CN114512994A - Frequency modulation method, system, equipment and medium for cluster temperature control load system - Google Patents

Frequency modulation method, system, equipment and medium for cluster temperature control load system Download PDF

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CN114512994A
CN114512994A CN202210182642.2A CN202210182642A CN114512994A CN 114512994 A CN114512994 A CN 114512994A CN 202210182642 A CN202210182642 A CN 202210182642A CN 114512994 A CN114512994 A CN 114512994A
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load
temperature control
state
cluster
frequency modulation
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潘玲玲
耿建
董昱
严亚勤
冷喜武
李峰
王勇
李亚平
熊浩
周竞
刘建涛
焦建林
宫成
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Jiangsu Electric Power Co Ltd
State Grid Beijing Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Jiangsu Electric Power Co Ltd
State Grid Beijing Electric Power Co Ltd
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Publication of CN114512994A publication Critical patent/CN114512994A/en
Priority to PCT/CN2022/137165 priority patent/WO2023160110A1/en
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H02J3/241—The oscillation concerning frequency
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention provides a frequency modulation method, a frequency modulation system, equipment and a medium for a cluster temperature control load system, wherein the method comprises the following steps: acquiring cluster temperature control load initialization parameters; establishing a 2D state bin cluster temperature control load model; according to the 2D state bin cluster temperature control load model, a cluster temperature control load state space model is established, and the state bin transition probability is solved based on a Markov chain, so that a state transition matrix is obtained; calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, and calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model; obtaining a cluster temperature control load control scheme by adopting rolling optimization solution; and carrying out controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting, and outputting a controllable load selection result. The method of the invention has better comprehensive performance in the aspects of control precision, response speed, load participation demand response fairness and the like.

Description

Frequency modulation method, system, equipment and medium for cluster temperature control load system
Technical Field
The invention belongs to the field of load frequency modulation control, and particularly relates to a frequency modulation method, a frequency modulation system, frequency modulation equipment and a frequency modulation medium for a cluster temperature control load system.
Background
With the advanced development of power system reform, power demand-side management will be substantially promoted as an important component of power reform. With the gradual implementation of intelligent power grid construction and demand response projects in China, the cluster temperature control load has great potential for participating in new energy consumption of the demand response projects, and important theoretical significance and engineering value are achieved when related key technologies are researched.
At present, the demand response items of cluster household temperature control load peak load shifting and emergency load management are available. The cluster home temperature control load participates in short-time (second level) demand response auxiliary items, such as: frequency regulation of the distribution network and balancing power consumption requirements. However, the power requirements of domestic temperature control loads are highly random and have a small capacity relative to industrial and commercial users; and large-scale industrial and commercial users have smaller randomness of power demand of temperature control loads and larger capacity due to own power utilization characteristics and operation flows. Further exploration of the regulation and control techniques for clustered temperature controlled load demand response is needed.
In the prior art, a second-order equivalent thermal parameter model considering dual-mass characteristics is adopted, and a 2D transfer model is used for modeling a cluster temperature control load. In the aspect of load control, model predictive control is adopted, the temperature control load is adjusted in advance, and rolling optimization is realized. However, the existing 2D transfer model cannot fully consider load heterogeneity and neglect ambient temperature time-varying characteristics, and the system matrix is a constant matrix, which will bring adverse effects to load optimization control.
And the existing control model is less optimized in the aspect of selecting the control object. On one hand, the quality of the output performance depends on the time-varying characteristic of a given tracking signal, and the superior response effect cannot be guaranteed; on the other hand, how to closely associate the optimal control signal and the optimal load control object to achieve a better control effect is not considered.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a frequency modulation method, a frequency modulation system, frequency modulation equipment and a frequency modulation medium for a cluster temperature control load system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a frequency modulation method for a cluster temperature control load system comprises the following steps:
acquiring cluster temperature control load initialization parameters;
establishing a 2D state bin cluster temperature control load model according to the single air conditioner load model, and determining the schedulable capacity of the current cluster temperature control load;
establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on a Markov chain so as to obtain a state transition matrix;
calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, and calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model so as to obtain a cluster temperature control load total power change value;
establishing a cluster temperature control load optimization control model according to the state transition matrix, and solving the total power change value of the cluster temperature control load by adopting rolling optimization to obtain a cluster temperature control load control scheme;
and carrying out controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting, and outputting a controllable load selection result.
As a further improvement of the present invention, the establishing a 2D state bin cluster temperature control load model according to a single air conditioner load model, and determining a schedulable capacity of a current cluster temperature control load specifically includes the following steps:
establishing a single air conditioner load model according to a second-order discretization differential equation of the equivalent thermal parameter model;
dividing the cluster temperature control load into a closing group and an opening group according to the current switching state of the cluster temperature control load; respectively according to the upper and lower limit values of the indoor air temperature
Figure BDA0003522034440000021
And upper and lower limits of the temperature of the indoor material
Figure BDA0003522034440000022
Dividing the temperature interval into N i2 indoor air temperature cell and N m2 temperature cells of indoor material to form Na*NmState bins for 4 closed groups and open groups; further forming a 2D state bin transfer model;
taking all the current closed bins in the 2D state bins, the temperature of which is from low to high, removing the sum of the rated power of all the air-conditioning equipment in the first state bin which is closest to the lower limit of the allowable temperature as the maximum power capacity which can be adjusted, and adding the sum of the load power of the currently opened air-conditioning equipment as the maximum power allowable value P which can be adjusted in a scheduling waymax(ii) a Taking all current open bins in the 2D state bins, the temperature of which is from low to high, removing the sum of all rated powers of the air-conditioning equipment of the last state bin which is closest to the upper limit of the allowable temperature as the maximum power capacity which can be adjusted downwards, and obtaining the minimum power allowable value P which can be adjusted in a dispatching way of the cluster air-conditioning groupmin。
As a further improvement of the invention, the second-order discretization differential equation of the equivalent thermal parameter model is as follows:
Figure BDA0003522034440000031
where Δ t represents the simulation step size, θaAn indoor air temperature indicative of an air conditioning load; thetamIndoor material temperature representing air conditioning load; theta.theta.sRepresenting the ambient temperature at which the air conditioning load is located; ra、RmEquivalent thermal resistances of indoor air and indoor substances respectively; ca、CmEquivalent heat capacities of air temperature and material temperature, respectively; when the air conditioner load is turned on, QaRated power for the air conditioner; when turned off, QaEqual to 0.
