CN111271839B - Method for adjusting short-term power of fixed-frequency air conditioner - Google Patents
Method for adjusting short-term power of fixed-frequency air conditioner Download PDFInfo
- Publication number
- CN111271839B CN111271839B CN202010093078.8A CN202010093078A CN111271839B CN 111271839 B CN111271839 B CN 111271839B CN 202010093078 A CN202010093078 A CN 202010093078A CN 111271839 B CN111271839 B CN 111271839B
- Authority
- CN
- China
- Prior art keywords
- air conditioner
- power
- load
- capacity
- fixed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a method for adjusting short-term power of a fixed-frequency air conditioner, belonging to the technical field of power adjustment, aiming at solving the technical problems of larger calculated amount and communication traffic in the power adjustment process of the fixed-frequency air conditioner in the prior art, predicting a load curve of the fixed-frequency air conditioner and evaluating the capacity of a schedulable fixed-frequency air conditioner load; determining the purchased air conditioner adjusting capacity, and further determining an adjustable instruction range; determining a final purchase result according to the available purchase capacity and the price; determining a distribution of purchased climate control groups; optimizing the triggering time of the air conditioner control group; and sending the triggering time of the air conditioner control groups to each air conditioner control group through the load aggregator. The method is based on the method for adjusting the set value of the air conditioner temperature, simultaneously considers the economy of the dispatching center and the load aggregator, realizes the accurate power adjustment effect based on the optimization algorithm, can effectively reduce the calculated amount and the real-time communication pressure during dispatching, and can provide higher adjustment precision on the basis of less calculated amount.
Description
Technical Field
The invention belongs to the technical field of power regulation, and particularly relates to a method for regulating short-term power of a fixed-frequency air conditioner.
Background
With the large-scale access of renewable energy and the annual growth of peak loads in urban centers, the demand of system balance resources is increased dramatically. The temperature control load has the characteristics of easy control and large schedulable potential, and a large amount of temperature control loads can be aggregated by a load aggregator to participate in the regulation and control operation of the main network. The proportion of the air conditioner load is highest, the air conditioner load model has great potential in the aspects of peak clipping and valley filling, maintenance of stability of an electric power system, auxiliary service providing and the like, and the demand on the air conditioner load model is gradually increased along with the increase of the proportion of the air conditioner load model in a power grid.
At present, the research ON the load of the air conditioner mostly adopts an ON/OFF switching method to realize power regulation, and the ON/OFF switching method is suitable for regulation in a short time and is not suitable for regulation for a longer time (namely short-term regulation for 30-120 min). Since frequent ON/OFF switching during longer conditioning causes wear ON the air conditioning hardware. Meanwhile, the requirement on communication is high in the whole adjusting process, and the control pressure of a dispatching center is increased. And the overall implementation is economically costly.
Since the air conditioning load usually appears in the form of load groups when participating in scheduling, it is desirable to achieve a high accuracy and a low amount of computation in the control, and the control cost can be reduced. When large-scale air conditioning load participates in power regulation, the method is one of key problems in researching that the air conditioning load provides auxiliary service for a system, and has important significance for realizing participation of the air conditioning load in demand response and providing the auxiliary service for a power system.
Disclosure of Invention
The invention aims to provide a method for adjusting the short-term power of a fixed-frequency air conditioner, which aims to solve the technical problem that the calculation amount and the communication amount are large in the power adjusting process of the fixed-frequency air conditioner in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a method for regulating the short-term power of a fixed-frequency air conditioner comprises the following steps:
a, simulating a change curve of the polymerization power of an air conditioning control group when different temperature set values are adjusted by using a constant-frequency air conditioning polymerization model and acquiring characteristic parameters;
b, predicting a next day fixed-frequency air conditioner load curve and evaluating the capacity of the schedulable fixed-frequency air conditioner load;
step C, determining the purchased air conditioner adjusting capacity according to the capacity of the schedulable fixed-frequency air conditioner load and the expected achieved scheduling effect, and further determining the adjustable instruction range;
step D, determining a final purchase result according to the purchasable capacity and the price uploaded by each load aggregator, and respectively issuing the purchase result to the corresponding load aggregators;
e, the load aggregator optimizes the distribution and price of the air-conditioning control groups with different local adjustment gears according to the final purchase result and the characteristic parameters of the variation curve of the aggregation power, and determines the distribution of the purchased air-conditioning control groups;
step F, determining a scheduling instruction according to the adjustable instruction range, and optimizing the triggering time of the air conditioner control group according to the scheduling instruction by combining the variation curve of the aggregation power and the distribution of the air conditioner control group;
and G, issuing the triggering time of the air conditioner control groups to each air conditioner control group through the load aggregator.
