CN116629534A - Demand side response optimization scheduling method considering multi-type adjustable resources of residential users - Google Patents
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Abstract
The invention relates to a demand side response optimization scheduling method considering multi-type adjustable resources of residential users, which comprises the following steps: constructing a flexible load model of a resident user, wherein the flexible load type comprises a temperature controllable load, a translatable load, an interruptible load and a reducible load; constructing a comfort level model of each type of flexible load of a resident user; constructing an excitation model based on peak shaving contribution index constraint; and constructing an optimal scheduling model with the minimum total cost of the user as a target by combining the flexible load model, the comfort level model and the excitation model, and outputting an optimal scheduling scheme. Compared with the prior art, the invention fully considers various load models and corresponding comfort models of resident users, and adopts peak shaving contribution index constraint from the power grid angle, so that the users can accord with peak shaving expectations of the users.
Description
Technical Field
The invention relates to the technical field of demand side response, in particular to a demand side response optimization scheduling method considering multi-type adjustable resources of residential users.
Background
In recent years, the economic development of China is rapid, and the electric power demand is increased year by year, which brings great challenges to the power grid. In order to relieve the power supply pressure of the power grid in the peak period of power consumption, the power grid stimulates the power consumers to actively participate in the demand side response. With the rapid innovation speed of household appliances, the proportion of electricity occupied by resident electricity is increased year by year, which means that the potential of resident users to participate in demand side response is huge. How to schedule these resident consumers is a considerable problem
Current research only attributes the residential electrical loads to a certain class of loads, such as translatable, interruptible, reducible, etc., but not specifically refined, which can lead to errors in the results of the scheduling from the actual ones. Secondly, the construction of the user comfort model is single when scheduling the resources, and the user comfort model is not established according to the electricity utilization characteristics of specific resident loads.
In the aspect of excitation, a plurality of excitation methods only give a fixed unit excitation cost, and then the excitation subsidy is issued according to the regulated electric quantity of the user, but the excitation method has larger uncertainty, and is easy to cause the situation that the voltage of the dispatching power grid cannot be relieved because the user does not want to participate.
Disclosure of Invention
The invention aims to provide a demand side response optimization scheduling method considering multi-type adjustable resources of residential users, and the demand side response of the users can be effectively mobilized on the basis of considering the comfort level of the users, so that the scheduling expectations of a power grid are met.
The aim of the invention can be achieved by the following technical scheme:
a demand side response optimization scheduling method considering multi-type adjustable resources of residential users comprises the following steps:
s1, constructing a flexible load model of a resident user, wherein the flexible load type comprises a temperature-controllable load, a translatable load, an interruptible load and a load reduction;
s2, constructing comfort models of various types of flexible loads of resident users;
s3, constructing an excitation model based on peak regulation contribution index constraint;
and S4, constructing an optimal scheduling model with the minimum total cost of the user as a target by combining the flexible load model, the comfort level model and the excitation model, and outputting an optimal scheduling scheme.
Further, the step S1 includes the steps of:
s11: constructing a temperature-controllable load model
The temperature-controllable load comprises a variable frequency air conditioner, a fixed frequency air conditioner and an electric water heater, and the corresponding load model is specifically as follows:
variable frequency air conditioner load operating power:
P bp (t)＝(θ out (t)-θ bp,set )A/η BP
wherein θ out (t) and θ bp,set Respectively outdoor temperature and set temperature, A is the guideCoefficient of heat, eta BP The energy efficiency ratio of the variable frequency air conditioner;
constant frequency air conditioner load operation power:
P dp ＝P min +α(P max -P min )
wherein θ dp (t) and θ out (t) the indoor and outdoor temperatures at the time t respectively; m (t) is the on-off state of the fixed-frequency air conditioner, 0 represents off, and 1 represents on; q is the refrigerating power of the air conditioner, and the size is eta dp P N ，η dp For the energy efficiency ratio of the fixed-frequency air conditioner,P N the rated power of the fixed-frequency air conditioner is set; θ - And theta + The upper limit and the lower limit of indoor temperature change of the constant-frequency air conditioner under the operation of a preset temperature are respectively set; θ set Setting a temperature for the fixed-frequency air conditioner; c is the induction sensitivity, and the size is 0.5; epsilon is the simulation time step; t (T) on And T off The on and off periods of the fixed-frequency air conditioner are respectively; p (P) on The on duty ratio of the fixed-frequency air conditioner; p (P) dp The average power of one operation period of the fixed-frequency air conditioner is calculated; p (P) max And P min The upper limit and the lower limit of the average power value of the fixed-frequency air conditioner are respectively set; alpha is a scale factor, and the size is 0.5;
