CN115549111A - Temperature control load cluster control method, system and medium for micro-grid - Google Patents

Temperature control load cluster control method, system and medium for micro-grid Download PDF

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CN115549111A
CN115549111A CN202211217777.4A CN202211217777A CN115549111A CN 115549111 A CN115549111 A CN 115549111A CN 202211217777 A CN202211217777 A CN 202211217777A CN 115549111 A CN115549111 A CN 115549111A
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load
temperature control
temperature
scheduling
power
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施星宇
张茂林
吴公平
肖辉
张永熙
曹一家
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Changsha University of Science and Technology
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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
    • H02J3/12—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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin

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Abstract

The invention discloses a temperature control load cluster control method, a system and a medium facing a micro-grid, wherein the method comprises the steps of collecting load electricity consumption and photovoltaic output of the micro-grid at the current moment, and calculating the total scheduling power of all temperature control loads by combining scheduling requirements; and aiming at an upper-layer object containing a plurality of temperature control loads, calculating a scheduling power increment required to be provided in the next control period based on the total scheduling power of the temperature control loads, calculating the scheduling power of each temperature control load according to the scheduling power increment, and realizing consensus control on temperature control load clusters in the same upper-layer object. The method can respond to the step scheduling signal of the main network, stabilize the power fluctuation of photovoltaic power generation and loads, simultaneously realize the consensus control aiming at temperature control load clusters in different upper-layer objects (such as buildings), relieve the communication pressure of a micro-grid system, optimize the distribution of scheduling power among the buildings by considering the difference of various building parameters, and effectively avoid the off-line of a comfortable state.

Description

Temperature control load cluster control method, system and medium for micro-grid
Technical Field
The invention belongs to the field of control of Temperature Controlled Loads (TCLs), and particularly relates to a temperature Controlled load cluster control method, system and medium for a micro-grid.
Background
Renewable energy is used in the microgrid to generate electricity, which can alleviate energy and environmental problems. However, the intermittency and uncertainty of renewable energy generation limits its ability to be dissipated, which if left uncontrolled, can cause significant impact on the main grid. Energy storage devices such as storage batteries and super capacitors are effective methods for stabilizing power fluctuation, but aging loss of the energy storage devices can influence scheduling performance of the energy storage devices, and meanwhile, operation expenditure of the micro-grid is increased. The installation of air conditioning Loads in a room constitutes a Temperature Controlled Load (TCL). With the increase of the number of TCLs, the proportion of the total load is increased, and the power fluctuation of the micro-grid can be effectively stabilized by adopting a proper control strategy, so that the scheduling requirement of the main grid is met. The influence of a single TCL on the system is small, and a large number of TCL groups need to be gathered to realize the regulation and control of the power.
The existing TCL cluster control method is a state queuing method based on temperature and a regulation and control method based on an intelligent algorithm. The temperature-based state queuing method sets the TCL control order with the distance between the current temperature and the upper temperature limit as a priority, thereby ensuring user comfort. By dynamically adjusting the temperature set point of the TCL, the problem of frequent start and stop of the part of the TCL in the state queuing algorithm can be further solved, and the influence of scheduling on the service life of the TCL is reduced. The TCL cluster control is expressed as a nonlinear programming problem based on the intelligent algorithm regulation and control method, each TCL is regulated and controlled through the intelligent algorithm, and the method has high calculation accuracy. However, these cluster control methods can only control TCLs with the same set temperature, and do not consider the problem of parameter heterogeneity of TCLs and buildings within the microgrid. In addition, these methods can be very computation and communication stressful for large-scale systems with wide spatial distribution. The two-dimensional state sequence modeling method is an effective method for solving parameter heterogeneity, heterogeneous parameters can be reflected through introduction of temperature change rate information, and although the method reduces the calculated amount, the method does not accurately describe the state of the TCL through an equivalent thermal parameter model. Moreover, as Variable Frequency Air Conditioners (VFACs) are popularized, a control method for VFAC to participate in scheduling needs to be further studied, an existing TCL cluster control method mainly focuses on configuration of scheduling power between loads, user comfort is generally used as a limiting condition for control, and fair distribution among TCLs is not achieved.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: aiming at the problems in the prior art, the invention provides a temperature control load cluster control method, a temperature control load cluster control system and a temperature control load cluster control medium for a micro-grid, which can respond to a step scheduling signal of a main grid, stabilize power fluctuation of photovoltaic power generation and loads, simultaneously realize consensus control of TCL power deviation and comfort level parameters in a building, and can complete scheduling under three conditions of partial buildings and TCL access and connection, different set temperatures of air conditioner loads and different parameters among buildings.
In order to solve the technical problems, the invention adopts the technical scheme that:
a temperature control load cluster control method facing a micro-grid comprises the following steps:
s1, collecting load electricity consumption P of the microgrid at the current moment t load (t) and photovoltaic output P pv (t), the initialization time Δ t is 0;
s2, electricity consumption P according to load load (t) photovoltaic output P pv (t) calculating the total scheduling power P of all temperature control loads according to the scheduling requirements req (t)；
S3, respectively aiming at upper-layer objects containing a plurality of temperature control loads, and scheduling power P based on the temperature control loads req (t) computing upper layer objectsL scheduled power increment delta P to be provided in the next control period L (t+Δt s )；
S4, aiming at the temperature control load in each upper layer object, respectively according to the scheduling power increment delta P required to be provided by the next control period of the corresponding upper layer object L (t+Δt s ) Calculating the scheduling power of each temperature control load and realizing consensus control on the temperature control load clusters in the same upper-layer object;
s5, adding the time delta t to the time interval delta t of the lower layer control s If the new time Δ t is equal to the preset load and photovoltaic data acquisition time interval Δ t c If yes, skipping to the step S6, otherwise skipping to the step S2;
s6, judging whether the scheduling is finished or not, and finishing and exiting if the scheduling is finished; otherwise, jumping to step S1.
Optionally, the temperature-controlled load scheduling power P is calculated in step S2 req The functional expression of (t) is:
P req (t)＝P pv (t)-P dreq (t)-P load (t)-P TCL (t)，
in the above formula, P dreq (t) output Power required for scheduling, P TCL (t) is the sum of the power consumed by the temperature controlled load clusters.