Further, the step a includes:
a1, establishing a polymerization model when the temperature set value of the fixed-frequency air conditioner is adjusted as follows:
wherein P is the aggregate power of the air conditioning groups, Pagg,1For adjusting the pre-steady state air-conditionerPolymerization power of Pagg,2For adjusting the polymerization power, delta P, of the air conditioner in the post-stationary stateaggFor adjusting the difference between the aggregate powers of the front and rear air-conditioning groups, PmagIs the lowest point of the aggregate power dip; k is a radical ofdownIs the rate of decrease of the polymerization power; k is a radical ofcA correction factor for the rate of decrease; k is a radical ofupIs the rate of rise of the polymerization power; t is the time in the scheduling process, t1Adjusting the temperature set value; t is t2The moment when the power drops to the lowest power; t is t3The polymerization power rise time; t is t4The moment of returning to the steady state; n is the number of controlled loads, eta is the energy efficiency ratio of the fixed-frequency air conditioner, R is the equivalent thermal resistance of the room, and delta TsetFor adjusted temperature set point, Δ ToIs the difference in ambient temperature;
a2, determining the scale of the air-conditioning control group, setting different adjusting gears, performing simulation through the established aggregation model to obtain an aggregation power change curve of the air-conditioning control group when different temperature setting values are adjusted, and acquiring characteristic parameters.
Further, the characteristic parameters comprise an overall power drop curve, power drop duration and a power difference from the original power after the power is stable in the adjusting process.
Further, the step B includes:
b1, dividing all air conditioners in a certain area into a plurality of air conditioner control groups according to geographic position or parameter similarity and using a plurality of load aggregators as agents;
b2, predicting the next day fixed-frequency air-conditioning load curve;
b3, according to the fixed-frequency air-conditioning load curve, estimating the capacity of the schedulable fixed-frequency air-conditioning load by combining the compensation price, the outdoor temperature and the user comfort level:
R＝[R1 R2 L Ri L Rn] (3)
Ri＝[Ri1 Ri2L RixL Rir] (4)
△θin,x＝x△θin (6)
in the formula: r is a purchasable air conditioning control group provided by each load aggregator, RiFor the distribution of air-conditioning control groups available in the ith, i is the ith load aggregator, n is the number of load aggregators participating in the bidding, x is the x-th gear of the adjusting temperature set value, RixNumber of air-conditioning control groups, lambda, adjusted for the x-th geariFor adjustable coefficient of potential, Delta thetain,xFor each of the changed temperature settings, delta thetainFor each adjustment amount corresponding to the difference between two adjacent gears, Delta thetainδ/r, δ being the maximum adjustable temperature, r being the adjustment effect gear, pc,iActual purchase price for reserve capacity, pcFor a compensation price desired by the user, To,dTo adjust the ambient temperature, T, of the dayoReference ambient temperature, D, established for contract makingpAdjustable scheduling potential, η, for a currently aggregated air conditioner groupiThe average energy consumption ratio of the air conditioner group under the ith load aggregation quotient is shown.
Further, in the step C, the purchased air conditioner adjusting capacity refers to a difference between the aggregated power of the air conditioners in the pre-adjustment steady state and the aggregated power of the air conditioners in the post-adjustment steady state.
Further, the area method is used to determine the purchased air conditioning capacity.
Further, in step F, the method for optimizing the trigger time includes:
in the formula, PdifAccumulating the sum of squares of differences between actual scheduling effects and required scheduling effectsAdding, Pch,tFor the actual scheduling effect at time t, Ptar,tThe scheduling effect required for the time t.
Compared with the prior art, the invention has the following beneficial effects: the method is based on the method for adjusting the temperature set value of the air conditioner, simultaneously considers the economy of the dispatching center and the load aggregator, realizes the accurate power adjustment effect based on the optimization algorithm, can effectively reduce the calculated amount and the real-time communication pressure during dispatching, can provide higher adjustment precision on the basis of less calculated amount, and provides high-quality auxiliary service for the system.
Drawings
Fig. 1 is a schematic flow chart of a method for adjusting short-term power of a fixed-frequency air conditioner according to an embodiment of the present invention;
FIG. 2 is a model of the polymerization when the fixed frequency air conditioner adjusts the set point temperature;
FIG. 3 is a detailed distinction between confusing parameters;
FIG. 4 is a schematic illustration of an area method;
FIG. 5 is an iterative flow chart for solving for purchasing air conditioning capacity.
FIG. 6 is a diagram illustrating the range of commands that can be issued by the dispatch center.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Referring to fig. 1-6, a method for hierarchical control and short-term power regulation of a fixed-frequency air conditioner is shown, and each step is described in detail below.
The method comprises the following steps: and simulating a change curve of the polymerization power of the air-conditioning control group when different temperature set values are adjusted by using a fixed-frequency air-conditioning polymerization model.
