Low-carbon court carbon neutralization potential evaluation method based on big data analysis

Abstract

The invention relates to a low-carbon transformer area carbon neutralization potential evaluation method based on big data analysis, and the method specifically comprises the steps: 1, employing a big data-based adjustable resource power feature analysis method in a transformer area energy management and control system architecture, combining the power feature analysis method with a low-carbon transformer area adjustable resource discrimination method, and carrying out the analysis of the adjustable resource power feature in the transformer area energy management and control system architecture; power characteristics of different resources are extracted, and accurate discrimination of the adjustable and controllable resources is realized; 2, constructing a low-carbon energy consumption management and control system framework of the power system transformer area; 3, constructing a low-carbon court adjustable resource aggregation model; step 4, evaluating polymerization potential; 5, a multi-time-scale elastic scheduling method oriented to the low-carbon transformer area is adopted, and the adjustment potential of a coupled energy system of the transformer area is fully excavated; step 6, combining big data analysis and applying actual data to carry out quantitative evaluation on the carbon neutralization potential of the transformer area; the method disclosed by the invention has the advantages of realizing effective polymerization, realizing low-carbon operation of the transformer area and realizing quantitative evaluation of the carbon neutralization potential.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon neutrality in low-carbon metropolitan areas, and specifically relates to a method for evaluating the carbon neutrality potential of low-carbon metropolitan areas based on big data analysis. Background Art

[0002] Theoretical research on user-side energy control has always been the focus of domestic and foreign scholars. User-side resource regulation is the basis for quantitatively analyzing the carbon neutrality potential of the substation, promoting the consumption of new energy, and realizing low-carbon operation of the substation. At present, the research on user-controllable resources mainly focuses on two aspects. On the one hand, it is the refined analysis of user power load and power consumption characteristics; on the other hand, it is the research on control strategies and mechanisms. At the regional power system level, especially at the distribution network and substation level, achieving effective aggregation of user-side controllable resources is also an important aspect of current domestic and foreign research. With the increasing proportion of renewable energy in the energy and power production structure, the volatility of its output has caused a certain impact on the safe and stable operation of the power system. With the development and utilization of non-renewable energy and the increase in global carbon dioxide concentration, the greenhouse effect has continued to intensify, and the carbon neutrality goal has become a national strategy. Taking the user side as the research object to achieve low-carbon operation of the entire substation has an important role in achieving the carbon neutrality goal. Therefore, it is very necessary to provide a low-carbon substation carbon neutrality potential assessment method based on big data analysis to achieve effective aggregation, low-carbon operation of the substation, and quantitative assessment of carbon neutrality potential. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a low-carbon area carbon neutrality potential assessment method based on big data analysis to achieve effective aggregation, low-carbon operation of the area, and quantitative assessment of carbon neutrality potential.

[0004] The purpose of the present invention is to achieve the following: a method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis, the method comprising the following steps:

[0005] Step 1: In the system architecture of the energy consumption control system of the power substation, a power characteristic analysis method of adjustable resources based on big data is adopted. The power characteristic analysis method is combined with the adjustable resource identification method of the low-carbon substation to extract the power characteristics of different resources and realize the accurate identification of adjustable resources.

[0006] Step 2: Build a low-carbon energy management and control system framework for power system substations: including the perception layer, network layer, and application layer, to achieve intelligent perception, effective aggregation, and precise control of adjustable resources in low-carbon substations;

[0007] Step 3: Construct a low-carbon area adjustable resource aggregation model: Construct an area adjustable resource aggregation model based on time-of-use electricity prices and energy storage charging and discharging strategies;

[0008] Step 4: Use the coordinated dispatching strategy of the adjustable resources of the distribution station area users to effectively aggregate the adjustable resources, evaluate their aggregation potential, and realize energy consumption control;

[0009] Step 5: Adopt a multi-time scale flexible dispatching method for low-carbon substations to fully tap the regulation potential of the coupled energy system in the substations and further promote the consumption of new energy: In view of the increasing peak-to-valley difference of the power grid, the strong uncertainty of new energy output and the characteristics of reverse peak regulation, conduct comprehensive regulation of various energy subsystems in the substations to solve the problem that the burden of the power system substations has greatly increased due to the dual pressure of source and load, and the large-scale new energy grid connection faces difficulties in consumption;

[0010] Step 6: Combine big data analysis with actual data to quantitatively evaluate the carbon neutrality potential of the substation: select adjustable potential data and corresponding indicators, classify them according to the clustering algorithm and evaluation algorithm, and based on the classification results, classify the adjustable potential according to the adjustable potential classification method, and use actual data to quantitatively evaluate the carbon neutrality potential of the substation.

[0011] The step 3 of constructing a low-carbon area controllable resource aggregation model specifically includes the following steps:

[0012] Step 3.1: Classification of user-side resources in distribution substations: According to different load operation characteristics, they can be divided into shiftable loads, transferable loads and interruptible loads; according to different time domain characteristics of load power consumption, they can be divided into weekday loads and holiday loads; in order to achieve energy control of adjustable resources of different types of users in the substation, considering the uncertainty of load response, the load adjustable potential and reliability are used as one of the user-side load classification principles, mainly considering the impact of temperature control loads, electric vehicles and user-side distributed energy storage on the energy control of the substation. In summary, the classification of user-side resources in the substation is shown in Table 1;

[0013] Step 3.2: Aggregation of adjustable resources on the user side of the distribution station area: Introduce the concept of user aggregators to effectively aggregate temperature-controlled loads, electric vehicles and distributed energy storage, and use user aggregators as representatives to participate in the energy consumption management and control of the station area.

[0014] The aggregation of adjustable resources on the user side of the distribution station area in step 3.2 specifically includes the following steps:

[0015] Step 3.21: Temperature control load aggregation model;

[0016] Step 3.22: Electric vehicle aggregation model: Assume that an electric vehicle starts charging at time t0, and the initial state of charge is SOC0, then the state of charge of the electric vehicle at time t is: Where P is the charging power; η is the charging efficiency; Q is the battery capacity; 24 hours in a day is taken as a cycle to aggregate the power of electric vehicles, the time step is 1 minute, and 24 hours is divided into 1440 time periods. After aggregation, the charging power in the i-th time period is: Where, M is the number of electric vehicles; i = 1, 2, 3, ..., 1440; P i,j is the charging power of the j-th electric vehicle in the i-th time period;

[0017] Step 3.23: Distributed energy storage aggregation model: The introduction of distributed energy storage on the user side can meet the user's flexible adjustment of energy load requirements, but the distributed energy storage on the user side is large in number and dispersed, and cannot be effectively controlled by the substation area. Therefore, it needs to be aggregated and managed. The distributed energy storage aggregation model is as follows:

[0018]

[0019] E(t)=E(t-1)+P char (t-1)Δt-P dis (t-1)Δt, where D is the amount of distributed energy storage; P char (t), P dis (t) are the distributed energy storage charging and discharging aggregate power at time t; P char,d (t), P dis,d (t) are the charging and discharging power of the dth distributed energy storage at time t; η char,d , η dis,d are the charging and discharging efficiency of the dth distributed energy storage; E(t) is the aggregated energy of the distributed energy storage at time t; U char,d (t), U dis,d (t) are the charging and discharging operation state variables of the dth distributed energy storage at time t. Its value is 1 for operation, and vice versa for stop. To avoid the dth distributed energy storage from charging and discharging at the same time, the following conditions must be met: In actual operation, each distributed energy storage must meet the charging and discharging constraints and capacity constraints, namely: Where, T is 24h; P char,d,max , P dis,d,max are the maximum charging and discharging power of the dth distributed energy storage; E d,max is the maximum capacity of the dth distributed energy storage; To limit the number of charge and discharge cycles.

