Area energy consumption multi-objective optimization scheduling method and system considering various elastic resources

Abstract

The invention discloses a transformer area energy consumption multi-objective optimization scheduling method and system considering various elastic resources. The method comprises the following steps: acquiring parameters of each elastic resource of a transformer area, distributed photovoltaic prediction output and temperature day-ahead prediction data; substituting the parameters of each elastic resource, the distributed photovoltaic prediction output and the day-ahead temperature prediction data into a pre-constructed multi-target optimization scheduling model to obtain a power output scheme of each elastic resource; wherein the pre-constructed multi-objective optimization scheduling model is constructed by combining constraint conditions with a peak clipping and valley filling objective function constructed by minimizing a load peak-valley difference and a new energy consumption objective function constructed by minimizing reverse power transmission electric quantity of a transformer area. According to the method, the multi-objective optimization scheduling model is adopted, the peak clipping and valley filling objective function constructed by minimizing the load peak-valley difference and the new energy consumption objective function constructed by minimizing the reverse power transmission electric quantity of the transformer area effectively stabilize the power load fluctuation of the transformer area, the voltage out-of-limit phenomenon is eliminated, and the new energy power generation consumption rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power dispatching, and in particular to a method and system for multi-objective optimization dispatching of power consumption in a substation taking into account a variety of elastic resources. Background Art

[0002] At present, the promotion and application of new technologies such as home appliance networking, orderly charging, and household energy storage has become a development trend. As the scale of smart home appliances in residents' homes continues to expand, the construction of "new infrastructure" facilities such as 5G base stations and charging piles is developing rapidly, the number of household distributed photovoltaic users and capacity are growing faster, and the power load in the area is surging, and the randomness and uncertainty are more prominent. During peak power consumption, it is easy to cause the distribution transformer in the area to overload, resulting in frequent power outages, low voltage, customer complaints and other problems. It is necessary to continuously enhance the balance and regulation capabilities of the power system, promote the full consumption of new energy, and achieve deep coordinated interaction between sources, grids and loads. Related research has become the focus.

[0003] How to formulate corresponding optimization scheduling plans for different elastic resources is the key to relevant research. The use of elastic resources can enrich the means of grid regulation. Electric vehicles, as mobile energy storage units, have certain dispatchable potential; 5G base stations, as emerging load-side equipment, can also be used as a dispatchable resource for substation scheduling as the number of base stations increases. Based on the heat storage characteristics of buildings and the comfortable temperature range of human bodies, the power of air conditioners can be reasonably adjusted to relieve the load pressure of the substation grid. For low-voltage substations, with the optimization goals of stable grid load operation and new energy consumption, the three elastic resources of electric vehicles, 5G base stations and residential air conditioners are coordinated and coordinated. The dispatch algorithm is designed to reduce the peak-to-valley difference of the grid load curve, improve the utilization rate of new energy, and improve the ability of low-voltage substations to operate independently and reliably.

[0004] At present, there have been some studies on the participation of elastic resources in optimal scheduling, but there are still few studies on how to optimize the scheduling of multiple new elastic resources at the same time. There are few studies on the regulation of new elastic resources such as 5G base station energy storage, which leads to the inability of the substation to adapt to the impact of the access of multiple elastic resources, reducing the stability of the substation operation. In summary, the current problems of low-voltage substations are: (1) After multiple elastic resources are connected to the substation, the power load of the substation is superimposed, resulting in large peak-to-valley differences in the grid load and voltage over-limit problems; (2) After the distributed photovoltaic power generation in the low-voltage substation is connected, it cannot be absorbed, resulting in the problem of reverse power transmission to the upper grid. Summary of the invention

[0005] In order to solve the problems that the power load of the power grid is superimposed after the multiple elastic resources are connected to the power grid, resulting in large peak-to-valley difference and voltage over-limit problems in the low-voltage power grid; and the problem that the distributed photovoltaic power grid in the low-voltage power grid cannot be absorbed after the connection, resulting in the reverse transmission of power to the superior power grid, the present invention proposes a multi-objective optimization scheduling method for power consumption in the power grid taking into account multiple elastic resources, including:

[0006] Obtain the parameters of each elastic resource in the substation area, the predicted output of distributed photovoltaic power generation and the day-ahead temperature prediction data;

[0007] The parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data are introduced into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource;

[0008] Among them, the pre-constructed multi-objective optimization scheduling model is constructed by combining the constraint conditions with the peak shaving and valley filling objective function constructed by minimizing the load peak-valley difference and the new energy consumption objective function constructed by minimizing the reverse power transmission amount of the substation.

[0009] Optionally, the construction of the multi-objective optimization scheduling model includes:

[0010] The peak shaving and valley filling objective function is constructed by minimizing the load peak-valley difference;

[0011] The objective function of new energy consumption is constructed by minimizing the reverse power transmission in the substation area;

[0012] Setting constraints for the peak shaving and valley filling objective function and the new energy consumption objective function;

[0013] A multi-objective optimization scheduling model is constructed based on the peak shaving and valley filling objective function, the new energy consumption objective function and the constraint conditions;

[0014] The constraints include: electric power balance constraint, thermal power balance constraint, various energy power constraints, various energy voltage constraints, and building temperature comfort constraints.

[0015] Optionally, the new energy consumption objective function is as follows:

[0016] R load (t) = min{max[P load (t)]-min[P load (t)]}

[0017]

[0018] In the formula, R load (t) is the variance of the power load curve, P load (t) is the power load of the substation, P l(t) is the net load of the power consumption in the substation, P mess (t) is the power consumption of the 5G base station energy storage battery, P EV (t) is the electric power consumed by the electric vehicle, P temp (t) is the power consumed by the electric cooling / heating system, P Imp (t) is the fixed electrical load of the substation, is the charging and discharging power of the 5G base station energy storage battery, P PV (t) is the distributed photovoltaic power generation power, It is the charging mark of the energy storage battery, 1 means charging, 0 means not charging, It is the discharge mark of the energy storage battery, 1 means discharge, 0 means no discharge, Charging and discharging power for electric vehicles, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, is the discharge mark of the electric vehicle, t is the time, and N is an integer.

[0019] Optionally, the new energy consumption objective function is as follows:

[0020]

[0021] Where: Q loss (t) is the total amount of electricity that the substation sends back to the grid in one day, P loss (t) is the internal power supply and demand difference of the area during period t, P op (t) is the internal power supply and demand difference in a certain period of time in the substation.

[0022] Optionally, the thermal power balance constraint is as follows:

[0023]

[0024] Q=U ER ·H ER +U EC ·H EC

[0025] Where, T d (t+1) is the floor surface temperature at time t+1, R d is the equivalent thermal resistance of floor convective heat transfer to indoor air and radiant heat transfer to building envelope, C d is the equivalent thermal conductivity of the floor, ΔT is the temperature difference between indoor and outdoor, T d (t) is the floor surface temperature at time t, Q is the heating / cooling capacity, T s (t+1) is the indoor temperature at time t+1, R w is the equivalent thermal resistance of the building envelope, T s(t) is the indoor temperature at time t, Q s is the solar radiation heat, T out (t) is the outdoor temperature, C w is the equivalent heat capacity of the building envelope, H ER Indicates the thermal power output of the electric heating system, H EC Indicates the thermal power output of the electric refrigeration system, U ER It is the heating flag, which is 1 when heating is in winter and 0 when not heating; U EC It is a cooling symbol.

[0026] Optionally, the parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data are brought into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource, including:

[0027] The multi-objective optimization scheduling model is solved by adopting a fast non-dominated sorting strategy based on grading, a comparison strategy of individuals with the same sequence value based on crowding distance, and an elite individual retention strategy in a genetic algorithm.

[0028] Optionally, the method of solving the multi-objective optimization scheduling model by using a hierarchical fast non-dominated sorting strategy, a crowding distance-based individual comparison strategy for individuals with the same sequence value, and an elite individual retention strategy in a genetic algorithm includes:

[0029] S1: Randomly generate an initial population as the parent population P of the first generation population t , and set the number of iterations g = 1;

[0030] S2: The parent population undergoes selection, crossover, and mutation to produce a child population Q of the same size t ;

[0031] S3: The parent population P t and the offspring population Q t Merge into a population R t , and based on the sequence value and crowding distance, the population R t Sort the individuals;

[0032] S4: From the sorted population R t Select the first N by sequence value pop Each individual constructs a new parent population Pt+1;

[0033] S5: Determine whether the preset population size Npop is reached and whether the maximum number of iterations g is reached max If it is not reached, set the number of iterations g = g + 1 and return to step S2; otherwise, output the Pareto optimal solution set and the frontier, and the algorithm ends.