(11) The polymerization model when the temperature set value of the fixed-frequency air conditioner is adjusted is established as follows:
wherein P is the aggregate power of the air conditioning groups, Pagg,1For adjusting the collective power of the air conditioner in the pre-stationary state, Pagg,2For adjusting the polymerization power, delta P, of the air conditioner in the post-stationary stateaggFor adjusting the difference between the aggregate powers of the front and rear air-conditioning groups, PmagIs the lowest point of the aggregate power dip; k is a radical ofdownIs the rate of decrease of the polymerization power; k is a radical ofcA correction factor for the rate of decrease; k is a radical ofupIs the rate of rise of the polymerization power; t is the time in the scheduling process, t1Adjusting the temperature set value; t is t2The moment when the power drops to the lowest power; t is t3The polymerization power rise time; t is t4The moment of returning to the steady state; n is the number of controlled loads, eta is the energy efficiency ratio of the fixed-frequency air conditioner, R is the equivalent thermal resistance of the room, and delta TsetFor adjusted temperature set point, Δ ToIs the difference in ambient temperature; FIG. 2 is a schematic diagram of a model of a fixed-frequency air conditioner for adjusting a temperature set value;
(12) simulation adjustment of polymerization power change curves of the space-time control group with different temperature set values:
for convenience of control, the size of the air conditioning control group is determined according to the command requirement. Because the smaller the size of the air conditioner control group, the higher the scheduling precision. And the single control of each air conditioner is replaced by controlling a small group, so that the calculation and control pressure of a dispatching center is reduced. Setting different adjusting gears, such as r gear, respectively simulating the r gear to obtain a polymerization power change curve of the air-conditioning control group when adjusting different temperature setting values, and extracting characteristic parameters according to the polymerization power change curve, wherein the characteristic parameters comprise an integral power drop curve, power drop duration and a power difference from original power after power is stable in an adjusting process; and the extracted characteristic parameters are used for optimizing and calculating in the fifth step to the sixth step.
Step two: before scheduling, the scheduling center predicts a next day load curve and evaluates the capacity of the schedulable air conditioner load, and the prediction results are respectively used for range planning of the scheduling time and the scheduling amplitude in the step three;
(21) determining different control bodies and control layers:
the air conditioner load can be divided according to the geographic position or parameter similarity, all air conditioners in a certain area are proxied by a plurality of load aggregators, and the control optimization is completed by the load aggregators and the dispatching center. Therefore, the dispatching system consists of an air conditioner load, a load aggregator and a dispatching center; the hierarchical architecture is divided as follows: a planning layer, a purchasing layer and an executing layer; the planning layer is mainly decided by a dispatching center, the purchasing layer is jointly born by the dispatching center and a load aggregator, and the execution layer is realized by air conditioner load
(22) Before dispatching, the dispatching center predicts the next day load curve:
currently, many studies are made on load prediction, and the load prediction is performed by using an LSTM neural network in the embodiment. The scheduling center determines the day-ahead purchase capacity of the air conditioner according to the day-ahead prediction; considering the existence of prediction error, the scheduling center performs ultra-short-term prediction on the load on the scheduling day; correcting the prediction result of the previous day by using the new prediction result, and if the purchased air conditioner capacity is more, correcting the adjustment effect of the day-ahead plan to realize reasonable utilization of resources and avoid excessive control in a certain period; if the purchased day-ahead air conditioning capacity is insufficient, additional purchasing of temporary air conditioning capacity is required to achieve the desired control effect;
(23) before scheduling, the scheduling center evaluates the capacity of the schedulable air conditioner load:
when the schedulable capacity of the air conditioner load is evaluated, the compensation price, the outdoor temperature and the user comfort degree are included; wherein the outdoor temperature can be obtained according to weather forecast, and the compensation price can be obtained by referring to past historical data; when dispatching, the most basic requirement is that the comfort degree of a user is not influenced, therefore, delta is set as the maximum adjustable temperature, and the temperature adjustment value can be obtained by a contract which is signed with the user in advance; dividing the adjusting effect into r gears, wherein the adjusting quantity corresponding to the difference between every two adjacent gears is delta thetainδ/r. R represents a purchasable air-conditioning group provided by each load aggregator, formula (3), formula(4) In, RiIs the distribution of the available air conditioning control groups in the ith. Equation (6) introduces the changed temperature setting Δ θ for each adjustment stepin,x. Equation (5) calculates the coefficient lambda of the schedulable potentialiAnd obtaining the adjustable scheduling potential D of the current aggregation air conditioner group in the formula (7)p；
In the formula: i represents the ith load aggregator, and n is the number of the load aggregators participating in bidding. x represents the x-th gear for adjusting the temperature set point. RixRepresenting the number of climate control subgroups corresponding to the x-th gear adjustment. p is a radical ofc,iActual purchase price for reserve capacity, pcThe price is compensated for the ideal price of the user. T iso,dTo adjust the ambient temperature, T, of the dayoA reference ambient temperature set up for contract making.