the first-order equivalent model of the electric water heater is as follows:
Q eh (t)＝η eh P eh (t)+Q eh,loss (t)
wherein θ eh,in (t) and θ out (t) is the internal temperature and the external temperature of the electric water heater at the moment t respectively; q (Q) eh (t) is the equivalent thermal power at time t; r is R eh And C eh The equivalent thermal resistance and the equivalent heat capacity of the room where the electric water heater is positioned are respectively; Δt (delta t) eh Simulating a time step for an electric water heater; η (eta) eh The heat efficiency of the electric water heater is that of the electric water heater; q (Q) eh,loss (t) is the heat loss power at time t; p (P) eh (t) is the electric power of the electric water heater at the moment t; d (t) is the water consumption of the electric water heater at the moment t; c (C) w Is the specific heat capacity of water; θ cw The temperature of cold water flowing into the electric water heater;
s12: constructing a translatable load constraint model:
wherein x is sh (t) is whether the translatable load is operational at time t, 1 indicating operational and 0 indicating non-operational; y is sh (t) indicates whether the translatable load is activated at time t, 1 indicates activation, and 0 indicates non-activation; alpha sh And beta sh Respectively representing start-stop time of the translatable load permission schedule; t (T) sh Representing a running time sum of translatable loads;
s13: constructing an interruptible load constraint model:
P tr (t)＝P N,tr x tr (t)
wherein P is tr (t) and P N，tr Respectively representing the running power and rated power of the interruptible load at the moment t; x is x tr (t) indicates whether or not the interruptible load is operating at time t, 1 indicates operating, and 0 indicates not operating; alpha tr And beta tr Respectively representing the start-stop time of the interruptible load allowable scheduling; t (T) tr Representing a running time sum of interruptible loads;
s14: constructing a load-reducible constraint model:
wherein x is cut,i Indicating that the load can be reduced at tThe running condition of the i-th gear power at the moment, 1 represents running, 0 represents non-running and alpha cut And beta cut Respectively representing start-stop time of load reduction allowable scheduling; p (P) cut (t) represents an operating power at which load can be reduced; p (P) cut,i Indicating that the operating power in the i-th power mode can be cut down.
Further, in the step S2, the comfort model of the temperature controllable load is:
wherein U is wk,i For the comfort level of the ith temperature-controllable load, the larger the value is, the lower the user comfort level is, and the value range is [0,1]；θ i,in (t) and θ i,in,best (t) representing the actual temperature of the i-th temperature controllable load and the optimal human body adaptation temperature, respectively; θ i,in,max The maximum regulation temperature of the ith temperature-controllable load; x is x i (t) indicates whether the ith temperature-controllable load has a requirement for temperature at time t, 1 representing the presence, 0 representing the absence; t denotes that the 24-hour scheduling period is equally divided into T time periods.
Further, in the step S2, the comfort model of the translatable load is:
wherein U is sh,i Indicating the comfort level of the ith translatable load, the greater the value, the lower the comfort level of the user, the range of values being [0,1]；T sh,i,de And T sh,i,de,max Representing the actual extension time and the maximum extension time of the i-th translatable load, respectively; alpha sh,i And beta sh,i Respectively representing the start-stop time of the i-th translatable load allowed scheduling; t (T) sh,i Representing the running time sum of the ith translatable load; y is sh,i (t) indicates whether the translatable load i is activated at time t, 1 indicates activation, and 0 indicates non-activation; t represents equally dividing a 24-hour scheduling period into T timesSegments.
Further, in the step S2, the comfort model of the interruptible load is:
wherein U is tr,i Indicating the comfort level of the ith interruptible load, the larger the value thereof is, the lower the comfort level of the user is, the value range is [0,1]；T tr,i,de And T tr,i,de,max Representing the actual extension time and the maximum extension time of the ith interruptible load, respectively; alpha tr,i And beta tr,i Respectively representing the start-stop time of the i-th interruptible load allowable scheduling; t (T) tr,i Representing the running time sum of the ith interruptible load; x is x tr,i (t) indicates whether the ith interruptible load t is operating at the moment, 1 representing operation and 0 representing non-operation.
Further, the comfort model capable of reducing load in the step S2 is as follows:
wherein U is cut,i Indicating the comfort level of the i type load reduction, wherein the larger the value is, the lower the comfort level of the user is, and the value range is [0,1]；x cut,down,i (t) represents whether or not the ith load reducible t moment is low power operation, 1 represents yes, and 0 represents no; alpha cut,i And beta cut,i The i-th start-stop time of load-reducible allowable schedule is shown.
