Demand side response resource aggregation and regulation potential evaluation method and system

Abstract

The invention discloses a demand side response resource aggregation and regulation potential evaluation method and system, and the method comprises the steps: building a temperature control load and an electric vehicle monomer operation physical model based on the operation characteristics of a monomer load; combining data driving to respectively establish a temperature control load aggregation model and an electric vehicle aggregation model, and establishing an energy storage aggregation model according to an energy storage equipment performance state; respectively establishing adjustable potential evaluation models of the temperature control load, the electric vehicle and the energy storage by combining the elastic load characteristics of the three types of demand side resources of the temperature control load, the electric vehicle and the energy storage and the corresponding aggregation models; and inputting elastic load related data of various demand side resources in the target area into the corresponding aggregation models and the adjustable potential evaluation models for solving to obtain aggregation power and adjustable potential evaluation results of the various demand side resources in the target area. According to the invention, maximum utilization and rapid adjustment of demand side resources can be realized so as to guarantee safe and stable operation of a power grid.

Description

Technical Field

[0001] The present invention relates to the field of power system and demand side management technology, and in particular to a method and system for evaluating demand side response resource aggregation and regulation potential. Background Art

[0002] With the continuous grid connection of clean energy with volatility and randomness and the continuous increase in load, the grid is under increasing pressure to absorb and ensure supply, and the system's flexible adjustment capability needs to be improved urgently, and the safe operation of the grid faces challenges. Promoting the participation of massive load-side flexible resources in the interactive regulation of the grid is an important means to improve the flexibility of the grid and an effective guarantee for the safe and stable operation of the grid.

[0003] Demand-side response can achieve the balance of supply and demand of the power system by flexibly adjusting the user load. Therefore, the aggregation and regulation potential evaluation of demand-side response resources are crucial in power system scheduling. However, there are still many challenges in the process of resource aggregation and regulation potential evaluation. Resource aggregation and regulation potential evaluation methods mainly include load curve analysis, user behavior modeling, equipment physical characteristics modeling, machine learning and data-driven, economic incentive mechanism analysis, etc. Although these methods have certain applicability in specific scenarios, there are still many technical challenges; for example, the load curve analysis method is difficult to dynamically adapt to changes in user behavior, and user behavior modeling is constrained by data loss and behavior complexity; equipment physical characteristics modeling faces the problems of insufficient diversity and real-time performance; machine learning methods can tap the potential of data but have low transparency and are easily affected by data bias; and economic incentive mechanism analysis is highly dependent on model parameters and it is difficult to take into account both short-term and long-term effects. In order to deal with these problems, it is urgent to explore directions such as data fusion, real-time optimization, model transparency improvement and privacy protection, and build a multi-dimensional and efficient demand-side response potential evaluation system. Summary of the invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method and system for demand-side response resource aggregation and regulation potential assessment. According to the operating characteristics of elastic load resources such as temperature-controlled loads, electric vehicles and energy storage, an aggregation model of the three resources of temperature-controlled loads, electric vehicles and energy storage is established on the basis of establishing a single load operating characteristic model; and based on the study of the load characteristics of elastic load resources, an elastic load resource adjustable potential assessment model is proposed, which deeply mines the resources on the power demand side, expands the regulation capability of the existing source-grid-load system, helps to maximize the utilization and rapid regulation of elastic load resources, and provides guidance for the scheduling strategy and scheme specification of elastic loads.

[0005] The present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides a method for aggregating demand-side response resources and evaluating regulation potential, the method comprising:

[0007] Step 1: Based on the single load operation characteristics, a single operation physical model of the temperature control load and the electric vehicle is established;

[0008] Step 2: Based on the operation models of each monomer, the aggregation models of the temperature control load and the electric vehicle are established respectively in combination with data drive, and the aggregation model of energy storage is established according to the performance status of the energy storage equipment;

[0009] Step 3: Based on the elastic load characteristics of the three types of demand-side resources, namely, the temperature control load, electric vehicles and energy storage, and their corresponding aggregation models, adjustable potential evaluation models of the temperature control load, electric vehicles and energy storage are established respectively;

[0010] Step 4: Input the elastic load-related data of various demand-side resources in the target area into the corresponding aggregation model and adjustable potential evaluation model for solution, and obtain the aggregated power and adjustable potential evaluation results of various demand-side resources in the target area.

[0011] Optionally, in step 1, the expression of the single-unit operation physical model of the temperature control load established is as follows:

[0012]

[0013] Where P AC (t) is the operating power of the single air conditioner at time t; wherein the temperature control load is the air conditioning load; is the indoor temperature at time t, represents the outdoor temperature at time t, R is the equivalent thermal resistance, and η is the air conditioning energy efficiency ratio; where, R and η satisfy the following air conditioning thermal equivalence relationship:

[0014]

[0015] Where Δt is the time step, e is a natural constant, is the switch status of the air conditioning compressor at time t.