R＝[R1 R2 L Ri L Rn] (3)
Ri＝[Ri1 Ri2L RixL Rir] (4)
△θin,x＝x△θin (6)
In the formula: i is the ith load aggregation quotient, n is the number of the load aggregation quotients participating in the competitive bidding, x is the x-th grade of the adjusting temperature set value, RixNumber of air-conditioning control groups, lambda, adjusted for the x-th geariFor adjustable coefficient of potential, Delta thetain,xFor each of the changed temperature settings, p, for each of the adjustment stepsc,iActual purchase price for reserve capacity, pcFor a compensation price desired by the user, To,dTo adjust the ambient temperature, T, of the dayoReference ambient temperature, η, set up for contract makingiEmptying for the ith load aggregation businessAverage energy consumption ratio of the clusters. For ease of understanding, fig. 3 is used to specifically illustrate the differences between confusing parameters. In the step, the predicted next day load curve is used for planning the input time of peak clipping and valley filling, which is beneficial to intensively clipping the peak load; and the prediction of schedulable capacity is to limit the planning range and avoid the meaningless and excessively high planning level. This predicted value can be used for the range constraint in step three and the optimization constraint in step six.
Step three: the dispatching center preliminarily determines the expected dispatching effect, determines the purchased air conditioner adjusting capacity according to the dispatching effect, and further determines the adjustable instruction range;
(31) the method for explicitly realizing power regulation comprises the following steps:
capacity purchased by the dispatch center refers to the before and after adjustment plateau Pagg,1And Pagg,2The power adjusting effect is realized by the power drop part of the air conditioner group which appears for a large amplitude and a long time after adjusting the temperature set values at different time. That is, the air conditioning capacity to be purchased cannot be directly obtained according to the power conditioning effect to be achieved as desired. Since there is no direct link between the two;
(32) introducing an area method calculation flow:
determining the purchased conditioning capacity of the air conditioner using an area method, the use of which is illustrated in fig. 4; psFor purchased capacity, SEThe area of the load reduced in the peak reduction process; spTo purchase capacity PsCorrespondingly reducing the power area; theoretically, the area SpShould be equal to the sum of the area clipped by all control subgroups; the distribution of the control groups in each load aggregator is shown as formula (8); normalizing the group distribution in all load aggregators, and calculating the average regulating temperature of all control groups according to the formula (9); therefore, all air conditioning control groups can be regarded as adjusting delta TatOn the basis, the power drop area S generated when all air-conditioning groups adjust the temperature set value can be calculatedp(formula (10));
Ri1>ai2Ri2>L>airRir (8)
in the formula, airNumber factor, DeltaT, between air conditioners for different scheduling levelsatThe average adjustable temperature of all air conditioners, eta is energy consumption ratio, S is area, and delta TsetFor the actual temperature set point adjusted, ncThe number of air conditioners contained in each control group;
(33) determining the purchased air conditioning capacity according to the scheduling effect:
since the scheduling effect and the purchased air conditioning capacity are not directly related, no formula can directly obtain the accurate purchased capacity; therefore, an iterative algorithm is proposed to complete the pair PsTo ensure the accuracy of the obtained result; firstly, the dispatching center randomly generates a value as PsThe initial value of (c). P is obtained according to the calculation methodsCorresponding reduced area Sp(ii) a Integrating the peak clipping effect to obtain the corresponding SE(ii) a To effectively achieve the desired scheduling effect, S needs to be satisfiedp>SE(ii) a In order to avoid the waste of resources, the purchase quantity is also restricted not to be too large, and S needs to be satisfiedE<Sp<1.05SE(ii) a If the two conditions are both satisfied, stopping iteration and outputting Ps(ii) a If at least one condition is not satisfied, PsAccording to the violated condition, the number of the violated conditions is increased or decreased in a small range, and the violated conditions are input again to start iteration until the two conditions are met, so that the final purchase result P is obtaineds(ii) a A specific iteration flowchart is shown in fig. 5;
(34) according to the obtained purchase capacity PsDetermining a specific adjustable instruction range:
(33) for calculating SEArea ofWhat is desired is a roughly desired adjustment effect, not an actual instruction; the actual reduction instruction is further planned on the basis; in order to reduce the planning difficulty of the dispatching center, the specific regulating instruction range of the air conditioner can be further restricted by the purchased air conditioner regulating capacity; the highest instruction boundary and the lowest instruction boundary can be deduced on the basis of fully utilizing the schedulable capacity of the air conditioner group; when the command requires to adjust the power to rise and fall in a step mode, the lowest command boundary can be obtained; when the instruction requirement reaches the regulation power and then drops immediately after reaching the top point, the highest instruction boundary can be obtained; in order to simplify the derivation process, the rising and falling rates of the adjustment power command issued by the scheduling center are set to be equal, as shown in fig. 6 in detail; therefore, the shape of the curve with the highest power can be regarded as a triangle, and the shape of the curve with the lowest power can be regarded as a rectangle; formula (11) derives PxAnd PsThe boundary power results are (12);
in the formula, PhighIs the maximum value of the power boundary, PlowIs the minimum value of the power boundary, t1For instruction rise time, t2For instruction fall time, Γ is the overall instruction duration, t1+t2K is the ascending/descending speed of the power instruction, and a, b and c are all substitution quantities used for simplifying a calculation formula and have no special meaning.
The dispatching center only needs to make the maximum and minimum regulating power value in the dispatching process be PhighAnd PlowIn the region of the cut area is not more than PSEffective scheduling can be achieved.