Further, in the step S3, the peak shaving contribution index constraint is specifically: the linearized pearson correlation coefficient is taken as a peak shaving contribution index (the smaller the negative value is, the better the peak shaving effect is indicated), and the peak shaving contribution index is constrained:
wherein H is sf (P total (t)) represents the peak shaving contribution of the user, P total (t) andrespectively representing the power at the time of a user t and the average power consumption of the user in one day; p (P) sys (t) and->The power consumption at the time t of the system and the average power consumption of the system in one day are respectively represented; mu is a resident contribution index constraint value; t denotes that the 24-hour scheduling period is equally divided into T time periods.
Further, the excitation model in the step S3 is:
wherein C is mot Compensation cost (constraint release according to peak shaver contribution index) for excitation; w (w) mot To encourage rates; h sf,base Normalization factors for peak regulation contribution; h sf (-P sys (t)) represents the peak shaving contribution of the peak shaving load curve of the system.
Further, in said step S4, the total cost of the user in the objective function of the optimized scheduling model includes the cost of the user power consumption, the cost of the user comfort and the incentive charge of the distribution network operator.
Further, in the step S4, an objective function of the optimized scheduling model is:
wherein C is use (t) represents the domestic electricity cost at time t; c (C) price (t) represents the operation of the power distribution network at the time tElectricity price given by the quotient; p (P) all (t) represents the total electricity purchasing power of residents at the time t; u (U) all Representing the sum of comfort levels of all resident electric equipment; a is a comfort influence factor, the size of which depends on the demand of a user on comfort, and the larger the value of a factor is, the higher the demand of the user on comfort is; c (C) mot Compensation costs for the excitation; Δt is the scheduling time interval; p (P) gx (t) represents the power at the moment of the rigid load t, P bp (t) represents the power at the moment of the load t of the variable frequency air conditioner, P dp (t) represents the power at the time of the constant-frequency air conditioner load t, P eh (t) represents the power at time t of the electric water heater, P sh (t) represents the power at the time of the translatable load t, P tr (t) represents the power at the time t of the interruptible load, P cut (t) represents the power at the moment t of the load, P pv (t) represents the power of the photovoltaic power generation system at time t; u (U) wk,i For the comfort level of the ith temperature-controllable load, U sh,i Indicating comfort level of i-th translatable load, U tr,i Indicating the comfort level of the ith interruptible load, U cut,i Indicating the comfort level at which class i can cut down the load.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention simply classifies domestic electric loads into four types, further subdivides each type of load according to actual conditions, particularly a temperature-controllable load with higher electricity occupation, respectively establishes different models according to the working characteristics of the temperature-controllable load, respectively establishes a user comfort model which is close to the load of different types according to the influence of each type of load on the user comfort, and more accords with the actual conditions.
(2) The peak regulation contribution degree index constraint model is built, the specific constraint value is determined by the power grid and the user together, and the aim of lowest cost of the user is achieved, so that the enthusiasm of the user for participating in scheduling can be fully mobilized, the expectation that the user participates in scheduling at each moment and the power grid is attached to the power grid can be guaranteed, and the requirements of both parties are met.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a graph of electricity price temperature in one embodiment;
FIG. 3 is a graph of photovoltaic output and a user initial electrogram in one embodiment;
FIG. 4 is a graph of a comparison of residential power load scheduling in one embodiment;
FIG. 5 is a diagram of user different comfort power variation in one embodiment;
FIG. 6 is a plot of example I versus initial power used by a user in one embodiment;
FIG. 7 is a plot of example II versus user initial power usage in one embodiment;
FIG. 8 is a graph showing the comparison of the power used by examples I and II in one embodiment.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
The embodiment provides a demand side response optimization scheduling method considering multi-type adjustable resources of residential users, as shown in fig. 1, comprising the following steps:
s1, constructing a flexible load model of a resident user, wherein the flexible load type comprises a temperature-controllable load, a translatable load, an interruptible load and a reducible load.
S11: constructing a temperature-controllable load model
The temperature-controllable load comprises a variable frequency air conditioner, a fixed frequency air conditioner and an electric water heater, and the corresponding load model is specifically as follows:
(1) Variable frequency air conditioner load operating power:
P bp (t)＝(θ out (t)-θ bp,set )A/η BP
wherein θ out (t) and θ bp,set Respectively outdoor temperature and set temperature, A is heat conductivity coefficient, eta BP Is the energy efficiency ratio of the variable frequency air conditioner.
