CN112838580A - Improved heterogeneous temperature control load bilinear polymerization model and distributed layered multi-target coordination control method thereof - Google Patents
Improved heterogeneous temperature control load bilinear polymerization model and distributed layered multi-target coordination control method thereof Download PDFInfo
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Abstract
The invention provides an improved heterogeneous temperature control load bilinear polymerization model and a distributed layered multi-target coordination control method thereof. Firstly, improving an original bilinear polymerization model by using a temperature control load second-order thermodynamic model, optimizing the problem of accumulated errors existing in the bilinear model, and deriving a higher-precision improved heterogeneous temperature control load bilinear polymerization model; secondly, a distributed layered multi-target control strategy based on the combination of consistency control and reverse control is provided, a plurality of temperature control load clusters in the system are regulated and controlled aiming at system power fluctuation exceeding AGC economic dispatching, and stability control services such as system load tracking and power angle and frequency are provided. The invention has the novelty that an original temperature control load bilinear polymerization model is improved, the model description precision is improved, and a distributed layered multi-target control strategy with good performance is designed, so that the TCL can better participate in the regulation and control of the power system.
Description
Technical Field
The invention belongs to the field of auxiliary service and demand side response of an electric power system, relates to an improved modeling and scheduling control strategy of temperature control loads, and particularly relates to an improved heterogeneous temperature control load bilinear polymerization model and a distributed hierarchical multi-target coordination control method thereof.
Background
The stability of power angle and frequency and the control thereof are the guarantee of safe and stable operation of the power system, and are closely related to the balance of the electric power for generation in the system. Traditionally, a power system adopts a power generation load tracking mode to meet the power balance and stability of the system, and the load is regarded as a passive physical terminal. When the output of the generator set is still difficult to maintain the stability of the system or needs to pay high cost according to the traditional way, the current load shedding/power supply measures have great social negative effects. And with the continuous rising of power load, the concentrated access of a large number of intermittent power supplies to the power system and the increasing of the proportion of a large-capacity supercritical unit in the system, the capacity of flexibly adjusting the output of the power generation side is gradually weakened. Due to the requirement of source-network-load interactive operation, the existing resources on the demand side are utilized to supplement the traditional power generation scheduling to carry out auxiliary services such as power frequency modulation and peak shaving, and the wide attention is paid.
Thermal Controlled Loads (TCLs) such as air conditioners, refrigerators, and water heaters have been one of the main subjects of flexible loads due to their advantages such as fast response, energy storage, and high controllability. Under the conditions that the load of the power grid fluctuates greatly and the spare capacity of the system is insufficient, the TCL with rich reserves can be used for supplementing the AGC power regulation capability of the system, quickly maintaining the balance of the system and improving the safety and the economy of the operation of the power grid. However, due to the characteristics of small capacity, large quantity, distributed distribution and strong response randomness of the TCL monomers, the scheduling center is not easy to obtain the information of the aggregated power consumption and the response potential, and therefore, how to effectively utilize the resources by the scheduling center is a major challenge currently facing.
The load system modeling is the basis of the TCL participating in the demand response, and the aggregation model can summarize the operation characteristics of a large-scale load group and guide related organizations to make control strategies. TCL modeling is one of the research hotspots of scholars at home and abroad, wherein a control-oriented TCL bilinear polymerization model can effectively reduce the calculated amount of the TCL model, avoids dimension disaster dilemma, provides an effective way for large-scale TCL polymerization scheduling, and is adopted by many research applications. However, the current original bilinear model considers too single factor, and has accumulated errors, and the description precision of the model is to be further improved. In addition, in order to reduce the calculation amount in the process of establishing the aggregation model, many scholars often assume that all load system model parameters are completely consistent, which not only violates the practical situation, but also often causes the oscillation problem due to the lack of load parameter diversity and is not beneficial to the effective control of the TCL, and the aggregation model with parameter diversity often generates a natural damping, so that the system has better stability in the regulation and control process. However, this tends to cause an increase in the amount of polymer, thereby posing another problem: how to coordinate regulation of numerous polymers. According to the decision position of the control signal, the control modes of the TCL demand response signal can be mainly classified into two types: the method has the advantages that the method is centralized control, namely, the TCL is controlled in a unified mode, and the method is characterized by high reliability and strong predictability, but communication lines need to be erected between a control center and all load clusters, so that the problems of high investment cost and communication delay exist. And secondly, distributed control can quickly respond according to the monitoring result and by combining the self condition, so that the response speed is high, the sensitivity is high, but the problem of insufficient response or excessive response can exist because a uniform control center is not provided, and the random response of the distributed control is high.