Step four: based on the purchased adjusted capacity obtained in the step three, the dispatching center determines a final purchase result according to the purchasable capacity and the price reported by the load aggregators, and respectively issues the purchase result to the corresponding load aggregators;
the load aggregators report the adjustable capacity and the corresponding quoted price to the dispatching center, and the optimized dispatching center determines the air conditioner adjusting capacity delta D purchased from each load aggregatori. The unit price of schedulable capacity purchased at the day is LiRThe unit price of the schedulable capacity of the temporary purchase is LiT. The more air conditioning capacity an emergency purchase, the higher the unit price, which is linear with the amount of purchased capacity. Since the load aggregator needs to temporarily and user purchase at a higher price, the smaller the temporarily purchased capacity is, the better, which is also of interest in increasing the correction of the ultra-short term prediction. Optimizing the purchasing capacity from each load aggregator according to the following formula (13) on the basis of the minimum economic cost of purchasing the capacity from the dispatching center;
equations (14) - (15) calculate the economic cost of purchasing the day-ahead reserve capacity and purchasing the temporary reserve capacity; formula (16) is used to ensure that the purchased capacity can meet the demand for regulation, and formula (17) ensures that the purchased capacity can meet the demand for planning at the day; equation (18) limits the purchased air conditioning capacity to within its dispatchable potential;
s.t. Cr(△DiR)＝LiR△DiR (14)
Ct(△DiT)＝LiT△DiT (15)
Ps≤Dp (18)
LiT＝AΔDiT+B (19)
in the formula,. DELTA.DiRAdjusting capacity, Δ D, for day ahead purchases from the ith load aggregatoriTTemporary capacity regulation for purchase to the ith load aggregator, Cr(ΔDiR) Cost for adjusting the capacity expenditure to the ith load-aggregator day by day, Ct(ΔDiT) For the purchase of a temporarily adjusted capacity expenditure from the ith load aggregator, CtotalThe cost of the dispatching center for purchasing the air conditioning capacity is determined by A, the price coefficient related to the purchase amount, B, the conventional price coefficient, and PyAdjusting capacity for total purchased days, LiTIs Δ DiTCorresponding unit price.
Step five: the load aggregator optimizes the distribution and price of the air-conditioning control groups of different local regulating gears according to the purchase result of the dispatching center and the characteristic parameters obtained in the step one, and determines the distribution of the purchased air-conditioning control groups;
not only does the scheduling center need to consider the economy during scheduling, but the load aggregators also need to realize the maximum economy under the condition that the scheduling process meets the basic scheduling requirement. And when the load aggregators receive the purchase amount issued by the dispatching center, each load aggregator determines the distribution of the purchased control groups in each regulating gear according to the local load characteristics and the dispatching requirement. Optimizing the number of control groups of each adjusting gear according to the optimal economic efficiency of the load aggregator, and obtaining an equation (20);
equation (21) constrains the actual distribution of the air conditioning control groups available for purchase under the jurisdiction of a certain load aggregator, i.e., the proportional relationship between the number of groups, which is different for different load aggregators; equation (22) constrains the relationship between dispatch center purchase capacity and all load aggregation commercially purchased climate control teams, which can be derived from the simultaneous solution of equations (2) and (7).
s.t.Ri1>ai2Ri2>L>airRir (8)
In the formula, CLA,iEconomic cost for the ith load aggregator to purchase air conditioning control team spending, CixPrice per unit for purchasing air-conditioning control group adjusted in x-th gear, Rp,ixNumber of control teams in x-gear purchased from ith LA
Step six: and uploading the purchased distribution of the air conditioner control groups at each gear to a dispatching center by the load aggregator, and optimizing the triggering time of the air conditioner control groups by the dispatching center according to the power adjusting instruction.
(61) Determining basic information of a scheduled air conditioner control group:
including the fall duration of different gear adjustments. Because the power change curve of the control group under different adjusting temperature setting values is calculated to be nonlinear according to the aggregation model when the fixed-frequency air conditioner adjusts the temperature setting values, a trigonometric function is adopted to fit the power change curve for further optimization, and the power change curve participates in the next optimization;
(62) uploading information:
all load aggregators report the number of the purchased air-conditioning control groups with different adjustment gears to a scheduling center, and the scheduling center calculates the number of the schedulable total control groups corresponding to each gear to carry out next optimization;
(63) optimizing the triggering time:
in the formula (24), PxThe corresponding fitted power curve for the temperature is adjusted for the x-th gear as a function of time. In the formula (32), JxAdjusting the maintaining time of a power drop part corresponding to the temperature for the x-th gear, wherein the dropping time of each gear is a fixed constant; pxAnd JxAre mutually corresponding, and both types are r.