(2) Constant frequency air conditioner load operation power:
P dp ＝P min +α(P max -P min )
wherein θ dp (t) and θ out (t) the indoor and outdoor temperatures at the time t respectively; m (t) is the on-off state of the fixed-frequency air conditioner, 0 represents off, and 1 represents on; q is the refrigerating power of the air conditioner, and the size is eta dp P N ，η dp P is the energy efficiency ratio of the fixed-frequency air conditioner N The rated power of the fixed-frequency air conditioner is set; θ - And theta + The upper limit and the lower limit of indoor temperature change of the constant-frequency air conditioner under the operation of a preset temperature are respectively set;θ set setting a temperature for the fixed-frequency air conditioner; c is the induction sensitivity, and the size is 0.5; epsilon is the simulation time step; t (T) on And T off The on and off periods of the fixed-frequency air conditioner are respectively; p (P) on The on duty ratio of the fixed-frequency air conditioner; p (P) dp The average power of one operation period of the fixed-frequency air conditioner is calculated; p (P) max And P min The upper limit and the lower limit of the average power value of the fixed-frequency air conditioner are respectively set; alpha is a scale factor, and the size is 0.5;
(3) The first-order equivalent model of the electric water heater is as follows:
Q eh (t)＝η eh P eh (t)+Q eh,loss (t)
wherein θ eh,in (t) and θ out (t) is the internal temperature and the external temperature of the electric water heater at the moment t respectively; q (Q) eh (t) is the equivalent thermal power at time t; r is R eh And C eh The equivalent thermal resistance and the equivalent heat capacity of the room where the electric water heater is positioned are respectively; Δt (delta t) eh Simulating a time step for an electric water heater; η (eta) eh The heat efficiency of the electric water heater is that of the electric water heater; q (Q) eh,loss (t) is the heat loss power at time t; p (P) eh (t) is the electric power of the electric water heater at the moment t; d (t) is the water consumption of the electric water heater at the moment t; c (C) w Is the specific heat capacity of water; θ cw For the temperature of the cold water flowing into the electric water heater.
S12: constructing translatable load constraint model
In this embodiment, the translatable load is mainly a washing machine, a thermos, a dishwasher, a sterilizing cabinet and a dryer, and the specific constraint model is:
wherein x is sh (t) is whether the translatable load is operational at time t, 1 indicating operational and 0 indicating non-operational; y is sh (t) indicates whether the translatable load is activated at time t, 1 indicates activation, and 0 indicates non-activation; alpha sh And beta sh Respectively representing start-stop time of the translatable load permission schedule; t (T) sh Representing the running time sum of translatable loads.
S13: construction of interruptible load constraint model
The interruptible load is mainly an electric automobile, and the specific constraint model is as follows:
P tr (t)＝P N,tr x tr (t)
wherein P is tr (t) and P N，tr Respectively representing the running power and rated power of the interruptible load at the moment t; x is x tr (t) indicates whether or not the interruptible load is operating at time t, 1 indicates operating, and 0 indicates not operating; alpha tr And beta tr Respectively representing the start-stop time of the interruptible load allowable scheduling; t (T) tr Representing the running time sum of the interruptible load.
S14: constructing a load shedding constraint model
The computer with the main power adjustable load can be cut down, and the specific constraint model is as follows:
wherein x is cut,i Indicating the operating condition of the i-th gear power at the t moment when the load can be reduced, 1 indicating the operation, 0 indicating the non-operation, alpha cut And beta cut Respectively representing start-stop time of load reduction allowable scheduling; p (P) cut (t) represents an operating power at which load can be reduced; p (P) cut,i Indicating that the operating power in the i-th power mode can be cut down.
The operating power and schedule time of the above various loads are shown in table 1:
TABLE 1 various load operating powers and scheduling times
| Load of | Operating power/kW | Scheduling time |
| Washing machine | 0.5 | 19：00-22:00 |
| Hot water kettle | 1.5 | 21:00-7:00 |
| Dish-washing machine | 0.5 | 20:00-2:00 |
| Sterilizing cabinet | 0.3 | 19:00-7:00 |
| Drying machine | 1.5 | 22:00-7:00 |
| Electric automobile | 3 | 18:00-6:00 |
| Computer with a computer program | 0.3/0.15 | 19:00-22:00 |
| Variable frequency air conditioner | 2 | All the day |
| Fixed-frequency air conditioner | 2 | All the day |
| Electric water heater | 2.5 | 7:00-0:00 |
The fixed frequency air conditioning parameters are shown in table 2:
table 2 constant frequency air conditioner parameters
The variable frequency air conditioner parameters are shown in table 3:
table 3 variable frequency air conditioner parameters
| Device category | Coefficient of thermal conductivity | Energy efficiency ratio | Temperature adjustment range (. Degree. C.) |
| Variable frequency air conditioner | 0.18 | 3.5 | [25,27] |
The parameters of the electric water heater are shown in table 4:
table 4 parameters of electric water heater
S2, constructing comfort models of various types of flexible loads of resident users.