As a further improvement of the present invention, the establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on the markov chain to obtain the state transition matrix includes the following steps:
on the basis of the 2D state bin cluster temperature control load model, a cluster temperature control load state space model is established and expressed by a time-varying discrete state space equation:
Figure BDA0003522034440000032
wherein, x (k) represents a system state vector at the k-th moment, and the expression is as follows:
x(k)＝[x1，off(k)x2，off(k)...xN/2，off(k)x1+N/2，on(k)x2+N/2，on(k)...xN，on(k)]T (3)
wherein N represents the total number of state bins, and N is Na*Nm/2, element x thereofi，off(k) The ratio of the number of the air conditioner loads in the closed group state bin i at the kth time divided by the total load number is shown, wherein i is 1, 2. x is the number ofj，on(k) The ratio of the number of the air conditioner loads in the opening group state bin j at the kth moment divided by the total load number is represented, wherein j is N/2+1, N/2+2, the. x (k +1) represents a system state vector at the k +1 th moment;
a (k) represents the system matrix at time k, whose element Aij(k) The transition probability of the air conditioning load of the state bin j to the state bin i in the kth time step is shown;
u (k) represents a control signal at the k-th time;
b (k) represents the input matrix at time k, whose elements Bij(k) Shows the transition probability of the air conditioning load of the state bin j needing to be switched to the state bin i under the action of u (k), and is shown as follows:
Figure BDA0003522034440000041
wherein diag denotes a diagonal matrix, diagsubRepresenting a sub-diagonal matrix;
c (k) represents the output matrix at the k-th time, that is, the average power vector of the air-conditioning loads of each state bin at the current time k, and is represented as follows:
C(k)＝mPave(k)*S (5)
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003522034440000042
m represents the total number of air conditioning loads, Pave(k) Represents the average power, P, of the air conditioning load of each state bin of the opening group at the kth timeagg(k) An observed value representing the aggregate output power of the air conditioning load group at the kth moment; s represents the switching function vector of each state bin, SiIndicating the switching state of the state bin i, wiRepresents the on-off state of the ith load, 0 is the off state, and 1 is the on state;
y (k) represents the output power of the cluster air conditioner load model at the k moment;
randomly selecting the space state at the initial moment as the simulation initial state, and setting the initial temperature of all air conditioner loads at theta-_ETPAnd theta+_ETPUniformly distributed among the layers;
performing model Carlo random simulation on the m air conditioner loads based on the formula (7) to obtain a time-temperature thermal operation curve of each air conditioner load at each simulation moment;
Figure BDA0003522034440000051
in the formula: x is the number ofsA set value representing an air conditioner temperature; x is the number ofin，tRepresents the indoor temperature at time t; Δ x represents an offset value allowed by an air conditioner temperature set value; sAC，tThe working state of the air conditioner at the moment t is represented, the value is 0 to represent that the air conditioner is closed, and the value is 1 to represent that the air conditioner is opened;
sequentially numbering the state bins according to the 2D temperature of each air conditioner load at different simulation moments;
counting the number of the air conditioner loads of the state bins i transferred to the state bins j between the adjacent simulation time k and k +1, wherein i, j is 1, 2.
Calculating the state transition probability of the air conditioning load of the state bin i in the k period of the first-order Markov chain to the state bin j:
Figure BDA0003522034440000052
in the formula, ni，j(k) The number of the air conditioner load of the state bin i transferred to the state bin j in the kth period is represented; n isi(k) Representing the total load number of the state transition occurring in the state bin i in the k period; n represents the total number of state bins;
according to the different values of i and j, the transition probability p of each state can be obtainedi，j(k) Thereby obtaining a state transition matrix p (k).
As a further improvement of the present invention, the calculating a cluster temperature control load primary frequency modulation power variation value according to the cluster temperature control load primary frequency modulation model specifically includes the following steps:
setting a primary frequency modulation coefficient of the cluster temperature control load according to the frequency adjustment characteristic of the cluster temperature control load;
establishing a cluster temperature control load primary frequency modulation system model; the cluster temperature control load primary frequency modulation system model comprises a single-region frequency modulation system model and a cluster air conditioner group frequency modulation module; the single-region frequency modulation system model is a closed-loop system with an integral regulation system, a power regulation signal of the single-region frequency modulation system model is converted into a turbine input power regulation variable through a system secondary frequency modulation transfer function, a generator governor transfer function and a prime mover transfer function respectively, and the input power regulation variable and a load fluctuation variable participate in system frequency modulation; the cluster air conditioner group frequency modulation module comprises a frequency modulation dead zone, a frequency modulation coefficient, a schedulable potential upper limit and a schedulable potential lower limit of the air conditioner group and an air conditioner response time delay;
on the basis of the cluster temperature control load primary frequency modulation system model, after each air-conditioning equipment monitors a frequency deviation signal, calculating to obtain a cluster temperature control load primary frequency modulation power change value delta PAC。
As a further improvement of the present invention, the calculating a cluster temperature control load secondary frequency modulation power variation value according to the cluster temperature control load secondary frequency modulation model specifically includes the following steps:
establishing a secondary frequency modulation simulation model of the cluster temperature control load on the basis of the cluster temperature control load primary frequency modulation system model; the secondary frequency modulation simulation model of the cluster temperature control load is based on the primary frequency modulation model of the cluster air conditioner, a control signal received by a cluster air conditioner group comprises a primary frequency modulation system frequency deviation and a setting value of a system power deviation signal calculated by a regional AGC of secondary frequency modulation, and the setting value of the regional AGC power deviation is determined by a setting multiplying factor r;
wherein r represents the setting multiplying factor of the regional AGC power deviation on the air-conditioning group, and is calculated by the following formula:
Figure BDA0003522034440000061
in the formula,. DELTA.Pg'(s) is represented by a secondary frequency modulation power deviation signal of the thermal power generating unit, delta PAC'(s) is denoted as the clustered air conditioning group chirp power offset signal, Δ Pc(s) calculating a quadratic frequency modulation power deviation signal for regional AGC;
after the regional AGC secondary frequency modulation power deviation signal monitored by each air conditioning equipment, calculating according to the formula (12) to obtain the total power change value delta P of the cluster temperature control loadAC′(s)。
As a further improvement of the present invention, the establishing of the cluster temperature control load optimization control model according to the state transition matrix, and the solving of the total power change value of the cluster temperature control load by rolling optimization to obtain the cluster temperature control load control scheme specifically includes the following steps:
setting the prediction time length as p, and setting the prediction state at the k + p time as x (k + ζ | k) under the current k time condition, wherein ζ is 1, 2. According to the cluster temperature control load state space model, establishing a state equation from the k +1 th moment to the k + p th moment:
X(k)＝AP(k)x(k|k)+BP(k)U(k) (13)
wherein the content of the first and second substances,
Figure BDA0003522034440000071
U(k)＝[u(k|k)u(k+1|k)…u(k+p-1|k)]T (15)
APinner block matrix Ap＝[A(k+p-1)...A(k+1)A(k)]The predicted value of the state transition matrix at the k + p th moment of the system at the current k moment is shown, and the element A of the predicted valuep(i, j) represents the predicted transition probability value of the air conditioning load of the state bin j to the state bin i in the k + p time step of the system only by knowing the load number vector x (k | k) of each state bin at the current time k;
the minimized tracking error is adopted as an objective function, and the optimized control objective function is as follows:
Figure BDA0003522034440000072
wherein, WerrA tracking error weight coefficient matrix representing the model output versus the actual target value, d (k) ═ diag { C (k +1) C (k + 2.. C (k + p) }, r (k) ═ r (k +1) r (k + 2.. r (k + p))]TAnd r (k + ζ) represents an output target track value at a time k + ζ;
converting an optimization control model of the cluster air conditioner load to obtain a quadratic programming function by taking the minimum tracking error of cluster output as a target:
Figure BDA0003522034440000073
and performing rolling optimization solution on the quadratic programming function, obtaining an optimization control sequence formed by the number of state bin air conditioner load switches in a control time domain p x delta t after the solution, and only issuing a first component u of the optimization sequence at the current scheduling time*(k | k); and repeating the rolling optimization process when the next scheduling period comes to obtain the cluster temperature control load control scheme.