Optionally, in step S3, a scheduling power increment Δ P that the upper layer object L needs to provide in the next control period is calculated L (t+Δt s ) The method comprises the following steps:
first, compute upper level object L at t + t d Scheduled power P of time L (t+t d )：
Figure BDA0003876083540000021
In the above formula, the subscripts L and M are numbers of upper layer objects, the upper layer objects are directly connected to the microgrid through the aggregator or indirectly connected to the microgrid through other upper layer objects, Δ P L (t+t d ) For the upper layer object L at t + t d Scheduled power increment of time, t d Time of single update for upper layer object, w tableShowing the coupling coefficient, N bc Number of upper level objects, c LM A correlation coefficient, c, for determining whether there is a communication link between the upper layer object L and the upper layer object M LM The value is 1 or 0; p is M (t) scheduling power, P, required to be provided by upper layer object M at time t L (t) scheduling power required to be provided by the upper layer object L at the time t, q L A drag coefficient, q, for whether an upper layer object is directly connected to the aggregator L A value of 1 or 0; p req (t) Total scheduled Power, P, for all temperature controlled loads at time t L (t+t d ) For the upper layer object L at t + t d Required delivered scheduled power, P, of a time instant L-max And P L-min Respectively representing the maximum and minimum scheduling power, beta, that the upper level object L can provide L Representing the containment consensus coefficient, alpha ML Is a scheduling capability scaling factor and has a delta t s ＝2t d ；
Then, it will be t + t d As the new current time t, the upper layer object L at t + t is recalculated d Scheduled power of time P L (t+t d ) And is used as the scheduled power P required to be provided by the upper layer object L in the next control period L (t+Δt s )；
Finally, the scheduling power increment delta P required to be provided by the upper layer object L in the next control period is calculated according to the following formula L (t+Δt s )：
ΔP L (t+Δt s )＝P L (t+Δt s )-P L (t)，
In the above formula, P L (t) is the scheduled power that the upper layer object L needs to provide at time t.
Optionally, the upper-level object is a building, one building corresponds to one upper-level object, and the calculation function expression of the containment consensus coefficient and the scheduling capability scaling factor is as follows:
Figure BDA0003876083540000031
in the above formula,. Beta. L Indicating a containment systemCoefficient of identity, α ML For the scheduling capability scaling factor, N L And N M Indicating the number of temperature control loads in which the upper layer object L and the upper layer object M participate in scheduling,
Figure BDA0003876083540000032
mean high power, R, representing temperature-controlled load L And η L Respectively representing the equivalent thermal resistance and thermal coefficient of the upper layer object L, R M And η M Respectively representing the equivalent thermal resistance and thermal coefficient of the upper layer object M, N 1 ～N Nbc Respectively represent upper layer objects 1 to N bc Number of temperature-controlled loads involved in scheduling, R 1 ～R Nbc Respectively represent upper layer objects 1 to N bc Equivalent thermal resistance of 1 ～η Nbc Respectively represent upper layer objects 1 to N bc Thermal coefficient of (2).
Optionally, step S1 is preceded by establishing a state space model for a single temperature-controlled load, as shown in the following formula:
Figure BDA0003876083540000033
in the above-mentioned formula, the compound has the following structure,
Figure BDA0003876083540000034
state variable x for the ith temperature-controlled load i (t)＝[γ i (t)；β i (t)]First derivative of gamma i (t) the temperature-controlled load power, β, of the ith temperature-controlled load i (t) user comfort for the ith temperature-controlled load, A and B are system state matrices, W is a constant matrix, u is a constant matrix i (t) is a control variable of the ith temperature-controlled load, subscript i is a control variable u representing the ith temperature-controlled load i (t) using percent change in rated power, and having:
γ i (t)＝α i (t)-α s ，
Figure BDA0003876083540000035
Figure BDA0003876083540000036
in the above formula, α i (t) represents the power of the ith temperature-controlled load as a percentage of the rated power at time t, α s Indicating temperature as a user set point T s Percentage of rated power, T, corresponding to time i (T) is the temperature when the ith temperature-controlled load stably operates at the moment T, delta T is the temperature deviation tolerance, eta represents the thermal coefficient of the variable frequency air conditioner,
Figure BDA0003876083540000041
to rated power, C th And R th Respectively representing the heat capacity and the heat resistance of a building; the step S4 comprises the following steps:
s4.1, forming an internal degree matrix D = diag { D ] according to the communication topology of N temperature control loads in the upper layer object 1 ,d 2 ,…,d N }∈R N×N Wherein d is 1 ,d 2 ,…,d N The internal degree of the 1 st to N temperature control loads, R N×N For dimensionality, the computational function expression for the degree of internalization is:
Figure BDA0003876083540000042
in the above formula, d i Is the internal degree of the ith temperature-controlled load, a ij The communication connection state of the ith temperature control load and the jth temperature control load is set to be 1 or 0; constructing an adjacency matrix of M = [ a ] according to communication topology of N temperature control loads in upper-layer objects ij ]∈R N×N And constructing a laplace matrix L = { L ] according to the following formula ij }∈R N×N ：
L＝D-M，
In the above formula, D is an interior matrix, M is an adjacent matrix, l ij The Laplace operator values of the ith temperature control load and the jth temperature control load are obtained;
s4.2, according to the scheduling power required to be provided by the next control cycle of the upper layer objectRate increment Δ P L (t+Δt s ) Determining scheduling information of each temperature control load receiving controller according to the following formula;
Figure BDA0003876083540000043
in the above formula,. DELTA.x i (t) the ith temperature-controlled load receives scheduling information of the controller at time t, k 1 For the number of temperature controlled loads directly connected to the controller,
Figure BDA0003876083540000044
average power, Δ t, for temperature-controlled loads s Time interval controlled for lower layer, q i For the control coefficient of the ith temperature control load, q is provided if the ith temperature control load is directly connected with the controller in communication i =1, otherwise q i ＝0；
S4.3, determining that the states among the temperature control loads with communication connection are relatively available, and adopting a function expression controlled by a distributed static consensus protocol as follows:
Figure BDA0003876083540000045
in the above formula, u i (t) is the control variable at time t, c is a coupling coefficient greater than 0, L' is E.R 1×2 Representing a feedback gain matrix, R 1×2 Represents dimension, x i (t) is the state variable of the ith temperature-controlled load, a ij For the communication connection state between the ith temperature control load and the jth temperature control load, x j (t) is the state variable of the jth temperature-controlled load,. DELTA.x i (t) scheduling information of the ith temperature control load receiving controller; substituting the function expression controlled by the distributed static consensus protocol into a state space model of a single temperature control load to obtain an ith temperature control load regulation and control model:
Figure BDA0003876083540000046
in the above formula, /) ij Is the Laplace operator value, deltax, of the ith and jth temperature control loads j (t) scheduling information of the jth temperature control load receiving controller;
and S4.4, determining a feedback gain matrix L 'and a coupling coefficient c, and substituting the determined feedback gain matrix L' and the coupling coefficient c into the control model of each temperature control load to control the state of each temperature control load.
