CN114862252A - Load-adjustable multi-layer aggregation scheduling potential analysis method, system, equipment and medium - Google Patents
Load-adjustable multi-layer aggregation scheduling potential analysis method, system, equipment and medium Download PDFInfo
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Abstract
The invention discloses a method, a system, equipment and a medium for analyzing the multi-layer aggregation scheduling potential of an adjustable load, wherein a plurality of adjustable loads are divided into three layers, namely an equipment layer, a user layer and an aggregation business layer; and meanwhile, considering the electricity price, an excitation mechanism and user wishes, calculating the adjustable potential layer by layer for the equipment layer, the user layer and the aggregation business layer, transmitting the constraint boundary layer by layer, and finally obtaining an aggregation scheduling potential evaluation curve of the multivariate adjustable load. According to the invention, each main body of the multi-element load resource can obtain regulation compensation and increase benefits by participating in regulation and control operation of the power grid, and in addition, the distributed new energy consumption level is improved through the multi-element load resource cooperative regulation and control, the carbon emission of power production is reduced, the achievement of a low-carbon target is facilitated, and multiple benefits are realized.
Description
Technical Field
The invention relates to the field of multivariate load regulation and control, in particular to a method, a system, equipment and a medium for analyzing the multi-layer aggregation scheduling potential of an adjustable load.
Background
With the continuous deepening of energy transformation, an electric power system enters a new era, the scale of an extra-high voltage alternating current-direct current hybrid power grid is rapidly expanded, high-permeability new energy is rapidly developed, novel load proportions such as a distributed power supply and energy storage are rapidly increased, a new generation electric power system which is characterized by wide interconnection, intelligent interaction, flexibility, safety and controllability is formed, and new requirements are provided for the supporting capability of a control technology. In addition to conventional power supply side regulation resources, new regulation resources, in particular distributed flexible regulation resources, have emerged in power systems, including: the users can not only use the end electric load, but also can realize the interaction with the dispatching mechanism through the load side management. Compared with a conventional power supply, the load side has large resource quantity and various types, and has dual roles of a producer and a consumer, and the running characteristics are complex and changeable. For example, the charging load of a large-scale electric automobile brings remarkable influence on an urban power grid, and the outstanding contradictions such as peak-valley difference, voltage deviation, local blockage and the like of a power system are increased; on the other hand, the distributed energy storage characteristic of the electric automobile provides abundant schedulable resources for power grid peak regulation, voltage regulation, new energy consumption and the like. Therefore, it is urgently needed to effectively incorporate multiple loads into the power grid adjustable resources to realize data communication so as to improve the optimal configuration capability of the power grid.
The following description of the related art is provided:
1) prior art 1: the patent application number: 201710425592.5
The method comprises the steps of calculating the power consumption of TCLs polymer according to a pre-established approximate polymerization model of the TCLs; calculating the reducible degree of the TCLs polymer according to the change rate of the electric power for the TCLs polymer, and determining the controllable degree and the acceptability of the TCLs polymer; and calculating expected values and distribution characteristics of the TCLs polymerization response potential according to the reducible degree, the controllable degree and the acceptability of the TCLs polymerization. The prior art 1 can effectively evaluate the TCLs polymerization response potential and the distribution characteristics thereof under a given demand response mechanism on the basis of temperature control load polymerization modeling, and lays a foundation for the participation of temperature control loads in power grid regulation and control operation or the formulation of a reasonable demand response policy.
2) Prior art 2: a thermal storage type electric heating virtual power plant day-ahead schedulable potential evaluation method and device are disclosed in the patent application number: 202011346151.4
The method comprises the steps of obtaining a demand response excitation time period and a subsidy price, obtaining user heat load data according to historical data, obtaining model parameters of heat accumulating type electric heating equipment according to equipment parameters, constructing a single heat accumulating type electric heating day-ahead optimized scheduling model, and determining single heat accumulating type electric heating output; according to the output of the single heat accumulating type electric heating, a scheduling potential evaluation model of the single heat accumulating type electric heating system is constructed, and scheduling power of the single heat accumulating type electric heating is calculated; and constructing a thermal storage type electric heating virtual power plant day-ahead scheduling potential evaluation model according to the thermal storage type electric heating scheduling power. This prior art 2 can respond to the price of electricity type excitation under the prerequisite of guarantee user's comfort level, and the better response potential of the demand of representation heat accumulation formula electric heating system.
3) Prior art 3: a method for evaluating the aggregation potential of load participation demand response is disclosed in the patent application No.: 201710834914.1
The method comprises the steps of providing a user comfort degree characterization index and calculating a comfort degree index value of each load based on input and output physical models of three types of household loads, namely an air conditioner, a water heater and an electric vehicle; considering the influences of factors such as load running characteristics, user comfort, user trip plans, demand response principles and the like, and establishing a load aggregation response model; it is proposed to characterize the aggregate response potential of this time period with the equivalent response power of the load population within this time period. This prior art 3 can effectively assess the aggregate response ability of intelligent load group to fully excavate the response potential of load when the urgent power shortage appears in the electric wire netting, alleviate the reserve pressure of generator primary and secondary frequency modulation.
According to the technical scheme, physical modeling is carried out on air conditioner load, electric heating load and household load of an air conditioner, a water heater and an electric automobile, and load adjustability potential is evaluated through single-type resource aggregation or household load group aggregation, on one hand, the air conditioner and the electric heating load are single, and the actual electricity utilization condition of residents cannot be accurately reflected by modeling and clustering of single loads; on the other hand, the household load considering the air conditioner, the water heater and the electric automobile occupies a small proportion to the user side load, the practicability is not strong, and the adjustable potential of residential load, commercial building and large industrial load needs to be fully considered.
Disclosure of Invention
The invention aims to provide a load-adjustable multi-layer aggregation scheduling potential analysis method, a system, equipment and a medium, so as to overcome the defects in the prior art, and the method enables all main bodies of multi-element load resources to participate in power grid regulation and control operation, can obtain regulation and compensation and increase benefits, and in addition, improves the distributed new energy consumption level through the multi-element load resource cooperative regulation and control, reduces the carbon emission of power production, is beneficial to promoting the achievement of a low-carbon target and realizes multiple wins.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for analyzing adjustable load multi-layer aggregation scheduling potential, the method comprising:
dividing the multi-element adjustable load into three layers of an equipment layer, a user layer and a polymer business layer;
and meanwhile, considering the electricity price, an excitation mechanism and user wishes, calculating the adjustable potential layer by layer for the equipment layer, the user layer and the aggregation business layer, transmitting the constraint boundary layer by layer, and finally obtaining an aggregation scheduling potential evaluation curve of the multivariate adjustable load.
Further, the adjustable potential calculated for the device layer is specifically: the adjustable potential calculation is carried out on single typical equipment of the equipment layer, wherein the single typical equipment comprises residential air conditioners, water heaters, electric automobiles, commercial central air conditioners and industrial production lines.
Further, the adjustable potential of the residential air conditioner is calculated as follows:
p AC,t ＝P AC ×S AC,t
in the formula, p AC,t For t time period air-conditioning actual power, P AC For rated power of air conditioner in refrigerating state, S AC,t For a period of time T, air-conditioning operating state AC,min Is the lowest room temperature setting, T AC,max Is the highest room temperature setting, T AC,t At room temperature for a period of T, T AC,t+1 、T AC,t Respectively representing the t +1 time interval and the t time interval room temperature, G t Denotes the outdoor and indoor heat exchange values during the period t,. DELTA.c denotes the indoor temperature coefficient, C AC Indicating the heat capacity of the air conditioner in a cooling state,
the value of the air conditioner acting on the change of the room temperature in the cooling state is shown, and delta t represents the time interval.
Further, the adjustable potential of the water heater is calculated as follows:
p WH,t ＝P WH ×S WH,t
in the formula, p WH,t For t period water heater actual power, P WH Is rated power of the water heater in a heating state S WH,t For the operating state of the water heater at a time T, T WH,s Is the set value of the maximum water temperature of the water heater, T WH,t The water temperature of the water heater is T time period T WH,t+1 、T WH,t Respectively represents the water temperature of the water heater in the T +1 time interval and the T time interval, T in Indicating the temperature of cold water injected into the water inlet of the water heater, flt indicating the hot water flow of the water heater in the period t, and V WH Representing the volume of the water heater, delta t representing the time interval, alpha representing the heating temperature coefficient of the water heater, P WH Is rated power of the water heater in a heating state S WH,t And xi represents a self-cooling temperature reduction value of hot water in the water heater in a unit time period at the normal room temperature.
Further, the adjustable potential of the electric vehicle is calculated as follows:
p EV,t ＝P EV ·S EV,t
in the formula, p EV,t Actual charging power of the electric automobile is t time period; p EV Rated charging power for the electric vehicle; s EV,t The charging state of the battery of the electric automobile is t time period; SOC 0 Representing the initial electric quantity of the battery of the electric automobile; SOC t The residual capacity of the battery of the electric automobile is t time period; SOC t+1 The residual capacity of the battery of the electric automobile is in a t +1 time period; SOC max The maximum value of the electric quantity of the battery when the electric automobile reaches a full charge state is represented; eta is charging efficiency; c batt The total capacity of the battery of the electric automobile; Δ t represents a time period interval; b is EV A limit value of the battery capacity of the electric vehicle; l is the travel distance of the electric automobile; e EV The running efficiency is obtained.
Further, the tunable potential of the commercial central air conditioner is calculated as follows:
in the formula, T in (t) room temperature at time t;
and
respectively representing the room temperature at the moment t and the initial moment of the central air conditioning cooling period;
and
respectively representing the room temperature at the moment t and the initial moment of the shutdown period of the central air conditioner; q p A rated refrigerating capacity of the refrigerating machine, a temperature variation parameter of the refrigerated water, and a parameter theta 1 、θ 2 、θ 3 、θ 4 The construction parameters are determined as follows:
in the formula, ρ a Represents the air density; c. C a Represents the specific heat capacity of air; v k Represents the indoor volume; k is a radical of s The heat storage coefficient of the inner wall surface is shown,
representing the area of the inner wall; k is a radical of roof And k wall Respectively representing the heat conduction coefficients of the roof and the wall; s roof And S wall Respectively representing the area of the roof and the wall; q er Represents the total heat dissipation cold load of indoor equipment, lighting and personnel; t is out Represents the outdoor temperature; m is z Represents the quality of the frozen water; c. C w Represents the specific heat capacity of the chilled water; t is w-in And T w-out Respectively indicating freezingThe water inlet and outlet temperatures;
indoor temperature set value is T set The variation range of the indoor temperature is
The operation power of the commercial central air conditioner is rated power p e And zero, the duration of the refrigeration and shutdown state of the commercial central air conditioner is t cooling And t standby Then the central air conditioner is in a period t 1 To t 2 Average refrigerating power p in av Comprises the following steps:
further, the adjustable potential of the industrial production line is determined by n processes, wherein the maximum adjustable potential of one process i is related to the basic parameters of the industrial user production:
wherein, P ei The power of a single machine in the working procedure i; n is ei The number of production equipment in the process i; t is t ai Is the unit working hour of the procedure i; t is t ti The accumulated working hours of the working procedure i; t is t aj Unit man-hour of the process j after the process is finished; t is t tj The accumulated working hours of the working procedure j after the operation is finished; a is a relation matrix of a process i and an immediately subsequent process j, a ij Is an element of the relationship matrix a, i ═ 1, 2.., n; j is 1,2,. n; Δ t mi Maximum interruptible time length for immediately following process i
The maximum adjustable potential of an industrial production line is:
further, the calculation of the adjustable potential for the user layer specifically includes: scalable potential calculations for residential user layers, scalable potential calculations for business user layers, and scalable potential calculations for industrial user layers.
Further, the adjustable potential for the residential user floor is calculated as follows:
in the formula, n 1 The number of air conditioners; p pot,AC,i Response capacity for the ith residential air conditioner; s i (t) is the ith air conditioning state; n is 2 The number of water heaters; p pot,WH,j The response power of the jth water heater; s j (t) is the jth water heater state; n is 3 The number of electric vehicles; p pot,EV,k Response power of the kth electric automobile; s k (t) represents a state of the kth electric vehicle.
Further, the scalable potential for the business user layer is calculated as follows:
in the formula, n 1 The number of air conditioners; p pot,HVAC,i Response capacity for the ith commercial central air conditioner; s i (t) is the ith air conditioning state; n is 3 The number of electric vehicles; p pot,EV,k Response power of the kth electric automobile; s k (t) represents a state of the kth electric vehicle.
Further, the calculation of the adjustable potential of the industrial user layer specifically includes: the adjustable potential of the industrial user layer is determined by the potential of each production line, the theoretical adjustable potential of the industrial production line capable of building the model is analyzed through modeling, and the adjustable potential of the industrial production line incapable of building the model is obtained through analyzing the process flow statistics of the industrial production line.
Further, the calculation of the physical regulation potential of the aggregation business layer specifically comprises:
and inputting the meteorological data, the equipment scale and the excitation strategy transmitted by the user layer into an aggregation characteristic prediction model based on machine learning to obtain an aggregation scheduling potential evaluation curve.
The adjustable load multi-layer aggregation scheduling potential analysis system comprises a hierarchical division module and an aggregation scheduling potential analysis module, wherein:
a hierarchical division module: the system is used for dividing the multi-element adjustable load into three layers of an equipment layer, a user layer and an aggregation business layer;
an aggregate schedulable potential analysis module: and meanwhile, considering the electricity price, an excitation mechanism and user wishes, calculating the adjustable potential layer by layer for the equipment layer, the user layer and the aggregation business layer, transmitting the constraint boundary layer by layer, and finally obtaining the aggregation adjustable potential of the multi-element adjustable load.
Further, the calculating of the adjustable potential of the device layer specifically includes: the adjustable potential calculation is carried out on single typical equipment of the equipment layer, wherein the single typical equipment comprises residential air conditioners, water heaters, electric automobiles, commercial central air conditioners and industrial production lines.
Further, the calculation of the adjustable potential for the user layer specifically includes: scalable potential calculations for residential user layers, scalable potential calculations for business user layers, and scalable potential calculations for industrial user layers.
Further, the calculation of the physical regulation potential of the aggregation business layer specifically comprises:
and inputting the meteorological data, the equipment scale and the excitation strategy transmitted by the user layer into an aggregation characteristic prediction model based on machine learning to obtain an aggregation scheduling potential evaluation curve.
A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the adjustable-load multi-tier aggregate scheduling potential analysis method when executing the computer program.
A computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the tunable load multi-tier aggregation scheduling potential analysis method.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention adopts a bottom-up evaluation method, which is integrally divided into three layers of equipment layer-user layer-polymer business layer according to different evaluation objects and evaluation ranges, and carries out the adjustable potential calculation of users layer by layer and transmits the constraint boundaries layer by layer to finally obtain the physical adjustable potential of massive multi-element users.
Further, for the device layer: the power utilization characteristics of a single typical device (residential air conditioner, water heater, electric automobile, commercial central air conditioner, industrial production line) are analyzed, or device-level modeling is performed based on historical power utilization data thereof. The role of device level modeling is reflected in: firstly, providing a foundation for potential analysis layer by layer; ② evaluating the tunable potential of a typical plant for a given control strategy.
Further, for the user layer: the method comprises the steps of classifying equipment of single users (residential users, commercial users and industrial users), and evaluating the adjustable potential of the single users on the basis of equipment-level adjustable potential evaluation by considering response time sequence and power utilization correlation among different equipment.
Further, for the aggregator layer: and potential evaluation is carried out according to the multi-type load aggregators, on the basis of the evaluation of the adjustable potential of the equipment layer and the user layer, the balance between the calculation speed and the precision of the evaluation result is considered, and the method for evaluating the adjustable potential of the aggregation provider layer is provided, so that the evaluation of the adjustable potential of the mass equipment or the users after aggregation is realized.
The physical regulation performance, economic characteristics and actual operation characteristics of the multi-element adjustable load resources are finely analyzed, and the multi-element load resources are optimally integrated, so that the power grid balance capacity and balance means are improved, the control capacity of a power grid regulation department on the distributed flexible regulation resources of regional levels and multi-region interconnection is improved, and the distributed flexible regulation resources are comprehensively supported to participate in power balance.
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The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a block diagram of a physical model of air conditioning load;
FIG. 2 is a block diagram of a physical model of a water heater;
FIG. 3 is a graph showing the change in indoor temperature;
FIG. 4 illustrates the operation status and power of the central air conditioner;
FIG. 5 is a process flow for potential evaluation based on process flow statistics;
FIG. 6 is a machine learning based aggregate property prediction model;
FIG. 7 is a graph of multivariate tunable load polymerization properties; wherein (a) is a multivariate load polymerization characteristic prediction curve, (b) is a descending dynamic time of polymerization, and (c) is an ascending dynamic time of polymerization;
FIG. 8 is a flow chart of a method of the present invention;
fig. 9 is a block diagram of the system of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides an adjustable load multi-level aggregation scheduling potential analysis method, which quantitatively analyzes potentials of different types of users participating in power grid regulation, adopts a bottom-up evaluation method, considers influence factors such as electricity price, an excitation mechanism and user wishes, calculates the adjustable potentials of an equipment layer, a user layer and an aggregation business layer by layer, transmits constraint boundaries layer by layer, and finally obtains the aggregation scheduling potential of multi-load.
As shown in fig. 8, the method specifically includes the following steps:
(1) device level schedulable potential assessment
1) The physical model of the resident air conditioning load participating in the electric power demand response is shown in fig. 1. The model input variables comprise a t-period demand response control signal, t-period room temperature, an air conditioner temperature set value, a temperature set range, outdoor temperature and the like; the output variables include the working state or actual power of the air conditioner in the period t, the room temperature in the period t +1 and the like. And the room temperature in the t +1 time period is used as an input variable and fed back to the input end, and the variable calculation in the next time period is carried out. Meanwhile, other variables in the model also comprise the self parameters of the air-conditioning equipment, the room volume, the indoor heat conductivity and the like, and the parameters are considered as fixed values according to the statistical conditions during the specific calculation of the model. the actual power of the air conditioner in the period t is calculated as follows:
p AC,t ＝P AC ×S AC,t (1)
in the formula, p AC,t The actual power of the air conditioner is t time period; p AC The rated power of the air conditioner in a refrigeration state; s AC,t The working state of the air conditioner is in a t period (the value is 0 to indicate power failure; the value is 1 to indicate power on); t is AC,min Is the lowest room temperature set value; t is AC,t Room temperature for a period of t.
the t +1 period and the t period room temperature are as follows:
in the formula, T AC,t+1 、T AC,t Respectively representing the room temperature in a t +1 period and a t period; g t Expressing the outdoor and indoor heat exchange value in the period t, and delta c expressing the indoor temperature coefficient, namely the heat required for increasing the room temperature by 10 ℃; c AC Indicating the heat capacity of the air conditioner in a cooling state,
the function value of the air conditioner on the change of the room temperature in the refrigeration state is represented; Δ t represents a time interval, here taken to be 1 min.
2) The physical model of the participation of the water heater load in the power demand response is shown in FIG. 2. The model input variables comprise a t-period demand response control signal, a t-period water temperature in the water heater, a temperature of cold water flowing into the water heater, a water heater temperature setting range, a room temperature and the like; the output variables comprise the working state or the actual power of the water heater in the t period, the water temperature of the water heater in the t +1 period and the like. And the water temperature of the water heater in the t +1 time period is fed back to the input end as an input variable, and the variable calculation in the next time period is carried out. Meanwhile, other variables in the model also comprise parameters of the water heater equipment, the amount of hot water, the flow rate of the hot water and the like, and are determined according to the statistical situation of the living habits of the user during the specific calculation of the model. the actual power of the water heater in the time period t is calculated as follows:
p WH,t ＝P WH ×S WH,t (4)
in the formula, p WH,t The actual power of the water heater is t time period; p WH The rated power of the water heater in a heating state; s WH,t The working state of the water heater is t time (the value is 0 to stop heating when power is off; the value is 1 to stop heating when power is on); t is a unit of WH,s The set value is the maximum water temperature of the water heater; t is WH,t The water temperature of the water heater is t time.
the water temperature of the water heater in the t +1 time period and the t time period is calculated as follows:
in the formula, T WH,t+1 、T WH,t Respectively representing the water temperature of the water heater in a t +1 time interval and a t time interval; t is in The temperature of cold water injected into the water inlet of the water heater is indicated; f. of lt The hot water flow of the water heater in the time period t is expressed and is related to the living habits of residents; v WH Representing the volume of the water heater; Δ t represents a time interval, taken as 1 min; alpha represents the heating temperature coefficient of the water heater, namely the water temperature increase value of the water heater under the rated heating power of the water heater in unit time; p WH The rated power of the water heater in a heating state; s WH,t The working state of the water heater is t time (the value is 0 to stop heating when power is off; the value is 1 to stop heating when power is on); xi represents the self-cooling temperature reduction value of the hot water in the water heater in a unit time period at the normal room temperature, and xi is related to the volume, the surface area, the room temperature, the temperature of the hot water in the water heater and the like.
3) The electric vehicle charging load model is related to an initial charging time, a rated charging power of the vehicle-mounted battery, a battery initial state SOC (state of charge), a final full charge demand, and the like. When the response control signal is not required, when the state of charge (SOC) of the electric automobile does not meet the requirement of electric quantity, the electric automobile is in a continuous charging stage until the requirement of full charge quantity is met. the actual charging power of the electric vehicle in the period t is calculated as follows:
p EV,t ＝P EV ·S EV,t (7)
in the formula, P EV Rated charging power for the electric vehicle; s EV,t The charging state of the battery of the electric automobile is t time (the value is 0 to stop charging when power is off; the value is 1 to indicate the charging state when power is on); SOC 0 Representing the initial electric quantity of the battery of the electric automobile; SOC t The residual capacity of the battery of the electric automobile is t time period; SOC max The maximum value of the electric quantity of the battery when the electric automobile reaches a full charge state is represented; SOC t+1 The residual capacity of the battery of the electric automobile is in a t +1 time period; eta is charging efficiency; c batt The total capacity of the battery of the electric automobile; Δ t represents a time period interval; b is EV A limit value of the battery capacity of the electric vehicle; l is the travel distance of the electric automobile; e EV The running efficiency is obtained.
4) The indoor heat change of the commercial building can be obtained according to the difference between the heat transferred into the room and the heat reduced by the load of the central air conditioner within a period of time, and the room temperature time-varying formula of the building provided with the central air conditioner is as follows:
in the formula, T in (t) room temperature at time t;
and
respectively shown in the central spaceModulating the room temperature at the t moment and the initial moment of the cold period;
and
respectively representing the room temperature at the moment t and the initial moment of the shutdown period of the central air conditioner; q p Indicating the rated refrigerating capacity of the refrigerating machine. a represents a parameter of temperature change of the chilled water, parameter theta 1 、θ 2 、θ 3 、θ 4 The construction parameters are determined as follows:
in the formula, ρ a Represents the air density; c. C a Represents the specific heat capacity of air; v k Represents the indoor volume; k is a radical of s The heat storage coefficient of the inner wall surface is shown,
representing the area of the inner wall; k is a radical of roof And k wall Respectively representing the heat conduction coefficients of the roof and the wall; s roof And S wall Respectively representing the area of the roof and the wall; q er Represents the total heat dissipation cold load of indoor equipment, lighting and personnel; t is out Represents the outdoor temperature; m is z Represents the quality of the frozen water; c. C w Represents the specific heat capacity of the chilled water; t is a unit of w-in And T w-out Respectively showing the inlet and outlet water temperatures of the chilled water.
Indoor temperature set value is T set As shown in FIG. 3, the room temperature is at the set value T set A fluctuation in the vicinity of
The running power of the central air conditioner is at the rated power P e And zero. The duration of the central air conditioner in the refrigeration state and the shutdown state is t respectively cooling And t standby As shown in fig. 4, the central air conditioner is operated for one weekPeriod t 1 To t 2 Average refrigerating power P in av Comprises the following steps:
5) the evaluation of the adjustability of an industrial production line is the sum of the maximum adjustability potentials of the n processes. The maximum adjustable potential of the procedure i is as follows:
wherein n represents the number of steps of an industrial production line, P ei The power of a single machine in the working procedure i; n is a radical of an alkyl radical ei The number of production equipment in the process i; t is t ai Is the unit working hour of the procedure i; t is t ti The accumulated working hours of the working procedure i; t is t aj Unit man-hour of the process j after the process is finished; t is t tj The accumulated working hours of the working procedure j after the operation is finished; a is a relation matrix of a process i and an immediately subsequent process j, a ij Is an element of the relationship matrix a, i ═ 1, 2.., n; j is 1,2,. n; Δ t mi The maximum interruptible time length of the subsequent process i.
Maximum adjustable capacity of industrial production line:
(2) user-level tunable potential evaluation
1) Adjustable potential assessment for residential customer floor
For the residential users, according to different equipment types, the adjustable potential calculation formula of the multivariate user is as follows:
in the formula, n 1 The number of air conditioners; p pot,AC,i Response capacity for the ith residential air conditioner; s i (t) is the ith air conditioning state; n is 2 The number of water heaters; p pot,WH,j The response power of the jth water heater; s j (t) is the jth water heater state; n is 3 The number of electric vehicles; p pot,EV,k Response power of the kth electric automobile; s k (t) represents a state of the kth electric vehicle.
2) Commercial user layer tunable potential:
for commercial users, the adjustable equipment is generally a central air conditioner, an electric automobile and the like, and the adjustable potential calculation formula of the multi-user is as follows:
in the formula, n 1 The number of air conditioners; p pot，HVAC，i Response capacity for the ith commercial central air conditioner; s i (t) is the ith air conditioning state; n is 3 The number of electric vehicles; p pot,EV，k Response power of the kth electric automobile; s k (t) represents a state of the kth electric vehicle.
3) Adjustable potential of industrial user layer:
the adjustable potential of the industrial user layer is determined by the adjustable potential of each industrial production line, the theoretical adjustable potential of the industrial production lines capable of building the model is analyzed through modeling, most of the industrial production lines incapable of building the model are obtained through analyzing the process flow statistics, the potential evaluation basic flow based on the process flow statistics is shown in the attached figure 5, the process flows of the industrial production lines are firstly analyzed, the power utilization characteristics of the industrial production lines are analyzed according to the load duty ratio of each process flow, and the load response type and the response time of each process flow are determined, so that the response potential of the industrial production lines is evaluated. The industrial users generally enter into interruptible contracts with the electric power company in advance according to own adjusting capacity, and the adjustable potential of the industrial users can be evaluated according to contract capacity.
(3) Assessment of tunable potential of polymeric layer
The adjustable load is huge, the invention provides an aggregation characteristic prediction model based on machine learning, and the network structure of the model is shown in FIG. 6. The prediction model includes a convolutional layer, a pooling layer, a fully-connected layer, and a prediction layer. The prediction layer is used for converting the information output by the full connection layer into corresponding class probability to play a classification role.
In the prediction model, input data is one-dimensional data such as meteorological data, equipment scale, excitation strategy, and the like, and the size is W × 1 × 3. Before putting the data into a convolutional neural network for training, data preprocessing is carried out on the data, repeated information and noise data are deleted, the data are kept consistent and normalized, and the size of an input image is 224 multiplied by 1 multiplied by 3. The cleaned data was then trained in a VGG16 network, VGG16 containing 16 total sublayers, layer 1 convolutional layer consisting of 2 conv3-64, layer 2 convolutional layer consisting of 2 conv3-128, layer 3 convolutional layer consisting of 3 conv3-256, layer 4 convolutional layer consisting of 3 conv3-512, layer 5 convolutional layer consisting of 3 conv3-512, followed by 2 FC4096 full-connectivity layers, 1 FC1000 softmax egress layers, 16 total layers.
The convolutional layer and the pooling layer can be divided into 5 blocks, the convolutional layers are separated by using a maximization pool maxpool, the activation units of all hidden layers adopt ReLU functions, and each Block processing flow is convolution, ReLU, convolution, ReLU and pooling.
The Block1 is composed of 2 conv3-64 convolutional layers and 1 maximum pooling layer, the size of a convolutional core in each convolutional base layer is 3 × 3, the number of the convolutional cores is 64, namely the number of output channels is 64, the input of each convolutional layer is 224 × 1 × 3, and the output of each convolutional layer is 224 × 1 × 64; the pooling layer size was 2 × 2 and the output was 112 × 1 × 64.
The Block2 is composed of 2 conv3-128 convolutional layers and 1 maximum pooling layer, the size of a convolutional core in each convolutional base layer is 3 × 3, the number of the convolutional cores is 128, namely the number of output channels is 128, the input of each convolutional layer is 112 × 1 × 64, and the output of each convolutional layer is 112 × 1 × 128; the pooling layer size was 2 × 2 and the output was 56 × 1 × 128.
The Block3 is composed of 3 conv3-256 convolutional layers and 1 maximum pooling layer, the convolutional core size in each convolutional base layer is 3 × 3, the number is 256, namely the number of output channels is 256, the convolutional layers have the input of 56 × 1 × 128, and the output of 56 × 1 × 256; the pooling layer size was 2 × 2 and the output was 28 × 1 × 256.
The Block4 is composed of 3 conv3-512 convolutional layers and 1 maximum pooling layer, the convolutional core size in each convolutional base layer is 3 × 3, the number is 512, namely the number of output channels is 512, the convolutional layers have the input of 28 × 1 × 256 and the output of 28 × 1 × 512; the pooling layer size was 2 × 2 and the output was 14 × 1 × 512.
The Block5 is composed of 3 conv3-512 convolutional layers and 1 maximum pooling layer, the convolutional core size in each convolutional base layer is 3 × 3, the number is 512, namely the number of output channels is 512, the convolutional layers have the input of 14 × 1 × 512 and the output of 14 × 1 × 512; the pooling layer size was 2 × 2 and the output was 7 × 1 × 512.
The fully-connected layer is composed of 2 FC4096, the processing flow of the layer is FC, ReLU and Dropout, and the Dropout has the function of randomly disconnecting some neurons of the fully-connected layer and preventing overfitting in a mode of not activating some neurons.
The layer 1 full connection layer FC4096 consists of 4096 neurons, where the FC input is a 7 × 1 × 512, i.e., a 7 × 1 × 512 one-dimensional vector, and the output is 4096 neurons.
The layer 2 full connection layer FC4096 consists of 4096 neurons, where the FC inputs are 4096 neurons and the outputs are 4096 neurons.
The layer 3 full junction layer FC1000 consists of 1000 neurons, corresponding to 1000 classes of the ImageNet dataset. The processing flow of the layer is as follows: FC. The FC input is 4096 neurons and the output is 1000 neurons.
And the softmax layer outputs the operation results of the 1000 neurons in a softmax function, and the prediction probability values corresponding to the 1000 categories are output.
The network has a deeper network structure, a smaller convolution kernel and a pooling sampling domain, so that the number of parameters can be controlled while more characteristic information is obtained, and excessive calculation amount and an excessively complex structure are avoided. After the forward propagation and the backward propagation are completed, the model predicts the rising dynamic time, the falling dynamic time, the delay time and the power recovery time in the aggregation characteristic, as shown in fig. 7, and finally obtains an aggregation scheduling potential evaluation curve.
The invention also provides an adjustable load multi-layer aggregation scheduling potential analysis system, as shown in fig. 9, which includes a hierarchical division module and an aggregation scheduling potential analysis module, wherein:
a hierarchical division module: the system is used for dividing the multi-element adjustable load into three layers of an equipment layer, a user layer and an aggregation business layer;
an aggregate schedulable potential analysis module: and meanwhile, considering the electricity price, an excitation mechanism and user wishes, calculating the adjustable potential layer by layer for the equipment layer, the user layer and the aggregation business layer, transmitting the constraint boundary layer by layer, and finally obtaining the aggregation adjustable potential of the multi-element adjustable load.
The adjustable potential for equipment layer calculation is specifically as follows: the adjustable potential calculation is carried out on single typical equipment of the equipment layer, wherein the single typical equipment comprises residential air conditioners, water heaters, electric automobiles, commercial central air conditioners and industrial production lines.
The adjustable potential for the user layer is specifically calculated as follows: scalable potential calculations are performed on a residential user level, scalable potential calculations are performed on a business user level, and scalable potential calculations are performed on an industrial user level.
The calculation physical regulation potential of the aggregation business layer is specifically as follows: and inputting the meteorological data, the equipment scale and the excitation strategy transmitted by the user layer into an aggregation characteristic prediction model based on machine learning to obtain an aggregation scheduling potential evaluation curve.
The invention adopts a bottom-up evaluation method, which is integrally divided into three layers of 'equipment layer-user layer-polymer business layer' according to different evaluation objects and evaluation ranges, and calculates the adjustable potential of users layer by layer, and transmits the constraint boundary layer by layer, thereby finally obtaining the physical adjustable potential of massive multi-user. On the equipment level, the adjustment potential of single users such as air conditioning equipment, water heater equipment, electric automobile equipment, industrial production lines, commercial central air conditioners and the like is mainly evaluated; on a user layer, the adjustment potential of the user layer is calculated by taking resident families, commercial buildings and large industrial loads as objects; and at the aggregation layer, the adjustment potential of the multiple users at the aggregation layer is calculated according to the aggregation of the multiple users.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that various changes, modifications and equivalents can be made in the embodiments of the invention without departing from the scope of the invention as defined by the appended claims.
Claims (18)
1. The method for analyzing the adjustable load multi-layer aggregation scheduling potential is characterized by comprising the following steps:
dividing the multi-element adjustable load into three layers of an equipment layer, a user layer and a polymer business layer;
and meanwhile, considering the electricity price, an excitation mechanism and user wishes, calculating the adjustable potential layer by layer for the equipment layer, the user layer and the aggregation business layer, transmitting the constraint boundary layer by layer, and finally obtaining an aggregation scheduling potential evaluation curve of the multivariate adjustable load.
2. The method for analyzing adjustable load multi-tier aggregation scheduling potential according to claim 1, wherein the calculating adjustable potential for the device tier specifically comprises: the adjustable potential calculation is carried out on single typical equipment of the equipment layer, wherein the single typical equipment comprises residential air conditioners, water heaters, electric automobiles, commercial central air conditioners and industrial production lines.
3. The adjustable load multi-floor aggregate dispatching potential analyzing method as claimed in claim 2, wherein the adjustable potential of the residential air conditioner is calculated as follows:
p AC,t ＝P AC ×S AC,t
in the formula, p AC,t For t time period air-conditioning actual power, P AC For rated power of air conditioner in refrigerating state, S AC,t For a period of time T, air-conditioning operating state AC,min Is the lowest room temperature setting, T AC,max Is the highest room temperature setting, T AC,t At room temperature for a period of T, T AC,t+1 、T AC,t Respectively representing the t +1 time interval and the t time interval room temperature, G t Denotes the outdoor and indoor heat exchange values during the period t,. DELTA.c denotes the indoor temperature coefficient, C AC Indicating the heat capacity of the air conditioner in a cooling state,
the value of the air conditioner acting on the change of the room temperature in the cooling state is shown, and delta t represents the time interval.
4. The adjustable-load multi-tier aggregate scheduling potential analysis method according to claim 2, wherein the adjustable potential of the water heater is calculated as follows:
p WH,t ＝P WH ×S WH,t
in the formula, p WH,t For t period water heater actual power, P WH Is rated power of the water heater in a heating state S WH,t For the operating state of the water heater at a time T, T WH,s Is the set value of the maximum water temperature of the water heater, T WH,t The water temperature of the water heater is T time period T WH,t+1 、T WH,t Respectively represents the water temperature of the water heater in the T +1 time interval and the T time interval, T in Indicating the temperature of cold water injected into the water inlet of the water heater, f lt Representing hot water flow of water heater in t periodAmount, V WH Representing the volume of the water heater, delta t representing the time interval, alpha representing the heating temperature coefficient of the water heater, P WH Is rated power of the water heater in a heating state S WH,t And xi represents a self-cooling temperature reduction value of hot water in the water heater in a unit time period at the conventional room temperature.
5. The adjustable load multi-layer aggregation scheduling potential analysis method according to claim 2, wherein the adjustable potential of the electric vehicle is calculated as follows:
p EV,t ＝P EV ·S EV,t
in the formula, p EV,t Actual charging power of the electric automobile is t time period; p EV Rated charging power is provided for the electric automobile; s EV，t The charging state of the battery of the electric automobile is t time period; SOC 0 Representing the initial electric quantity of the battery of the electric automobile; SOC t The residual capacity of the battery of the electric automobile is t time period; SOC t+1 The residual capacity of the battery of the electric automobile is in a t +1 time period; SOC max The maximum value of the electric quantity of the battery when the electric automobile reaches a full charge state is represented; eta is charging efficiency; c batt The total capacity of the battery of the electric automobile; Δ t represents a time period interval; b is EV A limit value of the battery capacity of the electric vehicle; l is the travel distance of the electric automobile; e EV The running efficiency is obtained.
6. The tunable load multi-tier aggregate scheduling potential analysis method of claim 2, wherein the tunable potential of the commercial central air conditioner is calculated as follows:
in the formula, T in (t) room temperature at time t;
and
respectively representing the room temperature at the moment t and the initial moment of the central air conditioning cooling period;
and
respectively representing the room temperature at the moment t and the initial moment of the shutdown period of the central air conditioner; q p A rated refrigerating capacity of the refrigerating machine, a temperature variation parameter of the refrigerated water, and a parameter theta 1 、θ 2 、θ 3 、θ 4 The construction parameters are determined as follows:
in the formula, ρ a Represents the air density; c. C a Represents the specific heat capacity of air; v k Represents the indoor volume; k is a radical of s The heat storage coefficient of the inner wall surface is shown,
representing the area of the inner wall; k is a radical of roof And k wall Respectively representing the heat conduction coefficients of the roof and the wall; s roof And S wall Respectively representing the area of the roof and the wall; q er Represents the total heat dissipation cold load of indoor equipment, lighting and personnel; t is out Represents the outdoor temperature; m is a unit of z Represents the quality of the frozen water; c. C w Represents the specific heat capacity of the chilled water; t is w-in And T w-out Respectively representing the inlet and outlet water temperatures of the chilled water;
indoor temperature set value is T se t, the variation range of the indoor temperature is
The operation power of the commercial central air conditioner is rated power p e And zero, the duration of the refrigeration and shutdown state of the commercial central air conditioner is t cooling And t standby Then the central air conditioner is in a period t 1 To t 2 Average refrigerating power p in av Comprises the following steps:
7. the tunable load multi-tier aggregation scheduling potential analysis method according to claim 2, wherein the tunable potential of the industrial production line is determined by n processes, wherein the maximum tunable potential of one process i is related to the basic parameters of industrial user production:
wherein, P ei The power of a single machine in the working procedure i; n is ei The number of production equipment in the process i; t is t ai Is the unit working hour of the procedure i; t is t ti The accumulated working hours of the working procedure i; t is t aj Unit man-hour of the process j after the process is finished; t is t tj The accumulated working hours of the working procedure j after the operation is finished; a is a relation matrix of a process i and an immediately subsequent process j, a ij Is an element of the relationship matrix a, i ═ 1, 2.., n; j is 1,2,. n; Δ t mi Maximum interruptible time length for immediately following process i
The maximum adjustable potential of an industrial production line is:
8. the method for analyzing adjustable load multi-tier aggregation scheduling potential according to claim 1, wherein the calculating of the adjustable potential for the user tier specifically comprises: scalable potential calculations for residential user layers, scalable potential calculations for business user layers, and scalable potential calculations for industrial user layers.
9. The adjustable load multi-tier aggregate scheduling potential analysis method according to claim 8, wherein the adjustable potential for the residential user tier is calculated as follows:
in the formula, n 1 The number of air conditioners; p pot,AC,i Response capacity for the ith residential air conditioner; s i (t) is the ith air conditioning state; n is 2 The number of water heaters; p pot，WH,j The response power of the jth water heater; s j (t) is the jth water heater state; n is 3 The number of electric vehicles; p pot,EV,k Response power of the kth electric automobile; s k (t) represents a state of the kth electric vehicle.
10. The tunable load multi-tier aggregate scheduling potential analysis method of claim 8, wherein the tunable potential for the business user tier is calculated as follows:
in the formula, n 1 The number of air conditioners; p pot,HVAC,i Response capacity for the ith commercial central air conditioner; s i (t) is the ith air conditioning state; n is 3 The number of electric vehicles; p pot,EV,k Response power of the kth electric automobile; s k (t) represents a state of the kth electric vehicle.
11. The method for analyzing adjustable load multi-tier aggregation scheduling potential according to claim 8, wherein the calculation of the adjustable potential for the industrial user tier specifically comprises: the adjustable potential of the industrial user layer is determined by the potential of each production line, the theoretical adjustable potential of the industrial production line capable of building the model is analyzed through modeling, and the adjustable potential of the industrial production line incapable of building the model is obtained through analyzing the process flow statistics of the industrial production line.
12. The method for analyzing the adjustable-load multi-layer aggregation scheduling potential according to claim 1, wherein the calculating the physical adjustment potential for the aggregation business layer specifically comprises:
and inputting the meteorological data, the equipment scale and the excitation strategy transmitted by the user layer into an aggregation characteristic prediction model based on machine learning to obtain an aggregation scheduling potential evaluation curve.
13. The adjustable load multi-layer aggregation scheduling potential analysis system is characterized by comprising a hierarchical division module and an aggregation scheduling potential analysis module, wherein:
a hierarchical division module: the system is used for dividing the multi-element adjustable load into three layers of an equipment layer, a user layer and an aggregation business layer;
an aggregate schedulable potential analysis module: and meanwhile, considering the electricity price, an excitation mechanism and user wishes, calculating the adjustable potential layer by layer for the equipment layer, the user layer and the aggregation business layer, transmitting the constraint boundary layer by layer, and finally obtaining the aggregation adjustable potential of the multi-element adjustable load.
14. The adjustable-load multi-tier aggregate scheduling potential analysis system according to claim 13, wherein the calculating of the adjustable potential for the device tier specifically comprises: the adjustable potential calculation is carried out on single typical equipment of the equipment layer, wherein the single typical equipment comprises residential air conditioners, water heaters, electric automobiles, commercial central air conditioners and industrial production lines.
15. The system for analyzing adjustable load multi-tier aggregate scheduling potential according to claim 13, wherein the calculating adjustable potential for the user tier specifically comprises: scalable potential calculations for residential user layers, scalable potential calculations for business user layers, and scalable potential calculations for industrial user layers.
16. The system for analyzing the adjustable-load multi-layer aggregation scheduling potential according to claim 13, wherein the calculating the physical adjustment potential for the aggregation business layer specifically comprises:
and inputting the meteorological data, the equipment scale and the excitation strategy transmitted by the user layer into an aggregation characteristic prediction model based on machine learning to obtain an aggregation scheduling potential evaluation curve.
17. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the adjustable load multi-tier aggregate scheduling potential analysis method according to any one of claims 1 to 12.
18. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the adjustable load multi-tier aggregate scheduling potential analysis method according to any one of claims 1 to 12.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115411730A (en) * | 2022-10-31 | 2022-11-29 | 国网浙江省电力有限公司金华供电公司 | Air conditioner load multi-period adjustable potential evaluation method and related device |
| CN115545579A (en) * | 2022-12-01 | 2022-12-30 | 国网浙江义乌市供电有限公司 | Aggregation intelligent control method and system for user flexible resources |
| CN116544930A (en) * | 2023-06-25 | 2023-08-04 | 国网浙江省电力有限公司丽水供电公司 | Distributed resource polymer adjustable capacity evaluation method and device |
| CN116544930B (en) * | 2023-06-25 | 2023-09-19 | 国网浙江省电力有限公司丽水供电公司 | Distributed resource polymer adjustable capacity evaluation method and device |
| CN117745043A (en) * | 2024-02-21 | 2024-03-22 | 国网数字科技控股有限公司 | Adjustment potential determining method, device and equipment |
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