load states of the 5G base station, and then obtain the adjustable potential of the base station energy storage; in order to ensure that the base station as energy storage participating in the grid interaction does not affect its reliability as a backup power source, the energy storage of the base station in a heavy load state is not considered to be regulated; Step S33: Combining the dispatchable potential model of electric vehicles, the dispatchable potential model of 5G base stations, and the dispatchable model of distributed energy storage, the Cplex solver in MATLAB is used to solve the load changes before and after the multivariate energy storage participates in the optimization and regulation.

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