Optionally, the determining the feedback gain matrix L' in step S4.4 includes:
s101, finding out a solution of a linear matrix inequality shown in the following formula when P > 0:
AP+PA T -2BB T <0，
in the above formula, A and B are system state matrixes, and P is the solution of a linear matrix inequality;
s102, a feedback gain matrix L' is calculated according to the following formula:
L′＝-B T P -1 ，
in the above formula, P is the solution of the linear matrix inequality.
Optionally, the determining the coupling coefficient c in step S4.4 comprises:
s201, determining the condition that the state variables of the temperature control loads in the upper layer object are commonly identified as a matrix A + c lambda i BL' is a Helveltz matrix, and the matrix A + c λ i The characteristic polynomial det (sI- (A + σ BL ')) of BL' is stable, where λ i Is the non-zero eigenvalue of Laplace matrix L, s is matrix A + c λ i The eigenvalues of BL', I is a second order identity matrix, A and B are system state matrices, σ is a coupling coefficient c and an eigenvalue λ i L' is a feedback gain matrix and has σ = c λ i = x + jy, wherein x and y are coordinates of a real axis and an imaginary axis respectively, and j is an imaginary unit;
s202, according to the matrix A + c lambda i The eigenvalues s of BL' establish a complex coefficient polynomial p(s) as shown below:
p(s)＝s 2 +(a+jb)s+e+jd，
in the formula, a, b, d and e are polynomial coefficients, j is an imaginary unit, and a, b and e belong to R, wherein R is a real number;
s203, according to the stable essential condition a of the complex coefficient polynomial p (S)>0 and abd + a 2 e-d 2 >And 0, determining the value range of the coupling coefficient c, thereby determining the value of the coupling coefficient c in the value range.
Optionally, after the determined feedback gain matrix L' and the coupling coefficient c are substituted into the control model of each temperature control load in step S4.4 to control the state of each temperature control load, the state variables of any ith temperature control load after each control are:
Figure BDA0003876083540000051
in the above formula, x i (t+Δt s ) For regulating the post-state variable, x i (t) is the state variable of the ith temperature-controlled load,
Figure BDA0003876083540000052
is the first derivative, Δ t, of the state variable of the ith temperature-controlled load s The time interval controlled for the lower layer.
In addition, the invention also provides a temperature control load cluster control system facing the microgrid, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the temperature control load cluster control method facing the microgrid.
In addition, the present invention also provides a computer readable storage medium, in which a computer program is stored, the computer program being programmed or configured by a microprocessor to execute the microgrid-oriented temperature control load cluster control method.
Compared with the prior art, the invention mainly has the following advantages: the method comprises the steps of collecting load electricity consumption and photovoltaic output of a micro-grid at the current moment, and calculating the total scheduling power of all temperature control loads by combining scheduling requirements; and aiming at an upper-layer object containing a plurality of temperature control loads, calculating a scheduling power increment required to be provided in the next control period based on the total scheduling power of the temperature control loads, calculating the scheduling power of each temperature control load according to the scheduling power increment, and realizing consensus control on temperature control load clusters in the same upper-layer object. The method can respond to the step scheduling signal of the main network, stabilize the power fluctuation of photovoltaic power generation and loads, simultaneously realize the consensus control aiming at temperature control load clusters in different upper-layer objects (such as buildings), relieve the communication pressure of a micro-grid system, optimize the distribution of scheduling power among the buildings by considering the difference of various building parameters, and effectively avoid the off-line of a comfortable state.
Drawings
FIG. 1 is a schematic diagram of a basic process flow of a method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a hierarchical control principle in the first embodiment of the present invention.
Fig. 3 is a schematic view of a topology structure between buildings according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a topology of a TCL in a building according to an embodiment of the present invention.
Fig. 5 is a graph comparing interaction power curves of a microgrid and a main grid with and without a method according to an embodiment of the present invention.
Fig. 6 is a graph comparing interaction power curves of a microgrid and a main grid with and without the method of the second embodiment of the invention.
FIG. 7 is the internal TCL user comfort index β for buildings # 1-5 i (t) graph of variation.
FIG. 8 is an index β of the comfort status of the internal TCL users in buildings No. 6-10 i (t) graph of variation.
FIG. 9 is the internal TCL user comfort index β for buildings 11-15 i (t) graph of variation.
FIG. 10 is the internal TCL user comfort index β for buildings 16-20 i (t) graph of variation.
Detailed Description
The first embodiment is as follows:
as shown in fig. 1, the temperature control load cluster control method for the microgrid in the present embodiment includes:
s1, collecting load electricity consumption P of the microgrid at the current moment t load (t) and photovoltaic output P pv (t), the initialization time Δ t is 0;
s2, electricity consumption P is used according to load load (t) photovoltaic output P pv (t) calculating the total scheduling power P of all temperature control loads according to the scheduling requirements req (t)；
S3, respectively aiming at upper-layer objects containing a plurality of temperature control loads, and scheduling power P based on the temperature control loads req (t) calculating the scheduled power increment Δ P that the upper layer object L needs to provide in the next control period L (t+Δt s )；
S4, aiming at temperature control Loads (TCLs for short) in each upper layer object, respectively according to scheduling power increment delta P required to be provided by the corresponding upper layer object in the next control period L (t+Δt s ) Calculating the scheduling power of each temperature control load and realizing consensus control on the temperature control load clusters in the same upper-layer object;
s5, adding the time delta t to the time interval delta t of the lower layer control s If the new time Δ t is equal to the preset load and photovoltaic data acquisition time interval Δ t c If yes, skipping to the step S6, otherwise skipping to the step S2;
s6, judging whether the scheduling is finished or not, and finishing and exiting if the scheduling is finished; otherwise, jumping to step S1.
As shown in fig. 2, the temperature control load cluster control method for the microgrid in the present embodiment includes two levels of an upper level control and a lower level control, where a control object of the upper level control is an upper level object, and a control means is step S3; the control object of the lower layer control is the temperature control load of the upper layer object, and the control means is step S4. In this embodiment, the temperature control load cluster control method for the microgrid is implemented by an aggregator and a controller in the microgrid which is composed of upper-level objects (a set of temperature control Loads, which may be a building, a floor, a unit, and the like). The aggregator establishes communication connection with the main network, the photovoltaic power generation system and part of the upper-layer objects, and the controller establishes communication connection with the TCL in the upper-layer objects.
In this embodiment, the temperature-controlled load scheduling power P is calculated in step S2 req The functional expression of (t) is:
P req (t)＝P pv (t)-P dreq (t)-P load (t)-P TCL (t)，(1)
in the above formula, P dreq (t) output Power required for scheduling, P TCL (t) is the sum of the power consumed by the temperature controlled load cluster.