As a further improvement of the present invention, the performing controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting and outputting a controllable load selection result includes the following steps:
establishing a ranking index based on the normalized temperature distance:
Figure BDA0003522034440000081
wherein, NTDi，kIs the normalized temperature distance of the ith air conditioner load at the current k moment; delta represents temperature dead zone, upper and lower limit values theta of comfort temperature of userhighAnd thetalowThe difference between the two; thetai，tIndicating the temperature, O, of the ith air conditioning load at the current time kkAnd CkRespectively representing an opening group and a closing group at the current k moment, wherein m is the total number of air conditioner loads;
establishing a ranking index based on power similarity:
Figure BDA0003522034440000082
wherein, the SIMi，(p，q)Is a similarity index, P, of the conditioned load power and the required conditioned power in a state bin (P, q)iRated power for the ith air conditioning load, Paim，(p，q)A target power for the state bin (p, q) to respond to; n is a radical of(p，q)Representing the number of air conditioning loads in the status bin (p, q);
establishing a sequencing index based on the accumulated control times:
NCi，k＝(Ci，k-Ck，min)/(Ck，max-Ck，max) (20)
wherein, Ci，kThe cumulative control times of the air conditioning load i at the time k, Ck，minAnd Ck，maxThe minimum value and the maximum value of the controlled times of k load at the current moment are represented;
obtaining a multi-scale priority comprehensive index gamma based on the ranking index based on the normalized temperature distance, the ranking index based on the power similarity and the ranking index based on the accumulated control timesopenAs shown in the following formula:
Figure BDA0003522034440000083
wherein, KT，KSAnd KCAre respectively corresponding weight coefficients;
according to a multi-scale priority synthetic index gammaopenThe air-conditioning loads in each 2D state bin corresponding to the open state group are sorted, and controllable load selection is performed from high to low according to the priority, so that a controllable load selection result is obtained.
A cluster temperature control load system frequency modulation system comprises:
the acquisition unit is used for acquiring cluster temperature control load initialization parameters;
the load model establishing unit is used for establishing a 2D state bin cluster temperature control load model according to the single air conditioner load model and determining the schedulable capacity of the current cluster temperature control load;
the space model establishing unit is used for establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on the Markov chain so as to obtain a state transition matrix;
the frequency modulation model calculation unit is used for calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model, and further obtaining a cluster temperature control load total power change value;
the control model optimization unit is used for establishing a cluster temperature control load optimization control model according to the state transition matrix, and solving the total power change value of the cluster temperature control load by adopting rolling optimization to obtain a cluster temperature control load control scheme;
and the controllable load selection unit is used for performing controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting and outputting a controllable load selection result.
An electronic device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of said cluster temperature controlled load system frequency modulation method when executing said computer program.
A computer-readable storage medium, storing a computer program which, when executed by a processor, implements the steps of the cluster temperature controlled load system frequency modulation method.
The invention has the beneficial effects that:
the invention provides a flexible frequency modulation method for a cluster temperature control load system, in particular to a cluster temperature control load modeling method based on a 2D state bin time-varying Markov chain, wherein primary and secondary frequency modulation models of a cluster temperature control load electric power system are established on the basis, and finally, an optimization control method for selecting loads based on multiple scales and priorities of normalized temperature distance, power similarity and accumulated control times is innovatively provided. The control process that the cluster air-conditioning group participates in the primary and secondary frequency modulation of the power system under the load aggregation organization is provided, the effectiveness that the cluster air-conditioning group participates in the system frequency modulation is verified, the frequency characteristic of a power grid can be improved when the load fluctuates, and the operation safety of the system is enhanced. The method provided by the invention can effectively improve the frequency characteristic of the power grid when the load fluctuates, and compared with the traditional method, the temperature control load modeling precision is higher, and the control method has better comprehensive performance in the aspects of control precision, response speed, load participation demand response fairness and the like.
Drawings
Fig. 1 is a control flow chart of frequency modulation of a cluster temperature-controlled load system.
Fig. 2 is a thermal operation characteristic diagram of the unitary air conditioner.
Fig. 3 is a 2D state bin transition model provided by the present invention.
Fig. 4 is a schematic diagram of a change curve of the power output of the cluster temperature control load regulation along with the system frequency.
Fig. 5 is a single-region primary frequency modulation simulation model based on cluster temperature control loads according to the present invention.
Fig. 6 is a single-region primary and secondary frequency modulation simulation model based on a cluster temperature control load according to the present invention.
Fig. 7 is a flowchart of a cluster temperature control load control method according to the present invention.
Fig. 8 is a schematic structural view of a frequency modulation system of a cluster temperature control load system according to an alternative embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to an alternative embodiment of the invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Interpretation of related terms
Load frequency modulation: when the frequency of the power system deviates from the rated value, the load can be controlled through the frequency deviation amount, so that the effects of reducing frequency fluctuation and maintaining the operation safety of the power system are achieved.
Temperature control load: the temperature control load is an electric load controlled by a constant temperature heater and has a certain heat energy storage capacity. In an actual power system, the temperature control load often refers to an air conditioning load of a residential or small commercial user.
And (3) demand response: the power demand response means that a power supplier guides the behavior of the power consumption mode of a power consumer through means such as power price signals or compensation excitation, so that the power load is reduced or increased, and the stable operation of a power grid is guaranteed.
Model prediction control: the model predictive control is a closed-loop optimization control method based on a model, and the core idea of the model predictive control is a rolling time-domain strategy. The basic idea is to predict the future dynamics of the system based on the current time, solve the constraint programming problem to obtain the current optimal solution, and after the system is updated, the prediction time domain rolls forward until the requirements are met.
Multi-scale prioritization: the invention particularly relates to a load selection method based on three indexes of load normalization temperature distance, power similarity and accumulated control times. When a control command is issued, the load is selected through multi-scale priority sequencing to implement control.