[0034] Optionally, the obtaining of parameters of each elastic resource in the substation, distributed photovoltaic forecast output and temperature day-ahead forecast data includes:

[0035] Obtain the forecasted output of distributed photovoltaic power, base load power and temperature in the substation area;

[0036] Get the historical records of each electric car through Monte Carlo random sampling,

[0037] The historical records include: daily mileage of electric vehicles, time of electric vehicles connecting to the grid, time of electric vehicles leaving the grid, power of electric vehicles when connected to the grid, and power of electric vehicles when the owner expects to leave the grid;

[0038] Obtain the 5G base station user load in the station area, the equivalent thermal conductivity of the floor, the equivalent thermal resistance of the floor's convection heat transfer to the indoor air and the radiation heat transfer to the building envelope, the equivalent heat capacity of the building envelope, and the equivalent thermal resistance of the building envelope.

[0039] On the other hand, the present invention also provides a multi-objective optimization scheduling system for energy consumption in a substation taking into account multiple elastic resources, comprising:

[0040] The parameter acquisition module is used to obtain the parameters of each elastic resource in the substation area, the distributed photovoltaic forecast output and the temperature forecast data;

[0041] An optimization module is used to bring the parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource;

[0042] Among them, the pre-constructed multi-objective optimization scheduling model is constructed by combining the constraint conditions with the peak shaving and valley filling objective function constructed by minimizing the load peak-valley difference and the new energy consumption objective function constructed by minimizing the reverse power transmission amount of the substation.

[0043] Optionally, a model building module is also included for:

[0044] The peak shaving and valley filling objective function is constructed by minimizing the load peak-valley difference;

[0045] The objective function of new energy consumption is constructed by minimizing the reverse power transmission in the substation area;

[0046] Setting constraints for the peak shaving and valley filling objective function and the new energy consumption objective function;

[0047] A multi-objective optimization scheduling model is constructed based on the peak shaving and valley filling objective function, the new energy consumption objective function and the constraint conditions;

[0048] The constraints include: electric power balance constraint, thermal power balance constraint, various energy power constraints, various energy voltage constraints, and building temperature comfort constraints.

[0049] Optionally, the new energy consumption objective function is as follows:

[0050] R load (t) = min{max[P load (t)]-min[P load (t)]}

[0051]

[0052] In the formula, R load (t) is the variance of the power load curve, P load (t) is the power load of the substation, P l (t) is the net load of the power consumption in the substation, P mess (t) is the power consumption of the 5G base station energy storage battery, P EV (t) is the electric power consumed by the electric vehicle, P temp (t) is the power consumed by the electric cooling / heating system, P Imp (t) is the fixed electrical load of the substation, is the charging and discharging power of the 5G base station energy storage battery, P PV (t) is the distributed photovoltaic power generation power, It is the charging mark of the energy storage battery, 1 means charging, 0 means not charging, It is the discharge mark of the energy storage battery, 1 means discharge, 0 means no discharge, Charging and discharging power for electric vehicles, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, is the discharge mark of the electric vehicle, t is the time, and N is an integer.

[0053] Optionally, the new energy consumption objective function is as follows:

[0054]

[0055] Where: Q loss (t) is the total amount of electricity that the substation sends back to the grid in one day, P loss (t) is the internal power supply and demand difference of the substation during period t, and Pop(t) is the internal power supply and demand difference of the substation during a certain period.

[0056] Optionally, the thermal power balance constraint is as follows:

[0057]

[0058] Q=UER ·H ER +U EC ·H EC

[0059] Where, T d (t+1) is the floor surface temperature at time t+1, R d is the equivalent thermal resistance of floor convective heat transfer to indoor air and radiant heat transfer to building envelope, C d is the equivalent thermal conductivity of the floor, ΔT is the temperature difference between indoor and outdoor, T d (t) is the floor surface temperature at time t, Q is the heating / cooling capacity, T s (t+1) is the indoor temperature at time t+1, R w is the equivalent thermal resistance of the building envelope, T s (t) is the indoor temperature at time t, Q s is the solar radiation heat, T out (t) is the outdoor temperature, C w is the equivalent heat capacity of the building envelope, H ER Indicates the thermal power output of the electric heating system, H EC Indicates the thermal power output of the electric refrigeration system, U ER It is the heating flag, which is 1 when heating is in winter and 0 when not heating; U EC It is a cooling symbol.

[0060] Optionally, the optimization module includes:

[0061] The solution submodule is used to solve the multi-objective optimization scheduling model by adopting the hierarchical-based fast non-dominated sorting strategy, the crowding distance-based same-order-value individual comparison strategy and the elite individual retention strategy in the genetic algorithm.

[0062] Optionally, the solution submodule is specifically used for:

[0063] S1: Randomly generate an initial population as the parent population P of the first generation population t , and set the number of iterations g = 1;

[0064] S2: The parent population undergoes selection, crossover, and mutation to produce a child population Q of the same size t ;

[0065] S3: The parent population P t and the offspring population Q t Merge into a population R t , and based on the sequence value and crowding distance, the population R t Sort the individuals;

[0066] S4: From the sorted population R tSelect the first N by sequence value pop Each individual constructs a new parent population Pt+1;

[0067] S5: Determine whether the preset population size Npop is reached and whether the maximum number of iterations g is reached max If it is not reached, set the number of iterations g = g + 1 and return to step S2; otherwise, output the Pareto optimal solution set and the frontier, and the algorithm ends.

[0068] Optionally, the parameter acquisition module is specifically used for:

[0069] Obtain the forecasted output of distributed photovoltaic power, base load power and temperature in the substation area;

[0070] Get the historical records of each electric car through Monte Carlo random sampling,

[0071] The historical records include: daily mileage of electric vehicles, time of electric vehicles connecting to the grid, time of electric vehicles leaving the grid, power of electric vehicles when connected to the grid, and power of electric vehicles when the owner expects to leave the grid;

[0072] Obtain the 5G base station user load in the station area, the equivalent thermal conductivity of the floor, the equivalent thermal resistance of the floor's convection heat transfer to the indoor air and the radiation heat transfer to the building envelope, the equivalent heat capacity of the building envelope, and the equivalent thermal resistance of the building envelope.

[0073] In another aspect, the present application further provides a computing device, comprising: at least one processor and a memory;

[0074] The memory is used to store one or more programs;

[0075] When the one or more programs are executed by the at least one processor, a multi-objective optimization scheduling method for energy consumption in a substation taking into account multiple elastic resources as described above is implemented.

[0076] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements a multi-objective optimization scheduling method for energy consumption in a substation taking into account a variety of elastic resources as described above.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] The present invention provides a multi-objective optimization scheduling method for energy consumption in a substation taking into account a variety of elastic resources, including: obtaining the parameters of each elastic resource in the substation, the distributed photovoltaic predicted output and the temperature day-ahead predicted data; bringing the parameters of each elastic resource, the distributed photovoltaic predicted output and the temperature day-ahead predicted data into a pre-constructed multi-objective optimization scheduling model to obtain the power output scheme of each elastic resource; wherein the pre-constructed multi-objective optimization scheduling model is constructed by combining the constraints with the peak shaving and valley filling objective function constructed by minimizing the peak-valley difference of the load and the new energy consumption objective function constructed by minimizing the reverse power transmission amount of the substation. The present invention adopts a multi-objective optimization scheduling model, a peak shaving and valley filling objective function constructed by minimizing the peak-valley difference of the load and a new energy consumption objective function constructed by minimizing the reverse power transmission amount of the substation, which effectively smooths the fluctuation of the power load in the substation, eliminates the voltage over-limit phenomenon, and improves the new energy power generation consumption rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A flow chart of a multi-objective optimization scheduling method for energy consumption in a substation taking into account various elastic resources of the present invention;

[0080] Figure 2 : Schematic diagram of the thermodynamic model of floor radiation cooling and heating of the present invention;

[0081] Figure 3 : A schematic diagram of the charge and discharge quantity trajectory of an electric vehicle of the present invention;

[0082] Figure 4 : The 5G base station energy storage battery scheduling capacity diagram of the present invention;

[0083] Figure 5 : The NSGA-Ⅱ genetic algorithm solution process of the present invention;

[0084] Figure 6 : The short-term optimization scheduling scheme diagram of the present invention;

[0085] Figure 7 : Comparison diagram of medium and long-term power load in the substation area of ​​the present invention;

[0086] Figure 8 : Comparison diagram of the mid-term electric vehicle and long-term electric vehicle area optimization scheduling schemes of the present invention;

[0087] Fig. 9 : Comparison diagram of the mid-term 5G base station and long-term 5G base station area optimization scheduling solutions of the present invention;

[0088] Fig.10 : Comparison diagram of the mid-term air conditioning and long-term air conditioning area optimization scheduling schemes of the present invention;

[0089] Fig.11 The figure is a schematic diagram of the structure of a computer device of the present invention. DETAILED DESCRIPTION

[0090] The present invention proposes a multi-objective optimization scheduling method for energy consumption in a substation taking into account a variety of elastic resources, which is close to actual engineering applications and has good effects.