In this embodiment, the data update time of the upper layer control is t d Having t of d ＝0.5Δt s The microgrid has N bc The upper level objects participate in the scheduling with i =1,2, \ 8230;, N bc And (4) showing. In step S3, a scheduling power increment Δ P required to be provided by the upper layer object L in the next control period is calculated L (t+Δt s ) The method comprises the following steps: first, compute upper level object L at t + t d Scheduled power of time P L (t+t d )：
Figure BDA0003876083540000071
In the above formula, subscripts L and M are numbers of upper layer objects, the upper layer objects are directly connected to the microgrid through the aggregator or indirectly connected to the microgrid through other upper layer objects, Δ P L (t+t d ) For the upper layer object L at t + t d Scheduled power increment of time, t d Time of single update for upper layer object, w represents coupling coefficient, N bc Number of upper level objects, c LM A correlation coefficient, c, for determining whether there is a communication link between the upper layer object L and the upper layer object M LM Value of 1 or 0 (c) LM =1 means that there is a communication link between two buildings, c LM =0 meaning no contact); p M (t) scheduling power, P, required to be provided by upper layer object M at time t L (t) scheduling power required to be provided by the upper layer object L at the time t, q L A drag coefficient, a drag coefficient q, for whether an upper object is directly connected to the aggregator L Value 1 or 0 (q) L =1 denotes that building is directly connected to aggregator, adjustment of aggregator can be acceptedInformation, q L =0 means that building and aggregator are not directly connected); p is req (t) Total scheduled Power, P, for all temperature controlled loads at time t L (t+t d ) For the upper layer object L at t + t d Required delivered scheduled power, P, of a time instant L-max And P L-min Respectively representing the maximum and minimum scheduling power, beta, that the upper level object L can provide L Representing the containment consensus coefficient, alpha ML Is a scheduling capability scaling factor and has a delta t s ＝2t d (ii) a The substitution constant can be given as:
Figure BDA0003876083540000081
then, it will be t + t d As the new current time t, the upper layer object L is recalculated at t + t d Scheduled power P of time L (t+t d ) And is used as the scheduled power P required to be provided by the upper layer object L in the next control period L (t+Δt s )；
Finally, the scheduling power increment delta P needed to be provided by the upper layer object L in the next control period is calculated according to the following formula L (t+Δt s )：
ΔP L (t+Δt s )＝P L (t+Δt s )-P L (t)， (3)
In the above formula, P L (t) is the scheduled power that the upper layer object L needs to provide at time t.
As shown in fig. 3, in the present embodiment, the upper layer object is a building, and one building corresponds to one upper layer object. Referring to fig. 3, as a specific embodiment, the present embodiment includes 20 buildings, which are respectively referred to as building 1 to building 20, wherein only buildings 6 to 10 are directly connected to the microgrid through the aggregator. As shown in fig. 4, 20 temperature-controlled loads, which are designated as temperature-controlled loads 1 to 20, are included in a building, and only the temperature-controlled loads 1 to 8 are directly connected to the controller. In this embodiment, the calculation function expression of the containment consensus coefficient and the scheduling capability scaling factor is:
Figure BDA0003876083540000082
Figure BDA0003876083540000083
in the above formula,. Beta. L Represents the containment consensus coefficient, α ML For scheduling capability scaling factor, N L And N M Represents the amount of temperature control loads that the upper layer object L and the upper layer object M participate in scheduling,
Figure BDA0003876083540000084
mean high power, R, representing temperature-controlled load L And η L Respectively representing the equivalent thermal resistance and thermal coefficient of the upper layer object L, R M And η M Respectively representing the equivalent thermal resistance and thermal coefficient of the upper layer object M, N 1 ～N Nbc Respectively represent upper layer objects 1 to N bc Number of temperature-controlled loads involved in scheduling, R 1 ～R Nbc Respectively represent upper layer objects 1 to N bc Equivalent thermal resistance of 1 ～η Nbc Respectively represent upper layer objects 1 to N bc Thermal coefficient of (a). The data update time for the above-described upper control is t d In order to prevent the divergence phenomenon of the scheduling power of each building in the upper control, it is necessary to prevent the coupling coefficient w from being too large, and the convergence rate of the upper control is slowed down by too small w, so the upper control speed is accelerated by reducing the update time. Setting t d ＝0.5Δt s I.e. the lower layer control is updated every twice the data update time t d Upper layer scheduling information is accepted once. Beta as defined above L And alpha ML The parameters of each upper layer object, namely the different TCLs which can participate in scheduling in the upper layer object, the different rated powers, the different heat capacities of buildings, the different thermal resistances and the different TCLs thermal coefficients, are fully considered by the definitions of the formulas (4) and (5), and the influence of the heterogeneity of the parameters on the scheduling capability of the upper layer object is taken into account.
In this embodiment, before step S1, a state space model shown by the following formula is established for a single temperature control load:
Figure BDA0003876083540000091
in the above formula, the first and second carbon atoms are,
Figure BDA0003876083540000092
state variable x for the ith temperature-controlled load i (t)＝[γ i (t)；β i (t)]First derivative of, gamma i (t) is the temperature-controlled load power, β, of the ith temperature-controlled load i (t) is the user comfort for the ith temperature controlled load, A and B are the system state matrix, W is the constant matrix, u is the constant matrix i (t) is a control variable of the ith temperature control load, subscript i is a control variable representing the ith temperature control load, and control variable u i (t) using percent change in rated power, and having:
γ i (t)＝α i (t)-α s ，(7)
Figure BDA0003876083540000093
Figure BDA0003876083540000094
Figure BDA0003876083540000095
Figure BDA0003876083540000096
in the above formula, α i (t) represents the power of the ith temperature-controlled load as a percentage of the rated power at time t, α s Indicating temperature as user set point T s Percentage of rated power, T, corresponding to time i (T) is the time temperature of the stable operation of the ith temperature control load at the moment T, delta T is the temperature deviation tolerance, and eta represents the variable frequency air conditionerThe thermal coefficient of (a) of (b),
Figure BDA0003876083540000097
to rated power, C th And R th Respectively representing the heat capacity and the thermal resistance of the building. The foregoing process of establishing a state space model for a single temperature controlled load (TLC) is as follows:
s101, establishing an equivalent thermal parameter model of a single TCL:
Figure BDA0003876083540000098
in the above formula, i =1,2, \8230;, N, N represents the number of TCLs in the building, T i (T) represents the indoor temperature at time T, T a (t) represents the external ambient temperature (e.g., 30 degrees Celsius in this example), C, at time t th And R th Respectively representing the thermal capacity and thermal resistance of the building,
Figure BDA0003876083540000099
denotes the rated power of TCL, eta denotes the thermal coefficient of VFAC (e.g., 12.5 in this example), and alpha i (t) represents the power of the ith TCL as a percentage of the rated power at time t. The equivalent thermal parametric model of a single TCL describes its internal temperature as a function of ambient temperature and operating power.