As shown in fig. 1, the invention provides a frequency modulation method for a cluster temperature control load system, which comprises the following steps:
s1, acquiring cluster temperature control load initialization parameters;
s2, establishing a 2D state bin cluster temperature control load model according to the single air conditioner load model, and determining the schedulable capacity of the current cluster temperature control load;
s3, establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on the Markov chain so as to obtain a state transition matrix;
s4, calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, and calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model, so as to obtain a cluster temperature control load total power change value;
s5, establishing a cluster temperature control load optimization control model according to the state transition matrix, and solving the total power change value of the cluster temperature control load by adopting rolling optimization to obtain a cluster temperature control load control scheme;
and S6, performing controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting, and outputting a controllable load selection result.
The frequency modulation method of the cluster temperature control load system is embodied in three aspects of cluster temperature control load running state modeling, cluster temperature control load frequency modulation model construction and optimization control algorithm, and specifically comprises the following steps:
1) on the basis of fully considering cluster temperature control load heterogeneity and diversity, a cluster temperature control load modeling method based on a 2D state bin transition time-varying Markov chain is provided, and compared with a traditional time-invariant Markov chain modeling method, the method has higher modeling precision.
2) The modeling method of the primary frequency modulation and the secondary frequency modulation of the cluster temperature control load fully utilizes the demand response potential of the load side, and has certain feasibility and superiority.
3) The cluster temperature control load control method based on model prediction rolling optimization control increases a multi-scale priority sorting load selection process, and further performs simulation analysis on the better comprehensive performance of the method in the aspects of control precision, response speed, load participation demand response fairness and the like.
The invention is described in detail below with reference to the following specific examples and figures:
a flexible frequency modulation method for a cluster temperature control load system comprises the following steps:
step 1: acquiring cluster temperature control load initialization parameters, wherein the initialization parameters comprise the switching states, indoor temperatures, outdoor temperatures and the like of the current N temperature control loads;
step 2: establishing a 2D state bin cluster temperature control load model;
the step 2 comprises the following specific steps:
21) establishing a single air conditioner load model: the second-order discretization differential equation of the equivalent thermal parameter model is as follows:
Figure BDA0003522034440000121
where Δ t represents the simulation step size, θaAn indoor air temperature indicative of an air conditioning load; thetamIndoor material temperature representing air conditioning load; thetasIndicating the ambient temperature at which the air conditioning load is located; ra、RmEquivalent thermal resistances of indoor air and indoor substances respectively; ca、CmEquivalent heat capacities of air temperature and material temperature, respectively; when the air conditioning load is on, QaRated power for the air conditioner; when turned off, QaEqual to 0.
FIG. 2 shows the thermal operation characteristics of the unitary air conditioner, where θ+_ETP、θ-_ETPRespectively representSetting an upper regulation limit and a lower regulation limit of temperature by an air conditioner load; thetas_ETPA temperature set value representing an air conditioning load; tau isonThe starting time of the air conditioning equipment; tau.offThe turn-on time of the air conditioning apparatus.
22) Establishing a state bin: dividing the cluster temperature control load into a closing group and an opening group according to the current switching state of the cluster temperature control load; for the closing group, the upper and lower limit values of the indoor air temperature are determined according to the comfort level of the user
Figure BDA0003522034440000131
And upper and lower limit values of indoor material temperature
Figure BDA0003522034440000132
Dividing the temperature interval into N i2 indoor air temperature cell and N m2 temperature cells of indoor material to form Na*Nm4 status bins; the same applies to the open group. A 2D state bin transition model is formed as shown in fig. 3.
23) Determining the schedulable capacity of the current cluster temperature control load: taking all the current closed bins in the 2D state bins, controlling the temperature from low to high, removing the sum of the rated power of all the air-conditioning equipment in the first state bin closest to the lower limit of the allowable temperature to be the maximum power capacity capable of being adjusted, and adding the sum of the current load power of the opened air-conditioner to be the maximum power allowable value P capable of being adjusted in a scheduling mannermax(ii) a Taking all current open bins in the 2D state bins, the temperature is from low to high, the sum of all the rated powers of the air-conditioning equipment except the last state bin closest to the upper limit of the allowable temperature is the maximum power capacity which can be adjusted downwards, namely the minimum power allowable value P which can be adjusted by the cluster air-conditioning group in a scheduling waymin。
And step 3: solving the state bin transition probability based on a Markov chain; the step 3 comprises the following specific steps:
31) establishing a cluster temperature control load state space model: on the basis of the 2D state bin transition model in the step 2, the method is further expressed by a time-varying discrete state space equation:
Figure BDA0003522034440000133
wherein, x (k) represents a system state vector at the k-th moment, and the expression is as follows:
x(k)＝[x1，off(k)x2，off(k)...xN/2，off(k)x1+N/2，on(k)x2+N/2，on(k)...xN，on(k)]T (3)
wherein N represents the total number of state bins, and N is Na*NmAnd/2, the same as below. Element x thereofi，off(k) The ratio of the number of the air conditioner loads in the closed group state bin i at the kth time divided by the total load number is shown, wherein i is 1, 2. x is a radical of a fluorine atomj，on(k) The ratio of the number of the air conditioning loads in the group state bin j opened at the k-th moment divided by the total load number is shown, wherein j is N/2+1, N/2+ 2. x (k +1) represents the system state vector at time k + 1.
A (k) represents the system matrix at time k, whose element Aij(k) And the transition probability of the air conditioning load of the state bin j to the state bin i in the k time step is shown.
u (k) represents a control signal at the k-th moment, namely the percentage of air-conditioning loads in each state bin needing to be switched at the current moment k; when the signal is positive, it represents opening action, and when it is negative, it represents closing action.
B (k) represents the input matrix at time k, whose elements Bij(k) The transition probability of the air conditioning load of the state bin j requiring the switching operation to the state bin i by the action of u (k) is shown as follows:
Figure BDA0003522034440000141
wherein diag denotes a diagonal matrix, diagsubRepresenting a secondary diagonal matrix.
C (k) represents the output matrix at the k-th time, that is, the average power vector of the air-conditioning loads of each state bin at the current time k, and is represented as follows:
C(k)＝mPave(k)*S (5)
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003522034440000142
m represents the total number of air conditioning loads, Pave(k) Represents the average power, P, of the air conditioning load of each state bin of the opening group at the kth momentagg(k) And an observed value representing the aggregate output power of the air conditioning load groups at the k-th moment. S represents the switching function vector of each state bin, SiRepresenting the switching state of the state bin i, wiThis indicates the on/off state of the ith load, 0 being off and 1 being on.
And y (k) represents the output power of the cluster air conditioner load model at the k moment.