According to the characteristics of the electric equipment, the comfort level models of users are respectively built.
S21, constructing a comfort level model of the temperature-controllable load
Wherein U is wk,i For the comfort level of the ith temperature-controllable load, the larger the value is, the lower the user comfort level is, and the value range is [0,1]；θ i,in (t) and θ i,in,best (t) representing the actual temperature of the i-th temperature controllable load and the optimal human body adaptation temperature, respectively; θ i,in,max Is the ith kind of cocoaMaximum regulation temperature of temperature control load; x is x i (t) indicates whether the ith temperature-controllable load has a requirement for temperature at time t, 1 representing the presence, 0 representing the absence; t denotes that the 24-hour scheduling period is equally divided into T time periods.
S22, constructing a comfort level model capable of translating load
Wherein U is sh,i Indicating the comfort level of the ith translatable load, the greater the value, the lower the comfort level of the user, the range of values being [0,1]；T sh,i,de And T sh,i,de,max Representing the actual extension time and the maximum extension time of the i-th translatable load, respectively; alpha sh,i And beta sh,i Respectively representing the start-stop time of the i-th translatable load allowed scheduling; t (T) sh,i Representing the running time sum of the ith translatable load; y is sh,i (t) indicates whether the translatable load i is activated at time t, 1 indicates activation, and 0 indicates non-activation.
S23, constructing a comfort level model capable of interrupting load
Wherein U is tr,i Indicating the comfort level of the ith interruptible load, the larger the value thereof is, the lower the comfort level of the user is, the value range is [0,1]；T tr,i,de And T tr,i,de,max Representing the actual extension time and the maximum extension time of the ith interruptible load, respectively; alpha tr,i And beta tr,i Respectively representing the start-stop time of the i-th interruptible load allowable scheduling; t (T) tr,i Representing the running time sum of the ith interruptible load; x is x tr,i (t) indicates whether the ith interruptible load t is operating at the moment, 1 representing operation and 0 representing non-operation.
S24, constructing a comfort level model capable of reducing load
Wherein U is cut,i Indicating the comfort level of the i type load reduction, wherein the larger the value is, the lower the comfort level of the user is, and the value range is [0,1]；x cut,down,i (t) represents whether or not the ith load reducible t moment is low power operation, 1 represents yes, and 0 represents no; alpha cut,i And beta cut,i The i-th start-stop time of load-reducible allowable schedule is shown.
S3, constructing an excitation model based on peak shaving contribution index constraint.
In order to enable the user to participate in peak shaving to meet the expectations of the power grid, peak shaving contribution degree index constraint is adopted, and final subsidy of the power grid to the user depends on whether the user schedules according to the constraint, wherein the peak shaving contribution degree index constraint is expressed as:
wherein H is sf (P total (t)) represents peak shaving contribution of the user, and μ is a resident contribution index constraint value; p (P) total (t) andrespectively representing the power at the time of a user t and the average power consumption of the user in one day; p (P) sys (t) and->The power consumption at the time t of the system and the average power consumption of the system in one day are respectively represented.
The constructed excitation model is specifically as follows:
wherein C is mot Compensation costs for the excitation; w (w) mot To encourage rates; h sf,base Normalization factors for peak regulation contribution; h sf (-P sys (t)) represents the peak shaving contribution of the peak shaving load curve of the system.
And S4, constructing an optimal scheduling model with the minimum total cost of the user as a target by combining the flexible load model, the comfort level model and the excitation model, and outputting an optimal scheduling scheme.
In this embodiment, the power supply side is directly powered by the grid and the residential roof is photovoltaic powered, and the load side is composed of a rigid load and a flexible load. The objective function is that the total cost of the user is the lowest, specifically:
wherein C is use (t) represents the domestic electricity cost at time t; c (C) price (t) represents the electricity price given by the power distribution network operator at the moment t; p (P) all (t) represents the total electricity purchasing power of residents at the time t; p (P) gx (t)、P pv (t) represents the power at the moment t of the rigid load and the photovoltaic power generation system, respectively; u (U) all Representing the sum of calling comfort levels of all resident electric equipment; a is a comfort influence factor, the size of which depends on the demand of a user on comfort, and the larger the value of a factor is, the higher the demand of the user on comfort is; Δt is the scheduling time interval.