S102, making the temperature set value of the user be T s Setting the temperature comfort level range accepted by the user as [ T min ,T max ]Tolerance of temperature deviation is DeltaT, having T min ＝T s -ΔT、T max ＝T s + Δ T. dT when TCL reaches steady state operation i (T)/dt =0, and substituting into the equivalent thermal parameter model of a single TCL to obtain the temperature T in stable operation i (t) and percent rated power α i (t) correspondence relationship:
Figure BDA0003876083540000101
s103, making the current temperature be a user set value T s The percentage of rated power corresponding to the time is set as alpha s Considering that the comfort range of users is different according to the set temperature of each TCL, the power offset index gamma of the TCL is defined i (t) and user comfort index β i (t) is represented by the formulae (7) and (8). Can obtain beta i (t)∈[0,1]In order to ensure user comfort, the comfort index must not exceed this range, β i (t) =0.5 is the most comfortable state for the user. And gamma is i The range of (t) needs to be determined according to parameters such as thermal coefficient and thermal resistance and the comfort state of the user at the moment t.
S104, defining u i (t) is the percentage change of the ith TCL rated power, i.e. the control input, and comprises:
Figure BDA0003876083540000102
take x i And (t) is a state variable, the state space expression of a single TCL is shown as formula (6). Two state quantities of temperature control load power and user comfort are taken, and x is i (t)＝[γ i (t)；β i (t)]The detailed expressions of the joint formula (12) to formula (14) and the formula (7) and formula (8) in the writable formula (6) are as follows:
Figure BDA0003876083540000103
substituting the parameters can obtain:
Figure BDA0003876083540000104
step S4 requires communication of topology information by means of the building and TCL, with adjacency matrix M = [ a = [ ] ij ]∈R N×N . Wherein, when there is a communication connection between the i, j TCLs, there is a ij =1; when both are not communicatively connected or i = j, there is a ij And =0. The communication map has an internal matrix of D = diag { D } 1 ,d 2 ,…,d N }∈R N×N The Laplace matrix is L = { L = { [ L ] ij }∈R N×N 。
In this embodiment, step S4 includes:
s4.1, forming an interior matrix D = diag { D) according to the communication topology of N temperature control loads in the upper layer object 1 ,d 2 ,…,d N }∈R N×N Wherein d is 1 ,d 2 ,…,d N The internal degree of the 1 st to N temperature control loads, R N×N For dimensionality, the computational function expression for the degree of inliness is:
Figure BDA0003876083540000105
in the above formula, d i Is the internal degree of the ith temperature-controlled load, a ij The communication connection state of the ith temperature control load and the jth temperature control load is set to be 1 or 0; constructing an adjacency matrix of M = [ a ] according to communication topology of N temperature control loads in upper-layer objects ij ]∈R N×N And constructing a laplace matrix L = { L ] according to the following formula ij }∈R N×N ：
L＝D-M，(17)
In the above formula, D is an interior matrix, M is an adjacent matrix, l ij The Laplace operator values of the ith temperature control load and the jth temperature control load are obtained;
s4.2, according to the scheduling power increment delta P required to be provided by the next control period of the upper layer object L (t+Δt s ) Determining the scheduling information of each temperature control load receiving controller according to the following formula;
Figure BDA0003876083540000111
in the above formula,. DELTA.x i (t) the ith temperature-controlled load receives scheduling information of the controller at time t, k 1 For the number of temperature controlled loads directly connected to the controller,
Figure BDA0003876083540000112
average power, Δ t, for temperature-controlled loads s Time interval controlled for lower layer, q i For the control coefficient of the ith temperature control load, q is provided if the ith temperature control load is directly connected with the controller in communication i =1, otherwise q i ＝0；
S4.3, determining that the states among the temperature control loads with communication connection are relatively available, and adopting a function expression controlled by a distributed static consensus protocol as follows:
Figure BDA0003876083540000113
in the above formula, u i (t) is the control variable at time t, c is a coupling coefficient greater than 0, L' is e.g. R 1×2 Representing a feedback gain matrix, R 1×2 Represents dimension, x i (t) is the state variable of the ith temperature-controlled load, a ij For the communication connection state between the ith temperature control load and the jth temperature control load, x j (t) is the state variable of the jth temperature-controlled load,. DELTA.x i (t) scheduling information of the ith temperature control load receiving controller; substituting the function expression controlled by the distributed static consensus protocol into a state space model of a single temperature control load to obtain an ith temperature control load regulation and control model:
Figure BDA0003876083540000114
in the above formula, /) ij Is the Laplace operator value, deltax, of the ith and jth temperature control loads j (t) scheduling information of the jth temperature control load receiving controller;
and S4.4, determining a feedback gain matrix L 'and a coupling coefficient c, and substituting the determined feedback gain matrix L' and the coupling coefficient c into the control model of each temperature control load to control the state of each temperature control load.
According to consensus reasoning, for the intra-building TCL population, the if and only if matrix A + c λ i Where BL' is a Hurwitz matrix, the state variables of all TCLs can be agreed upon. Wherein λ is i Is the non-zero eigenvalue of the laplacian matrix L. Considering that both matrices a, B are stable, the consensus problem of the protocol being equation (20) can be solved by finding the appropriate L' and c.
In this embodiment, the determining the feedback gain matrix L' in step S4.4 includes:
s101, finding out a solution of a Linear Matrix inequality (Linear Matrix indexes, LMI for short) shown in the following formula when P > 0:
AP+PA T -2BB T <0，(21)
in the above formula, A and B are system state matrixes, and P is a solution of a linear matrix inequality;
s102, a feedback gain matrix L' is calculated according to the following formula:
L′＝-B T P -1 ，(22)
in the above formula, P is the solution of the linear matrix inequality.
In this embodiment, the determining the coupling coefficient c in step S4.4 includes:
s201, determining the condition that the state variables of the temperature control loads in the upper layer object are commonly identified as a matrix A + c lambda i BL' is a Helveltz matrix, and the matrix A + c λ i The characteristic polynomial det (sI- (A + σ BL ')) of BL' is stable, where λ i Is the non-zero eigenvalue of the Laplace matrix L, s is the matrix A + c λ i The eigenvalues of BL', I is a second order identity matrix, A and B are system state matrices, σ is a coupling coefficient c and an eigenvalue λ i L' is a feedback gain matrix and has σ = c λ i = x + jy, where x, y are the coordinates of real and imaginary axes, respectively, and j is the unit of imaginary number;
s202, according to the matrix A + c lambda i The eigenvalues s of BL' establish a complex coefficient polynomial p(s) as shown below:
p(s)＝s 2 +(a+jb)s+e+jd，(23)
in the formula, a, b, d and e are polynomial coefficients, j is an imaginary unit, and a, b and e belong to R, wherein R is a real number;
s203, according to the stable essential condition a of the complex coefficient polynomial p (S)>0 and abd + a 2 e-d 2 >0, determining the couplingThe value range of the coefficient c, and thus the value of the coupling coefficient c is determined in the value range.