32) Randomly selecting the space state at the initial moment as the simulation initial state, and setting the initial temperature of all air conditioner loads at theta-_ETPAnd theta+_ETPUniformly distributed among the layers;
33) performing model Carlo random simulation on the m air conditioner loads based on the formula (7) to obtain a time-temperature thermal operation curve of each air conditioner load at each simulation time (the simulation time is one day, the sampling interval is 1 minute, and 1440 points are total);
Figure BDA0003522034440000151
in the formula: x is the number ofsA set value representing an air conditioner temperature; x is the number ofin，tRepresents the indoor temperature at time t; Δ x represents an offset value allowed by an air conditioner temperature set value; sAC，tThe working state of the air conditioner at the time t is shown, the air conditioner is closed when the value is 0, and the air conditioner is opened when the value is 1.
34) Sequentially numbering the state bins according to the 2D temperature of each air conditioner load at different simulation moments;
35) counting the number of the state bins j to which the air conditioning load of the state bin i is transferred between adjacent simulation time k and k +1 (i, j is 1, 2.. multidot.N);
36) calculating the state transition probability of the air conditioning load of the state bin i in the k period of the first-order Markov chain to the state bin j:
Figure BDA0003522034440000152
in the formula, ni，j(k) The number of the air conditioner load of the state bin i transferred to the state bin j in the k period is represented; n isi(k) Representing the total load number of the state transition occurring in the state bin i in the k period; n represents the total number of state bins.
37) From step 31), it can be known that each state transition probability p can be obtained according to the difference between the values of i and ji，j(k) Thereby obtaining a state transition matrix p (k).
Since any column j of a (k) represents the state transition probability that the air-conditioning load of the current k-th time period state bin i is transferred to the state bins 1 to N, and any row i of P (k) represents the state transition probability that the air-conditioning load of the current k-th time period state bin i is transferred to the state bins 1 to N, a (k) PT(k).
And 4, step 4: establishing a cluster temperature control load primary frequency modulation model; the step 4 comprises the following specific steps:
41) setting cluster temperature control load primary frequency modulation coefficient KAC *: the frequency adjustment characteristic of the cluster temperature control load is shown in FIG. 4, and the primary frequency modulation coefficient K of the cluster temperature control load is set according to the following formulaAC *：
Figure BDA0003522034440000161
Figure BDA0003522034440000162
Figure BDA0003522034440000163
In the formula, Kg *Expressing unified primary frequency modulation coefficient per-unit value delta f of regional thermal power generating unit*Representing system frequenciesPer unit value of rate deviation, KAC *The value of delta K is the primary frequency modulation coefficient of the cluster air conditioner groupAC *And a unit value representing the total power change of the cluster air-conditioning group.
42) Establishing a cluster temperature control load primary frequency modulation system model: the single-zone primary frequency modulation simulation model of the cluster temperature control load is shown in fig. 5. As shown in fig. 5, the cluster temperature control load primary frequency modulation system model is divided into a traditional single-region frequency modulation system model and a cluster air conditioner group frequency modulation module. The traditional single-region frequency modulation system model is a closed-loop system with an integral regulation system, and a power regulation signal is converted into a turbine input power regulation variable through a system secondary frequency modulation transfer function, a generator speed regulator transfer function and a prime mover transfer function respectively and participates in system frequency modulation together with a load fluctuation variable. The cluster air-conditioning group frequency modulation module is composed of an air-conditioning group frequency modulation dead zone, a frequency modulation coefficient, a schedulable potential upper limit and a schedulable potential lower limit, and an air-conditioning response time delay.
System quadratic frequency modulation transfer function
Figure BDA0003522034440000164
Generator speed governor transfer function
Figure BDA0003522034440000165
Transfer function of prime mover
Figure BDA0003522034440000166
Load fluctuation variable Δ PD(s)
Frequency modulation factor KAC
Air conditioner response time delay
Figure BDA0003522034440000171
Wherein, KnFor static gain of speed governors, TnIs the time constant of the speed regulator, and R is the difference adjustment coefficient of the speed regulator; knFor static gain of steam turbines, TTIs the time constant of the turbine, KrIs the reheat coefficient, TrIs the reheat time constant; kPFor the quadratic adjustment of the proportionality coefficient, KIAnd adjusting the coefficient for quadratic frequency adjustment integration. T isACThe value of the temperature control load response time delay is between 0.1s and 0.5 s.
43) Calculating a primary frequency modulation power change value of the cluster temperature control load: on the basis of the model in 32), after each air-conditioning equipment monitors a frequency deviation signal, calculating according to the formulas (9) to (11) to obtain a total power change value delta P of the temperature-controlled load of the clusterAC。
And 5: establishing a cluster temperature control load secondary frequency modulation model; the step 5 comprises the following specific steps:
51) establishing a cluster temperature control load secondary frequency modulation system model: and on the basis of the model in 42), establishing a secondary frequency modulation simulation model of the cluster temperature control load as shown in FIG. 6.
On the basis of a cluster air conditioner primary frequency modulation model, a control signal received by a cluster air conditioner group comprises a system frequency deviation delta f (primary frequency modulation) and a setting value (secondary frequency modulation) of a system power deviation signal calculated by a regional AGC, and the setting value of the regional AGC power deviation is determined by a setting multiplying factor r. The other structures are the same as the primary frequency modulation of the cluster air conditioner.
Wherein r represents the setting multiplying factor of the regional AGC power deviation on the air-conditioning group, and is calculated by the following formula:
Figure BDA0003522034440000172
in the formula,. DELTA.Pg'(s) is represented by a secondary frequency modulation power deviation signal of the thermal power generating unit, delta PAC'(s) is denoted as the clustered air conditioning group chirp power offset signal, Δ PcAnd(s) calculating a secondary frequency modulation power deviation signal for the regional AGC.
52) Calculating a cluster temperature control load secondary frequency modulation power change value: on the basis of the model 41), after an area AGC secondary frequency modulation power deviation signal monitored by each air conditioning equipment is obtained, the total power of the cluster temperature control load is calculated according to the formula (12)Variation value Δ PAC′(s)。
Step 6: rolling optimization cluster temperature control load control based on MPC; the step 6 comprises the following specific steps:
as shown in fig. 7, after modeling in step 2 and step 3, a primary frequency modulation signal or a primary and secondary frequency modulation signal is obtained in step 4 or step 5, and the cluster temperature control load is controlled by the following steps.
61) Establishing a prediction system state equation: let p be the prediction duration, and let x (k + ζ | k) be the prediction state at the k + p th time under the current time k, (ζ ═ 1, 2.., p). According to equation (2) in step 31), the equation of state written from the k +1 th time to the k + p time can be derived:
X(k)＝AP(k)x(k|k)+BP(k)U(k) (13)
here, the number of the first and second electrodes,
Figure BDA0003522034440000181
U(k)＝[u(k|k)u(k+1|k)…u(k+p-1|k)]T (15)
APinner block matrix Ap＝[A(k+p-1)...A(k+1)A(k)]The predicted value of the state transition matrix at the k + p th moment of the system at the current k moment is shown, and the element A of the predicted valuepAnd (i, j) represents a predicted transition probability value of the state bin i when the air conditioning load of the state bin j is transferred within the k + p time step by the system under the condition that only the load number vector x (k | k) of each state bin at the current time k is known. B isPCompletely similar and will not be described in detail.