The formula (25) restricts the input operation mode of the control group, once the control group is input for adjustment, the control group cannot quit midway until the whole adjustment process is finished, and the initial operation state can be recovered. Equation (26) constrains the capacity of the adjustment at each moment not to exceed the capacity purchased. Equation (27) constrains the drop part of all control teams to be completely contained within the scheduling duration to maximize the utilization of the control team's scheduling potential. Equations (28) - (31) are used to calculate some of the required parameters. Solving the optimized triggering time according to the formula (23) to maximally approach the instruction, which is also the only parameter to be optimized; the other parameters are fixed values;
Ti,x,y+Jx≤Γ (27)
Px∈{P1,P2,K,Pr},Jx∈{J1,J2,K,Jr} (32)
in the formula, PdifFor the summation of the square of the difference between the actual scheduling effect and the required scheduling effect, Pch,tFor the actual scheduling effect at time t, Ptar,tFor the scheduling effect required at time t, Rp,ixNumber of control teams in x gear for purchase from the ith LA, y being Rp,ixCounting, Ti,x,yThe time to participate in power regulation is triggered for each control group. Si,x,y,tFor the operating state of each subgroup at time t, Si,x,y,t-1For each subgroup operating state at time t-1, when Si,x,y,tThe control team is put into operation (i.e. adjusts the temperature set point) 1; when S isi,x,y,t0, the control group keeps the current operation state unchanged, N is the controlled air conditioning load number, NGTo control the number of subgroups, NG,xFor the number of x-th gear control subgroups, Δ θaveThe average adjusted temperature set point for all air conditioning loads under the current purchase profile conditions.
Step seven: the dispatching center issues the triggering time of the air conditioning group to each control group through a load aggregator so as to realize accurate short-term power regulation effect; the dispatching center distributes the number and the serial number of the control groups with different adjusting gears to each load aggregator according to the reporting proportion; the load aggregator distributes different trigger times to different control teams, requiring them to act at specified times.
The method is based on the method for adjusting the temperature set value of the air conditioner, simultaneously considers the economy of the dispatching center and the load aggregator, realizes the accurate power adjustment effect based on the optimization algorithm, can effectively reduce the calculated amount and the real-time communication pressure during dispatching, can provide higher adjustment precision on the basis of less calculated amount, and provides high-quality auxiliary service for the system.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (7)
1. A method for regulating the short-term power of a fixed-frequency air conditioner is characterized by comprising
A, simulating a change curve of the polymerization power of an air conditioning control group when different temperature set values are adjusted by using a constant-frequency air conditioning polymerization model and acquiring characteristic parameters;
b, predicting a next day fixed-frequency air conditioner load curve and evaluating the capacity of the schedulable fixed-frequency air conditioner load;
step C, determining the purchased air conditioner adjusting capacity according to the capacity of the schedulable fixed-frequency air conditioner load and the expected achieved scheduling effect, and further determining the adjustable instruction range;
step D, determining a final purchase result according to the purchasable capacity and the price uploaded by each load aggregator, and respectively issuing the purchase result to the corresponding load aggregators;
e, the load aggregator optimizes the distribution and price of the air-conditioning control groups with different local adjustment gears according to the final purchase result and the characteristic parameters of the variation curve of the aggregation power, and determines the distribution of the purchased air-conditioning control groups;
step F, determining a scheduling instruction according to the adjustable instruction range, and optimizing the triggering time of the air conditioner control group according to the scheduling instruction by combining the variation curve of the aggregation power and the distribution of the air conditioner control group;
g, issuing the trigger time of the air conditioner control groups to each air conditioner control group through a load aggregator;
the predicting of the next day fixed-frequency air conditioner load curve comprises the following steps: load prediction is carried out by using an LSTM neural network, and a scheduling center determines the day-ahead purchase capacity of the air conditioner according to the day-ahead prediction; considering the existence of prediction error, the scheduling center performs ultra-short-term prediction on the load on the scheduling day; correcting the prediction result of the previous day by using the new prediction result, and if the purchased air conditioner capacity is more, correcting the adjustment effect of the day-ahead plan to realize reasonable utilization of resources and avoid excessive control in a certain period; if the purchased day-ahead air conditioning capacity is insufficient, additional temporary air conditioning capacity needs to be purchased to achieve the desired control effect.
2. The method for the short-term power regulation of a fixed-frequency air conditioner as claimed in claim 1, wherein the step a comprises:
a1, establishing a polymerization model when the temperature set value of the fixed-frequency air conditioner is adjusted as follows:
wherein P is the aggregate power of the air conditioning groups, Pagg,1For adjusting the collective power of the air conditioner in the pre-stationary state, Pagg,2For regulating the collective power, Δ P, of the air conditioner in the post-stationary stateaggFor adjusting the difference between the aggregate powers of the front and rear air-conditioning groups, PmagIs the lowest point of the aggregate power dip; k is a radical ofdownIs the rate of decrease of the polymerization power; k is a radical ofcA correction factor for the rate of decrease; k is a radical ofupIs the rate of rise of the polymerization power; t is the time in the scheduling process, t1Adjusting the temperature set value; t is t2The moment when the power drops to the lowest power; t is t3The polymerization power rise time; t is t4The moment of returning to the steady state; n is the number of controlled loads, eta is the energy efficiency ratio of the fixed-frequency air conditioner, R is the equivalent thermal resistance of the room, and delta TsetFor adjusted temperature set point, Δ ToIs the difference in ambient temperature;
a2, determining the scale of the air-conditioning control group, setting different adjusting gears, performing simulation through the established aggregation model to obtain an aggregation power change curve of the air-conditioning control group when different temperature setting values are adjusted, and acquiring characteristic parameters.