Disclosure of Invention
Aiming at the problems, the invention aims to overcome the defects of the existing research and makes the following two innovations: firstly, an original TCL bilinear polymerization model is optimized, an improved heterogeneous TCL bilinear polymerization model is provided, and description accuracy of the polymerization model is improved; and secondly, a distributed layered multi-target coordination control strategy for coordinating a plurality of TCL aggregates is designed based on the consistency control and backstepping control principle, and multi-index regulation and control of the power angle, the frequency and the power of the power system are realized under the constructed inertia center system.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
first, a second order differential equation is usedAn Equivalent thermodynamic Parameter model (ETP) describes a single TCL, changes of indoor temperature and indoor substance temperature into two state variables observed in the ETP model, so that the TCL is also referred to as a heterogeneous model for short, mainly and additionally considers the heat exchange process of the indoor substance and the indoor air, and the load transfer rate alpha of each temperature interval is determinedon/offAre all approximated to the desired temperature set point
And starting material temperature Tm0Average load transfer rate of
And obtaining a heterogeneous temperature control load bilinear polymerization model.
Aiming at the pair alpha in the derivation process of the bilinear polymerization modelon/offOptimizing the accumulated error problem caused by the approximate acquisition by using the real-time temperature set value Tset(t) substitution
In (1)
By real-time mass temperature Tm(t) substitution
T in (1)m0And finally, deducing to obtain the improved heterogeneous temperature control load bilinear polymerization model. The improved model comprehensively reflects the real-time influence and the accumulated influence of the temperature set value change on the temperature control load and the influence of heterogeneity on the temperature control load.
Secondly, a distributed layered multi-target coordination control method is designed for controlling a plurality of temperature control load aggregates.
The consistency control is applied to the aggregator level for balancing the output of each temperature control load aggregate, and the control signal lambda of the control center0(t) an output power coefficient lambda of each temperature-controlled load constructed based on the PI control concepti(t) according to oneThe adaptive control iterates as long as the system power deficit Δ P ≠ 0, λ0(t)、λi(t) iterates until the system power is balanced, and each λiThe value of (t) tends to be consistent in the iterative calculation process, and the output power tracking value corresponding to the self maximum power ratio can be distributed to each aggregate.
The backward control is applied to the temperature control load aggregate level and is used for controlling the output power of the temperature control load to track the output quota distributed by the load aggregator; firstly, a combined mathematical model of a power system and a temperature control load differential form is constructed, and a power angle reference value is given as delta according to a reverse-thrust control principlerefSequentially deriving the virtual angular velocity control quantity omegarefAnd a temperature-controlled load output power virtual control quantity PTCL，refThen, the control input U of each temperature control load aggregate can be obtainedi(t)。
The system control flow is shown in fig. 1.
And finally, checking and improving the accuracy of the heterogeneous TCL bilinear polymerization model and the regulation and control performance of distributed hierarchical multi-target coordination control through algorithm simulation verification analysis.
Drawings
FIG. 1 is a system control flow diagram;
FIG. 2 is a graph showing the temperature coupling dynamics of air and indoor media;
FIG. 3 is a second order heterogeneous ETP model of a monolithic TCL;
FIG. 4 is a TCL finite difference discretization dynamic process;
FIG. 5 is a comparison of aggregate power of a heterogeneous TCL bilinear aggregation model and an original TCL bilinear aggregation model;
FIG. 6 is a comparison of aggregate power for the modified heterogeneous TCL bilinear aggregation model and the heterogeneous TCL bilinear aggregation model;
FIG. 7 is a block diagram of an example simulation algorithm for an IEEE 9 node system;
FIG. 8 is a plan of system power generation and load forecasting;
FIG. 9 is a power deficit that the TCL needs to absorb;
FIG. 10 shows power angle fluctuation of the system;
FIG. 11 is a system frequency fluctuation;
fig. 12 shows the total power tracking error in each control mode: (a) centralized control, (b) distributed control, (c) decentralized control;
FIG. 13 shows the polymer output power coefficient for each control mode: (a) centralized control, (b) distributed control, (c) decentralized control;
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
1 System model
1.1 improved heterogeneous TCL bilinear polymerization model
1.1.1 Single temperature control load thermal dynamic model
Most of the existing TCL polymerization modeling methods simulate the thermodynamic process thereof based on a first-order thermodynamic ordinary differential equation, and neglect the temperature T of a substancemThe second order dynamic effects caused. This simplification often results in the constructed model not accurately describing the true TCL dynamics in transient response, such as caused by temperature set point changes. FIG. 2 shows the air temperature T of the air conditioning load at a time T of 1(hr) with the temperature set point changed from 25 deg.C to 27 deg.CaAnd temperature T of mattermEvolution of (c). It can be seen that compared to the time period t3，t4]At a time period [ t ]1，t2]Period TaA longer time is required to increase from 25 c to 27 c. This is due to the fact that at [ t ]1，t2]During which the temperature T of the material is lowermSignificantly slows down the air temperature TaIs increased.
In the case where T is not consideredmIn the case of (1), TaDepends only on TaThis ignores the effect as shown in fig. 2, per se. In fact, T as presented in FIG. 2 cannot be accurately described by only a first-order modelaThe entire trajectory of (a). Therefore, consider T in detailmAnd TaIs necessary to obtain a good quality polymerization model that can accurately describe the transient and steady state polymerization responses of the TCL population.
The monomer TCL is described by using a second order differential equation Equivalent Thermodynamic Parameter (ETP) model, and changes of indoor temperature and indoor substance temperature are two state variables observed in the ETP model, so the model is also referred to as a heterogeneous model for short, mainly considering a heat exchange process of outdoor air and indoor air of a house, a heat exchange process of indoor substance and indoor air, heat loss in the exchange process, and a heat energy storage process, which are described by modeling with heat capacity and heat resistance, and a specific differential equation of the dual-medium model is as follows:
wherein,
Ta(T) is the room air temperature, Tm(T) is the temperature of the indoor material, T∝For outdoor ambient temperature, P is the operating electric power of a single TCL, Tmax、TminUpper and lower limits of temperature limit, TsetIs the temperature set point, ΔdbIs a temperature dead zone, CaIs the heat capacity of indoor air, CmIs the heat capacity of indoor material, RaIs the heat resistance of indoor air, RmIs the indoor material thermal resistance. The thermodynamic equivalent model is shown in FIG. 3, in which UAairCoefficient of heat loss of indoor air, UAmassIs the heat loss coefficient of the indoor material, Ra＝1/UAair，Rm＝1/UAmass。
The thermocouple model aggregate power PrComprises the following steps:
in the formula etaiIs the efficiency coefficient of the load i.
1.1.2 heterogeneous TCL bilinear polymerization model
Although the centralized TCL represented by the ETP model can describe the electricity utilization characteristics of the TCL cluster relatively accurately, the model has both continuous state variables (temperature) and discrete on/off state variables, and is difficult to be directly used for control design; and if each TCL is expressed as a group of independent differential equations, the model can face a dimension disaster dilemma when applied to the dynamic response of the power grid level load.
A control-oriented heterogeneous TCL bilinear aggregation model is derived below. In this model, it is assumed that all temperature-controlled loads are in a limited temperature range TL，TH]In the method, the whole temperature range is discretized into small segments with equal intervals, each temperature small segment contains TCLs in two states of on/off, and therefore, the total number of temperature state intervals is Q, and each TCL belongs to a certain discrete temperature state interval, as shown in fig. 4.
Constructing a heterogeneous TCL bilinear polymerization model as shown in a formula (3).
In the formula: x (t) ═ x1(t)，x2(t)，…，xQ(t)]TThe state variable matrix is Q multiplied by 1, and represents the TCL quantity in each temperature interval after finite difference discretization;
is a control input; pr(t) total output power of the aggregated TCL;
is an output matrix of 1 XQ order; a is a system matrix of Q × Q order and is described as
In the formula:
representing the load transfer rate of the TCL in the "on/off" state, respectively.
The matrix B is a Q multiplied by Q order bilinear matrix which has the same structure as the matrix A, and alpha in the matrix A ison/offSetting both to-1 results in a matrix B, i.e., B ═ a (-1, -1).
In order to reduce the iterative calculation of the matrix in the operation, the load transfer rate of each temperature interval is approximate to the expected temperature set value
And starting material temperature Tm0Average load transfer rate of:
then
The constant matrix can greatly reduce data calculation and memory space and improve the model operation speed.
1.1.3 improved heterogeneous TCL bilinear polymerization model
The heterogeneous TCL bilinear polymerization model is only suitable for the scene with small change range of the load temperature set value. When T isset(t) deviation from the desired set point
When larger, if still used
Average load transfer rate of
Substituted for alphaon/offThe bilinear polymerization model can be caused to fail, and obvious deviation can occur after long-time operation.
In the course of one control cycle,
is always constant and cannot reflect the real-time internal temperature Ta(T) Next, using the real-time material temperature Tm(T) and the real-time temperature setpoint Tset(t) to improve the para-alphaon/offEstimation of (2):
this can lead to an improved heterogeneous TCL bilinear polymerization model:
in the formula,
control input u1(t)、u2(t)、u3(t) respectively reflecting the real-time influence of the temperature set value change on the temperature control load, the cumulative influence of the temperature set value change on the temperature control load and the influence of heterogeneity on the temperature control load. Wherein T ism(T) is not easily available in reality and can be changed to Tset(T) instead of Ta(t) estimated and obtained according to the formula (1).
1.2 equivalent model of generator inertial center system
Assuming that S generator sets are shared in the system, the generators all adopt a classical second-order model which ignores salient pole effect, assuming that an excitation system of the generator is strong enough, the amplitude of transient electromotive force after transient reactance of the generator in a dynamic process can be kept unchanged, and the obtained generator model is as shown in the formula:
in the formula, deltaiIs the power angle, omega, of the generator ii、ωi，0Angular velocity and standard angular velocity, T, of the generator i, respectivelyj，iIs the inertia time constant, P, of the generator im，i、Pe，iMechanical power, electromagnetic power, D, of the generator i, respectivelyiConstant damping coefficient of generator i.
When the frequency control strategy of the power grid with large scale is calculated by adopting the model, the fact that the time consumed for solving is rapidly increased along with the increase of the scale of the system is found, and even an unsolvable situation can occur. The frequency control model provided by the section comprises large-scale differential algebraic equation system constraints, time-varying variables and non-time-varying variables are coupled with each other, and the complexity of model solution is rapidly increased along with the increase of the number of system nodes. Therefore, further analysis of the problem is necessary to improve the solution speed by improving the built model.
The electrical angular speeds of the generators can be regarded as approximately the same, and the generators in the power grid are equivalent to one generator under the inertial center system, so that the transient power angle stability constraint is simplified, and an equivalent generator model under the inertial center system is established, namely:
wherein,
δs、ωs、ωs，0、Tj，s、Dsrespectively setting an equivalent power angle, an equivalent angular velocity, a standard angular velocity, an equivalent time constant and an equivalent damping coefficient of the inertia center system; pm，sFor the total mechanical power of the system, PG，iFor the generated power of the generator set i, Pe，sIs the total electromagnetic power of the system, PTCLFor temperature-controlled loading of the total output power, PLFor a rigid load of the system, PnewGenerating power for a distributed power supply of the system, PAGCAnd increasing the generated power for the AGC unit.
Equivalent power angles and frequencies of the inertia center system can be regarded as the power angles and frequencies of the whole power system, and the whole regional power grid large system can be regarded as being in a stable operation state as long as the parameters of the inertia center system are kept stable.
2-distributed hierarchical multi-objective coordinated control design
2.1 problem description
In a hierarchical architecture based on a load aggregator, the load aggregator can be a load management center of a distribution company, a government entity or a power grid company in the traditional sense, or can be a third party organization representing a single type or multiple types of loads, and the load aggregator can be used as an intermediate organization for coordinating a large number of small and medium-sized users and a power grid control center, and has the common point that a large number of power terminal users are aggregated together to participate in power grid dispatching, and the established targets of the power grid company, the load aggregator and the power terminal users are realized. The load aggregation businessmen acquire the controllability and the dispatching response capacity of each load cluster from the managed load clusters, so that a response model of the whole load cluster can be established, and the self-distribution autonomy function is realized.
The main purpose of the design coordination controller is to enable a load aggregator to distribute the output power of each TCL aggregate in a balanced manner, regulate and control each TCL aggregate to accurately track the issued power quota, ensure the power balance of the system, and maintain the power angle and frequency stability, i.e., the research is a layered multi-target coordination control problem.
2.2 consistency pinning control strategy
According to the multi-subject consistency pinning control method, the control state information of each subject can be expressed as:
ui(t+Δt)＝fi[hi0(t)u0(t)，hi1(t)u1(t)，…，hiN(t)uN(t)] (11)
in the formula: u. ofi(t) control information of the ith host at time t; u. of0(t) control information of the control center at time t; h isijRepresenting the communication link between the ith self-body and the jth self-body, if the communication between the ith self-body and the jth self-body is possible, then h ij1, otherwise h ij0. Furthermore, if the ith autonomous body can communicate with the control center, then h 0i1, otherwise h0i＝0。h ii1 is suitable for arbitraryAnd the self-body indicates that all self-bodies can acquire self information.
The time-varying communication coefficients may be represented by a communication topology matrix:
the dispatching center can directly obtain the future generation power plan value of the units and the load agents for a plurality of hours in the future, the new energy power injection value in the system and the load predicted value of the system according to the historical data and the prediction rolling model stored by the dispatching center. The total power deficit of the system can be expressed as:
ΔP＝PL+PTCL-PAGC-Pm，s-Pnew (13)
to balance the output power of each load aggregate, one may set:
in the formula, PiConsumption Power of the i-th TCL aggregate, Pi，maxIs the maximum power of the ith TCL aggregate, and λ is the TCL aggregate output power coefficient.
Control signal lambda of the control center0(t) can be constructed based on the PI control concept as follows:
the distributed consistency control algorithm for each TCL aggregate can be described as:
in the formula, λi(t) is the time-varying output power coefficient, lk，iThe control gain of the load polymer is more than 0, and is taken as:
as long as the system power deficit Δ P ≠ 0, λ0(t)、λi(t) iterates until the system power is balanced, and each λiThe value of (t) tends to be consistent in the iterative calculation process, and the output power tracking value corresponding to the self maximum power ratio can be distributed to each aggregate.