62) Establishing a cluster temperature control load optimization control model: the minimized tracking error is adopted as an objective function, and the optimized control objective function is as follows:
Figure BDA0003522034440000182
wherein, WerrA tracking error weight coefficient matrix representing the output of the model and an actual target value is set as a unit matrix; d (k) ═ diag { C (k +1) C (k +2)...C(k+p)}，R(k)＝[r(k+1)r(k+2)...r(k+p)]TAnd r (k + ζ) represents an output target track value at a time k + ζ.
Based on the above 2D state bin transition model, the value range of the control variable u (k) should be between-1 and 1; the state variable x (k) should range in value from 0 to 1. Therefore, by targeting the minimum of the tracking error of the cluster output, the optimization control model of the cluster air conditioner load can be converted into a quadratic programming problem as shown below:
Figure BDA0003522034440000191
and (3) rolling optimization solution: 0 and 1 in the constraint condition of the formula (17) are in a vector form, and meanwhile, the quadratic programming model can be solved by calling a quadratic programming function provided by an MATLAB optimization tool box. Obtaining an optimized control sequence formed by the number of state bin air conditioner load switches in the control time domain p x delta t after solving, and only issuing the first component u of the optimized sequence at the current scheduling time*(k | k). And waiting for the next scheduling period to arrive, and repeating the rolling optimization process.
And 7: performing controllable load selection based on multi-scale priority sorting; the step 7 comprises the following specific steps:
71) establishing a ranking index based on the normalized temperature distance:
Figure BDA0003522034440000192
wherein, NTDi，kThe normalized temperature distance of the ith air conditioner load at the current k moment is a dimensionless coefficient with the value range between 0 and 1. Delta denotes the temperature dead zone, i.e. the upper and lower limit values theta of the comfort temperature of the userhighAnd thetalowThe difference between them. Thetai，tIndicating the temperature, O, of the ith air conditioning load at the current time kkAnd CkRespectively representing the opening group and the closing group at the current k moment, and m is the total number of the air conditioner loads.
72) Establishing a ranking index based on power similarity:
Figure BDA0003522034440000193
wherein, the SIMi，(p，q)Is the similarity index of the conditioned load power and the required conditioned power in the state bin (P, q), PiRated power for the ith air conditioning load, Paim，(p，q)The target power of the response is required for the state bin (p, q). N is a radical of(p，q)Indicates the number of air conditioning loads in the status bin (p, q).
73) Establishing a sequencing index based on the accumulated control times:
NCi，k＝(Ci，k-Ck，min)/(Ck，max-Ck，max) (20)
wherein, Ci，kThe cumulative control times of the air conditioning load i at the time k, Ck，minAnd Ck，maxThe minimum value and the maximum value of the controlled times of the k load at the current moment are shown.
74) Load sorting based on multi-scale priority: comprehensive sequencing reference value gamma of air conditioning load i of controllable opening state group at time kopenAs shown in the following formula:
Figure BDA0003522034440000201
KT，KSand KCAre respectively the corresponding weight coefficient, KTThe larger the value, the more the thermal comfort of the user can be guaranteed; kSThe larger the load response, the higher the accuracy of the load response; kCThe larger the load response, the better the fairness, which is set to 0.3, 0.4, respectively.
According to a multi-scale priority synthetic index gammaopenThe magnitude of the value of (a) ranks the air conditioning loads in each 2D state bin corresponding to the group of open states, ΓopenA smaller value indicates a higher priority of the air conditioning load within the status bin. And controlling the temperature control load from high to low according to the priority, thereby realizing the frequency modulation function.
As shown in fig. 8, the present invention provides a frequency modulation system for a cluster temperature-controlled load system, including:
the acquisition unit is used for acquiring cluster temperature control load initialization parameters;
the load model establishing unit is used for establishing a 2D state bin cluster temperature control load model according to the single air conditioner load model and determining the schedulable capacity of the current cluster temperature control load;
the space model establishing unit is used for establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on the Markov chain so as to obtain a state transition matrix;
the frequency modulation model calculation unit is used for calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model, and further obtaining a cluster temperature control load total power change value;
the control model optimization unit is used for establishing a cluster temperature control load optimization control model according to the state transition matrix, and solving the total power change value of the cluster temperature control load by adopting rolling optimization to obtain a cluster temperature control load control scheme;
and the controllable load selection unit is used for performing controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting and outputting a controllable load selection result.
A third object of the present invention is to provide an electronic device, as shown in fig. 9, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the cluster temperature-controlled load system frequency modulation method or the steps of the offer prediction method when executing the computer program.
The frequency modulation method of the cluster temperature control load system comprises the following steps:
acquiring cluster temperature control load initialization parameters;
establishing a 2D state bin cluster temperature control load model according to the single air conditioner load model, and determining the schedulable capacity of the current cluster temperature control load;
establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on a Markov chain so as to obtain a state transition matrix;
calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, and calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model so as to obtain a cluster temperature control load total power change value;
establishing a cluster temperature control load optimization control model according to the state transition matrix, and solving the total power change value of the cluster temperature control load by adopting rolling optimization to obtain a cluster temperature control load control scheme;
and carrying out controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting, and outputting a controllable load selection result.
A fourth object of the present invention is to provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the processor implements the steps of the cluster temperature control load system frequency modulation method or the steps of the offer prediction method.
The frequency modulation method for the cluster temperature control load system comprises the following steps:
acquiring cluster temperature control load initialization parameters;
establishing a 2D state bin cluster temperature control load model according to the single air conditioner load model, and determining the schedulable capacity of the current cluster temperature control load;
establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on a Markov chain so as to obtain a state transition matrix;
calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, and calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model so as to obtain a cluster temperature control load total power change value;
establishing a cluster temperature control load optimization control model according to the state transition matrix, and solving the total power change value of the cluster temperature control load by adopting rolling optimization to obtain a cluster temperature control load control scheme;
and carrying out controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting, and outputting a controllable load selection result.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (11)

1. A frequency modulation method for a cluster temperature control load system is characterized by comprising the following steps:
acquiring cluster temperature control load initialization parameters;
establishing a 2D state bin cluster temperature control load model according to the single air conditioner load model, and determining the schedulable capacity of the current cluster temperature control load;
establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on a Markov chain so as to obtain a state transition matrix;
calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, and calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model so as to obtain a cluster temperature control load total power change value;
establishing a cluster temperature control load optimization control model according to the state transition matrix, and solving the total power change value of the cluster temperature control load by adopting rolling optimization to obtain a cluster temperature control load control scheme;
and carrying out controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting, and outputting a controllable load selection result.