To verify the effectiveness of the technical solution of the present invention, this example comprises two examples (for comparative experiments):
example I: the peak shaving contribution index constraint is not carried out;
example II: and carrying out peak regulation contribution index constraint.
In this embodiment, the current day 14 is selected in consideration of the electricity consumption condition of the user on the working day: 00-the next day 14:00 is a scheduling period, the scheduling time scale is 30min, and the outdoor temperature and the grid electricity price during scheduling are shown in fig. 2. The home photovoltaic system output and home user initial power usage are shown in fig. 3.
The example takes a=1, μ=3, the scheduling situation is as shown in fig. 4, the peak electricity consumption of the user before scheduling, wherein 18: 30-20: 30, the maximum power consumption reaches 5.918kW, and after scheduling at 18: 30-20: the electricity consumption between 30 times is obviously reduced, the daily maximum load of the whole dispatching cycle is only 3.566kW, and the peak clipping effect is achieved. The daily peak Gu Chafen before and after scheduling was 5.779kW and 3.348kW, respectively, as shown in table 5, the fluctuation of the residential load electricity consumption after scheduling was smaller than that before scheduling. Thus, the daily load rate increases after scheduling.
Table 5 load characteristics before and after scheduling
| Scheduling case | Before scheduling | After scheduling |
| Daily maximum load/kW | 5.918 | 3.566 |
| Daily maximum peak-valley difference/kW | 5.779 | 3.348 |
| Daily load rate% | 22.61 | 36.31 |
Fig. 5 shows the electricity consumption change of the household user under different comfort requirements, when a=1, the daily maximum load is 3.566kW, and when a=3, the daily maximum load is 3.991kW, which indicates that when the user has higher comfort requirements, the adjustment will of the user is lower, and the scheduling effect is reduced.
Fig. 6 to 8 show that the comparison between the initial electricity consumption condition of the example i and the user, the comparison between the initial electricity consumption condition of the example ii and the user, and the comparison between the examples i and ii respectively, and it can be seen from fig. 6 and 7 that the examples i and ii do not both have the constraint on the peak regulation contribution index, but have a certain peak regulation effect. From fig. 8, it can be seen whether the peak shaving effect is constrained by the peak shaving contribution index or not, the daily maximum load of example i is 3.995kW, the daily maximum load of example ii is 3.566kW, and the peak shaving effect of example ii is better than that of example i.
In summary, the present technical solution proposes a demand side response optimization scheduling method considering participation of multiple types of adjustable resources of residential users, including: constructing a load model of resident users, wherein the load model comprises a temperature-controllable load, a translatable load, an interruptible load and a load-reducible load; constructing a comfort model of various flexible loads of resident users; constructing an excitation model based on peak shaving contribution index constraint; and constructing an optimal scheduling model with the lowest total cost of the users by combining the models, and giving an optimal scheduling scheme. The simulation result shows that the invention fully considers various load models of resident users and corresponding comfort models, regulates and controls the load according to the comfort requirements of the users, adopts peak regulation contribution index constraint from the power grid angle, and has the final dispatching effect obviously better than the situation that peak regulation contribution index is not constrained, and the constraint value power grid of the index can be determined with the user according to the actual situation of the power grid per se, thereby considering the requirements of the power grid and the users.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by a person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (10)
1. A demand side response optimization scheduling method considering multi-type adjustable resources of residential users is characterized by comprising the following steps:
s1, constructing a flexible load model of a resident user, wherein the flexible load type comprises a temperature-controllable load, a translatable load, an interruptible load and a load reduction;
s2, constructing comfort models of various types of flexible loads of resident users;
s3, constructing an excitation model based on peak regulation contribution index constraint;
and S4, constructing an optimal scheduling model with the minimum total cost of the user as a target by combining the flexible load model, the comfort level model and the excitation model, and outputting an optimal scheduling scheme.