[0016] Optionally, the air conditioning compressor switch state at time t The calculation formula is as follows:

[0017]

[0018] In the formula, is the compressor switch state at time t, Δt is the time step, is the compressor switch state at time t-Δt; T room is the indoor temperature at time t, T setis the air conditioner set temperature; θ is the air conditioner temperature detection threshold.

[0019] Optionally, in step 1, the step of establishing a single-unit operation physical model of the electric vehicle includes:

[0020] By collecting historical data on the use of electric vehicles by users in the target area, it is fitted that the daily mileage of electric vehicles satisfies the logarithmic normal distribution f(s) and the starting charging time of electric vehicles satisfies the normal distribution f(t b ), whose expressions are as follows:

[0021]

[0022] Where s is the daily driving distance of the electric vehicle, μ s is the average daily mileage of electric vehicles, σ s is the standard deviation of the probability distribution of daily mileage of electric vehicles; t b is the initial charging time of the electric vehicle, μ b is the average value of the initial charging time of the electric vehicle, σ b is the standard deviation of the initial charging time of the electric vehicle;

[0023] According to the initial state of charge of the electric vehicle, the charging time t of the electric vehicle is obtained. in The expression is as follows:

[0024]

[0025] In the formula, C EV is the battery capacity of electric vehicles, P EV is the charging power of the electric vehicle, k is the charging efficiency of the electric vehicle, SOC be is the initial state of charge of the electric vehicle, s is the daily driving distance of the electric vehicle, and s max The maximum driving range of an electric vehicle after a full charge.

[0026] Optionally, in step 2, the expression of the aggregation model of the temperature control load established is as follows:

[0027]

[0028]

[0029] Where P AC_all (t) is the aggregate power of the temperature control load, N is the total number of air conditioners turned on, represents the outdoor temperature at time t, is the average air conditioning set temperature, R i is the equivalent thermal resistance of the i-th air conditioner, η i is the air conditioning energy efficiency ratio of the i-th air conditioner, Req is the equivalent thermal resistance aggregate value, η eq is the aggregate value of air conditioner energy efficiency ratio.

[0030] Optionally, the three parameters of equivalent thermal resistance, air conditioner energy efficiency ratio and total number of air conditioners N in the aggregation model of the temperature control load are obtained by adopting a model based on parameter identification and data-driven fusion, combined with the measured total load curve of the air conditioner cluster, and using a particle swarm algorithm with dynamic parameters to identify the parameters of the thermal parameter model, wherein the parameter objective function is:

[0031]

[0032] Where N is the total number of air conditioners in the on state, R i is the equivalent thermal resistance of the i-th air conditioner, η i is the air conditioning energy efficiency ratio of the i-th air conditioner, i=1,2…N; P AC (t) is the actual operating power of the air conditioning cluster, P * (t) is the power calculated during the iteration of the particle swarm algorithm; a particle swarm optimization algorithm with dynamic parameters is used to find the optimal combination of model parameters, and the output parameters when the objective function is minimized are used as the final values ​​of the corresponding parameters in the aggregation model of the temperature control load.

[0033] Optionally, in step 2, the expression of the aggregation model of electric vehicles established is as follows:

[0034]

[0035] Where P EV_all (t) is the aggregate power of the total charging of electric vehicles in period t, is the charging power of the mth electric vehicle in time period t, M is the total number of electric vehicles; After obtaining the initial state of charge, initial charging time and charging duration through the single-unit operation physical model of the electric vehicle, the charging load curve of the electric vehicle m is calculated, and the power corresponding to time t in the charging load curve is taken as

[0036] Optionally, in step 2, the expression of the energy storage aggregation model established is as follows:

[0037]

[0038] Where P S_all (t) represents the aggregate power of the total energy storage equipment, L represents the total number of energy storage equipment, P l (t) is the operating power of the lth energy storage device in time period t.

[0039] Optionally, in step 3, the expression of the adjustable potential evaluation model of the temperature control load is as follows:

[0040]

[0041] Where P AC_all,tr (t) represents the adjustable potential value of the temperature control load in period t, The average set temperature before regulation, To set the average temperature after regulation, represents the outdoor temperature at time t, t co To control the time, C eq is the aggregate value of the equivalent heat capacity of all air conditioners in the turned-on state, R eq is the equivalent thermal resistance aggregate value, η eq is the aggregate value of air conditioner energy efficiency ratio.