3. The method for the short-term power regulation of a fixed-frequency air conditioner as claimed in claim 1 or 2, wherein the characteristic parameters comprise an overall power drop curve, a power drop duration and a power difference from an original power after power is stabilized in a regulation process.
4. The method for the short-term power regulation of a fixed-frequency air conditioner as claimed in claim 1, wherein the step B comprises:
b1, dividing all air conditioners in a certain area into a plurality of air conditioner control groups according to geographic position or parameter similarity and using a plurality of load aggregators as agents;
b2, predicting the next day fixed-frequency air-conditioning load curve;
b3, according to the fixed-frequency air-conditioning load curve, estimating the capacity of the schedulable fixed-frequency air-conditioning load by combining the compensation price, the outdoor temperature and the user comfort level:
R＝[R1 R2…Ri…Rn] (3)
Ri＝[Ri1 Ri2…Rix…Rir] (4)
Δθin,x＝xΔθin (6)
in the formula: r is a purchasable air conditioning control group provided by each load aggregator, RiFor the distribution of air-conditioning control groups available in the ith, i is the ith load aggregator, n is the number of load aggregators participating in the bidding, x is the x-th gear of the adjusting temperature set value, RixAir conditioner control group adjusted for corresponding x-th gearNumber of (A)iFor schedulable latent coefficient, Delta thetain,xFor each of the changed temperature settings, Delta thetainFor each adjustment corresponding to the difference between two adjacent gears, Δ θinδ/r, δ being the maximum adjustable temperature, r being the adjustment effect gear, pc,iActual purchase price for reserve capacity, pcFor a compensation price desired by the user, To,dTo adjust the ambient temperature, T, of the dayoReference ambient temperature, D, established for contract makingpAdjustable scheduling potential, η, for a currently aggregated air conditioner groupiThe average energy consumption ratio of the air conditioner group under the ith load aggregation quotient is shown.
5. The method as claimed in claim 1, wherein the air conditioner capacity purchased in the step C is a difference between the aggregate power of the air conditioners in the pre-conditioning plateau and the aggregate power of the air conditioners in the post-conditioning plateau.
6. The method of claim 4 wherein the purchased conditioning capacity is determined using an area method.
7. The method for the short-term power regulation of the fixed-frequency air conditioner as claimed in claim 1, wherein in the step F, the method for optimizing the triggering time comprises the following steps:
in the formula, PdifFor the summation of the sum of squares of the differences between the actual scheduling effect and the required scheduling effect, Pch,tFor the actual scheduling effect at time t, Ptar,tThe scheduling effect required for the time t.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010093078.8A CN111271839B (en) | 2020-02-14 | 2020-02-14 | Method for adjusting short-term power of fixed-frequency air conditioner |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010093078.8A CN111271839B (en) | 2020-02-14 | 2020-02-14 | Method for adjusting short-term power of fixed-frequency air conditioner |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111271839A CN111271839A (en) | 2020-06-12 |
| CN111271839B true CN111271839B (en) | 2021-05-11 |
Family
ID=70997067
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010093078.8A Active CN111271839B (en) | 2020-02-14 | 2020-02-14 | Method for adjusting short-term power of fixed-frequency air conditioner |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111271839B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115325666A (en) * | 2022-07-18 | 2022-11-11 | 东南大学溧阳研究院 | Air conditioner response capability assessment method and device based on LSTM and heat storage mechanism |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447171A (en) * | 2016-08-31 | 2017-02-22 | 清华大学 | Power demand side scheduling resource potential modeling method and system |
| CN107143968A (en) * | 2017-04-14 | 2017-09-08 | 东南大学 | Peak regulation control method based on air-conditioning polymerization model |
| JP2017215924A (en) * | 2016-05-31 | 2017-12-07 | エンコアード テクノロジーズ インク | System and method for guiding use of electrical appliances |
| CN108805445A (en) * | 2018-06-08 | 2018-11-13 | 浙江大学 | A kind of air conditioner load group provides the order of packets dispatching method of spinning reserve |
| CN109886463A (en) * | 2019-01-18 | 2019-06-14 | 杭州电子科技大学 | Consider the probabilistic user side optimal control method of demand response |
| CN110068110A (en) * | 2019-04-02 | 2019-07-30 | 深圳市海源节能科技有限公司 | A kind of central air-conditioning load prediction technique, intelligent terminal and storage medium |
| CN110336330A (en) * | 2019-06-06 | 2019-10-15 | 东南大学 | An a kind of air conditioner load virtual synchronous group of planes participates in the regulation method of power grid interaction |