In this embodiment, after the determined feedback gain matrix L' and the coupling coefficient c are substituted into the control model of each temperature control load in step S4.4 to control the state of each temperature control load, the state variables of any ith temperature control load after each control are:
Figure BDA0003876083540000121
in the above formula, x i (t+Δt s ) For regulating the post-state variable, x i (t) is the state variable of the ith temperature-controlled load,
Figure BDA0003876083540000122
is the first derivative, Δ t, of the state variable of the ith temperature-controlled load s The time interval for the lower layer control. After the determined feedback gain matrix L' and the coupling coefficient c are substituted into the control model of each temperature control load to control the state of each temperature control load, each TCL can meet the consensus requirement only by controlling according to the consensus protocol shown in the formulas (18) and (20).
In the embodiment, the installed capacity of the photovoltaic power generation system of the microgrid in a certain area is 1200kW. There are 20 buildings participating in regulation, and each building has 20 TCLs. Equivalent heat capacity C of building th =2 kWh/DEG C, equivalent thermal resistance R th =2 ℃/kW; the temperature deviation tolerance of the user Δ T =2 ℃; the ambient temperature is 30 ℃; rated power of VFAC
Figure BDA0003876083540000123
Thermal coefficient η =2.5. Lower unit control time Deltat s =1s, upper layer single update time t d =0.5s, load and photovoltaic data acquisition time interval Δ t c =1min, coupling coefficient w =0.6. Verifying the performance of the TCL system for stabilizing photovoltaic and load power fluctuation under the scheme of the patent, the embodiment is based on the following steps that 12-00 photovoltaic power generation output of a micro-grid and load consumption except VFAC on a certain day. The dispatching requirement of the main network is to exchange power of the micro-grid and the main networkP d (t) stabilize at 25kW, and all TCLs participate in scheduling. Finally, the method of the present embodiment finally determines c =12,l' = [ -1.4366 2]The obtained interaction power of the micro grid and the main grid is shown in fig. 5. Referring to fig. 5, it can be seen that the interaction power fluctuation of the unoptimized micro grid and the main grid is large, and the interaction power Pd (t) optimized by the method of the embodiment is stabilized at about 25kW, which indicates that the scheduling of the method of the embodiment meets the requirement of the main grid, and stabilizes the power fluctuation of the photovoltaic power generation and the load.
In summary, the method of the present embodiment is implemented by an aggregator and a controller in a microgrid consisting of buildings. The aggregator is in communication connection with the main network, the photovoltaic power generation system and a part of buildings, and the controller is in communication connection with the TCL in the buildings. Firstly, establishing a single TCL state space model, constructing a building communication topological graph in a microgrid and a building TCL communication topological graph in a building, and determining a load and photovoltaic data acquisition time interval delta t c And time interval deltat of lower layer control s (ii) a Then, the aggregator collects the current load electricity P load (t) photovoltaic output P pv (t) calculating TCL dispatching power P according to the load electricity utilization, photovoltaic output and power grid dispatching requirements req (t); and finally, the upper aggregator calculates the real-time scheduling power of each building based on the containment control, and transmits the result to the lower controller, and the lower controller calculates the real-time scheduling power of each TCL according to the consensus protocol. The implementation of the method can realize the consensus control of the TCL power deviation and comfort level parameters in the building, so that the microgrid responds to various scheduling signals of the main grid, and the power fluctuation of photovoltaic power generation and load is accurately and effectively stabilized.
In addition, the embodiment also provides a temperature control load cluster control system facing a microgrid, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the temperature control load cluster control method facing the microgrid. In addition, the present embodiment also provides a computer-readable storage medium, in which a computer program is stored, where the computer program is used to be programmed or configured by a microprocessor to execute the foregoing microgrid-oriented temperature control load cluster control method.
Example two:
the present embodiment is substantially the same as the first embodiment, and the main difference is the topology of the microgrid. In this embodiment, on the basis of the topology structures shown in fig. 2 to 4 in the first embodiment, due to reasons such as communication failure and user requirements, the TCLs of the buildings are different in operation, no. 14 building participates in system scheduling, no. 6 to No. 10 building has one TCL not operating, and No. 11 to No. 13 and No. 15 building has two TCLs not operating, so that the communication diagrams of the TCLs of the buildings are different. In addition, the temperature set point Ts of the TCL in the building is also different. Photovoltaic output and load consumption were the same as in example one. Finally, the method of this embodiment finally determines c =12, l' = [ -1.4366 ], and the obtained interaction power of the microgrid and the main grid is as shown in fig. 6. Referring to fig. 6, it can be seen that the fluctuation of the interaction power of the unoptimized micro grid and the main grid is large, and the interaction power Pd (t) optimized by the method of the present embodiment is stabilized at about 25kW, which indicates that the control strategy of the method of the present embodiment is still applicable under the conditions of different set temperatures and communication faults.
In addition, the embodiment also provides a temperature control load cluster control system facing a microgrid, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the temperature control load cluster control method facing the microgrid. In addition, the present embodiment also provides a computer-readable storage medium, in which a computer program is stored, where the computer program is used to be programmed or configured by a microprocessor to execute the foregoing microgrid-oriented temperature control load cluster control method.
Example three:
the present embodiment is basically the same as the first embodiment, and the main difference is that the topology of the building is different. In this embodiment, on the basis of the micro-grid of the first embodiment, the values of heat capacity, heat resistance and heat coefficient of each building are different and are respectively [1.5,2.5 ]]、[1,3]And [1.5,3.5]The distribution within the range, the building and the operation of the TCL are the same as in example two. Finally, the method of the present embodiment finally determines that c =12, l' = [ -1.4366 2]Finally, obtaining the TCL user comfort state index in each buildingβ i The (t) changes are shown in FIGS. 7 to 10. Wherein, FIG. 7 is the internal TCL user comfort state index β of buildings # 1 to # 5 i (t) variation diagram, FIG. 8 is the internal TCL user comfort index β for buildings Nos. 6-10 i (t) variation diagram, FIG. 9 is the internal TCL user comfort index β for buildings Nos. 11-15 i (t) variation diagram, FIG. 10 is the internal TCL user comfort index β for buildings # 16-20 i (t) graph of variation. Referring to fig. 7 to 10, the user comfort index is 0,1]Within the range. Namely, the interaction power is controlled to be about 75kW under the condition that the dispatching meets the requirement of comfort. The reason is that in steady operation, the temperature T i (t) percentage of rated power α i (t) is subject to a parameter η R th The influence of (c). For the same power offset gamma i (T) corresponding temperature deviation Δ T i Different. Parameter R th Affect the scheduling ability of the building, resulting in beta L And alpha ML A change in (c). In the method of the embodiment, the parameter η R is considered in the power scheduling of each building th The influence of (2) not only ensures the comfort level of the user, but also improves the dispatching capability of the system.