[0042] Optionally, in step 3, the expression of the adjustable potential evaluation model of the electric vehicle is as follows:

[0043]

[0044] Where P EV_all,up (t) represents the adjustable potential value of the electric vehicle in period t, is the potential increase in charging power of the mth electric vehicle in period t, is the maximum charging power of the mth electric vehicle, is the charging power used by the mth electric vehicle before the regulation begins.

[0045] Optionally, in step 3, the expression of the adjustable potential evaluation model of the energy storage is as follows:

[0046]

[0047] Where P S_all,up (t) and P S_all,down (t) are the upward and downward adjustment potentials of the energy storage equipment cluster operating power, and They respectively represent the upper and lower limits of the aggregated power of the energy storage device.

[0048] In a second aspect, the present invention provides a demand-side response resource aggregation and regulation potential assessment system, which runs the steps of any method described in the first aspect of the present invention, and the system includes:

[0049] A single-unit model building unit is used to build a single-unit operation physical model of the temperature control load and the electric vehicle based on the single-unit load operation characteristics;

[0050] An aggregation model building unit, used to establish aggregation models of temperature control load and electric vehicle based on the monomer operation models and in combination with data drive, and to establish an aggregation model of energy storage according to the performance status of energy storage equipment;

[0051] An evaluation model building unit, for building adjustable potential evaluation models for the temperature control load, electric vehicle and energy storage, respectively, by combining the elastic load characteristics of the three types of demand-side resources, namely, the temperature control load, electric vehicle and energy storage, and their corresponding aggregation models;

[0052] The adjustable potential assessment unit is used to input the elastic load-related data of various demand-side resources in the target area into the corresponding aggregation model and adjustable potential assessment model for solution, so as to obtain the aggregated power and adjustable potential assessment results of various demand-side resources in the target area.

[0053] In a third aspect, the present invention provides a terminal, including a processor and a storage medium;

[0054] The storage medium is used to store instructions;

[0055] The processor is used to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0057] The beneficial effects of the present invention are as follows:

[0058] The present invention establishes an operating characteristic model of a single load by deeply analyzing the operating characteristics of load resources such as temperature-controlled loads, electric vehicles and energy storage, and fully considers the typical behaviors of various loads, such as the charging / discharging mode of electric vehicles, the adjustment range of temperature-controlled loads, etc., which can more accurately calculate the behaviors and actual power of various loads under different conditions; on this basis, an aggregation model of three resources, temperature-controlled loads, electric vehicles and energy storage, is established, and data analysis is combined in the model, and the model parameters are continuously modified through real data to achieve more accurate calculations; furthermore, an evaluation model for the adjustable potential of various elastic load resources on the demand side is proposed, which deeply explores the resources on the power demand side, expands the adjustment capacity of the existing source-grid-load system, helps to maximize the utilization and rapid adjustment of elastic load resources, and provides guidance for the dispatching strategy and scheme designation of elastic loads, thereby achieving safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of the method for evaluating the demand-side response resource aggregation and regulation potential in the present invention;

[0060] Figure 2 It is a schematic diagram of the equivalent thermal parameter model of air conditioning load in the present invention;

[0061] Figure 3 It is a schematic diagram of evaluating the aggregate power and regulation potential of electric vehicles in a study area on a certain day in the simulation experiment of the present invention;

[0062] Figure 4 This is a structural principle block diagram of the demand-side response resource aggregation and regulation potential evaluation system in the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0064] Embodiment 1:

[0065] Reference Figure 1 The embodiment of the present invention provides a method for evaluating demand-side response resource aggregation and regulation potential, which specifically includes the following steps:

[0066] Step 1: Based on the single load operation characteristics, a single operation physical model of the temperature control load and the electric vehicle is established;

[0067] Step 2: Based on the operation models of each monomer, the aggregation models of the temperature control load and the electric vehicle are established respectively in combination with data drive, and the aggregation model of energy storage is established according to the performance status of the energy storage equipment;

[0068] Step 3: Based on the elastic load characteristics of the three types of demand-side resources, namely, the temperature control load, electric vehicles and energy storage, and their corresponding aggregation models, adjustable potential evaluation models of the temperature control load, electric vehicles and energy storage are established respectively;

[0069] Step 4: Input the elastic load-related data of various demand-side resources in the target area into the corresponding aggregation model and adjustable potential evaluation model for solution, and obtain the aggregated power and adjustable potential evaluation results of various demand-side resources in the target area.

[0070] As an embodiment of the present invention, the construction process of the physical model of each monomer operation in step 1 is as follows:

[0071] (101) The switch of the compressor inside the air conditioner determines the rise and fall of the indoor temperature. Its operating characteristic model is:

[0072]

[0073] In the formula, is the compressor switch state at time t, Δt is the time step, is the compressor switch state at time t-Δt; T room is the indoor temperature at time t, T set is the air conditioner set temperature; θ is the air conditioner temperature detection threshold.