2. A demand side response optimizing scheduling method considering multi-type adjustable resources of residential users according to claim 1, wherein said step S1 comprises the steps of:
s11: constructing a temperature-controllable load model
The temperature-controllable load comprises a variable frequency air conditioner, a fixed frequency air conditioner and an electric water heater, and the corresponding load model is specifically as follows:
variable frequency air conditioner load operating power:
P bp (t)＝(θ out (t)-θ bp,set )A/η BP
wherein θ out (t) and θ bp,set Respectively outdoor temperature and set temperature, A is heat conductivity coefficient, eta BP The energy efficiency ratio of the variable frequency air conditioner;
constant frequency air conditioner load operation power:
P dp ＝P min +α(P max -P min )
wherein θ dp (t) and θ out (t) the indoor and outdoor temperatures at the time t respectively; m (t) is the on-off state of the fixed-frequency air conditioner, 0 represents off, and 1 represents on; q is the refrigerating power of the air conditioner, and the size is eta dp P N ，η dp P is the energy efficiency ratio of the fixed-frequency air conditioner N The rated power of the fixed-frequency air conditioner is set; θ - And theta + The upper limit and the lower limit of indoor temperature change of the constant-frequency air conditioner under the operation of a preset temperature are respectively set; θ set Setting a temperature for the fixed-frequency air conditioner; c is the induction sensitivity; epsilon is the simulation time step; t (T) on And T off Respectively the on and off periods of the fixed frequency air conditioner；P on The on duty ratio of the fixed-frequency air conditioner; p (P) dp The average power of one operation period of the fixed-frequency air conditioner is calculated; p (P) max And P min The upper limit and the lower limit of the average power value of the fixed-frequency air conditioner are respectively set; alpha is a scale factor;
the first-order equivalent model of the electric water heater is as follows:
Q eh (t)＝η eh P eh (t)+Q eh,loss (t)
wherein θ eh,in (t) and θ out (t) is the internal temperature and the external temperature of the electric water heater at the moment t respectively; q (Q) eh (t) is the equivalent thermal power at time t; r is R eh And C eh The equivalent thermal resistance and the equivalent heat capacity of the room where the electric water heater is positioned are respectively; Δt (delta t) eh Simulating a time step for an electric water heater; η (eta) eh The heat efficiency of the electric water heater is that of the electric water heater; q (Q) eh,loss (t) is the heat loss power at time t; p (P) eh (t) is the electric power of the electric water heater at the moment t; d (t) is the water consumption of the electric water heater at the moment t; c (C) w Is the specific heat capacity of water; θ cw The temperature of cold water flowing into the electric water heater;
s12: constructing a translatable load constraint model:
wherein x is sh (t) is whether the translatable load is operational at time t, 1 indicating operational and 0 indicating non-operational; y is sh (t) indicates whether the translatable load is activated at time t, 1 indicates activation, and 0 indicates non-activation; alpha sh And beta sh Respectively representing start-stop time of the translatable load permission schedule; t (T) sh Representing a running time sum of translatable loads;
s13: constructing an interruptible load constraint model:
P tr (t)＝P N,tr x tr (t)
wherein P is tr (t) and P N，tr Respectively representing the running power and rated power of the interruptible load at the moment t; x is x tr (t) indicates whether or not the interruptible load is operating at time t, 1 indicates operating, and 0 indicates not operating; alpha tr And beta tr Respectively representing the start-stop time of the interruptible load allowable scheduling; t (T) tr Representing a running time sum of interruptible loads;
s14: constructing a load-reducible constraint model:
wherein x is cut,i Indicating the operating condition of the i-th gear power at the t moment when the load can be reduced, 1 indicating the operation, 0 indicating the non-operation, alpha cut And beta cut Respectively representing start-stop time of load reduction allowable scheduling; p (P) cut (t) represents an operating power at which load can be reduced; p (P) cut,i Representation ofThe operating power in the load i-th power mode can be cut down.
3. The method for optimizing and scheduling demand side response of multi-type adjustable resources of residential users according to claim 1, wherein in step S2, a comfort model of the temperature controllable load is:
wherein U is wk,i For the comfort level of the ith temperature-controllable load, the larger the value is, the lower the user comfort level is, and the value range is [0,1]；θ i,in (t) and θ i,in,best (t) representing the actual temperature of the i-th temperature controllable load and the optimal human body adaptation temperature, respectively; θ i,in,max The maximum regulation temperature of the ith temperature-controllable load; x is x i (t) indicates whether the ith temperature-controllable load has a requirement for temperature at time t, 1 representing the presence, 0 representing the absence; t denotes that the 24-hour scheduling period is equally divided into T time periods.