In addition, the embodiment also provides a microgrid-oriented temperature control load cluster control system, which comprises a microprocessor and a memory, which are connected with each other, wherein the microprocessor is programmed or configured to execute the aforementioned microgrid-oriented temperature control load cluster control method. In addition, the present embodiment also provides a computer-readable storage medium, in which a computer program is stored, where the computer program is used to be programmed or configured by a microprocessor to execute the foregoing microgrid-oriented temperature control load cluster control method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiments, and all technical solutions that belong to the idea of the present invention belong to the scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.

Claims (10)

1. A temperature control load cluster control method facing a micro-grid is characterized by comprising the following steps:
s1, collecting load electricity consumption P of the microgrid at the current moment t load (t) and photovoltaic output P pv (t), the initialization time Δ t is 0;
s2, electricity consumption P according to load load (t) photovoltaic output P pv (t) calculating the total scheduling power P of all temperature control loads according to the scheduling requirements req (t)；
S3, respectively aiming at upper-layer objects containing a plurality of temperature control loads, and scheduling power P based on the temperature control loads req (t) calculating the scheduled power increment Δ P that the upper layer object L needs to provide in the next control period L (t+Δt s )；
S4, aiming at the temperature control load in each upper-layer object, respectively according to the scheduling power increment delta P required to be provided by the corresponding upper-layer object in the next control period L (t+Δt s ) Calculating the scheduling power of each temperature control load and realizing consensus control on the temperature control load clusters in the same upper-layer object;
s5, adding the time delta t to the time interval delta t of the lower layer control s If the new time Δ t is equal to the preset load and photovoltaic data acquisition time interval Δ t c If yes, skipping to the step S6, otherwise skipping to the step S2;
s6, judging whether the scheduling is finished or not, and finishing and exiting if the scheduling is finished; otherwise, jumping to step S1.
2. The microgrid-oriented temperature control load cluster control method of claim 1, characterized in that in step S2, a temperature control load scheduling power P is calculated req The functional expression of (t) is:
P req (t)＝P pv (t)-P dreq (t)-P load (t)-P TCL (t)，
in the above formula, P dreq (t) output Power required for scheduling, P TCL (t) is the sum of the power consumed by the temperature controlled load cluster.
3. The microgrid-oriented temperature control load cluster control method of claim 1, characterized by steps ofIn S3, calculating a scheduling power increment delta P required to be provided by the upper-layer object L in the next control period L (t+Δt s ) The method comprises the following steps:
first, compute upper level object L at t + t d Scheduled power of time P L (t+t d )：
Figure FDA0003876083530000011
In the above formula, subscripts L and M are numbers of upper layer objects, the upper layer objects are directly connected to the microgrid through the aggregator or indirectly connected to the microgrid through other upper layer objects, Δ P L (t+t d ) For the upper layer object L at t + t d Scheduled power increment of time, t d Time of single update for upper layer object, w represents coupling coefficient, N bc Number of upper level objects, c LM A correlation coefficient, c, for determining whether there is a communication link between the upper layer object L and the upper layer object M LM The value is 1 or 0; p M (t) scheduling power, P, required to be provided by upper layer object M at time t L (t) scheduling power required to be provided by the upper layer object L at the time t, q L A drag coefficient, q, for whether an upper layer object is directly connected to the aggregator L The value is 1 or 0; p req (t) Total scheduled Power, P, for all temperature controlled loads at time t L (t+t d ) For the upper layer object L at t + t d Scheduled power required to be provided, P, for a time instant L-max And P L-min Respectively representing the maximum and minimum scheduling power, beta, that the upper level object L can provide L Representing the containment consensus coefficient, alpha ML Is a scheduling capability scaling factor and has a delta t s ＝2t d ；
Then, it will be t + t d As the new current time t, the upper layer object L at t + t is recalculated d Scheduled power P of time L (t+t d ) And as the scheduled power P to be provided by the upper layer object L in the next control period L (t+Δt s )；
Finally, the upper layer object is obtained through calculation according to the following formulaL scheduled power increment delta P to be provided in the next control period L (t+Δt s )：
ΔP L (t+Δt s )＝P L (t+Δt s )-P L (t)，
In the above formula, P L (t) is the scheduled power that the upper layer object L needs to provide at time t.
4. The microgrid-oriented temperature control load cluster control method of claim 3, wherein the upper level objects are buildings, one building corresponds to one upper level object, and the calculation function expressions of the containment consensus coefficient and the scheduling capability proportionality coefficient are as follows:
Figure FDA0003876083530000021
in the above formula,. Beta. L Represents the containment consensus coefficient, α ML For the scheduling capability scaling factor, N L And N M Represents the amount of temperature control loads that the upper layer object L and the upper layer object M participate in scheduling,
Figure FDA0003876083530000026
mean high power, R, representing temperature-controlled load L And η L Respectively representing the equivalent thermal resistance and thermal coefficient of the upper layer object L, R M And η M Respectively representing the equivalent thermal resistance and thermal coefficient of the upper layer object M, N 1 ～N Nbc Respectively represent upper layer objects 1 to N bc Number of temperature-controlled loads involved in scheduling, R 1 ～R Nbc Respectively represent upper layer objects 1 to N bc Equivalent thermal resistance of [ (. Eta. ]) 1 ～η Nbc Respectively represent upper layer objects 1 to N bc Thermal coefficient of (2).