[0074] If the air-conditioning compressor is idealized, the air-conditioning power can be considered constant and recorded as At this time, the air conditioner operating power is:

[0075]

[0076] In order to further explain the relationship between indoor temperature change and air conditioning operating power, an air conditioning thermal equivalent model is established:

[0077]

[0078] Where Δt is the time step, e is a natural constant, is the indoor temperature at time t; represents the outdoor temperature at time t, R is the equivalent thermal resistance, C is the equivalent heat capacity, and η is the air conditioning energy efficiency ratio. After simplification, the expression of the single-unit operation physical model of the temperature control load is as follows:

[0079]

[0080] Where P AC (t) is the operating power of the single air conditioner at time t; wherein the temperature control load is the air conditioning load; is the indoor temperature at time t, represents the outdoor temperature at time t, R is the equivalent thermal resistance, and η is the air conditioning energy efficiency ratio;

[0081] (102) The behavior pattern of electric vehicles is closely related to the daily behavior habits of car owners. By collecting historical data on the use of electric vehicles by users in the target area, it is fitted that the daily mileage of electric vehicles satisfies the log-normal distribution f(s) and the starting charging time of electric vehicles satisfies the normal distribution f(t b ), whose expressions are as follows:

[0082]

[0083] Where s is the daily driving distance of the electric vehicle, μ s is the average daily mileage of electric vehicles, σ s is the standard deviation of the probability distribution of daily mileage of electric vehicles; t bis the initial charging time of the electric vehicle, μ b is the average value of the initial charging time of the electric vehicle, σ b is the standard deviation of the initial charging time of the electric vehicle;

[0084] The starting state of charge of the electric vehicle is calculated by the following formula:

[0085]

[0086] In the formula, SOC be is the initial state of charge of the electric vehicle, s is the daily driving distance of the electric vehicle, and s max The maximum driving range of an electric vehicle after fully charging;

[0087] According to the initial state of charge of the electric vehicle, the charging time t of the electric vehicle can be calculated in :

[0088]

[0089] In the formula, C EV is the battery capacity of electric vehicles, P EV is the charging power of the electric vehicle, and k is the charging efficiency of the electric vehicle.

[0090] In this embodiment, the specific process of step 2 to establish the aggregation model of temperature control load, electric vehicle and energy storage is as follows:

[0091] (201) Air conditioning load aggregation power: When the air conditioning cluster reaches the set temperature, multiple air conditioners are aggregated into one air conditioner, and the aggregate satisfies:

[0092]

[0093] Where P AC_all (t) is the temperature control load aggregate power, is the operating power of the i-th unit air conditioner, N is the number of air conditioners, and the unit operation formula is substituted by the air conditioner set temperature to approximately replace the indoor temperature to obtain:

[0094]

[0095] In the formula, represents the outdoor temperature at time t, Set the temperature for the i-th air conditioner, R i is the equivalent thermal resistance of the i-th air conditioner, η i is the energy efficiency ratio of the i-th air conditioner, and N is the number of air conditioners;

[0096] The expression of the aggregation model of the temperature control load is further simplified as follows:

[0097]

[0098]

[0099] Where P AC_all (t) is the aggregate power of the temperature control load, N is the total number of air conditioners turned on, represents the outdoor temperature at time t, is the average air conditioning set temperature, R i is the equivalent thermal resistance of the i-th air conditioner, η i is the air conditioning energy efficiency ratio of the i-th air conditioner, R eq is the equivalent thermal resistance aggregate value, η eq is the aggregate value of air conditioner energy efficiency ratio.

[0100] It is further explained that under the traditional model drive, parameters are easily solidified and lead to deviations, and there will be a lack of physical concepts in the single data drive; therefore, the present invention adopts a method of integrating the model and data drive based on parameter identification; the thermodynamic model parameters of the building to which the air conditioner belongs will be affected by many factors, such as daily ambient temperature, humidity, air permeability, indoor personnel flow, etc.; based on the measured total load curve of the air conditioner cluster, the particle swarm algorithm containing dynamic parameters is used to carry out parameter identification of the thermal parameter model. The parameters to be identified include the equivalent thermal resistance of the room, the equivalent thermal capacity of the room and the total number of air conditioners turned on. The thermal parameter model constructed by identifying the optimal parameter combination can realize real-time tracking of the load power of the air conditioner cluster. The target optimization criterion is that the error between the simulated load of the air conditioner cluster in one day and the load curve measured by the load aggregator is minimized; among which, the parameter objective function is:

[0101]

[0102] Where N is the total number of air conditioners in the on state, R i is the equivalent thermal resistance of the i-th air conditioner, η i is the air conditioning energy efficiency ratio of the i-th air conditioner, i=1,2…N; P AC (t) is the actual operating power of the air conditioning cluster, P * (t) is the power calculated during the iteration of the particle swarm algorithm; a particle swarm optimization algorithm with dynamic parameters is used to find the optimal combination of model parameters, and the output parameters when the objective function is minimized are used as the final values ​​of the corresponding parameters in the aggregation model of the temperature control load, thereby improving the accuracy of the air conditioning load aggregation model.

[0103] (202) Aggregation model of electric vehicles:

[0104]

[0105] Where P EV_all (t) is the aggregate power of the total charging of electric vehicles in period t, is the charging power of the mth electric vehicle in time period t, M is the total number of electric vehicles; After obtaining the initial state of charge, initial charging time and charging duration through the single-unit operation physical model of the electric vehicle, the charging load curve of the electric vehicle m is calculated, and the power corresponding to time t in the charging load curve is taken as

[0106] Furthermore, the electric vehicle load aggregation calculation process is specifically as follows: using the electric vehicle behavior habits in 102, M electric vehicles are initialized, and after obtaining their initial state of charge, initial charging time and other data, the charging time is calculated to generate a charging load curve for each vehicle, and the electric vehicle aggregation power curve is obtained after superposition.

[0107] (203) Aggregation model and aggregate power of energy storage and upper and lower limits:

[0108]

[0109] Where P S_all (t) represents the aggregate power of the total energy storage equipment, L represents the total number of energy storage equipment, P l (t) is the operating power of the lth energy storage device in time period t, and They represent the upper and lower limits of the aggregated power of the energy storage device, and They represent the upper and lower limits of the operating power of the first energy storage device, and Respectively represent the upper and lower limits of the aggregated capacity of the energy storage device, and They respectively represent the upper and lower limits of the capacity of the lth energy storage device.

[0110] As an embodiment of the present invention, in step 3, the specific process of establishing the adjustable potential evaluation model of the demand-side temperature control load, electric vehicle and energy storage is as follows:

[0111] (301) Evaluation of adjustable potential of temperature control load:

[0112] Assuming the external temperature remains unchanged, the average set temperature before regulation is The average set temperature after adjustment is The control time is t co During the regulation period, assuming that the air conditioner is in a stable state, its aggregate power is recorded as P AC_all,co (t), the control time is obtained as follows:

[0113]

[0114] In the formula, C i is the equivalent heat capacity of the i-th air conditioner, R eq is the equivalent thermal resistance aggregate value, η eq is the aggregate value of air conditioning energy efficiency ratio, C eq is the aggregate value of the equivalent heat capacity of all air conditioners in the turned-on state,

[0115] Combining the above formula with the formula in (201) yields:

[0116]

[0117] If the power before regulation is P AC_all,before (t), then the polymer regulatory potential P AC_all,tr (t) can be calculated using the following formula:

[0118] P AC_all,tr (t) = P AC_all,before (t)-P AC_all,co (t)

[0119] Further substituting the above formula into the adjustable potential evaluation model of the temperature control load, the expression is as follows:

[0120]

[0121] Where P AC_all,tr (t) represents the adjustable potential value of the temperature control load in period t, The average set temperature before regulation, is the average set temperature after regulation, T t out represents the outdoor temperature at time t.

[0122] (302) Evaluation of the scalability potential of electric vehicles:

[0123] The maximum load increase obtained by increasing the charging power of electric vehicles to the maximum during the regulation period is called the electric vehicle's adjustable potential. The calculation formula for the power adjustment potential of each vehicle is as follows:

[0124]

[0125] In the formula, is the potential increase in charging power of the mth electric vehicle in period t, The maximum charging power of the mth electric vehicle, is the charging power used by the mth electric vehicle before the regulation begins.

[0126] According to the above-mentioned upward adjustment potential of single electric vehicles, the expression of the adjustable potential evaluation model of electric vehicles is as follows:

[0127]

[0128] Where P EV_all,up (t) represents the adjustable potential value of electric vehicles in period t, and M is the total number of electric vehicles.

[0129] (303) Assessment of the adjustable potential of energy storage:

[0130] For energy storage, there are many types of responsive devices, including lead-acid energy storage, dual-liquid flow energy storage, etc. These devices play an important role in demand response plans and can respond to changes in grid demand by adjusting usage time or power; the expression of the adjustable potential evaluation model of energy storage is as follows:

[0131]

[0132] Where P S_all,up (t) and P S_all,down (t) are the upward and downward adjustment potentials of the energy storage equipment cluster operating power, and They respectively represent the upper and lower limits of the aggregated power of the energy storage device.

[0133] Finally, in step 4 of this embodiment, the elastic load-related data of various demand-side resources in the target area (such as the number of single loads, power, user habits, etc.) are respectively input into the corresponding aggregation model and adjustable potential evaluation model, and each model is solved using GAMS software to obtain the aggregated power and adjustable potential evaluation results of various demand-side resources in the target area.

[0134] The effectiveness of the demand-side response resource aggregation and regulation potential evaluation method provided by the present invention is verified by a specific example below.

[0135] The air conditioning load data collected in Jiangning District of Nanjing City is used to simulate and analyze the air conditioning load aggregation model. The distribution range of the air conditioning load parameters is shown in Table 1; the air conditioning load equivalent thermal parameter model is shown in Table 1. Figure 2 As shown; assuming that the load parameters of 10,000 air conditioners obey random distribution within the interval shown, each air conditioner has been operating stably within its set temperature range before the interaction is implemented.

[0136] According to the air conditioning load aggregation model, the air conditioning temperature setting range is [24,26] degrees Celsius. The power aggregation values ​​of 10,000 air conditioners at different outside temperatures are shown in Table 2. The aggregation power is the largest at 32°C and the smallest at 28°C. The aggregation power of the air conditioning load increases with the increase of the outdoor temperature. This is because when the outdoor temperature is high, the temperature difference between indoor and outdoor is large, and the rate of heat transfer from outdoor to indoor is fast. In order to maintain the indoor temperature within the set range, the cooling capacity of the air conditioning load needs to be increased, and its operating power will also increase accordingly.

[0137] The electric vehicle data of Jiangning District, Nanjing is used to simulate and analyze the electric vehicle adjustable potential evaluation model. Electric vehicles are divided into three categories: the first category is private cars charged at night, the second category is online ride-hailing cars charged at night, and the third category is office workers charged during the day. The sampling parameters of the three types of electric vehicles are shown in Table 3. N(μ,σ 2 ) represents a normal distribution with a mathematical expectation of μ and a standard deviation of σ; U(a,b) represents a uniform distribution on the interval [a,b], S B is the rated capacity of the electric vehicle battery. The metering time interval is set to 1h, the battery capacity is 30kWh, the maximum charging and discharging power of the electric vehicle is 30kW, the maximum battery storage capacity is 0.9, the minimum storage capacity is 0.15, the charging and discharging efficiency of the electric vehicle is 90%, and the discharge compensation coefficient is 1. According to the electric vehicle power aggregation model and the adjustable potential evaluation model, it is calculated that Figure 3 Aggregate power and adjustable potential of electric vehicle load in Jiangning District, Nanjing City in one day.

[0138] Table 1 Air conditioning parameters

[0139] parameter R(℃ / kW) C(KW·h / ℃) Pc(KW) Tset(℃) δ η GT1 1.5-2.5 1.5-2.5 16-20 24-26 1 2.6-3

[0140] Table 2 Aggregate values ​​of air conditioning load at different temperatures

[0141] Outside temperature / ℃ 28 30 32 Aggregate power / MW 1.89 3.21 4.47

[0142] Table 3 Electric vehicle parameters

[0143]

[0144] The beneficial effects of the present invention are as follows:

[0145] The present invention establishes an operating characteristic model of a single load by deeply analyzing the operating characteristics of load resources such as temperature-controlled loads, electric vehicles and energy storage, and fully considers the typical behaviors of various loads, such as the charging / discharging mode of electric vehicles, the adjustment range of temperature-controlled loads, etc., which can more accurately calculate the behaviors and actual power of various loads under different conditions; on this basis, an aggregation model of three resources, temperature-controlled loads, electric vehicles and energy storage, is established, and data analysis is combined in the model, and the model parameters are continuously modified through real data to achieve more accurate calculations; furthermore, an evaluation model for the adjustable potential of various elastic load resources on the demand side is proposed, which deeply explores the resources on the power demand side, expands the adjustment capacity of the existing source-grid-load system, helps to maximize the utilization and rapid adjustment of elastic load resources, and provides guidance for the dispatching strategy and scheme designation of elastic loads, thereby achieving safe and stable operation of the power grid.