| CN110425706A (en) * | 2019-07-29 | 2019-11-08 | 南京理工大学 | Polymerization air conditioner load towards power grid peak clipping regulates and controls method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10354345B2 (en) * | 2012-01-23 | 2019-07-16 | Whisker Labs, Inc. | Optimizing and controlling the energy consumption of a building |
-
2020
- 2020-02-14 CN CN202010093078.8A patent/CN111271839B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017215924A (en) * | 2016-05-31 | 2017-12-07 | エンコアード テクノロジーズ インク | System and method for guiding use of electrical appliances |
| CN106447171A (en) * | 2016-08-31 | 2017-02-22 | 清华大学 | Power demand side scheduling resource potential modeling method and system |
| CN107143968A (en) * | 2017-04-14 | 2017-09-08 | 东南大学 | Peak regulation control method based on air-conditioning polymerization model |
| CN108805445A (en) * | 2018-06-08 | 2018-11-13 | 浙江大学 | A kind of air conditioner load group provides the order of packets dispatching method of spinning reserve |
| CN109886463A (en) * | 2019-01-18 | 2019-06-14 | 杭州电子科技大学 | Consider the probabilistic user side optimal control method of demand response |
| CN110068110A (en) * | 2019-04-02 | 2019-07-30 | 深圳市海源节能科技有限公司 | A kind of central air-conditioning load prediction technique, intelligent terminal and storage medium |
| CN110336330A (en) * | 2019-06-06 | 2019-10-15 | 东南大学 | An a kind of air conditioner load virtual synchronous group of planes participates in the regulation method of power grid interaction |
| CN110425706A (en) * | 2019-07-29 | 2019-11-08 | 南京理工大学 | Polymerization air conditioner load towards power grid peak clipping regulates and controls method |
Non-Patent Citations (4)
| Title |
|---|
| "大规模空调负荷集群的需求响应与优化互动研究";裴丛仙子;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;中国学术期刊（光盘版）电子杂志社;20180630;全文 * |
| "定频空调负荷聚合建模及其调控策略研究";楼家辉;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;中国学术期刊（光盘版）电子杂志社;20180630;全文 * |
| "居民温控负荷聚合功率及响应潜力评估方法研究";李亚平等;《中国电机工程学报》;中国电机工程学会;20171005;第37卷(第19期);全文 * |
| "空调负荷参与电力***需求响应的建模及控制策略研究";胡晓青;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;中国学术期刊（光盘版）电子杂志社;20180228;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111271839A (en) | 2020-06-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111915125B (en) | Multi-type resource optimal combination method and system for virtual power plant | |
| CN112803446B (en) | Multi-energy optimal control method and control system based on client side demand response | |
| CN107800157B (en) | Double-layer optimized scheduling method for virtual power plant containing polymer temperature control load and new energy | |
| CN110492498B (en) | Temperature control load participation power system primary frequency modulation method based on double-layer control | |
| CN109936130B (en) | Dynamic load control method | |
| CN105703355B (en) | Diversity load grading self-discipline collaborative demand response method | |
| CN107706932A (en) | A kind of energy method for optimizing scheduling based on dynamic self-adapting fuzzy logic controller | |
| CN106096790A (en) | Based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets Optimization Scheduling | |
| CN112488372A (en) | Double-layer optimized scheduling method for electric heating load under multiple time scales | |
| CN112186802A (en) | Multi-time scale rolling scheduling method and system for dynamic economic scheduling | |
| CN115765015A (en) | Source network load storage cooperative interaction scheme making method oriented to power grid practical application scene | |
| CN112116150A (en) | Method for regulating heat accumulating type electric heating power market by load aggregators | |
| CN113746089B (en) | Multi-user-oriented multi-time-scale electric power package and household energy optimization method | |
| CN112531746B (en) | Virtual power plant operation method based on local autonomous optimization of central air conditioner | |
| CN111271839B (en) | Method for adjusting short-term power of fixed-frequency air conditioner | |
| CN109494810A (en) | Dispatch evaluation method and the power limiting distribution method of the evaluation in intermittent energy source power station | |
| CN114825371A (en) | Aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation | |
| CN110535142B (en) | Power consumption intelligent control method based on improved discrete PSO algorithm and computer readable storage medium | |
| CN115800294A (en) | Flexible resource aggregation modeling and coordination control method | |
| CN114757507A (en) | Electric vehicle V2G regulation and control method based on dynamic regional dispatching electricity price | |
| CN114612001A (en) | Regulation and control instruction decomposition method and system for cluster electric vehicle participating in power grid peak clipping | |
| CN107947246B (en) | Wind power generation index distribution and increased power evaluation method considering frequency modulation and increased power | |
| CN113642918A (en) | Master-slave game-based virtual power plant regulation and control method, storage medium and device | |
| CN113283709A (en) | Hierarchical distributed load cooperative scheduling method for rural power grid | |
| CN113904331B (en) | Auxiliary regulation and control method, device and system for variable-frequency air conditioner cluster participation power system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |