CN115549111A - A microgrid-oriented temperature-controlled load cluster control method, system and medium - Google Patents

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

A microgrid-oriented temperature-controlled load cluster control method, system and medium

technical field

The invention belongs to the field of regulation and control of thermostatically controlled loads (TCL), and in particular relates to a microgrid-oriented temperature-controlled load cluster control method, system and medium.

Background technique

Adoption of renewable energy generation in microgrids can alleviate energy and environmental issues. However, the intermittence and uncertainty of renewable energy power generation limit its absorption capacity, and if it is not controlled, it will bring a lot of impact to the main grid. The use of energy storage devices such as batteries and supercapacitors is an effective way to stabilize power fluctuations, but the aging loss of energy storage devices will affect its dispatching performance, and at the same time increase the operating expenses of the microgrid. Installing an air-conditioning load in a room constitutes a thermostatically controlled load (TCL for short). As the number of TCLs increases, the proportion of the total load increases. By adopting an appropriate control strategy, the power fluctuation of the microgrid can be effectively stabilized and the dispatching requirements of the main grid can be met. A single TCL has little impact on the system, and a large number of TCL groups need to be gathered to achieve power regulation.

The existing TCL cluster control methods are temperature-based state queuing method and intelligent algorithm-based control method. The temperature-based state queuing method takes the distance between the current temperature and the temperature upper limit as the priority to set the TCL control sequence, thus ensuring the user's comfort. By dynamically adjusting the temperature set point of the TCL, the problem of frequent start and stop of some TCLs under the state queuing algorithm can be further solved, and the impact of scheduling on the life of the TCL can be reduced. The control method based on intelligent algorithm expresses the cluster control of TCL as a non-linear programming problem, and regulates each TCL through intelligent algorithm, which has high calculation accuracy. However, these cluster control methods can only control TCLs with the same set temperature, without considering the parameter heterogeneity of TCLs and buildings within the microgrid. In addition, these methods will have great computational and communication pressure for large-scale systems with wide spatial distribution. The two-dimensional state sequence modeling method is an effective method to solve parameter heterogeneity. The introduction of temperature change rate information can reflect heterogeneous parameters. Although this method reduces the amount of calculation, it does not accurately describe TCL through the equivalent thermal parameter model. status. Moreover, with the popularization of Variable Frequency Air Conditioners (VFAC), the control method of VFAC participating in dispatching needs to be further studied. The existing TCL cluster control method mainly focuses on the configuration of dispatching power between loads, and user comfort is usually used as The constraints of the control do not achieve a fair distribution among the various TCLs.

Contents of the invention

The technical problem to be solved by the present invention: Aiming at the above-mentioned problems of the prior art, a microgrid-oriented temperature-controlled load cluster control method, system and medium are provided. The present invention can respond to the step dispatching signal of the main network and stabilize photovoltaic power generation At the same time as the power fluctuation of the load, it can realize the consensus control of the two parameters of TCL power offset and comfort in the building. In some buildings and TCL access, the temperature setting of each air-conditioning load is different, and the parameters between buildings are different. Scheduling can still be completed under the three conditions, and the present invention can alleviate the communication pressure of the microgrid system, and can optimize the distribution of dispatching power among buildings by considering the differences of various building parameters, effectively avoiding crossing the line in a comfortable state.

In order to solve the problems of the technologies described above, the technical solution adopted in the present invention is:

A microgrid-oriented temperature control load cluster control method, comprising:

S1, collect the load power P _load (t) and photovoltaic output P _pv (t) of the microgrid at the current moment t, and the initialization time Δt is 0;

S2, calculate the total scheduling power P _req (t) of all temperature-controlled loads according to the load power consumption P _load (t), photovoltaic output P _pv (t) and scheduling requirements;

S3, for the upper-level objects containing multiple temperature-controlled loads, calculate the dispatching power increment ΔP _L (t+Δt _s ) that the upper-level object L needs to provide in the next control cycle based on the total dispatched power P _req (t) of the temperature-controlled loads;

S4. For the temperature-controlled loads in each upper-level object, calculate the dispatching power of each temperature-controlled load according to the dispatching power increment ΔP _L (t+Δt _s ) that the corresponding upper-level object needs to provide in the next control cycle, and realize the same upper-level Consensus control of the temperature control load cluster within the object;

S5, add the time Δt to the time interval Δt _s controlled by the lower layer, if the new time Δt is equal to the preset load and photovoltaic data collection time interval Δt _c , then go to step S6, otherwise go to step S2;

S6, judge whether the scheduling is over, if the scheduling is over, end and exit; otherwise, skip to step S1.

Optionally, the functional expression for calculating the temperature-controlled load scheduling power P _req (t) in step S2 is:

P _req (t) = P _pv (t) - P _dreq (t) - P _load (t) - P _TCL (t),

In the above formula, P _dreq (t) is the output power required by scheduling, and P _TCL (t) is the sum of power consumed by the temperature-controlled load cluster.

Optionally, the calculation of the scheduled power increment ΔP _L (t+Δt _s ) to be provided by the upper-level object L in the next control cycle in step S3 includes:

First, calculate the scheduling power P _L (t+t _d ) of the upper-level object L at time t+t _d :

In the above formula, the subscripts L and M are the numbers of the upper-level objects. The upper-level objects are directly connected to the microgrid through the aggregator or indirectly connected to the microgrid through other upper-level objects. ΔP _L (t+t _d ) is the upper-level object The scheduling power increment of L at time t+t _d , t _d is the single update time of the upper-layer object, w is the coupling coefficient, N _bc is the number of upper-layer objects, c _LM is whether the relationship between the upper-layer object L and the upper-layer object M is Correlation coefficient with communication connection, the value of correlation coefficient c _LM is 1 or 0; P _M (t) is the scheduling power that the upper object M needs to provide at time t, and P _L (t) is the scheduling power required by the upper object L at time t. The scheduling power to be provided, q _L is the pinning coefficient of whether the upper object is directly connected to the aggregator, the pinning coefficient q _L takes the value of 1 or 0; P _req (t) is the total scheduling power of all temperature-controlled loads at time t, P _L (t+t _d ) is the scheduling power required by the upper-layer object L at the time t+t _d , _PL-max and _PL-min respectively represent the maximum and minimum scheduling power that the upper-layer object L can provide, β _L Indicates the pinning consensus coefficient, α _ML is the scheduling capacity proportional coefficient, and Δt _s = 2t _d ;

Then, take t+t _d as the new current time t, recalculate the scheduled power _PL (t+t _d ) of the upper-level object L at the time t+t _d , and use it as the upper-level object L to provide in the next control cycle The scheduling power P _L (t+Δt _s );

Finally, the scheduling power increment ΔP _L (t+Δt _s ) that the upper-level object L needs to provide in the next control cycle is calculated according to the following formula:

ΔP _L (t+Δt _s )=P _L (t+Δt _s )-P _L (t),

In the above formula, _PL (t) is the scheduling power that the upper object L needs to provide at time t.

Optionally, the upper-level object is a building, and one building corresponds to one upper-level object, and the calculation function expressions of the diversion consensus coefficient and the dispatching capacity ratio coefficient are:

表示温控负荷的平均高功率，R_L和η_L分别表示上层对象L的等效热阻和热系数，R_M和η_M分别表示上层对象M的等效热阻和热系数，N₁～N_Nbc分别表示上层对象1～N_bc参与调度的温控负荷数量，R₁～R_Nbc分别表示上层对象1～N_bc的等效热阻，η₁～η_Nbc分别表示上层对象1～N_bc的热系数。In the above formula, β _L represents the pinning consensus coefficient, α _ML is the dispatching capacity proportional coefficient, N _L and N _M represent the number of temperature-controlled loads that the upper-layer object L and the upper-layer object M participate in scheduling,

Indicates the average high power of the temperature control load, R _L and η _L represent the equivalent thermal resistance and thermal coefficient of the upper object L respectively, R _M and η _M represent the equivalent thermal resistance and thermal coefficient of the upper object M respectively, N ₁ ~ N _Nbc respectively represent the number of temperature-controlled loads involved in the scheduling of upper-layer objects 1 to N _bc , R ₁ to R _Nbc represent the equivalent thermal resistance of upper-layer objects 1 to N _bc , and η ₁ to η _Nbc represent the upper-layer objects 1 to N _bc thermal coefficient.

Optionally, before step S1, it also includes establishing a state space model shown in the following formula for a single temperature control load:

为第i个温控负荷的状态变量x_i(t)＝[γ_i(t)；β_i(t)]的一阶导数，γ_i(t)为第i个温控负荷的温控负荷功率，β_i(t)为第i个温控负荷的用户舒适度，A和B为系统状态矩阵，W为常数矩阵，u_i(t)为第i个温控负荷的控制变量，下标i为表示第i个温控负荷，且控制变量u_i(t)采用额定功率百分比变化量，且有：In the above formula,

is the first derivative of the state variable x _i (t)=[γ _i (t); β _i (t)] of the i-th temperature-controlled load, and γ _i (t) is the temperature-controlled load of the i-th temperature-controlled load power, β _i (t) is the user comfort of the i-th temperature-controlled load, A and B are system state matrices, W is a constant matrix, u _i (t) is the control variable of the i-th temperature-controlled load, subscript i represents the i-th temperature control load, and the control variable u _i (t) adopts the percentage change of rated power, and there are:

γ _i (t) = α _i (t) - α _s ,

为额定功率，C_th和R_th分别表示建筑物的热容与热阻；步骤S4包括：In the above formula, α _i (t) represents the percentage of the power of the i-th temperature-controlled load to the rated power at time t, and α _s represents the corresponding percentage of rated power when the temperature is the user’s set value T _s , T _i (t) is The temperature when the i-th temperature-controlled load runs stably at time t, ΔT is the temperature deviation tolerance, η is the thermal coefficient of the inverter air conditioner,

is the rated power, C _th and R _th represent the thermal capacity and thermal resistance of the building respectively; Step S4 includes:

S4.1, according to the communication topology of N temperature-controlled loads in the upper object, construct the degree matrix D=diag{d ₁ ,d ₂ ,…,d _N }∈R ^N×N , where d ₁ ,d ₂ ,… ,d _N is the inner degree of the 1st to N temperature control loads, R ^N×N is the dimension, and the calculation function expression of the inner degree is:

In the above formula, d _i is the internal degree of the i-th temperature-controlled load, and a _ij is the communication connection status between the i-th temperature-controlled load and the j-th temperature-controlled load, and the value of whether the communication connection status is 1 or 0; Construct the adjacency matrix M=[a _ij ]∈R ^N×N according to the communication topology of N temperature-controlled loads in the upper object, and construct the Laplacian matrix L={l _ij }∈R ^N× according to the following formula ^N :

L=D-M,

In the above formula, D is the inner degree matrix, M is the adjacency matrix, l _ij is the Laplacian value of the i-th temperature control load and the j-th temperature control load;

S4.2, according to the scheduling power increment ΔP _L (t+Δt _s ) to be provided by the upper object in the next control cycle, determine the scheduling information of each temperature-controlled load receiving controller according to the following formula;

为温控负荷的平均功率，Δt_s为下层控制的时间间隔，q_i为第i个温控负荷的控制系数，若第i个温控负荷和控制器有通信直接连接则有q_i＝1，反之q_i＝0；In the above formula, Δx _i (t) is the scheduling information that the i-th temperature-controlled load receives from the controller at time t, k ₁ is the number of temperature-controlled loads directly connected to the controller,

is the average power of the temperature-controlled load, Δt _s is the time interval of lower-level control, q _i is the control coefficient of the i-th temperature-controlled load, and if the i-th temperature-controlled load has a direct communication connection with the controller, then q _i =1 , otherwise q _i =0;

S4.3, determine that the state between the temperature control loads with communication connections is relatively available, and the function expression of the distributed static consensus protocol control is as follows:

In the above formula, u _i (t) is the control variable at time t, c is the coupling coefficient greater than 0, L′∈R ^1×2 represents the feedback gain matrix, R ^1×2 represents the dimension, and _xi (t) is The state variable of the i-th temperature-controlled load, a _ij is the status of communication connection between the i-th temperature-controlled load and the j-th temperature-controlled load, x _j (t) is the state variable of the j-th temperature-controlled load, Δx _i (t) is the scheduling information of the i-th temperature-controlled load receiving controller; the function expression controlled by the distributed static consensus protocol is substituted into the state-space model of a single temperature-controlled load to obtain the regulation model of the i-th temperature-controlled load :

In the above formula, l _ij is the Laplacian value of the i-th temperature-controlled load and the j-th temperature-controlled load, and Δx _j (t) is the scheduling information of the j-th temperature-controlled load receiving controller;

S4.4. Determine the feedback gain matrix L' and the coupling coefficient c, and substitute the determined feedback gain matrix L' and the coupling coefficient c into the regulation model of each temperature-controlled load to control the state of each temperature-controlled load.

Optionally, determining the feedback gain matrix L' in step S4.4 includes:

S101, find out the solution of the 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 matrices, and P is the solution of linear matrix inequality;

S102, calculate the feedback gain matrix L' according to the following formula:

L'=-B ^T P ^-1 ,

In the above formula, P is the solution of the linear matrix inequality.

Optionally, determining the coupling coefficient c in step S4.4 includes:

S201, the condition for determining the state variables of each temperature control load in the upper object to reach a consensus is that the matrix A+cλ _i BL′ is a Hurwitz matrix _, and the characteristic polynomial det(sI-(A+ σBL′)) is stable, where λ _i is the non-zero eigenvalue of the Laplacian matrix L, s is the eigenvalue of the matrix A+cλ _i BL′, I is the second-order identity matrix, A and B are the system state matrices, σ is the product of the coupling coefficient c and the eigenvalue λ _i , L' is the feedback gain matrix, and σ=cλ _i =x+jy, where x and y are the coordinates of the real axis and the imaginary axis respectively, and j is the imaginary unit;

S202, according to the eigenvalue s of matrix A+cλ _i BL′, the complex coefficient polynomial p(s) shown in the following formula is established:

p(s)=s2 ⁺ (a+jb)s+e+jd,

In the above formula, a, b, d and e are polynomial coefficients, j is an imaginary number unit, and there are a, b, e∈R, where R is a real number;

S203, according to the stable necessary and sufficient conditions of the complex coefficient polynomial p(s) a>0 and abd+a ² ed ² >0, determine the value range of the coupling coefficient c, so as to determine the value of the coupling coefficient c in this value range value.

Optionally, in step S4.4, after substituting the determined feedback gain matrix L' and coupling coefficient c into the regulation model of each temperature-controlled load to control the state of each temperature-controlled load, any i-th temperature-controlled load is The post state variables are:

为第i个温控负荷的状态变量的一阶导数，Δt_s为下层控制的时间间隔。In the above formula, x _i (t+Δt _s ) is the state variable after regulation, and x _i (t) is the state variable of the i-th temperature control load,

is the first derivative of the state variable of the i-th temperature-controlled load, and Δt _s is the time interval of the lower layer control.

In addition, the present invention also provides a microgrid-oriented temperature-controlled load cluster control system, including an interconnected microprocessor and a memory, and the microprocessor is programmed or configured to execute the micro-grid-oriented temperature-controlled load cluster Control Method.

In addition, the present invention also provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and the computer program is used to be programmed or configured by a microprocessor to perform the microgrid-oriented temperature control Load cluster control method.

Compared with the prior art, the present invention mainly has the following advantages: the present invention includes collecting the load power consumption and photovoltaic output of the microgrid at the current moment, and calculating the total dispatching power of all temperature-controlled loads in combination with dispatching requirements; The upper-level object of the load calculates the dispatching power increment to be provided in the next control cycle based on the total dispatching power of the temperature-controlled load, and calculates the dispatching power of each temperature-controlled load based on this to achieve consensus on the temperature-controlled load cluster in the same upper-level object control. The present invention can respond to the step dispatching signal of the main network, stabilize the power fluctuation of photovoltaic power generation and load, and at the same time realize the consensus control of temperature-controlled load clusters in different upper-level objects (such as buildings), and can alleviate the problems of the micro-grid system. Communication pressure, and by considering the difference of various building parameters, it can optimize the distribution of dispatching power among buildings, effectively avoiding crossing the line in comfort state.

Description of drawings

Fig. 1 is a schematic flow chart of the basic method of the first embodiment of the present invention.

Fig. 2 is a schematic diagram of the layered control principle in Embodiment 1 of the present invention.

FIG. 3 is a schematic diagram of a topology structure between buildings in Embodiment 1 of the present invention.

FIG. 4 is a schematic diagram of a topology structure of a TCL in a building in Embodiment 1 of the present invention.

Fig. 5 is a comparison diagram of interactive power curves between the microgrid and the main grid with and without the method of the first embodiment of the present invention.

Fig. 6 is a comparison diagram of interactive power curves between the microgrid and the main grid with and without the method of Embodiment 2 of the present invention.

Fig. 7 is a graph showing the change of comfort state index β _i (t) of TCL users in buildings No. 1 to No. 5.

Fig. 8 is a diagram showing the change of comfort state index β _i (t) of TCL users in buildings No. 6 to No. 10.

Fig. 9 is a graph showing the change of comfort state index β _i (t) of TCL users in buildings No. 11 to No. 15.

Fig. 10 is a graph showing the change of comfort state index β _i (t) of TCL users in buildings No. 16-20.

detailed description

Embodiment one:

As shown in Figure 1, the temperature-controlled load cluster control method for microgrids in this embodiment includes:

S1, collect the load power P _load (t) and photovoltaic output P _pv (t) of the microgrid at the current moment t, and the initialization time Δt is 0;

S2, calculate the total scheduling power P _req (t) of all temperature-controlled loads according to the load power consumption P _load (t), photovoltaic output P _pv (t) and scheduling requirements;

S3, for the upper-level objects containing multiple temperature-controlled loads, calculate the dispatching power increment ΔP _L (t+Δt _s ) that the upper-level object L needs to provide in the next control cycle based on the total dispatched power P _req (t) of the temperature-controlled loads;

S4. For the thermostatically controlled loads (TCL for short) in each upper-level object, calculate each thermostatically-controlled load according to the dispatching power increment ΔP _L (t+Δt _s ) that the corresponding upper-level object needs to provide in the next control cycle Scheduling power and realize the consensus control of the temperature control load cluster in the same upper object;

S5, add the time Δt to the time interval Δt _s controlled by the lower layer, if the new time Δt is equal to the preset load and photovoltaic data collection time interval Δt _c , then go to step S6, otherwise go to step S2;

S6, judge whether the scheduling is over, if the scheduling is over, end and exit; otherwise, skip to step S1.

As shown in Figure 2, the temperature-controlled load cluster control method for microgrids in this embodiment includes two levels of upper-level control and lower-level control, the control object of the upper-level control is the upper-level object, and the control means is step S3; the control object of the lower-level control is The temperature control load of the upper object is controlled by step S4. In this embodiment, the temperature-controlled load cluster control method for micro-grids is oriented to micro-grids. The temperature-controlled load (Thermostatically Controlled Loads, TCL) cluster control needs to be controlled by an upper-level object (a collection of temperature-controlled loads, which can be a building, Aggregators and controllers in a microgrid consisting of one floor, one unit, etc.). Among them, the aggregator establishes communication connections with the main network, the photovoltaic power generation system, and some upper-layer objects, and the controller establishes communication connections with some TCLs in the upper-layer objects.

In this embodiment, the functional expression for calculating the temperature-controlled load scheduling power P _req (t) in step S2 is:

P _req (t) = P _pv (t) - P _dreq (t) - P _load (t) - P _TCL (t), (1)

In the above formula, P _dreq (t) is the output power required by scheduling, and P _TCL (t) is the sum of power consumed by the temperature-controlled load cluster.

In this embodiment, the data update time of the upper-layer control is t _d , and t _d =0.5Δt _s , and the microgrid has N _bc upper-layer objects participating in scheduling, represented by i=1, 2, ..., N _bc . In step S3, the calculation of the scheduled power increment ΔP _L (t+Δt _s ) that the upper-level object L needs to provide in the next control cycle includes: first, calculating the scheduled power _PL (t+ _{t d} ):

In the above formula, the subscripts L and M are the numbers of the upper-level objects. The upper-level objects are directly connected to the microgrid through the aggregator or indirectly connected to the microgrid through other upper-level objects. ΔP _L (t+t _d ) is the upper-level object The scheduling power increment of L at time t+t _d , t _d is the single update time of the upper-layer object, w is the coupling coefficient, N _bc is the number of upper-layer objects, c _LM is whether the relationship between the upper-layer object L and the upper-layer object M is Correlation coefficient with communication link, the value of correlation coefficient c _LM is 1 or 0 (c _LM = 1 means there is communication link between two buildings, c _LM = 0 means there is no link); P _M (t) is the upper object The scheduling power that M needs to provide at time t, P _L (t) is the scheduling power that the upper-level object L needs to provide at time t, q _L is the pinning coefficient of whether the upper-level object is directly connected to the aggregator, and the value of the pinning coefficient q _L is 1 or 0 (q _L = 1 means that the building is directly connected to the aggregator, and the adjustment information of the aggregator is acceptable; q _L = 0 means that the building and the aggregator are not directly connected); P _req (t) is the The total dispatching power of all temperature-controlled loads, _PL (t+t _d ) is the dispatching power required by the upper-level object L at the time t+t _d , and _PL-max and _PL-min represent the power that the upper-level object L can provide The maximum and minimum scheduling power provided, β _L represents the pinning consensus coefficient, α _ML is the scheduling capability proportional coefficient, and Δt _s = 2t _d ; substituting the constant can be obtained:

Then, take t+t _d as the new current time t, recalculate the scheduled power _PL (t+t _d ) of the upper-level object L at the time t+t _d , and use it as the upper-level object L to provide in the next control cycle The scheduling power P _L (t+Δt _s );

Finally, the scheduling power increment ΔP _L (t+Δt _s ) that the upper-level object L needs to provide in the next control cycle is calculated according to the following formula:

ΔP _L (t+Δt _s )=P _L (t+Δt _s )-P _L (t), (3)

In the above formula, _PL (t) is the scheduling power that the upper object L needs to provide at time t.

As shown in FIG. 3 , the upper-level object in this embodiment is a building, and one building corresponds to one upper-level object. Referring to Fig. 3, it can be seen that, as a specific implementation, this embodiment includes a total of 20 buildings, which are respectively marked as buildings 1 to 20, and only buildings 6 to 10 are directly connected to the microgrid through the aggregator. As shown in Figure 4, a building contains a total of 20 temperature-controlled loads, which are recorded as temperature-controlled load 1 to temperature-controlled load 20, of which only temperature-controlled loads 1 to 8 are directly connected to the controller. In this embodiment, the calculation function expressions of the pinning consensus coefficient and the scheduling capacity ratio coefficient are:

表示温控负荷的平均高功率，R_L和η_L分别表示上层对象L的等效热阻和热系数，R_M和η_M分别表示上层对象M的等效热阻和热系数，N₁～N_Nbc分别表示上层对象1～N_bc参与调度的温控负荷数量，R₁～R_Nbc分别表示上层对象1～N_bc的等效热阻，η₁～η_Nbc分别表示上层对象1～N_bc的热系数。对于上述的上层控制的数据更新时间为t_d，为防止上层控制中各楼栋的调度功率出现发散现象，需要防止耦合系数w的过大，而过小的w会使上层控制的收敛速度变慢，所以通过减小更新时间来加快上层控制速度。设置t_d＝0.5Δt_s，即下层控制每过两倍的数据更新时间t_d接受一次上层调度信息。上述的β_L和α_ML定义式(4)和(5)充分考虑了各上层对象的参数，即上层对象内部可参与调度的TCL数量不同、额定功率不同、建筑物的热容、热阻和TCL热系数不同，计及了这些参数异质性对上层对象调度能力的影响。In the above formula, β _L represents the pinning consensus coefficient, α _ML is the dispatching capacity proportional coefficient, N _L and N _M represent the number of temperature-controlled loads that the upper-layer object L and the upper-layer object M participate in scheduling,

Indicates the average high power of the temperature control load, R _L and η _L represent the equivalent thermal resistance and thermal coefficient of the upper object L respectively, R _M and η _M represent the equivalent thermal resistance and thermal coefficient of the upper object M respectively, N ₁ ~ N _Nbc respectively represent the number of temperature-controlled loads involved in the scheduling of upper-layer objects 1 to N _bc , R ₁ to R _Nbc represent the equivalent thermal resistance of upper-layer objects 1 to N _bc , and η ₁ to η _Nbc represent the upper-layer objects 1 to N _bc thermal coefficient. For the data update time of the above-mentioned upper-level control is t _d , in order to prevent the divergence phenomenon of the dispatching power of each building in the upper-level control, it is necessary to prevent the coupling coefficient w from being too large, and the too small w will make the convergence speed of the upper-level control slow Slow, so by reducing the update time to speed up the upper control speed. Set t _d =0.5Δt _s , that is, the lower-layer control receives the upper-layer scheduling information every time the data update time t _d is doubled. The above-mentioned definitions of β _L and α _ML (4) and (5) fully consider the parameters of each upper-level object, that is, the number of TCLs that can participate in scheduling in the upper-level object is different, the rated power is different, and the thermal capacity, thermal resistance and thermal resistance of the building are different. The TCL thermal coefficients are different, taking into account the impact of these parameter heterogeneity on the scheduling capabilities of upper-level objects.

In this embodiment, before step S1, it also includes establishing a state space model shown in the following formula for a single temperature control load:

为第i个温控负荷的状态变量x_i(t)＝[γ_i(t)；β_i(t)]的一阶导数，γ_i(t)为第i个温控负荷的温控负荷功率，β_i(t)为第i个温控负荷的用户舒适度，A和B为系统状态矩阵，W为常数矩阵，u_i(t)为第i个温控负荷的控制变量，下标i为表示第i个温控负荷，且控制变量u_i(t)采用额定功率百分比变化量，且有：In the above formula,

is the first derivative of the state variable x _i (t)=[γ _i (t); β _i (t)] of the i-th temperature-controlled load, and γ _i (t) is the temperature-controlled load of the i-th temperature-controlled load power, β _i (t) is the user comfort of the i-th temperature-controlled load, A and B are system state matrices, W is a constant matrix, u _i (t) is the control variable of the i-th temperature-controlled load, subscript i represents the i-th temperature control load, and the control variable u _i (t) adopts the percentage change of rated power, and there are:

γ _i (t) = α _i (t) - α _s , (7)

为额定功率，C_th和R_th分别表示建筑物的热容与热阻。前述为单个温控负荷(TLC)建立状态空间模型的过程如下：In the above formula, α _i (t) represents the percentage of the power of the i-th temperature-controlled load to the rated power at time t, and α _s represents the corresponding percentage of rated power when the temperature is the user’s set value T _s , T _i (t) is The temperature when the i-th temperature-controlled load runs stably at time t, ΔT is the temperature deviation tolerance, η is the thermal coefficient of the inverter air conditioner,

is the rated power, C _th and R _th represent the thermal capacity and thermal resistance of the building, respectively. The aforementioned 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:

表示TCL的额定功率，η表示VFAC的热系数(例如本实施例中为12.5)，α_i(t)表示t时刻第i个TCL的功率占额定功率的百分比。单个TCL的等效热参数模型描述了其内部温度随环境温度和运行功率的变化情况。In the above formula, i=1, 2,..., N, N represents the number of TCLs in the building, T _i (t) represents the indoor temperature at time t, and T _a (t) represents the external ambient temperature at time t (such as the present embodiment 30 degrees Celsius in the middle), C _th and R _th respectively represent the thermal capacity and thermal resistance of the building,

represents the rated power of the TCL, η represents the thermal coefficient of the VFAC (for example, 12.5 in this embodiment), and α _i (t) represents the percentage of the power of the i-th TCL to the rated power at time t. The equivalent thermal parameter model of a single TCL describes how its internal temperature varies with ambient temperature and operating power.

S102, set the temperature setting value of the user as T _s , set the temperature comfort range accepted by the user as [T _min , T _max ], and the temperature deviation tolerance as ΔT, T _min =T _s -ΔT, T _max = T _s +ΔT. When the TCL reaches the stable operation state, dT _i (t)/dt=0, substituting into the equivalent thermal parameter model of a single TCL, the correspondence between the temperature T _i (t) and the percentage of rated power α _i (t) in stable operation can be obtained relation:

S103, set the corresponding rated power percentage when the temperature is the user’s set value T _s as α _s , considering the different set temperatures of each TCL, the user’s comfort range is also different, define the TCL power offset index γ _i (t) and user comfort state index β _i (t) are shown in formulas (7) and (8). It can be obtained that β _i (t)∈[0,1], in order to ensure user comfort, the comfort state index cannot exceed this range, and β _i (t)=0.5 is the user's most comfortable state. The range of γ _i (t) needs to be determined according to parameters such as thermal coefficient, thermal resistance, and the comfort state of the user at time t.

S104, define u _i (t) as the percentage variation of the i-th TCL rated power, that is, the control input, which is:

Taking x _i (t) as the state variable, the state space expression of a single TCL is shown in formula (6). Taking the two state quantities of temperature-controlled load power and user comfort, let x _i (t) = [γ _i (t); β _i (t)], the simultaneous equations (12) ~ (14) and ) and (8), the detailed expression of formula (6) can be written as:

Substitute the parameters to get:

Step S4 requires the communication topology information of buildings and TCL, and its adjacency matrix is M=[a _ij ]∈R ^N×N . Wherein, a _ij =1 when there is a communication connection between the i-th and j-th TCLs; a _ij =0 when there is no communication connection between them or i=j. The in-degree matrix of the communication graph is D=diag{d ₁ ,d ₂ ,…,d _N }∈R ^N×N , and the Laplacian matrix is L={l _ij }∈R ^N×N .

In this embodiment, step S4 includes:

S4.1, according to the communication topology of N temperature-controlled loads in the upper object, construct the degree matrix D=diag{d ₁ ,d ₂ ,…,d _N }∈R ^N×N , where d ₁ ,d ₂ ,… ,d _N is the inner degree of the 1st to N temperature control loads, R ^N×N is the dimension, and the calculation function expression of the inner degree is:

In the above formula, d _i is the internal degree of the i-th temperature-controlled load, and a _ij is the communication connection status between the i-th temperature-controlled load and the j-th temperature-controlled load, and the value of whether the communication connection status is 1 or 0; Construct the adjacency matrix M=[a _ij ]∈R ^N×N according to the communication topology of N temperature-controlled loads in the upper object, and construct the Laplacian matrix L={l _ij }∈R ^N× according to the following formula ^N :

L=D-M, (17)

In the above formula, D is the inner degree matrix, M is the adjacency matrix, l _ij is the Laplacian value of the i-th temperature control load and the j-th temperature control load;

S4.2, according to the scheduling power increment ΔP _L (t+Δt _s ) to be provided by the upper object in the next control cycle, determine the scheduling information of each temperature-controlled load receiving controller according to the following formula;

为温控负荷的平均功率，Δt_s为下层控制的时间间隔，q_i为第i个温控负荷的控制系数，若第i个温控负荷和控制器有通信直接连接则有q_i＝1，反之q_i＝0；In the above formula, Δx _i (t) is the scheduling information that the i-th temperature-controlled load receives from the controller at time t, k ₁ is the number of temperature-controlled loads directly connected to the controller,

is the average power of the temperature-controlled load, Δt _s is the time interval of lower-level control, q _i is the control coefficient of the i-th temperature-controlled load, and if the i-th temperature-controlled load has a direct communication connection with the controller, then q _i =1 , otherwise q _i =0;

S4.3, determine that the state between the temperature control loads with communication connections is relatively available, and the function expression of the distributed static consensus protocol control is as follows:

In the above formula, u _i (t) is the control variable at time t, c is the coupling coefficient greater than 0, L′∈R ^1×2 represents the feedback gain matrix, R ^1×2 represents the dimension, and _xi (t) is The state variable of the i-th temperature-controlled load, a _ij is the status of communication connection between the i-th temperature-controlled load and the j-th temperature-controlled load, x _j (t) is the state variable of the j-th temperature-controlled load, Δx _i (t) is the scheduling information of the i-th temperature-controlled load receiving controller; the function expression controlled by the distributed static consensus protocol is substituted into the state-space model of a single temperature-controlled load to obtain the regulation model of the i-th temperature-controlled load :

In the above formula, l _ij is the Laplacian value of the i-th temperature-controlled load and the j-th temperature-controlled load, and Δx _j (t) is the scheduling information of the j-th temperature-controlled load receiving controller;

S4.4. Determine the feedback gain matrix L' and the coupling coefficient c, and substitute the determined feedback gain matrix L' and the coupling coefficient c into the regulation model of each temperature-controlled load to control the state of each temperature-controlled load.

According to the consensus inference, for the TCL group in the building, if and only if the matrix A+cλ _i BL' is a Hurwitz matrix, all TCL state variables can reach a consensus. Among them, _λi is the non-zero eigenvalue of Laplacian matrix L. Considering that the matrices A and B are both stable, the consensus problem of the protocol (20) can be solved by finding out the appropriate L' and c.

In this embodiment, determining the feedback gain matrix L' in step S4.4 includes:

S101, find out the solution of the linear matrix inequality (Linear Matrix Inequalities, referred to as LMI) 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 matrices, and P is the solution of linear matrix inequality;

S102, calculate the feedback gain matrix L' 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, determining the coupling coefficient c in step S4.4 includes:

S201, the condition for determining the state variables of each temperature control load in the upper object to reach a consensus is that the matrix A+cλ _i BL′ is a Hurwitz matrix _, and the characteristic polynomial det(sI-(A+ σBL′)) is stable, where λ _i is the non-zero eigenvalue of the Laplacian matrix L, s is the eigenvalue of the matrix A+cλ _i BL′, I is the second-order identity matrix, A and B are the system state matrices, σ is the product of the coupling coefficient c and the eigenvalue λ _i , L' is the feedback gain matrix, and σ=cλ _i =x+jy, where x and y are the coordinates of the real axis and the imaginary axis respectively, and j is the imaginary unit;

S202, according to the eigenvalue s of matrix A+cλ _i BL′, the complex coefficient polynomial p(s) shown in the following formula is established:

p(s)=s2 ⁺ (a+jb)s+e+jd, (23)

In the above formula, a, b, d and e are polynomial coefficients, j is an imaginary number unit, and there are a, b, e∈R, where R is a real number;

S203, according to the stable necessary and sufficient conditions of the complex coefficient polynomial p(s) a>0 and abd+a ² ed ² >0, determine the value range of the coupling coefficient c, so as to determine the value of the coupling coefficient c in this value range value.

In this embodiment, in step S4.4, after substituting the determined feedback gain matrix L' and coupling coefficient c into the regulation model of each temperature-controlled load to control the state of each temperature-controlled load, any i-th temperature-controlled load is The adjusted state variables are:

为第i个温控负荷的状态变量的一阶导数，Δt_s为下层控制的时间间隔。确定的反馈增益矩阵L′和耦合系数c代入各个温控负荷的调控模型以控制各个温控负荷的状态后，各TCL只需根据式(18)、(20)所示的共识协议调控便可满足共识要求。In the above formula, x _i (t+Δt _s ) is the state variable after regulation, and x _i (t) is the state variable of the i-th temperature control load,

is the first derivative of the state variable of the i-th temperature-controlled load, and Δt _s is the time interval of the lower layer control. After the determined feedback gain matrix L′ and coupling coefficient c are substituted into the regulation model of each temperature-controlled load to control the state of each temperature-controlled load, each TCL only needs to be regulated according to the consensus protocol shown in formulas (18) and (20). Satisfy the consensus requirement.

热系数η＝2.5。下层单位控制时间Δt_s＝1s，上层单次更新时间t_d＝0.5s，负荷和光伏数据采集时间间隔Δt_c＝1min，耦合系数w＝0.6。验证TCL系统在本专利方案下平抑光伏和负荷功率波动的性能表现，实施例依据微电网某天12:00-15:00光伏发电出力以及除VFAC外负荷消耗。主网的调度要求为将微电网和主网的交互功率P_d(t)稳定在25kW，所有TCL均参与调度。最终，本实施例方法最终确定c＝12，L′＝[-1.4366 2]，得到的微电网和主网的交互功率如图5所示。参见图5可知，未经优化的微电网和主网的交互功率波动较大，经过本实施例方法优化后的交互功率Pd(t)稳定在25kW左右，说明本实施例方法的调度满足了主电网的要求，平抑了光伏发电和负荷的功率波动。In this embodiment, the installed capacity of the micro-grid photovoltaic power generation system in a certain area is 1200kW. There are 20 buildings participating in regulation, and each building has 20 TCLs. Building equivalent heat capacity C _th = 2kWh/°C, equivalent thermal resistance R _th = 2°C/kW; user's temperature deviation tolerance ΔT = 2°C; ambient temperature is 30°C; rated power of VFAC

Thermal coefficient η = 2.5. The lower unit control time Δt _s =1s, the upper layer single update time t _d =0.5s, the load and photovoltaic data collection time interval Δt _c =1min, and the coupling coefficient w=0.6. To verify the performance of the TCL system in stabilizing photovoltaic and load power fluctuations under this patented solution, the embodiment is based on the output of photovoltaic power generation and the consumption of loads other than VFAC in the microgrid on a certain day from 12:00 to 15:00. The dispatching requirement of the main network is to stabilize the interactive power P _d (t) between the microgrid and the main network at 25kW, and all TCLs participate in the dispatching. Finally, the method of this embodiment finally determines c=12, L′=[-1.4366 2], and the obtained interactive power between the microgrid and the main grid is shown in FIG. 5 . Referring to Figure 5, it can be seen that the interaction power between the unoptimized microgrid and the main grid fluctuates greatly, and the interaction power Pd(t) after optimization by the method of this embodiment is stable at about 25kW, indicating that the scheduling of the method of this embodiment satisfies the requirements of the main grid. The requirements of the power grid stabilize the power fluctuations of photovoltaic power generation and loads.

To sum up, the method of this embodiment is implemented by the aggregator and the controller in the microgrid composed of buildings. Among them, the aggregator establishes communication connections with the main network, photovoltaic power generation system and some buildings, and the controller establishes communication connections with some TCLs in the buildings. Firstly, establish a single TCL state space model, construct the building communication topology diagram in the microgrid and the TCL communication topology diagram in the building, determine the load and photovoltaic data collection time interval Δt _c and the lower layer control time interval Δt _s ; then, use the aggregation The aggregator collects the load power P _load (t) and photovoltaic output P _pv (t) at the current moment, and calculates the TCL scheduling power P _req (t) according to the load power consumption, photovoltaic output and grid dispatching requirements; finally, the upper aggregator The control calculates the real-time scheduling power of each building, and transmits the result to the lower controller, and the lower layer calculates the real-time scheduling power of each TCL according to the consensus protocol. The implementation of the method of the invention can realize the consensus control of the two parameters of TCL power offset and comfort in the building, make the microgrid respond to various dispatching signals of the main network, and accurately and effectively stabilize the power fluctuations of photovoltaic power generation and loads.

In addition, this embodiment also provides a microgrid-oriented temperature-controlled load cluster control system, including interconnected microprocessors and memories, and the microprocessor is programmed or configured to execute the aforementioned micro-grid-oriented temperature-controlled load cluster control method . In addition, this embodiment also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and the computer program is used to be programmed or configured by a microprocessor to execute the above microgrid-oriented temperature control load cluster control method .

Embodiment two:

This embodiment is basically the same as the first embodiment, and the main difference is that the topological structure of the microgrid is different. In this embodiment, on the basis of the topology shown in Figure 2 to Figure 4 of Embodiment 1, due to reasons such as communication failures and user needs, the TCL operation conditions of each building are different. Building No. 14 does not participate in system scheduling, No. One TCL in the building is not running, and two TCLs in buildings 11-13 and 15 are not running, which makes the communication diagrams of the TCLs in each building different. In addition, the temperature set point Ts of the TCL in the building is also different. Photovoltaic output and load consumption are the same as in Embodiment 1. Finally, the method of this embodiment finally determines c=12, L′=[-1.4366 2], and the obtained interactive power between the microgrid and the main grid is shown in FIG. 6 . Referring to Figure 6, it can be seen that the interaction power between the unoptimized microgrid and the main grid fluctuates greatly, and the interaction power Pd(t) after optimization by the method of this embodiment is stable at about 25kW, indicating that the control strategy of the method of this embodiment is different. The set temperature and communication failure conditions still apply.

In addition, this embodiment also provides a microgrid-oriented temperature-controlled load cluster control system, including interconnected microprocessors and memories, and the microprocessor is programmed or configured to execute the aforementioned micro-grid-oriented temperature-controlled load cluster control method . In addition, this embodiment also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and the computer program is used to be programmed or configured by a microprocessor to execute the above microgrid-oriented temperature control load cluster control method .

Embodiment three:

This embodiment is basically the same as the first embodiment, and the main difference is that the topological structure of the building is different. In this embodiment, on the basis of the microgrid in Embodiment 1, the values of the thermal capacity, thermal resistance and thermal coefficient of each building are different, respectively in the range of [1.5, 2.5], [1, 3] and [1.5, 3.5] Internal distribution, building and TCL operation are the same as in Embodiment 2. Finally, the method of this embodiment finally determines c=12, L′=[-1.4366 2], and finally obtains the changes of the TCL user comfort state index β _i (t) in each building as shown in FIGS. 7 to 10 . Among them, Fig. 7 is a change diagram of the comfortable state index β _i (t) of TCL users in buildings 1 to 5, and Fig. 8 is a change diagram of the comfortable state index β _i (t) of TCL users in buildings 6 to 10, Fig. 9 is a change diagram of comfort state index β _i (t) of TCL users in buildings 11-15, and Fig. 10 is a change diagram of comfort state index β _i (t) of TCL users in buildings 16-20. Referring to Figures 7 to 10, it can be seen that the user comfort index is in the range of [0,1]. That is, the scheduling controls the interactive power at about 75kW under the condition of meeting the comfort requirements. The reason is that during stable operation, the corresponding relationship between temperature T _i (t) and rated power percentage α _i (t) is affected by parameter ηR _th . For the same power offset γ _i (t), the corresponding temperature offset ΔT _i is different. The parameter _Rth affects the dispatchability of the building, resulting in changes in _{βL and αML .} In the method of this embodiment, the influence of the parameter ηR _th is considered in the power scheduling of each building, which not only ensures the comfort of the user, but also improves the scheduling capability of the system.

In addition, this embodiment also provides a microgrid-oriented temperature-controlled load cluster control system, including interconnected microprocessors and memories, and the microprocessor is programmed or configured to execute the aforementioned micro-grid-oriented temperature-controlled load cluster control method . In addition, this embodiment also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and the computer program is used to be programmed or configured by a microprocessor to execute the above microgrid-oriented temperature control load cluster control method .

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. 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, etc.) having computer-usable program code embodied therein. The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram. These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

The above descriptions are only preferred implementations of the present invention, and the scope of protection of the present invention is not limited to the above-mentioned embodiments, and all technical solutions under the idea of the present invention belong to the scope of protection of the present invention. It should be pointed out that for those skilled in the art, some improvements and modifications without departing from the principles of the present invention should also be regarded as the protection scope of the present 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 )：

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:

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,

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:

in the above-mentioned formula, the compound has the following structure,

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 ，

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,

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:

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;

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,

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:

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:

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:

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,

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.

Images