[0091] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0092] Embodiment 1:

[0093] A multi-objective optimization scheduling method for energy consumption in a substation considering multiple elastic resources, such as Figure 1 As shown, including:

[0094] Step S1, obtaining parameters of each elastic resource in the area, distributed photovoltaic forecast output and temperature day-ahead forecast data;

[0095] Step S2, bringing the parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource;

[0096] Among them, the pre-constructed multi-objective optimization scheduling model is constructed by combining the constraint conditions with the peak shaving and valley filling objective function constructed by minimizing the load peak-valley difference and the new energy consumption objective function constructed by minimizing the reverse power transmission amount of the substation.

[0097] Before step S1, each model involved in this application is first introduced.

[0098] Step 1: Establish mathematical models and dispatchable potential models of various elastic resources in the low-voltage area, including: distributed photovoltaic power generation model, electric cooling / electric heating model, building cooling and heating load model, electric vehicle charging and discharging model, electric vehicle user behavior model, electric vehicle dispatchable potential model, 5G base station model, base station energy consumption model, and base station energy storage model.

[0099] The step 1 comprises the following steps:

[0100] S101: Establish mathematical models of various elastic resources in the low-voltage station area and dispatchable potential models;

[0101] 1. Distributed photovoltaic power generation model:

[0102] Distributed photovoltaic power generation is the power source of the park. Its power generation is usually affected by the intensity of sunlight and the outside temperature. The power generation model is as follows:

[0103]

[0104] Where: P PVis the actual output power of the photovoltaic power generation system, P0 is the rated output power of the photovoltaic panel, E is the actual solar radiation intensity received by the photovoltaic panel, and E ST is the standard solar radiation intensity, K is the temperature correlation coefficient, T S With T C is the actual temperature and ideal temperature of the photovoltaic panel.

[0105] 2. Electric cooling / electric heating model:

[0106] The electric cooling / heating system consumes electrical energy to provide cooling / heating power for the area, as follows:

[0107] H ER =P ER ×η ER

[0108] H EC =P EC ×η EC

[0109] Among them, H ER Represents the thermal power output of the electric refrigeration system, P ER Indicates the electrical energy consumed by the electric refrigeration system, η ER Indicates the energy conversion efficiency of the electric refrigeration system. Similarly, H EC Represents the thermal power output of the electric refrigeration system, P EC It represents the electric energy consumed by the electric refrigeration system, and ηEC represents the energy conversion efficiency of the electric refrigeration system.

[0110] 3. Building cooling and heating load model

[0111] When heat and cold energy is delivered to a building, the medium that conducts energy can be divided into air and floor enclosure. This method mainly considers the role of floor enclosure as a heat transfer medium in the energy consumption of building heat and cold loads. The floor enclosure mainly relies on radiation heat transfer between the floor and the building enclosure. At the same time, solar radiation heat and the temperature difference between indoor and outdoor of the house will also affect the actual temperature in the house. A thermodynamic heat transfer model is established as follows: Figure 2 , which specifically describes the dynamic process of indoor temperature changes.

[0112] According to the established equivalent thermodynamic model, the heat balance equation of heating / cooling and indoor temperature can be obtained:

[0113]

[0114] Where: Q represents heating / cooling capacity (W), T d Indicates floor surface temperature (℃), C d Represents the equivalent thermal conductivity of the floor (J / ℃), R dIt represents the equivalent thermal resistance (℃ / W) of the floor's convection heat transfer to the indoor air and radiation heat transfer to the building envelope. s Indicates indoor temperature (℃), Q s Indicates solar radiation heat (W), C w Represents the equivalent heat capacity of the building envelope (J / ℃), R w Indicates the equivalent thermal resistance of the building envelope (℃ / W), T out is the outdoor temperature (℃).

[0115] 4. Electric vehicle charging and discharging model

[0116] The electric vehicle charging and discharging model should include the electric vehicle battery state of charge model and the battery charging and discharging model.

[0117] (1) Battery state of charge model

[0118]

[0119] In the formula, is the state of charge of the k-th electric vehicle battery at time t, is the battery capacity of the kth electric vehicle at time t, C k,0 is the rated capacity of the battery of the kth electric vehicle.

[0120] (2) Battery charging and discharging model

[0121] The electric vehicle charging and discharging model is divided into a static charging and discharging model and a driving discharging model.

[0122] Static charge and discharge model:

[0123]

[0124] Where: is the state of charge of the kth electric vehicle battery at time t-1, η charge / η disch is the charging / discharging efficiency of electric vehicles, Charge / discharge power for electric vehicles.

[0125] Driving discharge model:

[0126]

[0127] Where: E k,init is the initial charge state of the electric vehicle when it enters the grid, E k,exp is the off-grid charge state of the electric vehicle, L r is the daily mileage of the electric vehicle, and L0 is the rated mileage of the electric vehicle.

[0128] 5. Electric vehicle user behavior model

[0129] Studying the behavior model of electric vehicles in the substation area can predict the charging and discharging rules of electric vehicles and formulate appropriate scheduling strategies in advance according to the load conditions. Electric vehicle user behavior usually includes the daily mileage of electric vehicles, the starting charging time, and the ending charging time.

[0130] (1) Daily mileage of electric vehicles

[0131]

[0132] Where f(L) is the probability density function of the daily mileage of electric vehicles, L is the daily mileage of electric vehicles, σ L is the standard deviation of the normal distribution of mileage, μ L is the expected value of the normal distribution of mileage, and the expected value of the normal distribution of mileage μ L is 3.2, standard deviation σ L It is 0.88.

[0133] (2) Electric vehicle access time

[0134] The time when electric vehicles enter the grid, that is, the time when electric vehicles start charging, approximately follows a normal distribution, and the probability density function is as follows:

[0135]

[0136] Where: f s (t) is the probability density function of the time when electric vehicles are connected to the grid, and the expected value of the normal distribution of the time when electric vehicles are connected to the grid is μ s is 17.6, standard deviation σ s is 3.4.

[0137] (3) Electric vehicle off-grid time

[0138] The time t when an electric vehicle leaves the grid, that is, the time when the electric vehicle finishes charging and leaves the charging station, is related to the charging start time and parking duration of the electric vehicle, and also approximately satisfies the log-normal distribution. The probability distribution function is:

[0139]

[0140] Where: f z (t) is the probability density function of the off-grid time of electric vehicles, and the expected value of the normal distribution of off-grid time is μ z is 2.74, and the standard deviation σ z is 1.21.

[0141] 6. Electric vehicle dispatch potential model

[0142] Assume that the time when the electric vehicle is connected to the grid is t start , the time to leave the grid is ten d, the amount of electricity of the electric vehicle when connected to the grid is The owner expects the electric vehicle to have a power level of The shortest time required for an electric vehicle to be fully charged immediately after being connected to the power grid is t min , the established electric vehicle charging and discharging capacity trajectory is as follows Figure 3 As shown in the figure, the battery capacity is limited by the upper and lower limits of the capacity. The upper limit of the battery capacity is that the electric vehicle is charged to the expected power immediately after entering the grid, and the lower limit of the capacity is that the electric vehicle fully accepts the dispatch first, and then charges to the expected power just when leaving the grid. The charging and discharging trajectory of the electric vehicle fluctuates between the upper and lower limits, providing a basis for the dispatch range.

[0143]

[0144] In the formula, are the upper and lower bounds of the charge and discharge capacity of the kth electric vehicle at time t, is the maximum and minimum charging power of the kth electric vehicle at time t, is the charging power of the kth electric vehicle at time t, is the maximum and minimum discharge power of the kth electric vehicle at time t, is the discharge power of the kth electric car at time t.

[0145] 7.5G base station energy consumption model

[0146] The daily energy consumption of 5G base stations is divided into two parts: static energy consumption and dynamic energy consumption. Static energy consumption is usually related to the need to maintain the normal operation of the base station, and will not change with the change of the base station load, and is not time-varying. Dynamic energy consumption is related to the base station load and has a linear relationship, which can fully reflect the changes in the number of users using the base station. The specific expression is as follows:

[0147] P G,t =P j,t +γP d,t

[0148] P d,t =λP d,max

[0149] 0≤λ≤1

[0150] Where P G,t is the total energy consumption of the base station at time t, P j,t is the static energy consumption of the base station at time t, P d,t is the dynamic energy consumption of the base station at time t, γ is the dynamic energy consumption efficiency of the base station, λ is the coefficient reflecting the traffic load of the base station, P d,max is the maximum dynamic energy consumption of the base station.

[0151] 8.5G base station energy storage model

[0152] The main function of base station energy storage is to ensure that the base station has uninterrupted power supply when the city power supply is cut off, so as to ensure the normal use of users. Therefore, at different times, the base station energy storage reserve power must be greater than the base station energy consumption at the corresponding time to ensure the reliability of the base station. The load of the base station will change due to the temporal and spatial distribution characteristics of the base station, resulting in the configuration of the base station energy storage battery usually being redundant, so there will be dispatchable capacity. Under the premise of ensuring the minimum backup power state of the base station, the redundant battery capacity is reasonably dispatched.

[0153] Base station energy storage battery charging and discharging model:

[0154]

[0155] Where: is the charging and discharging power of the base station energy storage battery, is the charging and discharging efficiency of the base station energy storage battery, C G is the rated capacity of the base station energy storage battery, SOC G,t+1 SOC is the state of charge of the 5G base station energy storage battery at time t+1. G,t is the charge state of the 5G base station energy storage battery at time t.

[0156] Take the base station energy storage battery of a certain scheduling period for analysis, and its dispatchable capacity distribution diagram is as follows: Figure 4 .

[0157] The dispatchable capacity model of 5G base station energy storage battery is as follows:

[0158] max(SOC low ,SOC min )≤SOC G,t ≤SOC high

[0159] In the formula, SOC high , SOC low The upper and lower limits of the state of charge (SOC) set for 5G base station energy storage batteries to avoid overcharging and over-discharging. min The SOC is the minimum charge state limit for 5G base station energy storage to ensure the normal operation of base station loads. G,t is the charge state of the 5G base station energy storage battery at time t.

[0160] Step 2: Consider constraints such as electric power balance constraints, thermal power balance constraints, electric vehicle constraints, 5G base station energy storage constraints, and building temperature comfort constraints, and establish a multi-objective function for peak shaving and valley filling and new energy consumption.

[0161] The step 2 comprises the following steps:

[0162] S201: Consider constraints such as electric power balance constraints, thermal power balance constraints, electric vehicle constraints, 5G base station energy storage constraints, and building temperature comfort constraints.

[0163] 1. Electric power balance constraints

[0164] P PV (t)+P grid (t) = P mess (t)+P EV (t)+P temp (t)+P Imp (t)

[0165] Where P PV (t) is the photovoltaic power generation power, P grid (t) is the power of interaction with the grid, P mess (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 temp (t) is the power of the air conditioning energy storage system, P Imp (t) is the fixed electrical load of the substation.

[0166] 2. Thermal power balance constraints

[0167] Heat transfer in a building takes a certain amount of time, which can be rewritten as a difference equation as follows:

[0168]

[0169] Q=U ER ·H ER +U EC ·H EC

[0170] Where: U ER It is the heating flag, which is 1 when heating is in winter and 0 when not heating. EC It is the cooling flag, which is 1 when cooling is provided in summer and 0 when not providing cooling.

[0171] 3. Power constraints of various energy sources

[0172]

[0173] Where: P temp (t) , P grid (t) , They are the upper and lower limits of photovoltaic power generation, the upper and lower limits of 5G base station energy storage charging power and discharging power, the upper and lower limits of electric vehicle charging power and discharging power, the upper and lower limits of electric heating / cooling system power, and the upper and lower limits of grid interaction power; P PV(t) is the photovoltaic power generation power at time t, is the energy storage charging power of the 5G base station at time t, is the energy storage discharge power of the 5G base station at time t, is the charging power of the electric vehicle at time t, is the discharge power of the electric vehicle at time t, P temp (t) is the power of the air conditioning energy storage system at time t, P grid (t) is the grid interaction power at time t.

[0174] 4. Power constraints of each energy source

[0175]

[0176] In the formula, It is the charging mark of the energy storage battery, 1 means charging, 0 means not charging, It is the discharge mark of the energy storage battery, 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.

[0177] 5. Building temperature comfort constraints

[0178] During the scheduling process, in order to ensure thermal comfort in the building, the indoor temperature should be maintained within the human comfort range and meet the following constraints:

[0179]

[0180] Where: T s (t) , is the upper and lower limits of indoor temperature, T s (t) is the indoor temperature.

[0181] S202: Establish a multi-objective function for peak shaving and valley filling and new energy consumption:

[0182] 1. Peak shaving and valley filling objective function

[0183] The essence of "peak shaving and valley filling" is to reduce the superposition of power loads during peak power consumption in the substation area through scheduling strategies, and appropriately increase the power load during low power consumption in the substation area, so as to achieve the effect of reducing the peak-to-valley difference of loads, that is, to minimize the peak-to-valley difference of loads:

[0184] R load (t) = min{max[P load (t)]-min[P load (t)]}

[0185]

[0186] Where: P load (t) is the power load of the substation, P l (t) is the net load of the power consumption in the substation, P mess (t) is the power consumption of the 5G base station energy storage battery, P EV (t) is the electric power consumed by the electric vehicle, P temp (t) is the power consumed by the electric cooling / heating system, P Imp (t) is the fixed electrical load of the substation, is the charging and discharging power of the 5G base station energy storage battery, P PV (t) is the distributed photovoltaic power generation power, It is the charging flag of the energy storage battery, 1 means charging, 0 means not charging. It is the discharge flag of the energy storage battery, 1 means discharge, 0 means no discharge. Charging and discharging power for electric vehicles, 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.

[0187] 2. New energy consumption objective function

[0188] The consumption of new energy in the substation area requires that the distributed photovoltaic power generation and the power load in the substation area be balanced with each other, and the interaction of power with the power grid due to the deviation of supply and demand of new energy power generation should be minimized, and the power transmission from the substation area to the upper grid should be reduced, that is, the reverse power transmission of the substation area should be minimized:

[0189]

[0190] Where: Q loss (t) is the total amount of electricity that the substation sends back to the grid in one day, P loss (t) is the internal power supply and demand difference of the area during period t, P op (t) is the internal power supply and demand difference in a certain period of time in the substation.

[0191] Step S1, obtaining parameters of each elastic resource in the area, distributed photovoltaic forecast output and temperature forecast data, specifically including:

[0192] Obtain hourly photovoltaic, base load power and temperature forecast data, and obtain the historical records of each electric vehicle through Monte Carlo random sampling, including: Number of electric vehicles N ev , daily mileage of electric vehicles L, time of electric vehicles accessing the grid t start , Electric vehicle off-grid time t end , Electric vehicle power when connected to the grid Owners expect electric vehicle power to be off-grid Obtain the day-ahead forecast data of hourly distributed photovoltaic, basic load power, 5G base station user load in the substation area, etc. within 24 hours where the electric vehicle is located; obtain data such as basic parameters of residential walls and enclosure structures in the low-voltage substation area.

[0193] The S1 comprises the following steps:

[0194] S301: Obtain the predicted output P of distributed photovoltaic power in the area PV , base load power P Imp and temperature forecast data t; obtain the historical records of each electric vehicle through Monte Carlo random sampling, including: the daily mileage of the electric vehicle L, the time the electric vehicle was connected to the grid t start , Electric vehicle off-grid time t end , Electric vehicle power when connected to the grid Owners expect electric vehicle power to be off-grid And the 5G base station user load P in the area mess , floor equivalent thermal conductivity C d , the equivalent thermal resistance R of the floor to indoor air convection heat transfer and radiation heat transfer to the building envelope d , Equivalent heat capacity of building envelope C w , equivalent thermal resistance of building envelope R w .

[0195] S302: Obtain the penetration rate of electric vehicles in the substation area under three time scales: short-term, medium-term, and long-term, and select three different periods as three scenarios for optimization scheduling to ensure the robustness of the optimization solution.

[0196] Step S2: bring the parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource.

[0197] To solve the multi-objective optimization scheduling model of energy consumption in the substation, firstly, according to the peak shaving and valley filling objective function and the new energy consumption objective function and the constraints in step 2, the established scheduling model is solved using the NSGA-Ⅱ algorithm to obtain the optimal scheduling scheme of various elastic resources and formulate a scheduling plan.

[0198] The step S2 comprises the following steps:

[0199] S401: NSGA-Ⅱ genetic algorithm is used to solve the multi-time scale optimization scheduling model. The algorithm flow is as follows: Figure 5. The following three key strategies are mainly adopted: a fast non-dominated sorting strategy based on grading, a strategy for comparing individuals with the same ordinal value based on crowding distance, and a strategy for retaining elite individuals. In addition, the NSGA-Ⅱ algorithm also gives the individual dominance problem considering constraints: (1) When individual x1 is a feasible solution and individual x2 is an infeasible solution, individual x1 is said to dominate individual x2; (2) When neither individual x1 nor individual x2 is a feasible solution, the constraint violation degree of the two is compared, and the individual with a smaller constraint violation degree is dominant; (3) When both individual x1 and individual x2 are feasible solutions, the ordinal value and crowding distance of the two are compared, and the individual with a smaller ordinal value is dominant. If the ordinal values ​​are the same, the individual with a larger crowding distance is dominant.

[0200] S402: Initialize the parent population P t . Randomly generate an initial population as the parent population of the first generation population;

[0201] S403: Generate offspring population Q t . Carry out the selection, crossover and mutation process to produce a temporary population of equal size;

[0202] S404: Merging populations and non-dominant sorting R t =P t ∪Q t . Merge the two populations into one population and sort the individuals in the population based on the ordinal value and crowding distance;

[0203] S405: Generate a new parent population P t+1 Individuals in the merged population are selected according to the sequence value to enter the next generation population.

[0204] S406: Determine whether the preset population size Npop is reached, and determine whether the algorithm reaches the maximum number of iterations gmax. If not, continue iterating; otherwise, output the Pareto optimal solution set and the frontier, and the algorithm ends.

[0205] S407: The established multi-objective optimization scheduling model of the substation is solved using the NSGA-Ⅱ algorithm to obtain the short-term, medium-term and long-term power output plans of various elastic resources. The outputs of various elastic resources are coordinated with each other. As the time scale changes, the peak-to-valley difference of the substation power load is effectively reduced and the new energy consumption rate is increased.

[0206] Example 2

[0207] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0208] The technical solution of the present invention is described in detail below by examples in conjunction with the accompanying drawings.

[0209] See also Figure 2 , Figure 3 , Figure 4 Establish mathematical models and dispatchable capacity models for the substation area including electric vehicles, 5G base stations, building temperature control, etc., obtain hourly forecast data on distributed photovoltaic, basic load, base station load, etc. in the dispatch period within 24 hours, and use Monte Carlo simulation to randomly sample the number of electric vehicles Nev, daily mileage L of electric vehicles, and access time t of electric vehicles at different penetration rates under three time scales: short-term, medium-term, and long-term. start , Electric vehicle off-grid time t end , Electric vehicle power when connected to the grid Owners expect electric vehicle power to be off-grid After obtaining the dispatchable capacity model of each elastic resource, the multi-objective function of peak shaving and valley filling and new energy consumption and the corresponding constraints are established. Based on the three scenarios of short, medium and long term, the dispatch period is set to T = 24h, the dispatch time interval Δt = 1h, and the algorithm is solved by NSGA-Ⅱ genetic algorithm to obtain the corresponding coordinated optimization dispatch strategy of multiple elastic resources, such as Figure 6 , Figure 7 and Figure 8 shown.

[0210] Step 1: Establish mathematical models and dispatchable potential models of various elastic resources in the low-voltage area, including: distributed photovoltaic power generation model, electric cooling / electric heating model, building cooling and heating load model, electric vehicle charging and discharging model, electric vehicle user behavior model, electric vehicle dispatchable potential model, 5G base station model, base station energy consumption model, and base station energy storage model.

[0211] The step 1 comprises the following steps:

[0212] S101: Establish mathematical models of various elastic resources in the low-voltage station area and dispatchable potential models;

[0213] Distributed photovoltaic power generation model

[0214] Distributed photovoltaic power generation is the power source of the park. Its power generation is usually affected by the intensity of sunlight and the outside temperature. The power generation model is as follows:

[0215]

[0216] Where: P PV is the actual output power of the photovoltaic power generation system, P0 is the rated output power of the photovoltaic panel, E is the actual solar radiation intensity received by the photovoltaic panel, and EST is the standard solar radiation intensity, K is the temperature correlation coefficient, T S With T C is the actual temperature and ideal temperature of the photovoltaic panel.

[0217] Electric cooling / electric heating model

[0218] The electric cooling / heating system consumes electrical energy to provide cooling / heating power for the area, as follows:

[0219] H ER =P ER ×η ER

[0220] H EC =P EC ×η EC

[0221] Among them, H ER Represents the thermal power output of the electric refrigeration system, P ER Indicates the electrical energy consumed by the electric refrigeration system, η ER Indicates the energy conversion efficiency of the electric refrigeration system. Similarly, H EC Represents the thermal power output of the electric refrigeration system, P EC Indicates the electrical energy consumed by the electric refrigeration system, η EC Indicates the energy conversion efficiency of the electric refrigeration system.

[0222] Building Cooling and Heating Load Model

[0223] When heat and cold energy is delivered to a building, the medium that conducts energy can be divided into air and floor enclosure. This method mainly considers the role of floor enclosure as a heat transfer medium in the energy consumption of building heat and cold loads. The floor enclosure mainly relies on radiation heat transfer between the floor and the building enclosure. At the same time, solar radiation heat and the temperature difference between indoor and outdoor of the house will also affect the actual temperature in the house. A thermodynamic heat transfer model is established as follows: Figure 2 , which specifically describes the dynamic process of indoor temperature changes.

[0224] According to the established equivalent thermodynamic model, the heat balance equation of heating / cooling and indoor temperature can be obtained:

[0225]

[0226] Where: Q represents heating / cooling capacity (W), T d Indicates floor surface temperature (℃), C d Represents the equivalent thermal conductivity of the floor (J / ℃), R d It represents the equivalent thermal resistance (℃ / W) of the floor's convection heat transfer to the indoor air and radiation heat transfer to the building envelope. s Indicates indoor temperature (℃), Qs Indicates solar radiation heat (W), C w Represents the equivalent heat capacity of the building envelope (J / ℃), R w Indicates the equivalent thermal resistance of the building envelope (℃ / W), T out is the outdoor temperature (℃).

[0227] Electric vehicle charging and discharging model

[0228] The electric vehicle charging and discharging model should include the electric vehicle battery state of charge model and the battery charging and discharging model.

[0229] (1) Battery state of charge model

[0230]

[0231] In the formula, is the state of charge of the k-th electric vehicle battery at time t, is the battery capacity of the kth electric vehicle at time t, C k,0 is the rated capacity of the battery of the kth electric vehicle.

[0232] (2) Battery charging and discharging model

[0233] The electric vehicle charging and discharging model is divided into a static charging and discharging model and a driving discharging model.

[0234] Static charge and discharge model:

[0235]

[0236] Where: is the state of charge of the kth electric vehicle battery at time t-1, η charge / η disch Charging / discharging efficiency for electric vehicles; Charge / discharge power for electric vehicles.

[0237] Driving discharge model:

[0238]

[0239] Where: E k,init is the initial charge state of the electric vehicle when it enters the grid, E k,exp is the off-grid charge state of the electric vehicle, L r is the daily mileage of the electric vehicle, and L0 is the rated mileage of the electric vehicle.

[0240] Electric vehicle user behavior model

[0241] Studying the behavior model of electric vehicles in the substation area can predict the charging and discharging rules of electric vehicles and formulate appropriate scheduling strategies in advance according to the load conditions. Electric vehicle user behavior usually includes the daily mileage of electric vehicles, the starting charging time, and the ending charging time.

[0242] (1) Daily mileage of electric vehicles

[0243]

[0244] Where f(L) is the probability density function of the daily mileage of electric vehicles, L is the daily mileage of electric vehicles, σ L is the standard deviation of the normal distribution of mileage, μ L is the expected value of the normal distribution of mileage, and the expected value of the normal distribution of mileage μ L is 3.2, standard deviation σ L It is 0.88.

[0245] (2) Electric vehicle access time

[0246] The time when electric vehicles enter the grid, that is, the time when electric vehicles start charging, approximately follows a normal distribution, and the probability density function is as follows:

[0247]

[0248] Where: The expected value of the normal distribution of network access time μ s is 17.6, standard deviation σ s is 3.4.

[0249] (3) Electric vehicle off-grid time

[0250] The time t when an electric vehicle leaves the grid, that is, the time when the electric vehicle finishes charging and leaves the charging station, is related to the charging start time and parking duration of the electric vehicle, and also approximately satisfies the log-normal distribution. The probability distribution function is:

[0251]

[0252] Where: The expected value of the normal distribution of off-grid time μ z is 2.74, and the standard deviation σ z is 1.21.

[0253] Electric vehicle dispatchable potential model:

[0254] Assume that the time when the electric vehicle is connected to the grid is t start , the time to leave the grid is t end , the amount of electricity of the electric vehicle when connected to the grid is The owner expects the electric vehicle to have a power level of The shortest time required for an electric vehicle to be fully charged immediately after being connected to the power grid is tmin , the established electric vehicle charging and discharging capacity trajectory is as follows Figure 3 As shown in the figure, the battery capacity is limited by the upper and lower limits of the capacity. The upper limit of the battery capacity is that the electric vehicle is charged to the expected power immediately after entering the grid, and the lower limit of the capacity is that the electric vehicle fully accepts the dispatch first, and then charges to the expected power just when leaving the grid. The charging and discharging trajectory of the electric vehicle fluctuates between the upper and lower limits, providing a basis for the dispatch range.

[0255]

[0256] In the formula, are the upper and lower bounds of the charge and discharge capacity of the kth electric vehicle at time t, is the maximum and minimum charging power of the kth electric vehicle at time t, are the maximum and minimum discharge powers of the kth electric vehicle at time t.

[0257] 5G base station energy consumption model

[0258] The daily energy consumption of 5G base stations is divided into two parts: static energy consumption and dynamic energy consumption. Static energy consumption is usually related to the need to maintain the normal operation of the base station, and will not change with the change of the base station load, and is not time-varying. Dynamic energy consumption is related to the base station load and has a linear relationship, which can fully reflect the changes in the number of users using the base station. The specific expression is as follows:

[0259] P G,t =P j,t +γP d,t

[0260] P d,t =λP d,max

[0261] 0≤λ≤1

[0262] Where P G,t is the total energy consumption of the base station at time t, P j,t is the static energy consumption of the base station at time t, P d,t is the dynamic energy consumption of the base station at time t, γ is the dynamic energy consumption efficiency of the base station, λ is the coefficient reflecting the traffic load of the base station, P d,max is the maximum dynamic energy consumption of the base station.

[0263] 5G base station energy storage model

[0264] The main function of base station energy storage is to ensure that the base station has uninterrupted power supply when the city power supply is cut off, so as to ensure the normal use of users. Therefore, at different times, the base station energy storage reserve power must be greater than the base station energy consumption at the corresponding time to ensure the reliability of the base station. The load of the base station will change due to the temporal and spatial distribution characteristics of the base station, resulting in the configuration of the base station energy storage battery usually being redundant, so there will be dispatchable capacity. Under the premise of ensuring the minimum backup power state of the base station, the redundant battery capacity is reasonably dispatched.

[0265] Base station energy storage battery charging and discharging model:

[0266]

[0267] Where: is the charging and discharging power of the base station energy storage battery, is the charging and discharging efficiency of the base station energy storage battery, C G It is the rated capacity of the base station energy storage battery.

[0268] Take the base station energy storage battery of a certain scheduling period for analysis, and its dispatchable capacity distribution diagram is as follows: Figure 4 .

[0269] The dispatchable capacity model of 5G base station energy storage battery is as follows:

[0270] max(SOC low ,SOC min )≤SOC G,t ≤SOC high

[0271] In the formula, SOC high , SOC low The upper and lower limits of the state of charge (SOC) set for 5G base station energy storage batteries to avoid overcharging and over-discharging. min The SOC is the minimum charge state limit for 5G base station energy storage to ensure the normal operation of base station loads. G,t The charge status of the 5G base station energy storage battery.

[0272] Step 2: Considering constraints such as electric power balance constraints, thermal power balance constraints, electric vehicle constraints, 5G base station energy storage constraints, and building temperature comfort constraints, a multi-objective function for peak shaving and valley filling and new energy consumption is established;

[0273] The step 2 comprises the following steps:

[0274] S201: Consider constraints such as electric power balance constraints, thermal power balance constraints, electric vehicle constraints, 5G base station energy storage constraints, and building temperature comfort constraints.

[0275] 1. Electric power balance constraints

[0276] PPV (t)+P grid (t) = P mess (t)+P EV (t)+P temp (t)+P Imp (t)

[0277] 2. Thermal power balance constraints

[0278] Heat transfer in a building takes a certain amount of time, which can be rewritten as a difference equation as follows:

[0279]

[0280] Q=U ER ·H ER +U EC ·H EC

[0281] Where: U ER It is the heating flag, which is 1 when heating is in winter and 0 when not heating. EC It is the cooling flag, which is 1 when cooling is provided in summer and 0 when not providing cooling.

[0282] 3. Power constraints of various energy sources

[0283]

[0284] Where: P temp (t) , P grid (t) , They are the upper limit of photovoltaic power generation power, the upper and lower limits of 5G base station energy storage charging power and discharging power, the upper and lower limits of electric vehicle charging power and discharging power, the upper and lower limits of electric heating / cooling system power, and the upper and lower limits of grid interaction power.

[0285] 4. Power constraints of each energy source

[0286]

[0287] In the formula, It is the charging mark of the energy storage battery, 1 means charging, 0 means not charging, It is the discharge mark of the energy storage battery, 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.

[0288] 5. Building temperature comfort constraints

[0289] During the scheduling process, in order to ensure thermal comfort in the building, the indoor temperature should be maintained within the human comfort range and meet the following constraints:

[0290]

[0291] Where: T s (t) , is the upper and lower limits of indoor temperature, T s (t) is the indoor temperature.

[0292] S202: Establish a multi-objective function for peak shaving and valley filling and new energy consumption:

[0293] 1. Peak shaving and valley filling objective function

[0294] The essence of "peak shaving and valley filling" is to reduce the superposition of power loads during peak power consumption in the substation area through scheduling strategies, and appropriately increase the power load during low power consumption in the substation area, so as to achieve the effect of reducing the peak-to-valley difference of loads, that is, to minimize the peak-to-valley difference of loads:

[0295] R load (t) = min{max[P load (t)]-min[P load (t)]}

[0296]

[0297] Where: R load (t) is the load peak-to-valley difference, P load (t) is the power load of the substation, P l (t) is the net load of the power consumption in the substation, P mess (t) is the power consumption of the 5G base station energy storage battery, P EV (t) is the electric power consumed by the electric vehicle, P temp (t) is the power consumed by the electric cooling / heating system, P Imp (t) is the fixed electrical load of the substation, is the charging and discharging power of the 5G base station energy storage battery, P PV (t) is the distributed photovoltaic power generation power, It is the charging flag of the energy storage battery, 1 means charging, 0 means not charging. It is the discharge flag of the energy storage battery, 1 means discharge, 0 means no discharge. Charging and discharging power for electric vehicles, 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.

[0298] 2. New energy consumption objective function

[0299] The consumption of new energy in the substation area requires that the distributed photovoltaic power generation and the power load in the substation area be balanced with each other, and the interaction of power with the power grid due to the deviation of supply and demand of new energy power generation should be minimized, and the power transmission from the substation area to the upper grid should be reduced, that is, the reverse power transmission of the substation area should be minimized:

[0300]

[0301] Where: Q loss (t) is the total amount of electricity that the substation sends back to the grid in one day, P loss (t) is the internal power supply and demand difference of the area during period t, P op (t) is the internal power supply and demand difference in a certain period of time in the substation.

[0302] Step 3: Obtain the hourly photovoltaic, base load power and temperature forecast data, and obtain the historical records of each electric vehicle through Monte Carlo random sampling, including: the number of electric vehicles N ev , daily mileage of electric vehicles L, time of electric vehicles accessing the grid t start , Electric vehicle off-grid time t end , Electric vehicle power when connected to the grid Owners expect electric vehicle power to be off-grid Obtain the day-ahead forecast data of hourly distributed photovoltaic, basic load power, 5G base station user load in the substation area, etc. within 24 hours where the electric vehicle is located; obtain data such as basic parameters of residential walls and enclosure structures in the low-voltage substation area.

[0303] The step 3 comprises the following steps:

[0304] S301: Obtain the predicted output P of distributed photovoltaic power in the area PV , base load power P Imp and temperature forecast data t; obtain the historical records of each electric vehicle through Monte Carlo random sampling, including: the daily mileage of the electric vehicle L, the time the electric vehicle was connected to the grid t start , Electric vehicle off-grid time t end , Electric vehicle power when connected to the grid Owners expect electric vehicle power to be off-grid And the 5G base station user load P in the area mess , floor equivalent thermal conductivity C d , the equivalent thermal resistance R of the floor to indoor air convection heat transfer and radiation heat transfer to the building envelope d , Equivalent heat capacity of building envelope C w , equivalent thermal resistance of building envelope R w .

[0305] S302: Obtain the penetration rate of electric vehicles in the substation area under three time scales: short-term, medium-term, and long-term, and select three different periods as three scenarios for optimization scheduling to ensure the robustness of the optimization solution.

[0306] Step 4: Solve the multi-objective optimization scheduling model for energy consumption in the substation. First, according to the peak shaving and valley filling objective function and the new energy consumption objective function and the constraints in step 2, the established scheduling model is solved using the NSGA-Ⅱ algorithm to obtain the optimal scheduling scheme for multiple elastic resources and formulate a scheduling plan.

[0307] The step 4 comprises the following steps:

[0308] S401: NSGA-Ⅱ genetic algorithm is used to solve the multi-time scale optimization scheduling model. The algorithm flow is as follows: Figure 5 . The following three key strategies are mainly adopted: a fast non-dominated sorting strategy based on grading, a strategy for comparing individuals with the same ordinal value based on crowding distance, and a strategy for retaining elite individuals. In addition, the NSGA-Ⅱ algorithm also gives the individual dominance problem considering constraints: (1) When individual x1 is a feasible solution and individual x2 is an infeasible solution, individual x1 is said to dominate individual x2; (2) When neither individual x1 nor individual x2 is a feasible solution, the constraint violation degree of the two is compared, and the individual with a smaller constraint violation degree is dominant; (3) When both individual x1 and individual x2 are feasible solutions, the ordinal value and crowding distance of the two are compared, and the individual with a smaller ordinal value is dominant. If the ordinal values ​​are the same, the individual with a larger crowding distance is dominant.

[0309] S402: Initialize the parent population P t . Randomly generate an initial population as the parent population of the first generation population;

[0310] S403: Generate offspring population Q t . Carry out the selection, crossover and mutation process to produce a temporary population of equal size;

[0311] S404: Merging populations and non-dominant sorting R t =P t ∪Q t . Merge the two populations into one population and sort the individuals in the population based on the ordinal value and crowding distance;

[0312] S405: Generate a new parent population P t+1 Individuals in the merged population are selected according to the sequence value to enter the next generation population.

[0313] S406: Determine whether the preset population size Npop is reached, and determine whether the algorithm reaches the maximum number of iterations gmax. If not, continue iterating; otherwise, output the Pareto optimal solution set and the frontier, and the algorithm ends.

[0314] S407: The established multi-objective optimization scheduling model of the substation is solved using the NSGA-Ⅱ algorithm to obtain the short-term, medium-term and long-term power output plans of each elastic resource. The output of each elastic resource is coordinated with each other. As the time scale changes, the peak-to-valley difference of the power load in the substation is effectively reduced and the new energy consumption rate is improved. The short-term optimization scheduling plan of each adjustable resource involved in the regulation, the comparison chart of the medium-term and long-term substation power load, the comparison chart of the medium-term electric vehicle and long-term electric vehicle substation optimization scheduling plan, the comparison chart of the medium-term 5G base station and long-term 5G base station substation optimization scheduling plan, and the comparison chart of the medium-term air conditioning and long-term air conditioning substation optimization scheduling plan, such as Figures 6 to 10 shown.

[0315] The present invention provides a multi-objective optimization scheduling method for energy consumption in a substation that takes into account multiple elastic resources. In order to alleviate the voltage over-limit phenomenon in the substation and solve the problem of redundant consumption of new energy, an optimization scheduling strategy that integrates multiple new elastic resources such as electric vehicles, 5G base station energy storage, and air conditioners into the power grid and coordinates with each other is proposed. The multi-objective optimization scheduling strategy for energy consumption in the substation is obtained, which effectively smooths the fluctuation of power load in the substation, eliminates the voltage over-limit phenomenon, and improves the consumption rate of new energy power generation. When specifying the scheduling strategy, the changes in the penetration rate of electric vehicles in the substation in different periods are considered to formulate optimization scheduling plans under different time scales, ensuring the robustness of the scheduling strategy.

[0316] Example 3

[0317] The present invention based on the same inventive concept also provides a multi-objective optimization dispatching system for energy consumption in a substation taking into account a variety of elastic resources, including:

[0318] The parameter acquisition module is used to obtain the parameters of each elastic resource in the substation area, the distributed photovoltaic forecast output and the temperature forecast data;

[0319] An optimization module is used to bring the parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource;

[0320] Among them, the pre-constructed multi-objective optimization scheduling model is constructed by combining the constraint conditions with the peak shaving and valley filling objective function constructed by minimizing the load peak-valley difference and the new energy consumption objective function constructed by minimizing the reverse power transmission amount of the substation.

[0321] Optionally, a model building module is also included for:

[0322] The peak shaving and valley filling objective function is constructed by minimizing the load peak-valley difference;

[0323] The objective function of new energy consumption is constructed by minimizing the reverse power transmission in the substation area;

[0324] Setting constraints for the peak shaving and valley filling objective function and the new energy consumption objective function;

[0325] A multi-objective optimization scheduling model is constructed based on the peak shaving and valley filling objective function, the new energy consumption objective function and the constraint conditions;

[0326] The constraints include: electric power balance constraint, thermal power balance constraint, various energy power constraints, various energy voltage constraints, and building temperature comfort constraints.

[0327] Optionally, the new energy consumption objective function is as follows:

[0328] R load (t) = min{max[P load (t)]-min[P load (t)]}

[0329]

[0330] Optionally, the new energy consumption objective function is as follows:

[0331]

[0332] Optionally, the thermal power balance constraint is as follows:

[0333]

[0334] Q=U ER ·H ER +U EC ·H EC

[0335] Optionally, the optimization module includes:

[0336] The solution submodule is used to solve the multi-objective optimization scheduling model by adopting the hierarchical-based fast non-dominated sorting strategy, the crowding distance-based same-order-value individual comparison strategy and the elite individual retention strategy in the genetic algorithm.

[0337] Optionally, the solution submodule is specifically used for:

[0338] S1: Randomly generate an initial population as the parent population P of the first generation population t , and set the number of iterations g = 1;

[0339] S2: The parent population undergoes selection, crossover, and mutation to produce a child population Q of the same size t ;

[0340] S3: The parent population P t and the offspring population Qt Merge into a population R t , and based on the sequence value and crowding distance, the population R t Sort the individuals;

[0341] S4: From the sorted population R t Select the first N by sequence value pop Each individual constructs a new parent population Pt+1;

[0342] S5: Determine whether the preset population size Npop is reached and whether the maximum number of iterations g is reached max If it is not reached, set the number of iterations g = g + 1 and return to step S2; otherwise, output the Pareto optimal solution set and the frontier, and the algorithm ends.

[0343] Optionally, the parameter acquisition module is specifically used for:

[0344] Obtain the forecasted output of distributed photovoltaic power, base load power and temperature in the substation area;

[0345] Get the historical records of each electric car through Monte Carlo random sampling,

[0346] The historical records include: daily mileage of electric vehicles, time of electric vehicles connecting to the grid, time of electric vehicles leaving the grid, power of electric vehicles when connected to the grid, and power of electric vehicles when the owner expects to leave the grid;

[0347] Obtain the 5G base station user load in the station area, the equivalent thermal conductivity of the floor, the equivalent thermal resistance of the floor's convection heat transfer to the indoor air and the radiation heat transfer to the building envelope, the equivalent heat capacity of the building envelope, and the equivalent thermal resistance of the building envelope.

[0348] Example 4

[0349] like Fig.11 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0350] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of a multi-objective optimization scheduling method for energy consumption in a substation taking into account multiple elastic resources in the above-mentioned embodiment.

[0351] Example 5

[0352] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a multi-objective optimization scheduling method for energy consumption in a substation taking into account multiple elastic resources in the above embodiment.

[0353] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0354] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0355] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0356] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0357] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A multi-objective optimization scheduling method for energy consumption in a substation taking into account multiple elastic resources, characterized in that: include: Obtain the parameters of each elastic resource in the substation area, the predicted output of distributed photovoltaic power generation and the day-ahead temperature prediction data; The parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data are introduced into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource; Among them, the pre-constructed multi-objective optimization scheduling model is constructed by combining the constraint conditions with the peak shaving and valley filling objective function constructed by minimizing the load peak-valley difference and the new energy consumption objective function constructed by minimizing the reverse power transmission amount of the substation.

2. The method according to claim 1, characterized in that The construction of the multi-objective optimization scheduling model includes: The peak shaving and valley filling objective function is constructed by minimizing the load peak-valley difference; The objective function of new energy consumption is constructed by minimizing the reverse power transmission in the substation area; Setting constraints for the peak shaving and valley filling objective function and the new energy consumption objective function; A multi-objective optimization scheduling model is constructed based on the peak shaving and valley filling objective function, the new energy consumption objective function and the constraint conditions; The constraints include: electric power balance constraint, thermal power balance constraint, various energy power constraints, various energy voltage constraints, and building temperature comfort constraints.

3. The method according to claim 2, characterized in that The new energy consumption objective function is as follows: minR load (t)=min{max[P load (t)]-min[P load (t)]} In the formula, R load (t) is the variance of the power load curve, P load (t) is the power load of the substation, P l (t) is the net load of the power grid, P mess (t) is the power consumption of the 5G base station energy storage battery, P EV (t) is the electric power consumed by the electric vehicle, P temp (t) is the electrical power consumed by the electric cooling / heating system, P Imp (t) is the fixed electric load of the substation, is the charging and discharging power of the 5G base station energy storage battery, P PV (t) is the distributed photovoltaic power generation power, It is the charging mark of the energy storage battery, 1 means charging, 0 means not charging, It is the discharge mark of the energy storage battery, 1 means discharge, 0 means no discharge, Charging and discharging power for electric vehicles, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, is the discharge mark of the electric vehicle, t is the time, and N is an integer.

4. The method according to claim 3, characterized in that The new energy consumption objective function is as follows: Where: Q loss (t) is the total amount of electricity that the substation sends back to the grid in one day, P loss (t) is the internal power supply and demand difference of the area during period t, P op (t) is the internal power supply and demand difference in a certain period of time in the substation.

5. The method according to claim 3, characterized in that The thermal power balance constraint is as follows: Q=U ER ·H ER +U EC ·H EC Where, T d (t+1) is the floor surface temperature at time t+1, R d is the equivalent thermal resistance of floor convective heat transfer to indoor air and radiant heat transfer to building envelope, C d is the equivalent thermal conductivity of the floor, ΔT is the temperature difference between indoor and outdoor, T d (t) is the floor surface temperature at time t, Q is the heating / cooling capacity, T s (t+1) is the indoor temperature at time t+1, R w is the equivalent thermal resistance of the building envelope, T s (t) is the indoor temperature at time t, Q s is the solar radiation heat, T out (t) is the outdoor temperature, C w is the equivalent heat capacity of the building envelope, H ER Indicates the thermal power output of the electric heating system, H EC Indicates the thermal power output of the electric refrigeration system, U ER It is the heating flag, which is 1 when heating is in winter and 0 when not heating; U EC It is a cooling symbol.

6. The method according to claim 1, characterized in that The parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data are brought into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource, including: The multi-objective optimization scheduling model is solved by adopting a fast non-dominated sorting strategy based on grading, a comparison strategy of individuals with the same sequence value based on crowding distance, and an elite individual retention strategy in a genetic algorithm.

7. The method according to claim 6, characterized in that The method adopts the hierarchical fast non-dominated sorting strategy, the crowding distance-based individual comparison strategy and the elite individual retention strategy in the genetic algorithm to solve the multi-objective optimization scheduling model, including: S1: Randomly generate an initial population as the parent population P of the first generation population t , and set the number of iterations g = 1; S2: The parent population undergoes selection, crossover, and mutation to produce a child population Q of the same size t ; S3: The parent population P t With the offspring population Q t Merge into a population R t , and based on the sequence value and crowding distance, the population R t Sort the individuals; S4: From the sorted population R t Select the first N by sequence value pop Each individual constructs a new parent population Pt+1; S5: Determine whether the preset population size Npop is reached and whether the maximum number of iterations g is reached max If it is not reached, set the number of iterations g = g + 1 and return to step S2; otherwise, output the Pareto optimal solution set and the frontier, and the algorithm ends.

8. The method according to claim 1, characterized in that The obtaining of parameters of each elastic resource in the substation, distributed photovoltaic predicted output and temperature day-ahead predicted data includes: Obtain the forecasted output of distributed photovoltaic power, base load power and temperature in the substation area; Get the historical records of each electric car through Monte Carlo random sampling, The historical records include: daily mileage of electric vehicles, time of electric vehicles connecting to the grid, time of electric vehicles leaving the grid, power of electric vehicles when connected to the grid, and power of electric vehicles when the owner expects to leave the grid; Obtain the 5G base station user load in the station area, the equivalent thermal conductivity of the floor, the equivalent thermal resistance of the floor's convection heat transfer to the indoor air and the radiation heat transfer to the building envelope, the equivalent heat capacity of the building envelope, and the equivalent thermal resistance of the building envelope.

9. A multi-objective optimization scheduling system for energy consumption in a substation taking into account multiple elastic resources, characterized in that: include: The parameter acquisition module is used to obtain the parameters of each elastic resource in the substation area, the distributed photovoltaic forecast output and the temperature forecast data; An optimization module is used to bring the parameters of each elastic resource, the distributed photovoltaic predicted output, and the temperature day-ahead predicted data into a pre-built multi-objective optimization scheduling model to obtain a power output plan for each elastic resource; Among them, the pre-constructed multi-objective optimization scheduling model is constructed by combining the constraint conditions with the peak shaving and valley filling objective function constructed by minimizing the load peak-valley difference and the new energy consumption objective function constructed by minimizing the reverse power transmission amount of the substation.

10. The system according to claim 9, characterized in that Also included are model building modules specifically for: The peak shaving and valley filling objective function is constructed by minimizing the load peak-valley difference; The objective function of new energy consumption is constructed by minimizing the reverse power transmission in the substation area; Setting constraints for the peak shaving and valley filling objective function and the new energy consumption objective function; A multi-objective optimization scheduling model is constructed based on the peak shaving and valley filling objective function, the new energy consumption objective function and the constraint conditions; The constraints include: electric power balance constraint, thermal power balance constraint, various energy power constraints, various energy voltage constraints, and building temperature comfort constraints.

11. The system according to claim 10, characterized in that The new energy consumption objective function is as follows: minR load (t)=min{max[P load (t)]-min[P load (t)]} In the formula, R load (t) is the variance of the power load curve, P load (t) is the power load of the substation, P l (t) is the net load of the power grid, P mess (t) is the power consumption of the 5G base station energy storage battery, P EV (t) is the electric power consumed by the electric vehicle, P temp (t) is the electrical power consumed by the electric cooling / heating system, P Imp (t) is the fixed electric load of the substation, is the charging and discharging power of the 5G base station energy storage battery, P PV (t) is the distributed photovoltaic power generation power, It is the charging mark of the energy storage battery, 1 means charging, 0 means not charging, It is the discharge mark of the energy storage battery, 1 means discharge, 0 means no discharge, Charging and discharging power for electric vehicles, It is the charging sign of electric vehicle, 1 means charging, 0 means not charging, is the discharge mark of the electric vehicle, t is the time, and N is an integer.

12. The system according to claim 11, characterized in that The new energy consumption objective function is as follows: Where: Q loss (t) is the total amount of electricity that the substation sends back to the grid in one day, P loss (t) is the internal power supply and demand difference of the area during period t, P op (t) is the internal power supply and demand difference in a certain period of time in the substation.

13. The system of claim 12, wherein: The thermal power balance constraint is as follows: Q=U ER ·H ER +U EC ·H EC Where, T d (t+1) is the floor surface temperature at time t+1, R d is the equivalent thermal resistance of floor convective heat transfer to indoor air and radiant heat transfer to building envelope, C d is the equivalent thermal conductivity of the floor, ΔT is the temperature difference between indoor and outdoor, T d (t) is the floor surface temperature at time t, Q is the heating / cooling capacity, T s (t+1) is the indoor temperature at time t+1, R w is the equivalent thermal resistance of the building envelope, T s (t) is the indoor temperature at time t, Q s is the solar radiation heat, T out (t) is the outdoor temperature, C w is the equivalent heat capacity of the building envelope, H ER Indicates the thermal power output of the electric heating system, H EC Indicates the thermal power output of the electric refrigeration system, U ER It is the heating flag, which is 1 when heating is in winter and 0 when not heating; U EC It is a cooling symbol.

14. The system of claim 9, wherein: The optimization module comprises: The solution submodule is used to solve the multi-objective optimization scheduling model by adopting the hierarchical-based fast non-dominated sorting strategy, the crowding distance-based same-order-value individual comparison strategy and the elite individual retention strategy in the genetic algorithm.

15. The system of claim 14, wherein: The solution submodule is specifically used for: S1: Randomly generate an initial population as the parent population P of the first generation population t , and set the number of iterations g = 1; S2: The parent population undergoes selection, crossover, and mutation to produce a child population Q of the same size t ; S3: The parent population P t With the offspring population Q t Merge into a population R t , and based on the sequence value and crowding distance, the population R t Sort the individuals; S4: From the sorted population R t Select the first N by sequence value pop Each individual constructs a new parent population Pt+1; S5: Determine whether the preset population size Npop is reached and whether the maximum number of iterations g is reached max If it is not reached, set the number of iterations g = g + 1 and return to step S2; otherwise, output the Pareto optimal solution set and the frontier, and the algorithm ends.

16. The system of claim 9, wherein: The parameter acquisition module is specifically used for: Obtain the forecasted output of distributed photovoltaic power, base load power and temperature in the substation area; Get the historical records of each electric car through Monte Carlo random sampling, The historical records include: daily mileage of electric vehicles, time of electric vehicles connecting to the grid, time of electric vehicles leaving the grid, power of electric vehicles when connected to the grid, and power of electric vehicles when the owner expects to leave the grid; Obtain the 5G base station user load in the station area, the equivalent thermal conductivity of the floor, the equivalent thermal resistance of the floor's convection heat transfer to the indoor air and the radiation heat transfer to the building envelope, the equivalent heat capacity of the building envelope, and the equivalent thermal resistance of the building envelope.

17. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a multi-objective optimization scheduling method for energy consumption in a substation taking into account multiple elastic resources as described in any one of claims 1 to 8 is implemented.

18. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a multi-objective optimization scheduling method for energy consumption in a substation taking into account multiple elastic resources as described in any one of claims 1 to 8 is implemented.

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