[0146] Embodiment 2:

[0147] like Figure 4 As shown, the present invention provides a demand-side response resource aggregation and regulation potential evaluation system, which is used to implement the steps of the method in the above embodiment 1, and the system specifically includes:

[0148] A single-unit model building unit is used to build a single-unit operation physical model of the temperature control load and the electric vehicle based on the single-unit load operation characteristics;

[0149] An aggregation model building unit, used to establish aggregation models of temperature control load and electric vehicle based on the monomer operation models and in combination with data drive, and to establish an aggregation model of energy storage according to the performance status of energy storage equipment;

[0150] An evaluation model building unit, for building adjustable potential evaluation models for the temperature control load, electric vehicle and energy storage, respectively, by combining the elastic load characteristics of the three types of demand-side resources, namely, the temperature control load, electric vehicle and energy storage, and their corresponding aggregation models;

[0151] The adjustable potential assessment unit is used to input the elastic load-related data of various demand-side resources in the target area into the corresponding aggregation model and adjustable potential assessment model for solution, so as to obtain the aggregated power and adjustable potential assessment results of various demand-side resources in the target area.

[0152] The demand-side response resource aggregation and regulation potential assessment system provided in the embodiment of the present invention and the demand-side response resource aggregation and regulation potential assessment method provided in Example 1 are based on the same technical concept, and can produce the beneficial effects described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0153] Embodiment three:

[0154] A terminal provided by an embodiment of the present invention includes a processor and a storage medium;

[0155] The storage medium is used to store instructions;

[0156] The processor is used to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0157] Embodiment 4:

[0158] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any method described in Embodiment 1 are implemented.

[0159] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0160] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0161] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0162] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating demand-side response resource aggregation and regulation potential, characterized in that: Methods include: Step 1: Based on the single load operation characteristics, a single operation physical model of the temperature control load and the electric vehicle is established; Step 2: Based on the operation models of each monomer, the aggregation models of the temperature control load and the electric vehicle are established respectively in combination with data drive, and the aggregation model of energy storage is established according to the performance status of the energy storage equipment; Step 3: Based on the elastic load characteristics of the three types of demand-side resources, namely, the temperature control load, electric vehicles and energy storage, and their corresponding aggregation models, adjustable potential evaluation models of the temperature control load, electric vehicles and energy storage are established respectively; Step 4: Input the elastic load-related data of various demand-side resources in the target area into the corresponding aggregation model and adjustable potential evaluation model for solution, and obtain the aggregated power and adjustable potential evaluation results of various demand-side resources in the target area.

2. The method for demand-side response resource aggregation and regulation potential assessment according to claim 1, characterized in that: In step 1, the expression of the single-unit operation physical model of the temperature control load established is as follows: Where P AC (t) is the operating power of the single air conditioner at time t; wherein the temperature control load is the air conditioning load; T t room is the indoor temperature at time t, T t out represents the outdoor temperature at time t, R is the equivalent thermal resistance, and η is the air conditioning energy efficiency ratio; where T t room 、T t out , R and η satisfy the following air conditioning thermal equivalence relationship: Where Δt is the time step, e is the natural constant, is the switch status of the air conditioning compressor at time t.

3. The method for evaluating demand-side response resource aggregation and regulation potential according to claim 2, characterized in that: The switch state of the air conditioning compressor at time t The calculation formula is as follows: In the formula, is the compressor switch state at time t, Δt is the time step, is the compressor switch state at time t-Δt; T room is the indoor temperature at time t, T set Set the temperature for the air conditioner; θ is the air conditioning temperature detection threshold.

4. The method for demand-side response resource aggregation and regulation potential assessment according to claim 1, characterized in that: In step 1, the steps of establishing a single-unit operation physical model of an electric vehicle include: By collecting historical data on the use of electric vehicles by users in the target area, it is fitted that the daily mileage of electric vehicles satisfies the logarithmic normal distribution f(s) and the starting charging time of electric vehicles satisfies the normal distribution f(t b ), whose expressions are as follows: Where s is the daily driving distance of the electric vehicle, μ s is the average daily mileage of electric vehicles, σ s is the standard deviation of the probability distribution of daily mileage of electric vehicles; t b is the initial charging time of the electric vehicle, μ b is the average value of the initial charging time of the electric vehicle, σ b is the standard deviation of the initial charging time of the electric vehicle; According to the initial state of charge of the electric vehicle, the charging time t of the electric vehicle is obtained. in The expression is as follows: In the formula, C EV is the battery capacity of electric vehicles, P EV is the charging power of the electric vehicle, k is the charging efficiency of the electric vehicle, SOC be is the initial state of charge of the electric vehicle, s is the daily driving distance of the electric vehicle, and s max The maximum driving range of an electric vehicle after a full charge.