5. The microgrid-oriented temperature control load cluster control method of claim 1, further comprising, before step S1, establishing a state space model represented by the following formula for a single temperature control load:
Figure FDA0003876083530000024
in the above-mentioned formula, the compound has the following structure,
Figure FDA0003876083530000027
state variable x for the ith temperature-controlled load i (t)＝[γ i (t)；β i (t)]First derivative of gamma i (t) the temperature-controlled load power, β, of the ith temperature-controlled load i (t) is the user comfort for the ith temperature controlled load, A and B are the system state matrix, W is the constant matrix, u is the constant matrix i (t) is a control variable of the ith temperature control load, subscript i is a control variable representing the ith temperature control load, and control variable u i (t) using a percentage change in rated power, and having:
γ i (t)＝α i (t)-α s ，
Figure FDA0003876083530000022
Figure FDA0003876083530000023
in the above formula, α i (t) represents the power of the ith temperature-controlled load as a percentage of the rated power at time t, α s Indicating temperature as user set point T s Percentage of rated power, T, corresponding to time i (T) is the time temperature of the stable operation of the ith temperature control load at the moment T, delta T is the temperature deviation tolerance, eta represents the thermal coefficient of the variable frequency air conditioner,
Figure FDA0003876083530000036
to rated power, C th And R th Respectively representing the heat capacity and the heat resistance of a building; step S4 comprises the following steps:
s4.1, according to the communication topology of N temperature control loads in the upper layer objectForming degree matrix D = diag { D } 1 ,d 2 ,…,d N }∈R N×N Wherein d is 1 ,d 2 ,…,d N The internal degree of the 1 st to N temperature control loads, R N×N For dimensionality, the computational function expression for the degree of internalization is:
Figure FDA0003876083530000031
in the above formula, d i Is the internal degree of the ith temperature-controlled load, a ij The communication connection state of the ith temperature control load and the jth temperature control load is set to be 1 or 0; constructing an adjacency matrix according to the communication topology of N temperature control loads in the upper layer object, wherein M = [ a = ij ]∈R N×N And constructing a laplace matrix L = { L ] according to the following formula ij }∈R N×N ：
L＝D-M，
In the above formula, D is an interior matrix, M is an adjacent matrix, l ij The Laplace operator values of the ith temperature control load and the jth temperature control load are obtained;
s4.2, according to the scheduling power increment delta P needed to be provided by the next control period of the upper layer object L (t+Δt s ) Determining scheduling information of each temperature control load receiving controller according to the following formula;
Figure FDA0003876083530000032
in the above formula,. DELTA.x i (t) the ith temperature-controlled load receives scheduling information of the controller at time t, k 1 For the number of temperature controlled loads directly connected to the controller,
Figure FDA0003876083530000035
average power, Δ t, of the temperature-controlled load s For the time interval of the lower layer control, q i For the control coefficient of the ith temperature control load, q is provided if the ith temperature control load is directly connected with the controller in communication i =1, otherwise q i ＝0；
S4.3, determining that the states among the temperature control loads with communication connection are relatively available, and adopting a function expression controlled by a distributed static consensus protocol as follows:
Figure FDA0003876083530000033
in the above formula, u i (t) is the control variable at time t, c is a coupling coefficient greater than 0, L' is e.g. R 1×2 Representing a feedback gain matrix, R 1×2 Represents dimension, x i (t) is the state variable of the ith temperature-controlled load, a ij For the communication connection state between the ith temperature control load and the jth temperature control load, x j (t) is the state variable of the jth temperature-controlled load, Δ x i (t) scheduling information of the ith temperature control load receiving controller; substituting the function expression controlled by the distributed static consensus protocol into a state space model of a single temperature control load to obtain an ith temperature control load regulation and control model:
Figure FDA0003876083530000034
in the above formula, l ij Laplace operator value, Δ x, for the ith and jth temperature controlled loads j (t) scheduling information of the jth temperature control load receiving controller;
and S4.4, determining a feedback gain matrix L 'and a coupling coefficient c, and substituting the determined feedback gain matrix L' and the coupling coefficient c into the control model of each temperature control load to control the state of each temperature control load.
6. The microgrid-oriented temperature control load cluster control method of claim 5, wherein the determination of the feedback gain matrix L' in step S4.4 includes:
s101, finding out a solution of a linear matrix inequality shown in the following formula when P > 0:
AP+PA T -2BB T <0，
in the above formula, A and B are system state matrixes, and P is a solution of a linear matrix inequality;
s102, a feedback gain matrix L' is calculated according to the following formula:
L′＝-B T P -1 ，
in the above formula, P is the solution of the linear matrix inequality.
7. The microgrid-oriented temperature controlled load cluster control method of claim 6, wherein the determining of the coupling coefficient c in step S4.4 includes:
s201, determining the condition that the state variables of the temperature control loads in the upper layer object are commonly identified as a matrix A + c lambda i BL' is a Helvelz matrix, and the matrix A + c λ i The characteristic polynomial det (sI- (A + σ BL ')) of BL' is stable, where λ i Is the non-zero eigenvalue of Laplace matrix L, s is matrix A + c λ i The eigenvalues of BL', I is a second order identity matrix, A and B are system state matrices, σ is a coupling coefficient c and an eigenvalue λ i L' is a feedback gain matrix and has σ = c λ i = x + jy, where x, y are the coordinates of real and imaginary axes, respectively, and j is the unit of imaginary number;
s202, according to the matrix A + c lambda i The eigenvalues s of BL' establish a complex coefficient polynomial p(s) as shown below:
p(s)＝s 2 +(a+jb)s+e+jd，
in the formula, a, b, d and e are polynomial coefficients, j is an imaginary unit, and a, b and e belong to R, wherein R is a real number;
s203, according to the stable essential condition a of the complex coefficient polynomial p (S)>0 and abd + a 2 e-d 2 >And 0, determining the value range of the coupling coefficient c, thereby determining the value of the coupling coefficient c in the value range.
8. The microgrid-oriented temperature control load cluster control method of claim 6, wherein in step S4.4, after substituting the determined feedback gain matrix L' and coupling coefficient c into the control model of each temperature control load to control the state of each temperature control load, the state variables of any ith temperature control load after each control are:
Figure FDA0003876083530000041
in the above formula, x i (t+Δt s ) For regulating the post-state variables, x i (t) is a state variable of the ith temperature-controlled load,
Figure FDA0003876083530000042
is the first derivative, Δ t, of the state variable of the ith temperature-controlled load s The time interval controlled for the lower layer.
9. A microgrid-oriented temperature controlled load cluster control system comprising a microprocessor and a memory connected to each other, characterized in that the microprocessor is programmed or configured to perform the microgrid-oriented temperature controlled load cluster control method of any one of claims 1 to 8.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is used for being programmed or configured by a microprocessor to execute the microgrid-oriented temperature control load cluster control method according to any one of claims 1 to 8.
CN202211217777.4A 2022-09-30 2022-09-30 Temperature control load cluster control method, system and medium for micro-grid Pending CN115549111A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117638995A (en) * 2024-01-24 2024-03-01 电子科技大学 Temperature control load cluster power comprehensive inertia control method based on time triggering

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117638995A (en) * 2024-01-24 2024-03-01 电子科技大学 Temperature control load cluster power comprehensive inertia control method based on time triggering
CN117638995B (en) * 2024-01-24 2024-04-05 电子科技大学 Temperature control load cluster power comprehensive inertia control method based on time triggering

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