2. The method of claim 1,
the method for establishing the 2D state bin cluster temperature control load model according to the single air conditioner load model and determining the schedulable capacity of the current cluster temperature control load specifically comprises the following steps:
establishing a single air conditioner load model according to a second-order discretization differential equation of the equivalent thermal parameter model;
dividing the cluster temperature control load into a closing group and an opening group according to the current switching state of the cluster temperature control load; respectively according to the upper and lower limit values of the indoor air temperature
Figure FDA0003522034430000011
And upper and lower limits of the temperature of the indoor material
Figure FDA0003522034430000012
Dividing the temperature interval into Ni2 indoor air temperature cell and Nm2 temperature cells of indoor material to form Na*NmState bins for 4 closed groups and open groups; further forming a 2D state bin transfer model;
taking all the current closed bins in the 2D state bins, the temperature is from low to high, removing the sum of the rated power of all the air-conditioning equipment in the first state bin which is closest to the lower limit of the allowable temperature to be the maximum power capacity which can be adjusted upwards, and adding the sum of the current load power of the opened air-conditioner as the maximum power allowable value P which can be adjusted in a scheduling waymax(ii) a Taking all current open bins in the 2D state bins, the temperature of which is from low to high, removing the sum of all rated powers of the air-conditioning equipment of the last state bin which is closest to the upper limit of the allowable temperature as the maximum power capacity which can be adjusted downwards, and obtaining the minimum power allowable value P which can be adjusted in a dispatching way of the cluster air-conditioning groupmin。
3. The method of claim 2,
the second-order discretization differential equation of the equivalent thermal parameter model is as follows:
Figure FDA0003522034430000021
where Δ t represents the simulation step size, θaAn indoor air temperature indicative of an air conditioning load; thetamIndoor material temperature representing air conditioning load; thetasIndicating the ambient temperature at which the air conditioning load is located; ra、RmEquivalent thermal resistances of indoor air and indoor substances respectively; ca、CmEquivalent heat capacities of air temperature and material temperature, respectively; when the air conditioner load is turned on, QaRated power for the air conditioner; when turned off, QaEqual to 0.
4. The method of claim 1,
the method comprises the following steps of establishing a cluster temperature control load state space model according to a 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving a state bin transition probability based on a Markov chain to obtain a state transition matrix, wherein the method comprises the following steps:
on the basis of the 2D state bin cluster temperature control load model, a cluster temperature control load state space model is established and expressed by a time-varying discrete state space equation:
Figure FDA0003522034430000022
wherein, x (k) represents a system state vector at the k-th moment, and the expression is as follows:
x(k)＝[x1，off(k)x2，off(k)...xN/2，off(k)x1+N/2，on(k)x2+N/2，on(k)...xN，on(k)]T (3)
in the formula (I), the compound is shown in the specification,n represents the total number of state bins, N ═ Na*Nm/2, element x thereofi,off(k) The ratio of the number of the air conditioning loads in the closed group state bin i at the k-th time divided by the total load number is shown, wherein i is 1,2, … and N/2; x is a radical of a fluorine atomj,on(k) The ratio of the number of the air conditioner loads in the opening group state bin j at the k-th moment to the total load number is shown, wherein j is N/2+1, N/2+2, … and N; x (k +1) represents a system state vector at the k +1 th moment;
a (k) represents the system matrix at time k, whose element Aij(k) The transition probability of the air conditioning load of the state bin j to the state bin i in the kth time step is shown;
u (k) represents a control signal at the k-th time;
b (k) represents the input matrix at time k, whose elements Bij(k) The transition probability of the air conditioning load of the state bin j requiring the switching operation to the state bin i by the action of u (k) is shown as follows:
Figure FDA0003522034430000031
wherein diag denotes a diagonal matrix, diagsubRepresenting a sub-diagonal matrix;
c (k) represents the output matrix at the k-th time, that is, the average power vector of the air-conditioning loads of each state bin at the current time k, and is represented as follows:
C(k)＝mPave(k)*S (5)
wherein the content of the first and second substances,
Figure FDA0003522034430000032
m represents the total number of air conditioning loads, Pave(k) Represents the average power, P, of the air conditioning load of each state bin of the opening group at the kth momentagg(k) An observed value representing the aggregate output power of the air conditioning load groups at the k-th moment; s represents the switching function vector of each state bin, SiIndicating the switching state of the state bin i, wiIndicating the on-off state of the ith load, 0 being the off state,1 is in an on state;
y (k) represents the output power of the cluster air conditioner load model at the k moment;
randomly selecting the space state at the initial moment as the simulation initial state, and setting the initial temperature of all air conditioner loads at theta-_ETPAnd theta+_ETPUniformly distributed among the layers;
performing model Carlo random simulation on the m air conditioner loads based on the formula (7) to obtain a time-temperature thermal operation curve of each air conditioner load at each simulation moment;
Figure FDA0003522034430000041
in the formula: x is the number ofsA set value representing an air conditioner temperature; x is the number ofin,tRepresents the indoor temperature at time t; Δ x represents an offset value allowed by an air conditioner temperature set value; sAC,tThe working state of the air conditioner at the moment t is represented, the value is 0 to represent that the air conditioner is closed, and the value is 1 to represent that the air conditioner is opened;
sequentially numbering the state bins according to the 2D temperature of each air conditioner load at different simulation moments;
counting the number of the air conditioning load of the state bin i transferred to the state bin j between the adjacent simulation time k and k +1, wherein i, j is 1,2, …, N;
calculating the state transition probability of the air conditioning load of the state bin i in the k period of the first-order Markov chain to the state bin j:
Figure FDA0003522034430000042
in the formula, ni,j(k) The number of the air conditioner load of the state bin i transferred to the state bin j in the k period is represented; n isi(k) Representing the total load number of the state transition occurring in the state bin i in the k period; n represents the total number of state bins;
according to the different values of i and j, the transition probability p of each state can be obtainedi,j(k) Thereby obtaining a state transition matrix p (k).