[0020] The temperature control load aggregation model in step 3.21 is specifically as follows: a single temperature control load model is established using an equivalent thermal parameter modeling method, and the state equation of a single temperature control load is obtained as follows: Where C is the equivalent heat capacity; R is the equivalent thermal resistance; P(t) is the air conditioner operating power at time t; T out (t), T in (t) represents the outdoor and indoor temperature at time t; η is the air conditioning energy efficiency ratio; u(t) is the air conditioning operating state at time t; further rearrangement of the above formula can obtain the relationship between the air conditioning operating power and the indoor temperature change: T in (t+Δt)=T out (t)-ηu(t)P(t)R-[T out (t)-ηu(t)P(t)RT in (t)]e -ΔU(RC) , where Δt is the time step; assuming that the room temperature must be maintained at [T min ,T max ] range, and without considering the instantaneous power impact of air conditioner start and stop, according to the law of conservation of energy, the average cooling power of air conditioner can be equal to the average rate of heat loss, then the average power of a single temperature control load is: In the formula, is the average power of air conditioning load at time t; is the average room temperature at time t; T max 、T min are the upper and lower limits of the room temperature respectively; since the air conditioners operate on the same principle, the area is more concerned about the total power consumption of the temperature control load group. Assuming there are N air conditioners, the total power of the temperature control load group can be estimated based on the average power of a single temperature control load, that is: Considering that the average room temperature at time t can be replaced by the average set temperature of the air conditioner at time t, and the η and R of N air conditioners are independent of each other, the total power consumption of the temperature control load group at time t is: in, In the formula, R eq is the equivalent resistance of the polymerization model; η eq is the equivalent energy efficiency ratio of the aggregate model; T set,eq (t) is the equivalent set temperature of the air conditioner of the aggregation model at time t; T set,n (t) is the set temperature of the nth air conditioner at time t.

[0021] The coordinated scheduling strategy of user-adjustable resources in the distribution station area in step 4 specifically includes the following steps:

[0022] Step 4.1: Objective function: ① User aggregated commercial electricity cost: The user aggregated commercial electricity cost is the difference between the revenue from selling electricity to the grid and the cost of purchasing electricity, and the expression is as follows: In the formula, C p Aggregate commercial electricity costs for users; buy (t), P sell(t) are the electric power that the user aggregator needs to purchase and sell at time t, which is the difference between the output of new energy, the charging and discharging power of energy storage and the power load. When the output of new energy and the discharge power of energy storage do not meet the load demand, it is necessary to purchase electric energy from the power grid. When the output of new energy meets the charging and load demand of energy storage and there is a surplus, it sells electric energy to the power grid; c fs (t), c sw (t) are the grid time-of-use electricity price and on-grid electricity price at time t respectively; ② User aggregated commercial energy comfort: User aggregated commercial energy comfort mainly considers the comfort of using the temperature control load, i.e., the air conditioner, and tries to meet the user's setting requirements for the room temperature. The evaluation criteria of temperature comfort are related to the room temperature and the upper and lower limits of the optimum temperature. Within the range of the user's set temperature, the greater the deviation percentage between the room temperature and the optimum temperature, the lower the user's energy comfort. The expression is as follows: Where Ω(t) is the percentage of room temperature deviation from the optimum temperature at time t; T in (t) is the indoor temperature; T fit,max 、T fit,min are the upper and lower limits of the optimum temperature respectively; k(t) is the air conditioning operation state variable, and its value is 1, indicating that the air conditioning is running, otherwise it means that the air conditioning is stopped; F is the energy comfort index; In summary, the user aggregator objective function is: minM=λ1C p +λ2F, where λ1 and λ2 are linear weights;

[0023] Step 4.2: Power supply reliability constraints: Considering that the balance of power supply and consumption of users in the distribution station area is an important prerequisite for saving users' electricity costs and improving energy comfort, the system should meet the load power, new energy output, energy storage device charging and discharging power, and user aggregator and grid interaction power balance constraints in each period, which can be expressed as: P load,fix (t)+P load,con (t) = P p,w (t)+P grid (t)+P dis (t), where P load,fix (t) is the unadjustable load power at time t; P load,con (t) is the adjustable load power at time t, i.e., the charging power of temperature-controlled load electric vehicles and distributed energy storage; P p,w (t) is the output of new energy at time t; P grid (t) is the interaction power between the user aggregator and the power grid at time t. When the output of new energy and the discharge power of energy storage do not meet the load demand, it is a positive value, otherwise it is a negative value; P dis (t) is the distributed energy storage discharge power at time t, which satisfies the constraint that the distributed energy storage is not charged and discharged simultaneously;

[0024] Step 4.3: Coordinate scheduling strategies;

[0025] Step 4.4: Solve the model: The adjustable resource scheduling model of the substation requires historical meteorological data and electricity consumption data to predict the wind power, photovoltaic output and unadjustable resource energy power of the next day. It is necessary to generate the optimal solution of the objective function based on the boundary conditions of the feasible solution. Therefore, the differential evolution algorithm is used to solve the model, and the optimal solution is found through three processes of mutation, crossover and selection.

[0026] The coordination scheduling strategy in step 4.3 specifically includes the following steps:

[0027] Step 4.31: When the output of renewable energy meets the load demand, the energy storage is charged first. When the energy storage SOC reaches the upper limit, if there is any surplus, it is sold to the grid for profit;

[0028] Step 4.32: When the output of renewable energy cannot meet the load demand, it is divided into the following three situations: when the TOU electricity price is at a valley value, the user side purchases electricity from the grid to make up the load difference, and at the same time purchases electricity to charge the energy storage until the SOC upper limit; when the TOU electricity price is at a flat value, the user side purchases electricity from the grid to make up the load difference, and the energy storage does not work; when the TOU electricity price is at a peak value, the energy storage discharge is used first to meet the load demand. When the SOC reaches the minimum value and is still in short supply, electricity is purchased from the grid again.

[0029] The multi-time scale flexible dispatching method for low-carbon areas in step 5 adopts a multi-time scale joint optimization dispatching strategy for the power system under the low-carbon background, which specifically includes the following steps:

[0030] Step 5.1: Weekly scheduling model;

[0031] Step 5.2: Day-ahead dispatch model: The objective function of day-ahead dispatch is to minimize the operating cost of the entire system, including fuel cost, generator startup cost, spinning reserve cost, regulating reserve cost, and wind curtailment cost. Considering that the state of large units has been determined, in the day-ahead dispatch model, the state variables of the generator units only refer to small units. In the formula, m represents the number of the small generator set; M∈I;

[0032] Step 5.3: Intraday dispatch model: The objective function of intraday dispatch is to minimize the operating cost of the entire system, including fuel cost, regulation reserve cost and wind curtailment cost.

[0033] The pre-week dispatch model in step 5.1 is specifically as follows: the objective function of the pre-week dispatch model is to minimize the system operation cost, including fuel cost, generator start-up cost, spinning reserve cost, regulating reserve cost and wind curtailment cost. Considering the characteristics of small capacity, dispersion and volatility of demand-side response resources on the user side, load aggregators are used to participate in the optimal dispatch of the power system. Gi is the fuel cost of generator set i, which can be expressed by formula (2); S Si is the startup cost of generator set i, which can be expressed by formula (3); the spinning reserve can be provided by traditional generator sets or loads, and the cost of spinning reserve is It can be expressed by formula (4); the cost of frequency regulation is It can be expressed by formula (5); the frequency regulation reserve is provided by traditional generators, and the cost is It can be expressed by formula (6); the cost of wind abandonment is It can be expressed by formula (7), S Gi [P Gi (t)]=a i +b i P Gi (t)+c i P Gi (t) 2 (2), S Si =S i α i (t)[1-α i (t-1)], In the formula, a i , b i 、c i is a parameter of the operating cost of generator i.

[0034] The constraints of the pre-scheduling model in step 5.1 are: ① Node power balance: ②Line flow constraints: -F max ≤P ij (t)≤F max ,t=1,2,...,T(9);③Generator output constraint: ④ Reserve demand constraints: ⑤ Reserve usage: ⑥Minimum startup time constraint: ⑦Demand-side response constraints:

[0035] The quantitative evaluation indicators of the carbon neutrality potential of the substation in step 6 are specifically as follows: from the user's perspective, the population size, income level, living area, low-carbon attitude and energy usage habits indicators are selected to quantitatively evaluate the carbon neutrality potential of the substation.

[0036] Beneficial effects of the invention: The present invention is a method for evaluating the carbon neutrality potential of low-carbon substations based on big data analysis. During use, the present invention effectively aggregates controllable resources and evaluates their aggregation potential, thereby reflecting the carbon neutrality potential of the substation. The method of the present invention provides decision-making support for low-carbon energy use management and control in the substation; unlike the previously established low-carbon economic evaluation indicators of power systems, the present invention considers the user side and selects indicators such as population size, income level, living area, low-carbon attitude and energy use habits to quantitatively evaluate the carbon neutrality potential of the substation, which can objectively and effectively reflect the low-carbon operation level and regulation potential of the substation, so as to better take action to reduce energy-related carbon emissions and achieve operability. ; The method of the present invention calculates and evaluates carbon emissions from a new perspective, taking into account the differences in the aggregation characteristics of adjustable resources, the aggregation potential, and the key factors affecting the construction of the aggregation model, and then aggregates and models the adjustable resources from multiple dimensions such as time and object, and evaluates their aggregation potential to achieve a quantitative assessment of the carbon neutrality potential of the substation; the present invention can enhance the ability to mine adjustable resources in the substation, achieve centralized control and effective aggregation of adjustable resources, control adjustable resources, achieve low-carbon operation of the substation, improve economic benefits, and help the power grid achieve the "carbon peak and carbon neutrality" goals as soon as possible; the present invention has the advantages of achieving effective aggregation, achieving low-carbon operation of the substation, and achieving quantitative assessment of carbon neutrality potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a framework diagram of the energy consumption management and control system of a distribution station area according to the method for evaluating the carbon neutrality potential of a low-carbon station area based on big data analysis of the present invention.

[0038] Figure 2 This is a temperature control load equivalent aggregation model diagram of the low-carbon area carbon neutrality potential assessment method based on big data analysis of the present invention.

[0039] Figure 3 This is a flow chart of electric vehicle charging power aggregation for the method for assessing carbon neutrality potential in low-carbon areas based on big data analysis of the present invention.

[0040] Figure 4 This is a flow chart of the solution model of the carbon neutrality potential assessment method for low-carbon areas based on big data analysis in the present invention.

[0041] Figure 5 This is a multi-time-scale optimization scheduling flowchart of the low-carbon area carbon neutrality potential assessment method based on big data analysis of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be further described below in conjunction with the accompanying drawings.

[0043] Example 1

[0044] like Figure 1-5As shown, a method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis includes the following steps:

[0045] Step 1: In the system architecture of the energy consumption control system of the power substation, a power characteristic analysis method of adjustable resources based on big data is adopted. The power characteristic analysis method is combined with the adjustable resource identification method of the low-carbon substation to extract the power characteristics of different resources and realize the accurate identification of adjustable resources.

[0046] Step 2: Build a low-carbon energy management and control system framework for power system substations: including the perception layer, network layer, and application layer, to achieve intelligent perception, effective aggregation, and precise control of adjustable resources in low-carbon substations;

[0047] Step 3: Construct a low-carbon area adjustable resource aggregation model: Construct an area adjustable resource aggregation model based on time-of-use electricity prices and energy storage charging and discharging strategies;

[0048] Step 4: Use the coordinated dispatching strategy of the adjustable resources of the distribution station area users to effectively aggregate the adjustable resources, evaluate their aggregation potential, and realize energy consumption control;

[0049] Step 5: Adopt a multi-time scale flexible dispatching method for low-carbon substations to fully tap the regulation potential of the coupled energy system in the substations and further promote the consumption of new energy: In view of the increasing peak-to-valley difference of the power grid, the strong uncertainty of new energy output and the characteristics of reverse peak regulation, conduct comprehensive regulation of various energy subsystems in the substations to solve the problem that the burden of the power system substations has greatly increased due to the dual pressure of source and load, and the large-scale new energy grid connection faces difficulties in consumption;

[0050] Step 6: Combine big data analysis with actual data to quantitatively evaluate the carbon neutrality potential of the substation: select adjustable potential data and corresponding indicators, classify them according to the clustering algorithm and evaluation algorithm, and based on the classification results, classify the adjustable potential according to the adjustable potential classification method, and use actual data to quantitatively evaluate the carbon neutrality potential of the substation.

[0051] In this embodiment, the system architecture of the energy consumption control system of the substation is specifically as follows: guided by the application needs of the substation field, the construction of the distribution Internet of Things energy consumption control system based on the substation intelligent fusion terminal is carried out. The framework of the substation energy consumption control system is as follows: Figure 1As shown in the figure, under the overall architecture of "cloud, pipe, edge and end" of the distribution Internet of Things, big data technology and cloud-edge collaborative technology are used to build the framework of the substation energy consumption management and control system, focusing on the five core business functions of the substation: monitoring and controlling the operating status of low-voltage distribution network equipment; providing data support for substation line loss electricity prices and management; realizing the digital transformation of distribution by the joint action of intelligent sensing units and intelligent distribution transformers; providing guarantee for the stable operation of distributed power supply access; and managing the safe charging and discharging of electric vehicles. The perception layer is responsible for collecting data collected by users' distributed power sources, electric vehicles and other load resources, as well as smart meters, fault diagnosis equipment, switching equipment, etc., identifying and analyzing them through smart distribution transformers, and interacting with the cloud master station, using the micro-application management and control system to realize data visualization through client APP downloads, and striving to improve the business capabilities of the substation to achieve "full data collection, full status perception, and full business coverage."

[0052] The step 3 of constructing a low-carbon area controllable resource aggregation model specifically includes the following steps:

[0053] Step 3.1: Classification of user-side resources in distribution substations: According to different load operation characteristics, they can be divided into shiftable loads, transferable loads and interruptible loads; according to different time domain characteristics of load power consumption, they can be divided into weekday loads and holiday loads; in order to achieve energy control of adjustable resources of different types of users in the substation, considering the uncertainty of load response, the load adjustable potential and reliability are used as one of the user-side load classification principles, mainly considering the impact of temperature control loads, electric vehicles and user-side distributed energy storage on the energy control of the substation. In summary, the classification of user-side resources in the substation is shown in Table 1;

[0054] Table 1 Classification of user-side resources in the substation area

[0055]

[0056] Step 3.2: Aggregation of adjustable resources on the user side of the distribution station area: Introduce the concept of user aggregators to effectively aggregate temperature-controlled loads, electric vehicles and distributed energy storage, and use user aggregators as representatives to participate in the energy consumption management and control of the station area.

[0057] The aggregation of adjustable resources on the user side of the distribution station area in step 3.2 specifically includes the following steps:

[0058] Step 3.21: Temperature control load aggregation model;

[0059] Step 3.22: Electric vehicle aggregation model: Assume that an electric vehicle starts charging at time t0, and the initial state of charge is SOC0, then the state of charge of the electric vehicle at time t is: Where P is the charging power; η is the charging efficiency; Q is the battery capacity; 24 hours in a day is taken as a cycle to aggregate the power of electric vehicles, the time step is 1 minute, and 24 hours is divided into 1440 time periods. After aggregation, the charging power in the i-th time period is: Where, M is the number of electric vehicles; i = 1, 2, 3, ..., 1440; P i,j is the charging power of the jth electric vehicle in the i-th time period; due to different subjective wishes of users, the initial charging time, end charging time, initial state of charge and expected state of charge of each electric vehicle are different. In order to simplify the model, it is assumed that the initial charging time and charging power of each electric vehicle are independent of each other, the state of charge of each electric vehicle reaches the maximum value at the end of charging, and the charging power of each electric vehicle is constant. The Monte Carlo scenario simulation method is used to initialize the number of electric vehicles first, and then calculate the charging time by randomly selecting the initial charging time and initial state of charge. The simulation curve of the electric vehicle charging power and time is superimposed until the number of superpositions reaches the upper limit of the number of electric vehicles. Finally, the electric vehicle charging power aggregation curve is drawn. The specific process is as follows Figure 3 As shown;

[0060] Step 3.23: Distributed energy storage aggregation model: The introduction of distributed energy storage on the user side can meet the user's flexible adjustment of energy load requirements, but the distributed energy storage on the user side is large in number and dispersed, and cannot be effectively controlled by the substation area. Therefore, it needs to be aggregated and managed. The distributed energy storage aggregation model is as follows: E(t)=E(t-1)+P char (t-1)Δt-P dis (t-1)Δt, where D is the amount of distributed energy storage; P char (t), P dis (t) are the distributed energy storage charging and discharging aggregate power at time t; P char,d (t), P dis,d (t) are the charging and discharging power of the dth distributed energy storage at time t; η char,d , η dis,d are the charging and discharging efficiency of the dth distributed energy storage; E(t) is the aggregated energy of the distributed energy storage at time t; U char,d (t), U dis,d (t) are the charging and discharging operation state variables of the dth distributed energy storage at time t. Its value is 1 for operation, and vice versa for stop. To avoid the dth distributed energy storage from charging and discharging at the same time, the following conditions must be met: In actual operation, each distributed energy storage must meet the charging and discharging constraints and capacity constraints, namely: Where, T is 24h; P char,d,max , P dis,d,maxare the maximum charging and discharging power of the dth distributed energy storage; E d,max is the maximum capacity of the dth distributed energy storage; To limit the number of charge and discharge cycles.

[0061] The temperature control load aggregation model in step 3.21 is specifically as follows: an equivalent thermal parameter modeling method is used to establish a model of a single temperature control load. This method equates the thermal resistance of building materials and the heat capacity of air to the resistance and capacitance elements of the circuit, and equates the light and ambient temperature to the power supply of the circuit. This method can be used to establish the relationship between power and temperature, time and energy efficiency based on the heat capacity and thermal resistance of a single temperature control load. Since different temperature control loads have the same operating mechanism but different equivalent heat capacity C, equivalent thermal resistance R and energy efficiency ratio η, under the same temperature control target, multiple temperature control load output models can be equivalent to: Figure 2 The equivalent thermal parameter aggregation model shown is given by Figure 2 The state equation of a single temperature control load is as follows: Where C is the equivalent heat capacity; R is the equivalent thermal resistance; P(t) is the air conditioner operating power at time t; T out (t), T in (t) represents the outdoor and indoor temperature at time t; η is the air conditioning energy efficiency ratio; u(t) is the air conditioning operating state at time t; further rearrangement of the above formula can obtain the relationship between the air conditioning operating power and the indoor temperature change: T in (t+Δt)=T out (t)-ηu(t)P(t)R-[T out (t)-ηu(t)P(t)RT in (t)]e -ΔU(RC) , where Δt is the time step; assuming that the room temperature must be maintained at [T min ,T max ] range, and without considering the instantaneous power impact of air conditioner start and stop, according to the law of conservation of energy, the average cooling power of air conditioner can be equal to the average rate of heat loss, then the average power of a single temperature control load is: In the formula, is the average power of air conditioning load at time t; is the average room temperature at time t; T max 、T min are the upper and lower limits of the room temperature respectively; since the air conditioners operate on the same principle, the area is more concerned about the total power consumption of the temperature control load group. Assuming there are N air conditioners, the total power of the temperature control load group can be estimated based on the average power of a single temperature control load, that is: Considering that the average room temperature at time t can be replaced by the average air conditioner set temperature at time t, and the η and R of N air conditioners are independent of each other, we can use Figure 2 The equivalent η and R of the temperature control load aggregation model can be calculated, and the total power consumption of the temperature control load group at time t is: in, In the formula, R eq is the equivalent resistance of the polymerization model; η eq is the equivalent energy efficiency ratio of the aggregate model; T set,eq (t) is the equivalent set temperature of the air conditioner of the aggregation model at time t; T set,n (t) is the set temperature of the nth air conditioner at time t.

[0062] The coordinated scheduling strategy of user-adjustable resources in the distribution station area in step 4 specifically includes the following steps:

[0063] Step 4.1: Objective function: ① User aggregated commercial electricity cost: The user aggregated commercial electricity cost is the difference between the revenue from selling electricity to the grid and the cost of purchasing electricity, and the expression is as follows: In the formula, C p Aggregate commercial electricity costs for users; buy (t), P sell (t) are the electric power that the user aggregator needs to purchase and sell at time t, which is the difference between the output of new energy, the charging and discharging power of energy storage and the power load. When the output of new energy and the discharge power of energy storage do not meet the load demand, it is necessary to purchase electric energy from the power grid. When the output of new energy meets the charging and load demand of energy storage and there is a surplus, it sells electric energy to the power grid; c fs (t), c sw (t) are the grid time-of-use electricity price and on-grid electricity price at time t respectively; ② User aggregated commercial energy comfort: User aggregated commercial energy comfort mainly considers the comfort of using the temperature control load, i.e., the air conditioner, and tries to meet the user's setting requirements for the room temperature. The evaluation criteria of temperature comfort are related to the room temperature and the upper and lower limits of the optimum temperature. Within the range of the user's set temperature, the greater the deviation percentage between the room temperature and the optimum temperature, the lower the user's energy comfort. The expression is as follows: Where Ω(t) is the percentage of room temperature deviation from the optimum temperature at time t; T in (t) is the indoor temperature; T fit,max 、T fit,min are the upper and lower limits of the optimum temperature, which are 27℃ and 23℃ in this paper; k(t) is the air conditioning operation state variable, and its value is 1, indicating that the air conditioning is running, otherwise it means that the air conditioning is stopped; F is the energy comfort index; In summary, the user aggregator objective function is: minM=λ1C p +λ2F, where λ1 and λ2 are linear weights;

[0064] Step 4.2: Power supply reliability constraints: Considering that the balance of power supply and consumption of users in the distribution station area is an important prerequisite for saving users' electricity costs and improving energy comfort, the system should meet the load power, new energy output, energy storage device charging and discharging power, and user aggregator and grid interaction power balance constraints in each period, which can be expressed as: Pload,fix (t)+P load,con (t) = P p,w (t)+P grid (t)+P dis (t), where P load,fix (t) is the unadjustable load power at time t; P load,con (t) is the adjustable load power at time t, i.e., the charging power of temperature-controlled load electric vehicles and distributed energy storage; P p,w (t) is the output of new energy at time t; P grid (t) is the interaction power between the user aggregator and the power grid at time t. When the output of new energy and the discharge power of energy storage do not meet the load demand, it is a positive value, otherwise it is a negative value; P dis (t) is the distributed energy storage discharge power at time t, which satisfies the constraint that the distributed energy storage is not charged and discharged simultaneously;

[0065] Step 4.3: Coordinate scheduling strategies;

[0066] Step 4.4: Solve the model: The adjustable resource scheduling model of the substation requires historical meteorological data and electricity consumption data to predict the wind power, photovoltaic output and energy consumption of unadjustable resources for the next day. It is necessary to generate the optimal solution of the objective function based on the boundary conditions of the feasible solution. Therefore, the differential evolution algorithm is used to solve the model. Its fast convergence speed and few control parameters are used to improve the accuracy of the model solution and the diversity of non-inferior solutions. The optimal solution is found through the three processes of mutation, crossover and selection. The specific solution process is as follows: Figure 4 .

[0067] The coordinated dispatching strategy in step 4.3 is as follows: considering the impact of electricity prices on user electricity consumption behavior and the regulatory effect on the charging and discharging operations of distributed energy storage, and at the same time to improve the effectiveness of the multi-objective optimization dispatching model of adjustable resources in the substation and the safety of distributed energy storage operation, this paper proposes two coordinated dispatching strategies; ① The charging and discharging of distributed energy storage on the user side should be based on the impact of the grid time-of-use electricity price, that is, the energy storage is charged when the electricity price is valley, and the energy storage is used for discharge when the electricity price is peak, so as to reduce the electricity cost on the user side; ② The charging and discharging of distributed energy storage on the user side should avoid over-limit situations, that is, when the energy storage SOC has reached the maximum value at time t-1, the charging operation will no longer be performed at time t, and when the energy storage SOC has reached the minimum value at time t-1, the discharging operation will no longer be performed at time t; Based on the above strategy, the following steps are specifically included:

[0068] Step 4.31: When the output of renewable energy meets the load demand, the energy storage is charged first. When the energy storage SOC reaches the upper limit, if there is any surplus, it is sold to the grid for profit;

[0069] Step 4.32: When the output of renewable energy cannot meet the load demand, it is divided into the following three situations: when the TOU electricity price is at a valley value, the user side purchases electricity from the grid to make up the load difference, and at the same time purchases electricity to charge the energy storage until the SOC upper limit; when the TOU electricity price is at a flat value, the user side purchases electricity from the grid to make up the load difference, and the energy storage does not work; when the TOU electricity price is at a peak value, the energy storage discharge is used first to meet the load demand. When the SOC reaches the minimum value and is still in short supply, electricity is purchased from the grid again.

[0070] The present invention is a method for evaluating the carbon neutrality potential of low-carbon power stations based on big data analysis. During use, the present invention effectively aggregates controllable resources and evaluates their aggregation potential, thereby reflecting the carbon neutrality potential of the power station. The method of the present invention provides decision-making support for the low-carbon energy use management and control of the power station; unlike the previously established low-carbon economic evaluation indicators of the power system, the present invention considers the user side and selects indicators such as population size, income level, living area, low-carbon attitude and energy use habits to quantitatively evaluate the carbon neutrality potential of the power station, which can objectively and effectively reflect the low-carbon operation level and regulation potential of the power station, so as to better take action to reduce energy-related carbon emissions and achieve operability; the method of the present invention The method calculates and evaluates carbon emissions from a new perspective, taking into account the differences in the aggregation characteristics of adjustable resources, the aggregation potential, and the key factors affecting the construction of the aggregation model, and then aggregates and models the adjustable resources from multiple dimensions such as time and object, and evaluates their aggregation potential to achieve a quantitative assessment of the carbon neutrality potential of the substation; the present invention can enhance the ability to mine adjustable resources in the substation, realize centralized control and effective aggregation of adjustable resources, control adjustable resources, achieve low-carbon operation of the substation, improve economic benefits, and help the power grid achieve the "carbon peak and carbon neutrality" goals as soon as possible; the present invention has the advantages of achieving effective aggregation, achieving low-carbon operation of the substation, and achieving quantitative assessment of carbon neutrality potential.

[0071] Example 2

[0072] like Figure 1-5 As shown, a method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis includes the following steps:

[0073] Step 1: In the system architecture of the energy consumption control system of the power substation, a power characteristic analysis method of adjustable resources based on big data is adopted. The power characteristic analysis method is combined with the adjustable resource identification method of the low-carbon substation to extract the power characteristics of different resources and realize the accurate identification of adjustable resources.

[0074] Step 2: Build a low-carbon energy management and control system framework for power system substations: including the perception layer, network layer, and application layer, to achieve intelligent perception, effective aggregation, and precise control of adjustable resources in low-carbon substations;

[0075] Step 3: Construct a low-carbon area adjustable resource aggregation model: Construct an area adjustable resource aggregation model based on time-of-use electricity prices and energy storage charging and discharging strategies;

[0076] Step 4: Use the coordinated dispatching strategy of the adjustable resources of the distribution station area users to effectively aggregate the adjustable resources, evaluate their aggregation potential, and realize energy consumption control;

[0077] Step 5: Adopt a multi-time scale flexible dispatching method for low-carbon substations to fully tap the regulation potential of the coupled energy system in the substations and further promote the consumption of new energy: In view of the increasing peak-to-valley difference of the power grid, the strong uncertainty of new energy output and the characteristics of reverse peak regulation, conduct comprehensive regulation of various energy subsystems in the substations to solve the problem that the burden of the power system substations has greatly increased due to the dual pressure of source and load, and the large-scale new energy grid connection faces difficulties in consumption;

[0078] Step 6: Combine big data analysis with actual data to quantitatively evaluate the carbon neutrality potential of the substation: select adjustable potential data and corresponding indicators, classify them according to the clustering algorithm and evaluation algorithm, and based on the classification results, classify the adjustable potential according to the adjustable potential classification method, and use actual data to quantitatively evaluate the carbon neutrality potential of the substation.

[0079] The multi-time scale flexible dispatching method for low-carbon areas in step 5 adopts a multi-time scale joint optimization dispatching strategy for the power system under the low-carbon background, which specifically includes the following steps:

[0080] Step 5.1: Weekly scheduling model;

[0081] Step 5.2: Day-ahead dispatch model: The objective function of day-ahead dispatch is to minimize the operating cost of the entire system, including fuel cost, generator startup cost, spinning reserve cost, regulating reserve cost, and wind curtailment cost. Considering that the state of large units has been determined, in the day-ahead dispatch model, the state variables of the generator units only refer to small units. Where m represents the number of the small generator set; M∈I; the corresponding constraints are the same as those in the pre-week scheduling model, and only need to be modified accordingly, which can be expressed by formulas (8) to (14); the number of the large generator set is defined as n, N∈I; λ n (t) represents the state of the large generator set n at time t, which is the input quantity; m (t) represents the state of the small generator set m at time t, which is a variable in the model; for example, in formula (10), the generator output constraint can be modified as follows:

[0082] Step 5.3: Intraday dispatch model: The objective function of intraday dispatch is to minimize the operating cost of the entire system, including fuel cost, regulation reserve cost and wind curtailment cost. The expressions of fuel cost, regulation reserve cost and wind curtailment cost are the same as those in the week-ahead dispatch model, expressed as formula (2), formula (5), formula (6) and formula (7); the constraints of intraday dispatch are the same as those in the day-ahead dispatch model except for the time interval, and only need to be modified accordingly, including node power balance, line flow constraint, generator output constraint, reserve demand constraint, reserve constraint and demand side response constraint, corresponding to expressions (8), formula (9), formula (10), formula (11), formula (12) and formula (14); the generator set status and spinning reserve capacity allocation obtained in the day-ahead dispatch are used as the input of intraday dispatch, and the generator output level and frequency regulation reserve allocation are used as variables of intraday dispatch. For example, the generator n output constraint in formula (16) needs to be modified accordingly to the formula: λ n (t) represents the state of large generator set n at time t, which is calculated in the pre-week scheduling model; It represents the spinning reserve capacity of large generator set n at time t, which can be calculated in the day-ahead dispatch model.

[0083] The pre-week dispatch model in step 5.1 is specifically as follows: the objective function of the pre-week dispatch model is to minimize the system operation cost, including fuel cost, generator start-up cost, spinning reserve cost, regulating reserve cost and wind curtailment cost. Considering the characteristics of small capacity, dispersion and volatility of demand-side response resources on the user side, load aggregators are used to participate in the optimal dispatch of the power system. Gi is the fuel cost of generator set i, which can be expressed by formula (2); S Si is the startup cost of generator set i, which can be expressed by formula (3); the spinning reserve can be provided by traditional generator sets or loads, and the cost of spinning reserve is It can be expressed by formula (4); the cost of frequency regulation is It can be expressed by formula (5); the frequency regulation reserve is provided by traditional generators, and the cost is It can be expressed by formula (6); the cost of wind abandonment is It can be expressed by formula (7), S Gi [P Gi (t)]=a i +b i P Gi (t)+c i P Gi (t) 2 (2), S Si =S i α i (t)[1-α i (t-1)], In the formula, a i , b i 、c i is a parameter of the operating cost of generator i.

[0084] The constraints of the pre-scheduling model in step 5.1 are: ① Node power balance: ②Line flow constraints: -F max ≤P ij (t)≤F max ,t=1,2,...,T(9);③Generator output constraint: ④ Reserve demand constraints: ⑤ Reserve usage: ⑥Minimum startup time constraint: ⑦Demand-side response constraints:

[0085] The present invention is a method for evaluating the carbon neutrality potential of low-carbon power stations based on big data analysis. During use, the present invention effectively aggregates controllable resources and evaluates their aggregation potential, thereby reflecting the carbon neutrality potential of the power station. The method of the present invention provides decision-making support for the low-carbon energy use management and control of the power station; unlike the previously established low-carbon economic evaluation indicators of the power system, the present invention considers the user side and selects indicators such as population size, income level, living area, low-carbon attitude and energy use habits to quantitatively evaluate the carbon neutrality potential of the power station, which can objectively and effectively reflect the low-carbon operation level and regulation potential of the power station, so as to better take action to reduce energy-related carbon emissions and achieve operability; the method of the present invention The method calculates and evaluates carbon emissions from a new perspective, taking into account the differences in the aggregation characteristics of adjustable resources, the aggregation potential, and the key factors affecting the construction of the aggregation model, and then aggregates and models the adjustable resources from multiple dimensions such as time and object, and evaluates their aggregation potential to achieve a quantitative assessment of the carbon neutrality potential of the substation; the present invention can enhance the ability to mine adjustable resources in the substation, realize centralized control and effective aggregation of adjustable resources, control adjustable resources, achieve low-carbon operation of the substation, improve economic benefits, and help the power grid achieve the "carbon peak and carbon neutrality" goals as soon as possible; the present invention has the advantages of achieving effective aggregation, achieving low-carbon operation of the substation, and achieving quantitative assessment of carbon neutrality potential.

Claims

1. A method for assessing the carbon neutrality potential of low-carbon areas based on big data analysis, characterized by: The method comprises the following steps: Step 1: In the system architecture of the energy consumption control system of the power substation, a power characteristic analysis method of adjustable resources based on big data is adopted. The power characteristic analysis method is combined with the adjustable resource identification method of the low-carbon substation to extract the power characteristics of different resources and realize the accurate identification of adjustable resources. Step 2: Build a low-carbon energy management and control system framework for power system substations: including the perception layer, network layer, and application layer, to achieve intelligent perception, effective aggregation, and precise control of adjustable resources in low-carbon substations; Step 3: Construct a low-carbon area adjustable resource aggregation model: Construct an area adjustable resource aggregation model based on time-of-use electricity prices and energy storage charging and discharging strategies; Step 4: Use the coordinated dispatching strategy of the adjustable resources of the distribution station area users to effectively aggregate the adjustable resources, evaluate their aggregation potential, and realize energy consumption control; Step 5: Adopt a multi-time scale flexible dispatching method for low-carbon substations to fully tap the regulation potential of the coupled energy system in the substations and further promote the consumption of new energy: In view of the increasing peak-to-valley difference of the power grid, the strong uncertainty of new energy output and the characteristics of reverse peak regulation, conduct comprehensive regulation of various energy subsystems in the substations to solve the problem that the burden of the power system substations has greatly increased due to the dual pressure of source and load, and the large-scale new energy grid connection faces difficulties in consumption; Step 6: Combine big data analysis with actual data to quantitatively evaluate the carbon neutrality potential of the substation: select adjustable potential data and corresponding indicators, classify them according to the clustering algorithm and evaluation algorithm, and based on the classification results, classify the adjustable potential according to the adjustable potential classification method, and use actual data to quantitatively evaluate the carbon neutrality potential of the substation.

2. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 1 is characterized in that: The step 3 of constructing a low-carbon area controllable resource aggregation model specifically includes the following steps: Step 3.1: Classification of user-side resources in distribution substations: According to different load operation characteristics, they can be divided into shiftable loads, transferable loads and interruptible loads; according to different time domain characteristics of load power consumption, they can be divided into weekday loads and holiday loads; in order to achieve energy control of adjustable resources of different types of users in the substation, considering the uncertainty of load response, the load adjustable potential and reliability are used as one of the user-side load classification principles, mainly considering the impact of temperature control loads, electric vehicles and user-side distributed energy storage on the energy control of the substation. In summary, the classification of user-side resources in the substation is shown in Table 1; Step 3.2: Aggregation of adjustable resources on the user side of the distribution station area: Introduce the concept of user aggregators to effectively aggregate temperature-controlled loads, electric vehicles and distributed energy storage, and use user aggregators as representatives to participate in the energy consumption management and control of the station area.

3. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 2 is characterized in that: The aggregation of adjustable resources on the user side of the distribution station area in step 3.2 specifically includes the following steps: Step 3.21: Temperature control load aggregation model; Step 3.22: Electric vehicle aggregation model: Assume that an electric vehicle starts charging at time t0, and the initial state of charge is SOC0, then the state of charge of the electric vehicle at time t is: Where P is the charging power; η is the charging efficiency; Q is the battery capacity; 24 hours in a day is taken as a cycle to aggregate the power of electric vehicles, the time step is 1 minute, and 24 hours is divided into 1440 time periods. After aggregation, the charging power in the i-th time period is: Where, M is the number of electric vehicles; i = 1, 2, 3, ..., 1440; P i,j is the charging power of the j-th electric vehicle in the i-th time period; Step 3.23: Distributed energy storage aggregation model: The introduction of distributed energy storage on the user side can meet the user's flexible adjustment of energy load requirements, but the distributed energy storage on the user side is large in number and dispersed, and cannot be effectively controlled by the substation area. Therefore, it needs to be aggregated and managed. The distributed energy storage aggregation model is as follows: E(t)=E(t-1)+P char (t-1)Δt-P dis (t-1)Δt, where D is the amount of distributed energy storage; P char (t), P dis (t) are the distributed energy storage charging and discharging aggregate power at time t; P char,d (t), P dis,d (t) are the charging and discharging power of the dth distributed energy storage at time t; η char,d , η dis,d are the charging and discharging efficiency of the dth distributed energy storage; E(t) is the aggregated energy of the distributed energy storage at time t; U char,d (t), U dis,d (t) are the charging and discharging operation state variables of the dth distributed energy storage at time t. Its value is 1 for operation, and vice versa for stop. To avoid the dth distributed energy storage from charging and discharging at the same time, the following conditions must be met: In actual operation, each distributed energy storage must meet the charging and discharging constraints and capacity constraints, namely: Where, T is 24h; P char,d,max , P dis,d,max are the maximum charging and discharging power of the dth distributed energy storage; E d,max is the maximum capacity of the dth distributed energy storage; To limit the number of charge and discharge cycles.

4. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 3 is characterized in that: The temperature control load aggregation model in step 3.21 is specifically as follows: a single temperature control load model is established using an equivalent thermal parameter modeling method, and the state equation of a single temperature control load is obtained as follows: Where C is the equivalent heat capacity; R is the equivalent thermal resistance; P(t) is the air conditioner operating power at time t; T out (t), T in (t) represent the outdoor and indoor temperatures at time t respectively; η is the air conditioning energy efficiency ratio; u(t) is the air conditioning operating state at time t; further rearrangement of the above formula yields the relationship between the air conditioning operating power and the indoor temperature change: T in (t+Δt)=T out (t)-ηu(t)P(t)R-[T out (t)-ηu(t)P(t)RT in (t)]e -ΔU(RC) , where Δt is the time step; assuming that the room temperature must be maintained at [T min ,T max ] range, and without considering the instantaneous power impact of air conditioner start and stop, according to the law of conservation of energy, the average cooling power of air conditioner can be equal to the average rate of heat loss, then the average power of a single temperature control load is: In the formula, is the average power of air conditioning load at time t; is the average room temperature at time t; T max 、T min are the upper and lower limits of the room temperature respectively; since the operating principles of air conditioners are the same, the total power consumption of the temperature control load group is more concerned for the substation. Assuming there are N air conditioners, the total power of the temperature control load group can be estimated based on the average power of a single temperature control load, that is: Considering that the average room temperature at time t can be replaced by the average set temperature of the air conditioner at time t, and that η and R of N air conditioners are independent of each other, the total power consumption of the temperature control load group at time t is: in, In the formula, R eq is the equivalent resistance of the polymerization model; η eq is the equivalent energy efficiency ratio of the aggregate model; T set,eq (t) is the equivalent set temperature of the air conditioner of the aggregation model at time t; T set,n (t) is the set temperature of the nth air conditioner at time t.

5. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 1 is characterized in that: The coordinated scheduling strategy of user-adjustable resources in the distribution area in step 4 specifically includes the following steps: Step 4.1: Objective function: ① User aggregated commercial electricity cost: The user aggregated commercial electricity cost is the difference between the revenue from selling electricity to the grid and the cost of purchasing electricity, and the expression is as follows: In the formula, C p Aggregate commercial electricity costs for users; buy (t), P sell (t) are the electric power that the user aggregator needs to purchase and sell at time t, which is the difference between the output of new energy, the charging and discharging power of energy storage and the power load. When the output of new energy and the discharge power of energy storage do not meet the load demand, it is necessary to purchase electric energy from the power grid. When the output of new energy meets the charging and load demand of energy storage and there is a surplus, it sells electric energy to the power grid; c fs (t), c sw (t) are the grid time-of-use electricity price and on-grid electricity price at time t respectively; ② User aggregated commercial energy comfort: User aggregated commercial energy comfort mainly considers the comfort of using the temperature control load, i.e., the air conditioner, and tries to meet the user's setting requirements for the room temperature. The evaluation criteria of temperature comfort are related to the room temperature and the upper and lower limits of the optimum temperature. Within the range of the user's set temperature, the greater the deviation percentage between the room temperature and the optimum temperature, the lower the user's energy comfort. The expression is as follows: Where Ω(t) is the percentage of room temperature deviation from the optimum temperature at time t; T in (t) is the indoor temperature; T fit,max 、T fit,min are the upper and lower limits of the optimum temperature respectively; k(t) is the air conditioning operation state variable, and its value is 1, indicating that the air conditioning is running, otherwise it means that the air conditioning is stopped; F is the energy comfort index; In summary, the user aggregator objective function is: minM=λ1C p +λ2F, where λ1 and λ2 are linear weights; Step 4.2: Power supply reliability constraints: Considering that the balance of power supply and consumption of users in the distribution station area is an important prerequisite for saving users' electricity costs and improving energy comfort, the system should meet the load power, new energy output, energy storage device charging and discharging power, and user aggregator and grid interaction power balance constraints in each period, which can be expressed as: P load,fix (t)+P load,con (t) = P p,w (t)+P grid (t)+P dis (t), where P load,fix (t) is the unadjustable load power at time t; P load,con (t) is the adjustable load power at time t, i.e., the charging power of temperature-controlled load electric vehicles and distributed energy storage; P p,w (t) is the output of new energy at time t; P grid (t) is the interaction power between the user aggregator and the power grid at time t. When the output of new energy and the discharge power of energy storage do not meet the load demand, it is a positive value, otherwise it is a negative value; P dis (t) is the distributed energy storage discharge power at time t, which satisfies the constraint that the distributed energy storage is not charged and discharged simultaneously; Step 4.3: Coordinate scheduling strategies; Step 4.4: Solve the model: The adjustable resource scheduling model of the substation requires historical meteorological data and electricity consumption data to predict the wind power, photovoltaic output and unadjustable resource energy power of the next day. It is necessary to generate the optimal solution of the objective function based on the boundary conditions of the feasible solution. Therefore, the differential evolution algorithm is used to solve the model, and the optimal solution is found through three processes of mutation, crossover and selection.

6. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 5 is characterized in that: The coordination scheduling strategy in step 4.3 specifically includes the following steps: Step 4.31: When the output of renewable energy meets the load demand, the energy storage is charged first. When the energy storage SOC reaches the upper limit, if there is any surplus, it is sold to the grid for profit; Step 4.32: When the output of renewable energy cannot meet the load demand, it is divided into the following three situations: when the TOU electricity price is at a valley value, the user side purchases electricity from the grid to make up the load difference, and at the same time purchases electricity to charge the energy storage until the SOC upper limit; when the TOU electricity price is at a flat value, the user side purchases electricity from the grid to make up the load difference, and the energy storage does not work; when the TOU electricity price is at a peak value, the energy storage discharge is used first to meet the load demand. When the SOC reaches the minimum value and is still in short supply, electricity is purchased from the grid again.

7. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 1 is characterized in that: The multi-time scale flexible dispatching method for low-carbon areas in step 5 adopts a multi-time scale joint optimization dispatching strategy for the power system under the low-carbon background, which specifically includes the following steps: Step 5.1: Weekly scheduling model; Step 5.2: Day-ahead dispatch model: The objective function of day-ahead dispatch is to minimize the operating cost of the entire system, including fuel cost, generator startup cost, spinning reserve cost, regulating reserve cost, and wind curtailment cost. Considering that the state of large units has been determined, in the day-ahead dispatch model, the state variables of the generator units only refer to small units. In the formula, m represents the number of the small generator set; M∈I; Step 5.3: Intraday dispatch model: The objective function of intraday dispatch is to minimize the operating cost of the entire system, including fuel cost, regulation reserve cost and wind curtailment cost.

8. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 7 is characterized in that: The pre-week dispatch model in step 5.1 is specifically as follows: the objective function of the pre-week dispatch model is to minimize the system operation cost, including fuel cost, generator start-up cost, spinning reserve cost, regulating reserve cost and wind curtailment cost. Considering the characteristics of small capacity, dispersion and volatility of demand-side response resources on the user side, load aggregators are used to participate in the optimal dispatch of the power system. Gi is the fuel cost of generator set i, which can be expressed by formula (2); S Si is the startup cost of generator set i, which can be expressed by formula (3); the spinning reserve can be provided by traditional generator sets or loads, and the cost of spinning reserve is It can be expressed by formula (4); the cost of frequency regulation is It can be expressed by formula (5); the frequency regulation reserve is provided by traditional generators, and the cost is It can be expressed by formula (6); the wind abandonment cost is S i wind , can be expressed by formula (7), S Gi [P Gi (t)]=a i +b i P Gi (t)+c i P Gi (t) 2 (2), S Si =S i α i (t)[1-α i (t-1)], In the formula, a i , b i 、c i is a parameter of the operating cost of generator i.

9. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 8 is characterized in that: The constraints of the pre-scheduling model in step 5.1 are: ① Node power balance: ②Line flow constraints: -F max ≤P ij (t)≤F max ,t=1,2,...,T(9);③Generator output constraint: ④ Reserve demand constraints: ⑤ Reserve usage: ⑥Minimum startup time constraint: ⑦Demand-side response constraints:

10. The method for evaluating the carbon neutrality potential of low-carbon areas based on big data analysis according to claim 1 is characterized in that: The quantitative evaluation indicators of the carbon neutrality potential of the substation in step 6 are specifically as follows: from the user's perspective, the population size, income level, living area, low-carbon attitude and energy usage habits indicators are selected to quantitatively evaluate the carbon neutrality potential of the substation.

Images