4. The method for optimizing and scheduling demand side response in consideration of multiple types of adjustable resources for residential users according to claim 1, wherein in step S2, a comfort model of translatable load is:
wherein U is sh,i Indicating the comfort level of the ith translatable load, the greater the value, the lower the comfort level of the user, the range of values being [0,1]；T sh,i,de And T sh,i,de,max Representing the actual extension time and the maximum extension time of the i-th translatable load, respectively; alpha sh,i And beta sh,i Respectively representing the start-stop time of the i-th translatable load allowed scheduling; t (T) sh,i Representing the running time sum of the ith translatable load; y is sh,i (t) represents a translatable negativeWhether the load i is started at the time t or not, wherein 1 represents starting and 0 represents non-starting; t denotes that the 24-hour scheduling period is equally divided into T time periods.
5. The method for optimizing and scheduling demand side response in consideration of multiple types of adjustable resources of residential users according to claim 1, wherein in step S2, a comfort model of interruptible load is:
wherein U is tr,i Indicating the comfort level of the ith interruptible load, the larger the value thereof is, the lower the comfort level of the user is, the value range is [0,1]；T tr,i,de And T tr,i,de,max Representing the actual extension time and the maximum extension time of the ith interruptible load, respectively; alpha tr,i And beta tr,i Respectively representing the start-stop time of the i-th interruptible load allowable scheduling; t (T) tr,i Representing the running time sum of the ith interruptible load; x is x tr,i (t) indicates whether the ith interruptible load t is operating at the moment, 1 representing operation and 0 representing non-operation.
6. The method for optimizing scheduling of demand side response in consideration of multiple types of adjustable resources for residential users according to claim 1, wherein the comfort model capable of reducing load in step S2 is as follows:
wherein U is cut,i Indicating the comfort level of the i type load reduction, wherein the larger the value is, the lower the comfort level of the user is, and the value range is [0,1]；x cut,down,i (t) represents whether or not the ith load reducible t moment is low power operation, 1 represents yes, and 0 represents no; alpha cut,i And beta cut,i The i-th start-stop time of load-reducible allowable schedule is shown.
7. The method for optimizing and scheduling demand side response of multi-type adjustable resources of residential users according to claim 1, wherein in the step S3, peak shaving contribution index constraint is specifically: taking the linearized pearson correlation coefficient as a peak shaving contribution index and restraining the peak shaving contribution index:
wherein H is sf (P total (t)) represents the peak shaving contribution of the user, P total (t) andrespectively representing the power at the time of a user t and the average power consumption of the user in one day; p (P) sys (t) and->The power consumption at the time t of the system and the average power consumption of the system in one day are respectively represented; mu is a resident contribution index constraint value; t denotes that the 24-hour scheduling period is equally divided into T time periods.
8. The method for optimizing scheduling of demand side response in consideration of multiple types of tunable resources of residential users according to claim 7, wherein the excitation model in step S3 is:
wherein C is mot Compensation costs for the excitation; w (w) mot Is excited byTariff rate; h sf,base Normalization factors for peak regulation contribution; h sf (-P sys (t)) represents the peak shaving contribution of the peak shaving load curve of the system.
9. A method for optimizing scheduling of demand side response in consideration of multiple types of tunable resources of residential subscribers according to claim 1, wherein in step S4, the total cost of subscribers in the objective function of the optimized scheduling model includes the cost of electricity consumption of subscribers, the cost of comfort of subscribers and the incentive charge of the distribution network operator.
10. The method for optimizing scheduling of demand side response in consideration of multi-type adjustable resources of residential users according to claim 9, wherein in step S4, the objective function of the optimizing scheduling model is:
wherein C is use (t) represents the domestic electricity cost at time t; c (C) price (t) represents the electricity price given by the power distribution network operator at the moment t; p (P) all (t) represents the total electricity purchasing power of residents at the time t; u (U) all Representing the sum of comfort levels of all resident electric equipment; a is a comfort influence factor, the size of which depends on the demand of a user on comfort, and the larger the value of a factor is, the higher the demand of the user on comfort is; c (C) mot Compensation costs for the excitation; Δt is the scheduling time interval; p (P) gx (t) represents the power at the moment of the rigid load t, P bp (t) represents the power at the moment of the load t of the variable frequency air conditioner, P dp (t) represents the power at the time of the constant-frequency air conditioner load t, P eh (t) represents the power at time t of the electric water heater, P sh (t) represents the power at the time of the translatable load t, P tr (t) represents the power at the time t of the interruptible load, P cut (t) represents the power at the moment t of the load, P pv (t) represents the power of the photovoltaic power generation system at time t; u (U) wk,i For the comfort level of the ith temperature-controllable load, U sh,i Represents the ith kind of can be flatComfort level of load shifting, U tr,i Indicating the comfort level of the ith interruptible load, U cut,i Indicating the comfort level at which class i can cut down the load.
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