5. The method for demand-side response resource aggregation and regulation potential assessment according to claim 2, characterized in that: In step 2, the expression of the aggregation model of the temperature control load established is as follows: Where P AC_all (t) is the aggregate power of the temperature control load, N is the total number of air conditioners turned on, T t out represents the outdoor temperature at time t, is the average air conditioning set temperature, R i is the equivalent thermal resistance of the i-th air conditioner, η i is the air conditioning energy efficiency ratio of the i-th air conditioner, R eq is the equivalent thermal resistance aggregate value, η eq is the aggregate value of air conditioner energy efficiency ratio.

6. The method for demand-side response resource aggregation and regulation potential assessment according to claim 5, characterized in that: The three parameters of equivalent thermal resistance, air conditioner energy efficiency ratio and total number of air conditioners N in the aggregation model of the temperature control load are obtained by adopting a model based on parameter identification and data-driven fusion, combined with the measured total load curve of the air conditioner cluster, and using a particle swarm algorithm with dynamic parameters to identify the parameters of the thermal parameter model, where the parameter objective function is: Where N is the total number of air conditioners in the on state, R i is the equivalent thermal resistance of the i-th air conditioner, η i is the air conditioning energy efficiency ratio of the i-th air conditioner, i=1,2…N; P AC (t) is the actual operating power of the air conditioning cluster, P * (t) is the power calculated during the iteration of the particle swarm algorithm; a particle swarm optimization algorithm with dynamic parameters is used to find the optimal combination of model parameters, and the output parameters when the objective function is minimized are used as the final values ​​of the corresponding parameters in the aggregation model of the temperature control load.

7. The method for demand-side response resource aggregation and regulation potential assessment according to claim 4, characterized in that: In step 2, the expression of the aggregation model of electric vehicles established is as follows: Where P EV_all (t) is the aggregate power of the total charging of electric vehicles in period t, is the charging power of the mth electric vehicle in time period t, M is the total number of electric vehicles; After obtaining the initial state of charge, initial charging time and charging duration through the single-unit operation physical model of the electric vehicle, the charging load curve of the electric vehicle m is calculated, and the power corresponding to time t in the charging load curve is taken as 8. The method for demand-side response resource aggregation and regulation potential assessment according to claim 1, characterized in that: In step 2, the expression of the established energy storage aggregation model is as follows: Where P S_all (t) represents the aggregate power of the total energy storage equipment, L represents the total number of energy storage equipment, P l (t) is the operating power of the lth energy storage device in time period t.

9. The method for evaluating demand-side response resource aggregation and regulation potential according to claim 5, characterized in that: In step 3, the expression of the adjustable potential evaluation model of the temperature control load is as follows: Where P AC_all,tr (t) represents the adjustable potential value of the temperature control load in period t, The average set temperature before regulation, is the average set temperature after regulation, T t out represents the outdoor temperature at time t, t co To control the time, C eq is the aggregate value of the equivalent heat capacity of all air conditioners in the turned-on state, R eq is the equivalent thermal resistance aggregate value, η eq is the aggregate value of air conditioner energy efficiency ratio.

10. The method for demand-side response resource aggregation and regulation potential assessment according to claim 7, characterized in that: In step 3, the expression of the adjustable potential evaluation model of the electric vehicle is as follows: Where P EV_all,up (t) represents the adjustable potential value of the electric vehicle in period t, is the potential increase in charging power of the mth electric vehicle in period t, is the maximum charging power of the mth electric vehicle, is the charging power used by the mth electric vehicle before the regulation begins.

11. The method for demand-side response resource aggregation and regulation potential assessment according to claim 8, characterized in that: In step 3, the expression of the adjustable potential evaluation model of the energy storage is as follows: Where P S_all,up (t) and P S_all,down (t) are the upward and downward adjustment potentials of the energy storage equipment cluster operating power, and They respectively represent the upper and lower limits of the aggregated power of the energy storage device.

12. A demand-side response resource aggregation and regulation potential assessment system, running the demand-side response resource aggregation and regulation potential assessment method as claimed in any one of claims 1 to 11, characterized in that: The system includes: A single-unit model building unit is used to build a single-unit operation physical model of the temperature control load and the electric vehicle based on the single-unit load operation characteristics; An aggregation model building unit, used to establish aggregation models of temperature control load and electric vehicle based on the monomer operation models and in combination with data drive, and to establish an aggregation model of energy storage according to the performance status of energy storage equipment; An evaluation model building unit, for building adjustable potential evaluation models for the temperature control load, electric vehicle and energy storage, respectively, by combining the elastic load characteristics of the three types of demand-side resources, namely, the temperature control load, electric vehicle and energy storage, and their corresponding aggregation models; The adjustable potential assessment unit is used to input the elastic load-related data of various demand-side resources in the target area into the corresponding aggregation model and adjustable potential assessment model for solution, so as to obtain the aggregated power and adjustable potential assessment results of various demand-side resources in the target area.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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