5. The method of claim 1,
the method for calculating the change value of the primary frequency modulation power of the cluster temperature control load according to the primary frequency modulation model of the cluster temperature control load specifically comprises the following steps:
setting a primary frequency modulation coefficient of the cluster temperature control load according to the frequency adjustment characteristic of the cluster temperature control load;
establishing a cluster temperature control load primary frequency modulation system model; the cluster temperature control load primary frequency modulation system model comprises a single-region frequency modulation system model and a cluster air conditioner group frequency modulation module; the single-region frequency modulation system model is a closed-loop system with an integral regulation system, a power regulation signal of the single-region frequency modulation system model is converted into a turbine input power regulation variable through a system secondary frequency modulation transfer function, a generator governor transfer function and a prime mover transfer function respectively, and the input power regulation variable and a load fluctuation variable participate in system frequency modulation; the cluster air conditioner group frequency modulation module comprises a frequency modulation dead zone, a frequency modulation coefficient, a schedulable potential upper and lower limit outer limit and an air conditioner response time delay of the air conditioner group;
on the basis of the cluster temperature control load primary frequency modulation system model, after each air-conditioning equipment monitors a frequency deviation signal, calculating to obtain a cluster temperature control load primary frequency modulation power change value delta PAC。
6. The method of claim 1,
the method for calculating the cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model specifically comprises the following steps:
establishing a secondary frequency modulation simulation model of the cluster temperature control load on the basis of the cluster temperature control load primary frequency modulation system model; the secondary frequency modulation simulation model of the cluster temperature control load is based on the primary frequency modulation model of the cluster air conditioner, a control signal received by a cluster air conditioner group comprises a primary frequency modulation system frequency deviation and a setting value of a system power deviation signal calculated by a regional AGC of secondary frequency modulation, and the setting value of the regional AGC power deviation is determined by a setting multiplying factor r;
wherein r represents the setting multiplying factor of the regional AGC power deviation on the air-conditioning group, and is calculated by the following formula:
Figure FDA0003522034430000051
in the formula,. DELTA.Pg′(s) is expressed as a secondary frequency modulation power deviation signal, delta P, of the thermal power generating unitAC′(s) is expressed as a clustered air conditioning group secondary FM power deviation signal, Δ Pc(s) calculating a quadratic frequency modulation power deviation signal for regional AGC;
after the regional AGC secondary frequency modulation power deviation signal monitored by each air conditioning equipment, calculating according to the formula (12) to obtain the total power change value delta P of the cluster temperature control loadAC′(s)。
7. The method of claim 1,
the method comprises the following steps of establishing a cluster temperature control load optimization control model according to a state transition matrix, and solving a cluster temperature control load control scheme by adopting rolling optimization on a total power change value of the cluster temperature control load, wherein the cluster temperature control load optimization control model comprises the following steps:
setting the prediction time length as p, and setting the prediction state at the k + p th time as x (k + ζ | k) under the current k time condition, wherein ζ is 1,2, …, p; according to the cluster temperature control load state space model, establishing a state equation from the k +1 th moment to the k + p th moment:
X(k)＝AP(k)x(k|k)+BP(k)U(k) (13)
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003522034430000061
U(k)＝[u(k|k)u(k+1|k)…u(k+p-1|k)]T (15)
APinner block matrix Ap＝[A(k+p-1)…A(k+1)A(k)]The predicted value of the state transition matrix at the k + p th moment of the system at the current k moment is shown, and the element A of the predicted valuep(i, j) represents onlyKnowing the load number vector x (k | k) of each state bin at the current moment k, the system transfers the air conditioning load of the state bin j to the transfer probability predicted value of the state bin i in the k + p time step;
the minimized tracking error is adopted as an objective function, and the optimized control objective function is as follows:
Figure FDA0003522034430000062
wherein, WerrA tracking error weight coefficient matrix representing the model output versus the actual target value, d (k) ═ diag { C (k +1) C (k +2) … C (k + p) }, r (k) ═ r (k +1) r (k +2) … r (k + p)]TAnd r (k + ζ) represents an output target track value at a time k + ζ;
converting the optimization control model of the cluster air conditioner load to obtain a quadratic programming function by taking the minimum cluster output tracking error as a target:
Figure FDA0003522034430000063
and performing rolling optimization solution on the quadratic programming function, obtaining an optimization control sequence formed by the number of state bin air conditioner load switches in a control time domain p x delta t after the solution, and only issuing a first component u of the optimization sequence at the current scheduling time*(k | k); and repeating the rolling optimization process when the next scheduling period comes to obtain the cluster temperature control load control scheme.
8. The method of claim 1,
the controllable load selection is carried out on the cluster temperature control load control scheme based on the multi-scale priority sorting, and a controllable load selection result is output, and the method comprises the following steps:
establishing a ranking index based on the normalized temperature distance:
Figure FDA0003522034430000071
wherein, NTDi,kIs the normalized temperature distance of the ith air conditioning load at the current time k; delta represents temperature dead zone, upper and lower limit values theta of comfort temperature of userhighAnd thetalowThe difference between the two; thetai,tIndicating the temperature, O, of the ith air conditioning load at the current time kkAnd CkRespectively representing an opening group and a closing group at the current k moment, wherein m is the total number of air conditioner loads;
establishing a ranking index based on power similarity:
Figure FDA0003522034430000072
wherein, the SIMi,(p,q)Is the similarity index of the conditioned load power and the required conditioned power in the state bin (P, q), PiRated power, P, for the ith air conditioning loadaim,(p,q)A target power for which a response is required for the state bin (p, q); n is a radical of(p,q)Representing the number of air conditioning loads in the status bin (p, q);
establishing a sequencing index based on the accumulated control times:
NCi，k＝(Ci，k-Ck，min)/(Ck，max-Ck，max) (20)
wherein, Ci,kThe cumulative control times of the air conditioning load i at the time k, Ck,minAnd Ck,maxThe minimum value and the maximum value of the controlled times of k load at the current moment are represented;
obtaining a multi-scale priority comprehensive index gamma based on the ranking index based on the normalized temperature distance, the ranking index based on the power similarity and the ranking index based on the accumulated control timesopenAs shown in the following formula:
Figure FDA0003522034430000073
wherein, KT，KSAnd KCAre respectively corresponding weight coefficients;
according to a multi-scale priority synthetic index gammaopenThe air-conditioning loads in each 2D state bin corresponding to the open state group are sorted, and controllable load selection is performed from high to low according to the priority, so that a controllable load selection result is obtained.
9. A cluster temperature control load system frequency modulation system is characterized by comprising:
the acquisition unit is used for acquiring cluster temperature control load initialization parameters;
the load model establishing unit is used for establishing a 2D state bin cluster temperature control load model according to the single air conditioner load model and determining the schedulable capacity of the current cluster temperature control load;
the space model establishing unit is used for establishing a cluster temperature control load state space model according to the 2D state bin cluster temperature control load model and the current cluster temperature control load schedulable capacity, and solving the state bin transition probability based on the Markov chain so as to obtain a state transition matrix;
the frequency modulation model calculation unit is used for calculating a cluster temperature control load primary frequency modulation power change value according to the cluster temperature control load primary frequency modulation model, calculating a cluster temperature control load secondary frequency modulation power change value according to the cluster temperature control load secondary frequency modulation model, and further obtaining a cluster temperature control load total power change value;
the control model optimization unit is used for establishing a cluster temperature control load optimization control model according to the state transition matrix, and solving the total power change value of the cluster temperature control load by adopting rolling optimization to obtain a cluster temperature control load control scheme;
and the controllable load selection unit is used for performing controllable load selection on the cluster temperature control load control scheme based on multi-scale priority sorting and outputting a controllable load selection result.
10. An electronic device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the cluster temperature controlled load system frequency modulation method according to any one of claims 1 to 8 when executing said computer program.
11. A computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method of frequency modulation for a cluster temperature controlled load system as claimed in any one of claims 1 to 8.
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