CN110729728A - Demand response method considering real-time comfort of user and load rate of power grid - Google Patents
Demand response method considering real-time comfort of user and load rate of power grid Download PDFInfo
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Abstract
The invention provides a demand response method considering real-time comfort of a user and a load rate of a power grid, which is different from a traditional passive control terminal, and considers a schedulable strategy of integrating the intention of the user to actively participate in response to improve the operation load rate of the power grid. The method comprises the steps of classifying user household appliances, determining the types of the intelligent household appliances, calculating comfort level indexes of users based on the operating characteristics, the working states and external environment information of the intelligent household appliances, increasing the demands according to the load rate of a power grid, setting power grid power dispatching limit values, updating the comfort level indexes of the users in real time, and achieving demand response control of the intelligent household appliances. The invention ensures the comfort of users and can effectively improve the operation load rate of the power grid.
Description
Technical Field
The invention relates to a demand response control method, belongs to the technical field of demand response intelligent control, and particularly relates to a demand response method considering real-time comfort of a user and a load rate of a power grid.
Background
The load factor refers to the ratio of the average active load to the highest active load of the power grid, namely the percentage of the ratio of the average active load to the highest active load of the power utilization in a period of time. Has a general formula of
In the formula: λ -load factor; pmax-counting the maximum active load during a time period; t is t1,t2-a start time, an end time of the statistical time period; p (t) -a power function with respect to t.
In recent years, the power grids in various regions develop rapidly, so that the peak-to-valley difference of the power load is increased continuously, the power load rate is reduced gradually, and the power generation peak regulation is difficult day by day. Effectively increasing the load rate also becomes a significant issue in the development of power grids. However, under the situation of large-scale new energy grid connection, peak shaving of the power grid faces a great challenge, and the power system power instantaneous balance is maintained only by scheduling power generation side resources, so that it becomes increasingly difficult to maintain a load factor with a large value. Demand Response (DR) means that after a power consumer receives a price signal or an incentive mechanism sent by a power supplier, the power consumer changes an inherent power consumption mode and reduces or shifts the behavior of a power load for a certain period of time. If the user can actively and reasonably participate in demand response, the purposes of peak clipping and valley filling, improvement of power utilization load rate and the like can be achieved. The traditional demand response signal mainly depends on manual transmission, and personnel manually shut down equipment or adjust the running power of the equipment, so that a user side cannot obtain DR information of a power grid side in time, the power grid side cannot adjust the DR signal in real time according to the latest power consumption information of the user, and the reliability and efficiency of realizing peak clipping and valley filling by DR are reduced. Therefore, the traditional demand response is difficult to realize peak clipping and valley filling of the power grid in the true sense, and the supply and demand balance of the power grid is ensured. On the other hand, Automatic Demand Response (ADR) can automatically realize DR response of a user on the basis of no manual operation, and thus, real power grid supply and demand balance is realized.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method for improving the load rate of a power grid by fully considering the real-time comfort of a user and utilizing participation of intelligent household appliances in demand response, mainly aiming at the problems and the defects of the current intelligent household appliance response strategy.
The technical scheme is as follows: a demand response method considering real-time comfort of a user and a load rate of a power grid comprises the following steps:
after entering a DR scheduling time interval of demand response, the total power P of all intelligent household appliances at the current t momenttotal(t) and a preset power limit plimitComparing the sizes of the two components;
when P is presenttotal(t)>PlimitAnd when the intelligent household appliance is switched, calculating a comfort index K value according to real-time data feedback of the intelligent household appliance, and sequentially judging whether the corresponding household appliance is switched according to the ascending order of the K value: if the household appliance is in the power-on state, the household appliance is powered off and the P is updatedtotal(t); if the household appliance is in the power-off state, the next household appliance state is judged and decided, and when the household appliance operation state changes, the P is updated in timetotal(t) value until the time Ptotal(t)<PlimitThe requirements are met;
and after meeting the requirement or after powering off all the household appliances in sequence, the requirement that the total power at the moment is lower than the limit value cannot be met, entering the next moment, and repeating the steps until the DR scheduling time zone is exited.
Furthermore, the intelligent household appliances comprise an air conditioner, a water heater and an electric automobile, and the total power P of all the intelligent household appliancestotalAnd (t) is the sum of the running power of the air conditioner, the water heater and the electric automobile at the time t.
The operating characteristics of the air conditioner are as follows:
in the formula: pAC(t) is the operating power (kW) of the air conditioner at the moment t; pACRated power (kW) for the air conditioner; t isAC(t) is the indoor temperature (. degree. C.) at time t;is the temperature set point (DEG C) at the time t;
temperature dead zone (. degree. C.); Δ t is the length of the time interval between the time t and the next time t + 1; g (t) is the heat increase rate of the house at the time t, positive numbers represent heat increase, and negative numbers represent heat loss (Btu/h); cACFor cooling capacity (Btu/h); Δ c is the energy required for a 1F change in room temperature (Btu/< F.).
The operating characteristics of the water heater are as follows:
in the formula: pWH(t) is the running power (kW) of the water heater at the moment t; pWHRated power (kW) for the water heater; t isWH(t) is the water temperature (. degree. C.) at time t;
set value (DEG C) of water temperature at time t;
water temperature dead zone (. degree. C.); fr (t) is the flow rate of hot water (gpm) at time t; vtankIs the volume of the tank (gallons); t isinletWater inlet injection temperature (F.); alpha is the heating temperature coefficient of the water heater; xi is the speed of the water temperature of the water heater falling in unit time.
The running characteristics of the electric automobile are as follows:
pEV(t)＝PEV·NEV(t)·wEV(t) (6)
in the formula: p is a radical ofEV(t) is charging power (kW) of the electric automobile at the moment t; pEVRated power (kW) for the electric vehicle; n is a radical ofEV(t) is the connection state of the electric automobile at the moment t, wherein 1 represents that the electric automobile is connected with the charging pile, and 0 represents that the electric automobile is not connected with the charging pile; w is aEV(t) is the charging state of the electric automobile under the uncontrolled condition at the time t, wherein 1 represents that the electric automobile is charged, and 0 represents that the electric automobile is not charged; SOC (t) is the state of charge at time t; SOCminThe minimum state of charge required to be reached at the expected end-of-charge time.
The preset power limit PlimitObtained according to the following method: the power grid dispatching center gives a load rate value lambda which needs to be improved after response according to the real-time load rate conditionafter，
In the formula: lambda [ alpha ]afterThe load rate of the power grid after implementing the demand response method; t is t1And t2Respectively as the starting time and the ending time of the statistical time interval; t is tDRaAnd tDRbRespectively as the beginning time and the ending time of the DR interval time interval; p (t) is the load power at the time t when the demand response is not implemented; Δ p (t) is the power variation of the intelligent household appliance participating in demand response at the time t; pmaxCounting the maximum active load in a time period; delta P is the power variation of the intelligent household appliance participating in demand response in the peak period;
p is calculated from the formula (14)limit＝P(t)-ΔP(t)＝Pmax-ΔP。
The intelligent household appliance comfort index K value ascending form is as follows:
K(t)＝min(KAPP0(t)) (13)
in the formula (I), the compound is shown in the specification,
is the largest integer less than x; min (x) is a function that can order x from small to large; kAPPExpressing the power utilization priority of the intelligent household appliances, and respectively calculating and obtaining the air conditioner, the water heater and the charging automobile according to respective calculation formulas and then sequencing the air conditioner, the water heater and the charging automobile together; t represents a time point within the statistical period, T represents a data update period of the household appliance, KAPP0(T) denotes K within one update period TAPPOf the sampling value(s).
Has the advantages that: under the condition of low load rate of the power grid, the use comfort level of the household appliances of the user is fully considered, the peak load is effectively reduced through the participation of the intelligent household appliances in demand response, and the reasonability and the effectiveness of corresponding strategies of the load are reflected. The optimization method provided by the invention can effectively improve the load rate of the power grid and meet the requirement of comfort level of users to the greatest extent.
Drawings
FIG. 1 is a flow chart of a demand response method of the present invention that considers user comfort and increases grid load rate;
FIG. 2 is an example of a simulation of the operating state of an intelligent appliance without implementing the demand response method of the present invention;
FIG. 3 is a total load simulation example of intelligent appliances without implementing the demand response method proposed by the present invention;
FIG. 4 is an example of a total load simulation without implementing the demand response method proposed by the present invention;
fig. 5 is a comfort comparison example of 3 types of home appliances when the demand response method of the present invention is implemented and the update period is 1 min;
fig. 6 is a comparison example of comfort levels of the same household appliance of different residents when the demand response method provided by the present invention is implemented and the update period is 1 min;
FIG. 7 is a comparative example of the total load simulation results before and after the implementation and with an update period of 1 min;
fig. 8 is a comfort comparison example of 3 types of home appliances when the demand response method of the present invention is implemented and the update period is 10 min;
fig. 9 is a comparison example of comfort levels of the same household appliance of different residents when the demand response method is implemented and the update period is 10 min;
FIG. 10 is a comparative example of the total load simulation results before and after the implementation and with an update cycle of 10 min;
fig. 11 is a comfort comparison example of 3 types of home appliances when the demand response method of the present invention is implemented and the update period is 30 min;
fig. 12 is a comparison example of comfort levels of the same household appliance for different residents when the demand response method is implemented and the update period is 30 min;
FIG. 13 is a comparative example of the total load simulation results before and after the implementation and at an update period of 30 min.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
Fig. 1 shows a flowchart of a schedulable policy for actively participating in response to increase of a grid operation load rate in consideration of the willingness of a user himself/herself, where participating objects include a scheduling center, an intelligent appliance, and a user. As shown in the figure, the DR scheduling time interval [ t ] is entered intoDRa，tDRb]Then, the total power P of all the current household appliances is firstly determinedtotalAnd power limit PlimitThe size of (2) is compared and judged. When P is presenttotal>PlimitAnd calculating a comfort level index K value according to real-time data feedback of the household appliances, and sequentially judging whether the state of the corresponding household appliances is switched according to the ascending order of the K value. If the household appliance is in the power-on state, the household appliance is powered off and the P is updatedtotal(ii) a If the household appliance is in the power-off state, the state of the next household appliance is directly determined and the same decision is made. As a house electric fortuneWhen the line state changes, updating P in timetotalValue until the moment Ptotal<PlimitThe requirements are met. And after the data of all the household appliances meet the requirements, updating the data in real time to enter the next moment, and repeating the steps until the DR scheduling time zone is exited. And finally, calculating the load rate of the power grid according to the scheduled household appliance data, and comparing the load rate with the load rate before scheduling to prove the effectiveness of the scheme provided by the invention.
Referring to the figures, the demand response method comprises the steps of:
step 1) classifying the user household appliances according to the operating characteristics and the importance degrees of different household appliances, and determining the types of the intelligent household appliances.
The invention selects various household appliances from common household appliances, establishes mathematical models of the various household appliances, and finally selects three intelligent household appliances of an air conditioner, a water heater and an electric automobile according to the operation characteristics, the degree of the user's requirement on the household appliances and the influence of the state change on the power utilization condition of the user. The step 1) specifically comprises the following steps:
1.1) analyzing the dynamic operating characteristics of the household appliances according to the household appliance power consumption condition of a user, researching other important loads of the household appliances including an air conditioner, a water heater, a dryer, an electric automobile, a refrigerator, an electric lamp and the like, wherein the corresponding operating characteristic formulas are as follows (1) - (9):
air conditioning:
in the formula: pAC(t) is the operating power (kW) of the air conditioner at the time t; pACRated power (kW) for the air conditioner; t isAC(t) is the indoor temperature (. degree. C.) at time t;
is the temperature set point (DEG C) at the time t;
the temperature dead zone (DEG C), namely the air conditioner temperature setting range, is a constant after being manually set; Δ t is the length of the time interval between time t and the next time t + 1, and is set to 1hour in terms of each parameter unit in equation (2); g (t) is the heat increase rate of the house at the time t, positive numbers represent heat increase, and negative numbers represent heat loss (Btu/h); cACFor cooling capacity (Btu/h); Δ c is the energy required for a 1F change in room temperature (Btu/< F.).
Water heater:
in the formula: pWH(t) is the running power (kW) of the water heater at the moment t; pWHRated power (kW) for the water heater; t isWH(t) is the water temperature (. degree. C.) at time t;set value (DEG C) of water temperature at time t;the water temperature dead zone (DEG C) is the temperature setting range of the water heater and is a constant after being manually set; fr (t) is the flow rate of hot water (gpm) at time t; vtankIs the volume of the tank (gallons); t isinletWater inlet injection temperature (F.); Δ t is the length of the time interval between time t and the next time t + 1, and is set to 1minute in terms of each parameter unit in equation (4); alpha is the heating temperature coefficient of the water heater, namely the water temperature increment of the water heater in unit time under unit power operation; xi is the speed of the water temperature of the water heater falling in unit time, and is related to parameters such as the volume, the surface area and the room temperature of the water heater.
A dryer:
in the formula: pCD(t) is the operating power (kW) of the dryer at time t; phHeating coil rated power (kW) for the dryer; k is a drying grade (k is 1, …, M); m is the drying grade number; pmEngine power (kW) for the dryer; wCDAnd (t) is the working state of the heating coil of the dryer at the time t, wherein 0 represents non-working, and 1 represents working.
Electric automobile:
pEV(t)＝PEV·NEV(t)·wEV(t) (6)
in the formula: p is a radical ofEV(t) is the charging power (kW) of the electric automobile at the moment t; pEVRated power (kW) for the electric vehicle; n is a radical ofEV(t) the electric automobile is in a connection state at the moment t, wherein 1 represents that the electric automobile is connected with the charging pile, and 0 represents that the electric automobile is not connected with the charging pile; w is aEV(t) is the charging state of the electric automobile under the uncontrolled condition at the time t, wherein 1 represents that the electric automobile is charged, and 0 represents that the electric automobile is not charged; SOC (t) is the state of charge at time t; SOCminThe minimum state of charge required to be reached at the expected end-of-charge time.
A refrigerator:
in the formula: t is a certain time(s); τ is a time constant; t is the temperature (K) of the wall surface of the refrigerator at the moment T; t isbeginIs the initial temperature, TstableTo a stabilized temperature; qabsorbThe cold quantity for cooling the refrigerator wall; cdThe heat capacity of the box body (J/(kg. K)); v is the volume of the box body (m)3) (ii) a Rho is the density of the box body (kg/m)3)；t1Initial time for stabilization, t2Is the end time; and E is the energy consumption of one period.
Electric lamps and other important loads:
in the formula: l iscThe total load (kw) required for one hour for the appliances contained in the category; f. oftypeThe annual load hour fraction of the ith class load in the class; l isavg_typeThe annual average load of the i-th class load in the category.
1.2) the household appliances can be divided into 2 types of important loads and intelligent controllable loads. Important loads such as necessary household appliances such as lighting household appliances and refrigeration household appliances can cause great influence on the life of a user when the household appliances are powered off, so that the important loads do not participate in DR control; controllable load of intelligence is like air conditioner, electric automobile etc. and power consumption time and law are comparatively stable, and carry out the short time outage to it through the electric wire netting control layer and hardly influence resident normal life, are convenient for participate in DR control. The intelligent controllable load researched by the invention comprises an air conditioner, a water heater and an electric automobile. When the room temperature or the water temperature is in a comfortable range and the charging time of the electric automobile is sufficient, the air conditioner, the water heater or the electric automobile can be powered off, the electricity utilization of a user cannot be greatly influenced, and therefore after the electric appliances are powered off in order according to the comfort level in the DR period, the load can be reduced in the electricity utilization peak time period. However, different from the air conditioner, the water heater and the electric automobile are energy-storing household appliances, and the requirements of residents on the water heater and the electric automobile are higher, so that the latter two appliances are necessary. Thus, the water heater and electric vehicle are affected by DR as a result of the transfer of the electrical load from the peak area to the valley area. After the DR control is finished, the electric energy still needs to be consumed to meet the requirement of the user, namely, the user is influenced by the DR control, and the time period of the two household appliances after the user uses the household appliances is changed.
Therefore, according to the operating characteristics of each household appliance and the household appliance electricity utilization condition of a user, intelligent household appliances related to the demand response method in the demand response method considering the real-time comfort degree of the user and the load rate of a power grid are determined, wherein the intelligent household appliances comprise an air conditioner, a water heater and an electric automobile. According to the electricity utilization characteristics of three interruptible intelligent household appliances and the different requirements of electricity utilization residents on the three, the effects of the intelligent household appliances in DR can be divided into 2 types: one is to reduce the load, such as air conditioning. Because the energy storage characteristic and the power consumption necessity are not possessed, the power consumption can be reduced through power failure, and no corresponding association exists between the power consumption and subsequent power failure. And secondly, loads such as a water heater and an electric automobile are transferred, and the total power consumption of the loads is basically unchanged in the whole research time period due to the energy storage property and the electricity consumption necessity.
And 2) calculating a comfort level index of the user using the intelligent household appliance according to the running characteristic, the working state and the external environment related parameters of the intelligent household appliance.
The step 2) specifically comprises the following steps: based on the dynamic operating characteristics of the intelligent household appliances, the actual requirements of users on the comfort level of the intelligent household appliances are considered, external environment information such as indoor temperature, water temperature and the like is collected, comfort level indexes of air conditioners, water heaters and electric automobiles and the comfort level index sequencing in the update period of all the intelligent household appliances are calculated and are respectively as shown in formulas (10) to (13):
in the formula: t isAC(t) is the indoor temperature (. degree. C.) at time t;
is the lowest room temperature set value;
the temperature dead zone (DEG C), namely the air conditioner temperature setting range, is a constant after being manually set; kAC(t) is the difference between the current room temperature and the lowest value of the set room temperature at the time t after the per unit processing, the higher the room temperature is, KACThe larger (t) is, the lower the satisfaction of the user is, and thus the higher the priority of electricity use thereof is. And in the DR period, the power-on and power-off state of the air conditioner is controlled according to the priority of the air conditioner.
In the formula: t isWH(t) is the water temperature (. degree. C.) at time t;
is the lowest water temperature set value;
the water temperature dead zone (DEG C), namely the temperature setting range of the water heater, is a constant after being manually set; kWH(t) setting the difference between the maximum water temperature and the current water temperature at t time after per unit, wherein the lower the water temperature, KWHThe larger (t) is, the lower the satisfaction of the user is, and thus the higher the priority of electricity use thereof is. And in the DR period, controlling the power-on and power-off states of the water heaters according to the priority of the water heaters.
In the formula: t isQThe length of time still required for completing charging of the electric vehicle; t isSTime required for completing charging of the electric vehicle; t is the running time t; kEV(t) is the priority of electric automobile power consumption, if the electric automobile can finish charging within the required time, then user's satisfaction is the highest among all intelligent household electrical appliances, and its priority of power consumption is the lowest, therefore KEV(t) — 1; and if the charging cannot be completed, the satisfaction degree of the user is the lowest among all the intelligent household appliances. It has the highest priority for electricity consumption, thereforeDuring DR period, according toThe priority of the electric automobile controls the power-on and power-off state of the electric automobile.
K(t)＝min(KAPP0(t)) (13)
In the formula (I), the compound is shown in the specification,
is the largest integer less than x; min (x) is a function that can order x from small to large; kAPPThe invention can represent the comfort level of a user for intelligent household appliances, namely the comfort level of the user is in inverse proportion to the comfort level of the user, and K values of three intelligent household appliances are ranked together after being calculated according to respective formulas; kAPP0(T) is K in one period TAPPOf the sampling value(s).
And 3) setting a power grid power dispatching limit value by the dispatching center according to the load rate promotion requirement of the power grid, wherein the total power in the DR interval is lower than the value.
The step 3) specifically comprises the following steps: the method comprises the following steps of utilizing different types of intelligent household appliances to participate in demand response, and achieving the improvement of the load rate of a power grid, wherein the expression is as follows (14):
in the formula: lambda [ alpha ]afterThe load rate of the power grid after implementing the demand response method; t is t1And t2Respectively as the starting time and the ending time of the statistical time interval; t is tDRaAnd tDRbThe DR interval time interval is a time interval for counting the data of the user household appliances, and is selected at the left and right of a peak area of the counting time interval, because the DR is not needed in a low valley area and the power cannot reach a limit value, the response speed is influenced by taking the power into consideration; p (t) is the load power at the time t when the demand response is not implemented; Δ p (t) is the power variation of the intelligent household appliance participating in demand response at the time t; pmaxCounting the maximum active load in a time period; delta P is power participating in demand response during peak time of intelligent household applianceThe amount of change.
The power grid dispatching center gives a load rate value lambda which needs to be improved after response according to the real-time load rate conditionafter. The total load power at each time t in the non-DR interval is still P (t), and the total load power P (t) at each time t in the DR interval is reduced by delta P (t) after the demand response considering the comfort of the user is considered, so that the reduced total load power is the power scheduling limit value PlimitI.e. P (t) - Δ P (t) ═ Plimit. Maximum load P within the same available statistical time periodmaxAfter response reduces delta P, the power scheduling limit value P is obtainedlimitI.e. Pmax-ΔP＝Plimit. Thus, P can be calculated from equation (14)limit＝P(t)-ΔP(t)＝PmaxAnd the power variation delta P (t) of the parameter response to the demand in the statistical time interval and the power variation delta P at the highest peak are obtained.
And 4) updating the comfort level index of the user in real time, and realizing the demand response control of the intelligent household appliance according to the power dispatching limit value of the power grid.
The step 4) specifically comprises the following steps:
4.1) calculating comfort level indexes of all intelligent household appliances and arranging K1, K2 and K3 … Kn in ascending order, wherein n is the number of the intelligent household appliances;
4.2) judging the total power P of all intelligent household appliances at the current t momenttotal(t) and a power limit PlimitThe size of (2). When P is presenttotal(t)>PlimitAnd when the intelligent household appliance is in a working state and the comfort level meets the requirements of users, the intelligent household appliance does not work according to the judgment standard. Finding out the intelligent household appliance with the minimum K value at the moment and judging the running state of the intelligent household appliance, wherein the intelligent household appliance is in the power-on state and has the total power Ptotal(t) subtracting the corresponding power of the household appliance, and judging the total power P at the moment againtotal(t) and a power limit PlimitThe intelligent household appliance operation state corresponding to the next comfort level index is judged and corresponding same measures are taken; and if the current state is the power-off state, directly judging the running state of the intelligent household appliance corresponding to the next comfort level index and taking corresponding same measures. The above steps are circulated untilTotal power P at that momenttotal(t) is less than or equal to power limit PlimitOr after all the household appliances are powered off in sequence, the requirement that the total power at the moment is lower than the limit value cannot be met, and the next moment is entered to carry out the same steps until the DR control interval is exited.
The effect of the scheme of the invention is verified by a specific example as follows:
the intelligent household appliances used by the users in the residential area are taken as research objects, a certain residential area comprises 1000 households, and each household has 3 related intelligent household appliances: air conditioner, water heater and electric automobile. The load power of the intelligent household appliance, i.e., the controllable household appliance, and the set power demand range thereof are referred to as examples, and the load power of the common household appliance, i.e., the uncontrollable household appliance, is shown in table 1.
TABLE 1 Intelligent household appliance and general household appliance information
According to the practical situation, the night is assumed to be a day-internal electricity utilization peak section, the next morning is assumed to be a day-internal second-use electricity peak section, and the middle is assumed to be an electricity utilization valley area. Thus, in the example 17: 00 to the next day 8: and (3) carrying out analysis before and after DR and calculation and comparison of load rate on the load power of 1000 households in the 00 time period. The initial value and the maximum temperature of the air conditioner are set to be 28 ℃, and the time point of starting the air conditioner is 17: 00 to 19: and randomly taking values in 00, and finally closing the air conditioner at a time point of 3: 00 to 6: the value is randomly selected in 00, and two conditions exist in the operation of the air conditioner: continuously operating the air conditioner after most families are started to be closed, closing the air conditioner midway (randomly taking values in a range from 20: 00 to 21: 00) for at least some families, and starting the air conditioner for the second time after 1 to 2 hours until the air conditioner is finally closed; the water heater is always in an opening state and is divided into a natural cooling state and a cold water adding state, and the initial temperature is set between 45 ℃ and 50 ℃ according to the range of requirements in table 1. Considering different water demands of residents, the residents are set at 17: 00 to 19: 00 In and the next day 6: 00 to 8: water is used within a short time (randomly taking values within 1min to 5 min) within 00, 19: 00 to 23: water is used for a long time (randomly taking values within 15min to 30 min) within 00. 23: 00 to the next day 6: 00 is a natural cooling state; the initial value of the SOC of the electric automobile is set between 10% and 30%, and the SOC of residents is set between 17: 00 to 22: charging is started at any time point within 00 until the SOC reaches 90%, and charging is considered to be finished.
The power grid dispatching center gives a load rate value lambda which needs to be improved after response according to the real-time load rate conditionafterThen, the corresponding load power limit value P can be calculatedlimit. The intelligent household appliance selects a proper DR interval near the peak time period of power utilization according to the power limit value to respond to the demand in consideration of the comfort level of the user.
The comfort level of the intelligent household appliance needs to be calculated and sequenced to meet the basic requirement of considering the comfort level of the user. First, K in a data updating period T is calculatedAPPAnd sequencing the sampling values of all the household appliances from small to large. KAPPThe smaller, the higher the user comfort, i.e. the higher the power-down priority. Therefore, the power is cut off from the priority with small K value until the power load reaches the requirement of the power grid, and then the next time is started. In addition, the selection of the updating period T needs to be reasonable, if the updating period T is too short, the switching of the power-on and power-off states of the household appliance is frequent, the service life of the household appliance is shortened, the use experience of a user is influenced, and the basic significance of the invention in consideration of the comfort level of the user cannot be embodied; if the load change is too long, hysteresis is generated, so that the DR control carries out power-off treatment on household appliances which are not required to be powered off, the normal life of residents is influenced, and the comfort level of power utilization of users is also influenced. Therefore, it is necessary to select a suitable long update cycle to improve the system load rate in real time and efficiently while preventing adverse effects on the service life of the home appliance and the feeling of electricity consumption of residents.
As can be seen from fig. 2-4, about 18: 30 to 23: 00 is a peak section for 3 household appliances, 23: 00 to the next day 3: 00 mainly for air-conditioning and electric vehicle operation, 3: 00 to 6: 00 mainly for air conditioning operation, 6: 00 to 8: 00 operate primarily for water heaters. Therefore, the running time of the electric automobile can be moved to the low valley region of the total load curve on the premise of meeting the set requirement, so that the purposes of peak clipping, valley filling and load maximum value reduction are achieved. Meanwhile, on the premise of considering the comfort degree of residents, the air conditioner and the water heater in the high peak area can be disconnected so as to reduce the total load.
Assuming that the DR scheduling interval is 18: 30 to 3: 00. firstly, the household appliance data and the comfort level are updated by taking T-1 min as a period. From fig. 5, the same user appliance is shown in fig. 1: before 30, the air conditioner and the water heater work alternately, and the working ratio of 1: 30-rear-cause electric vehicles do not meet 8: and finishing the charging requirement before 00, wherein the comfort index of the charging requirement is the highest, namely, the charging requirement is preferentially given to the electric automobile. The comfort ranking problem exists between the same household appliances of different users in fig. 6, that is, the household appliances of different users work alternately. As can be seen from fig. 7, the total load at each moment in the DR interval is lower than the given load limit of the grid, i.e. the portion higher than the given load is the limit value, and the portion lower than the given load is not changed. After DR is finished, the total load of the electric automobile is higher than that before DR in a period of time because the electric automobile is not fully charged, and the curve is consistent before and after the SOC meets the condition. Although the comfort levels of the household appliances of the same user are quite different, the comfort levels of the air conditioners of different users are quite close to each other as shown in fig. 6, so that the working states of the air conditioners of different users are frequently switched, the normal operation of the air conditioners is damaged, the use feeling of the users is influenced, and the basic requirements considering the comfort levels of the users are not met.
In order to reduce the condition that the working state of the household appliance is switched too densely, the comfort level updating period T of the household appliance is set to be 10min, and the DR interval is not changed. About 1 from FIG. 8: before 30, the electric automobile can be charged within a specified time, so that the priority of electricity utilization is lowest, and the total load and the peak value are reduced mainly by the alternative work of an air conditioner and a water heater. Then the electric automobile works independently until DR is finished. Fig. 9 shows that the alternation between the same home appliances of different users is not too frequent, compared to the case of T ═ 1 min. The alternating time interval of the household appliances is not less than 10min, the normal operation of the household appliances is not influenced, the DR effect is obvious, and the final purposes of peak clipping, valley filling and load rate improvement are achieved. From fig. 10, it can be seen that in the DR interval, the total load at each moment is lower than the given load limit of the grid, and the part lower than the given load is not changed, but the part higher than the given load is slightly lower than the limit value, because the comfort calculation is slightly delayed but has no great influence at 10min compared with the period of 1 min. After DR is finished, the total load is higher than that before DR in a period of time because the electric automobile is not fully charged, and the curve is consistent after the SOC meets the condition.
The effect is observed by prolonging the updating period to 30min, and the DR interval is still 18: 30 to 3: 00. as can be seen from fig. 11, the power on/off states of the 3 kinds of home appliances are the same as those in the first two examples, except that the state duration is extended. The user satisfaction may be reduced by having certain appliances of some users' homes remain on or off for too long as shown in fig. 12. As can be seen from fig. 13, the peak power consumption interval is obviously no longer in a straight line state after DR, but is a curve with large volatility. This illustrates the case where there is some hysteresis in the calculation of the comfort index K when the update period is too long, resulting in multiple cutbacks when cutting power above the limit. Although the load can be further reduced, reducing unnecessary power load affects the electricity comfort of residents, and the basic significance in consideration of the user comfort cannot be realized.
Because of the influence of the scheduling scheme provided by the invention on the load power of the intelligent household appliance, the corresponding change of the load rate before and after DR is shown in table 2.
TABLE 2 example comparison of intelligent household appliance load situation and load rate
As shown in the table 2, after the demand response scheme provided by the invention, the load rate of the domestic electricity of the residents is obviously improved. When the updating period is 1min, although the switching of the power-on and power-off states of the household appliances is too frequent, the load factor is improved to the highest extent; when the updating period is 10min, the switching frequency of the state of the household appliance is moderate, the load rate is slightly lower than 1min, but the whole is the most reasonable period set value; when the update period is 30min, not only a certain hysteresis is provided, but also the effect of increasing the load factor is much different from the former two cases.
In summary, when the working state update cycles of the intelligent household appliances are different, the load rate fluctuates in a small range near the required value of the power grid dispatching center. Thus, in the case where the power limit calculated from a given load factor is the same, the variation in the update period will have a slight effect on the load factor obtained after the actual response. Therefore, on the premise of selecting a proper household appliance state updating period, an intelligent household appliance participation demand response scheme considering the power utilization comfort level of the user is implemented, the power utilization load rate can be effectively improved according to the requirement, and the normal life of the user is not influenced.
Claims (10)
1. A demand response method considering real-time comfort of a user and a load rate of a power grid is characterized by comprising the following steps:
after entering a DR scheduling time interval of demand response, the total power P of all intelligent household appliances at the current t momenttotal(t) and a preset power limit PlimitComparing the sizes of the two components;
when P is presenttotal(t)＞PlimitAnd when the intelligent household appliance is switched, calculating a comfort index K value according to real-time data feedback of the intelligent household appliance, and sequentially judging whether the corresponding household appliance is switched according to the ascending order of the K value: if the household appliance is in the power-on state, the household appliance is powered off and the P is updatedtotal(t); if the household appliance is in the power-off state, the next household appliance state is judged and decided, and when the household appliance operation state changes, the P is updated in timetotal(t) value until the time Ptotal(t)＜PlimitThe requirements are met;
and after meeting the requirement or after powering off all the household appliances in sequence, the requirement that the total power at the moment is lower than the limit value cannot be met, entering the next moment, and repeating the steps until the DR scheduling time zone is exited.
2. The demand response method considering user real-time comfort and grid load rate according to claim 1, wherein the smart appliances comprise an air conditioner, a water heater and an electric vehicle, and the total power P of all the smart appliances istotalAnd (t) is the sum of the running power of the air conditioner, the water heater and the electric automobile at the time t.
3. The demand response method considering user real-time comfort and grid load rate according to claim 2, wherein the operation characteristics of the air conditioner are as follows:
in the formula: pAC(t) is the operating power (kW) of the air conditioner at the moment t; pACRated power (kW) for the air conditioner; t isAC(t) is the indoor temperature (. degree. C.) at time t;
is the temperature set point (DEG C) at the time t;
temperature dead zone (. degree. C.); Δ t is the length of the time interval between the time t and the next time t + 1; g (t) is the heat increase rate of the house at the time t, positive numbers represent heat increase, and negative numbers represent heat loss (Btu/h); cACFor cooling capacity (Btu/h); Δ c is the energy required for a 1 ° F change in room temperature (Btu/° F).
4. The demand response method considering real-time user comfort and grid load rate according to claim 2, wherein the operating characteristics of the water heater are as follows:
in the formula: pWH(t) is the running power (kW) of the water heater at the moment t; pWHRated power (kW) for the water heater; t isWH(t) is the water temperature (. degree. C.) at time t;
set value (DEG C) of water temperature at time t;
water temperature dead zone (. degree. C.); fr (t) is the flow rate of hot water (gpm) at time t; vtankIs the volume of the tank (gallons); t isinletThe temperature of the injected water at the water inlet (F); alpha is the heating temperature coefficient of the water heater; xi is the speed of the water temperature of the water heater falling in unit time.
5. The demand response method considering user real-time comfort and grid load rate according to claim 2, wherein the operating characteristics of the electric vehicle are as follows:
pEV(t)＝PEV·NEV(t)·wEV(t) (6)
in the formula: p is a radical ofEV(t) is charging power (kW) of the electric automobile at the moment t; pEVRated power (kW) for the electric vehicle; n is a radical ofEV(t) is the connection state of the electric automobile at the moment t, wherein 1 represents that the electric automobile is connected with the charging pile, and 0 represents that the electric automobile is not connected with the charging pile; w is aEV(t) is the charging state of the electric automobile under the uncontrolled condition at the time t, wherein l represents that the electric automobile is charged, and 0 represents that the electric automobile is not charged; SOC (t) is the state of charge at time t; SOCminThe minimum state of charge required to be reached at the expected end-of-charge time.
6. Demand response method taking into account user real-time comfort and grid load rate according to claim 1, characterized in that the preset power limit P islimitObtained according to the following method:
the power grid dispatching center gives a load rate value lambda which needs to be improved after response according to the real-time load rate conditionafter，
In the formula: lambda [ alpha ]afterThe load rate of the power grid after implementing the demand response method; t is t1And t2Respectively as the starting time and the ending time of the statistical time interval; t is tDRaAnd tDRbRespectively as the beginning time and the ending time of the DR interval time interval; p (t) is the load power at the time t when the demand response is not implemented; Δ p (t) is the power variation of the intelligent household appliance participating in demand response at the time t; pmaxCounting the maximum active load in a time period; delta P is the power variation of the intelligent household appliance participating in demand response in the peak period;
p is calculated from the formula (14)limit＝P(t)-ΔP(t)＝Pmax-ΔP。
7. The demand response method considering real-time user comfort and grid load rate according to claim 2, wherein the air conditioning comfort index calculation formula is as follows:
in the formula: t isAC(t) is the indoor temperature (. degree. C.) at time t;
is the lowest room temperature set value;
temperature dead zone (. degree. C.); kAC(t) is the difference between the room temperature at time t after the per unit analysis and the lowest value of the set room temperature, and K is the higher the room temperature isACThe larger (t) is, the lower the satisfaction of the user is, and thus the higher the priority of electricity use thereof is.
8. The demand response method considering real-time user comfort and grid load rate according to claim 2, wherein the water heater comfort index calculation formula is as follows:
in the formula: t isWH(t) is the water temperature (DEG C) at the time t of the water heater;
is the lowest water temperature set value;
water temperature dead zone (. degree. C.); kWH(t) the difference between the maximum water temperature and the water temperature at time t is set after per unit, and the lower the water temperature is, KWHThe larger (t) is, the lower the satisfaction of the user is, and thus the higher the priority of electricity use thereof is.
9. The demand response method considering the real-time comfort of the user and the load rate of the power grid according to claim 2, wherein the comfort index calculation formula of the electric vehicle is as follows:
in the formula: t isQThe length of time still required for completing charging of the electric vehicle; t isSTime required for completing charging of the electric vehicle; t is the running time t; kEV(t) is the priority of electric automobile power consumption, if the electric automobile can finish charging within the required time, then user's satisfaction is the highest among all intelligent household electrical appliances, and its priority of power consumption is the lowest, therefore KEV(t) — 1; if the charging can not be finished, the satisfaction degree of the user is the lowest of all intelligent household appliances, and the electricity utilization priority is the highest, so that the charging method has the advantages of being simple in structure, convenient to use, and capable of being used for charging the intelligent household appliances and achieving the purpose of improving the charging effect
。
10. The demand response method considering real-time user comfort and grid load rate according to claim 1, wherein the K value is in an ascending order of magnitude as follows:
K(t)＝min(KAPP0(t)) (13)
in the formula (I), the compound is shown in the specification,
is the largest integer less than x; min (x) is a function that can order x from small to large; kAPPRepresenting the power utilization priority of the intelligent household appliance; t represents a time point within the statistical period, T represents a data update period of the household appliance, KAPP0(T) denotes K within one update period TAPPOf the sampling value(s).
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