2.3 reverse thrust control strategy
The joint type (8) and the formula (10) can obtain:
the control theory of the reverse thrust is as follows:
1) let eδ＝δs，ref-δsWherein e isδAs a function of the phase error of the system, deltas，refIs the reference phase. By the principle of reverse-push control, for eδTaking the derivative, we can get:
designing a first virtual control quantity ωs，refThe following were used:
ωs，ref＝ωs，0-kδeδ (20)
in the formula, kδIs an adjustable phase control parameter greater than 0. By substituting formula (20) for formula (19):
2) let eω＝ωs，ref-ωsWherein e isωAs a system angular velocity error variable, ωs，refIs a reference angular frequency. To eωTaking the derivative, we can get:
designing a second virtual control quantity PTCL，refThe following were used:
in the formula, kωIs an adjustable angular velocity control parameter greater than 0. Formula (23) may be substituted for formula (22):
3) by
The tracking reference power of each temperature control load aggregate can be calculated as follows:
then can order eP，i＝Pi，ref-PiWherein e isP，iFor the system power error variable, P, corresponding to the ith TCLi，refFor the tracking reference power allocated to the ith TCL, Pi＝CiXi(t) is the polymerization power of the ith polymerization TCL. To eP，iTaking the derivative, we can get:
designing actual control quantity Ui(t) the following:
in the formula, kP，iIs an adjustable power control parameter greater than 0. By substituting formula (28) for formula (27):
3 model verification and algorithm implementation and analysis
3.1 improved heterogeneous TCL bilinear polymerization model simulation analysis
In order to test the accuracy of the optimized dual-medium bilinear model, 1000 TCLs are selected for simulation (C)a＝7.5，Cm＝2.5，Ra＝2，Rm＝1，P＝7，ηr＝2.5，δdb1), the results are shown in fig. 5 to 6.
It can be seen that, under the same parameters, compared with the original bilinear polymerization model only considering the air medium in fig. 5, the thermal resistance property embodied by the indoor substance makes the power fluctuation period of the heterogeneous bilinear model longer and the fluctuation amplitude larger when the temperature setting value is changed; in fig. 6, the optimization of formula (7) improves the problem of accumulated errors existing in the original bilinear model, so that the long-term control accuracy of the improved heterogeneous TCL bilinear model is greatly improved, and feasibility is provided for the TCL cluster to participate in system regulation and control.
3.2 distributed hierarchical Multi-target coordinated control simulation analysis
The distributed hierarchical multi-target coordination control strategy is verified in a small-sized power system, wherein 3 generator sets and 1 demand response load aggregator exist in a regional power grid, the generator sets and load agents are placed in an improved IEEE 9 node system, and the interior of each load agent is considered as a refrigeration type air conditioning device. The system is also provided with a wind power plant and a photovoltaic power station, and the physical connection and the communication connection between the unit and the load agent are shown in figure 7.
The parameters of each unit are as follows:
TABLE 1 Unit parameters
Assuming 8 load clusters, a total of 25418 refrigeration type temperature control devices participate in power system regulation. The parameters of the temperature controlled load polymer are selected from the value ranges of the following table according to the average distribution.
TABLE 2 typical TCL Polymer parameter intervals
The electricity load data of the system within 2h in the future at a certain time can be taken as shown in the following figure 8. It can be known that the first half of the system in the 120min period is a load peak period, and the new energy generating capacity of the second half is increased suddenly. In an actual operation system, the AGC adjusting capacity is generally selected to be 2% -5% of the maximum load of the system, wherein a large system takes a small percentage value, and a small system takes a large percentage value. In this example, the maximum load of the system is 1, and the AGC economic scheduling capacity is 4%, that is, the AGC economic scheduling capacity is ± 0.04. The system power deficit after filtering out the AGC economic schedule is shown in fig. 9 below.
In order to better check the regulation and control effect of the distributed hierarchical multi-target coordination control strategy, a comparison simulation experiment with centralized control and distributed control is designed. Wherein, the centralized control can be realized by setting h in the communication matrix h0i＝1，h ii1 and hij0(i ≠ j); the decentralized control is constructed based on a decentralized hierarchical control idea. The simulation experiment results are shown in fig. 10 to 13.
As can be seen from fig. 10 and 11, the multi-target coordination control strategy based on the back-stepping control structure can correctly control each TCL aggregate response regulation target in both the centralized control mode and the distributed control mode, and maintain the stability of the system. Further observation is made by combining fig. 12, the centralized control has the best regulation and control effect on the power angle and frequency of the system, and the load tracking error is also the smallest; the distributed control has secondary regulation effect, but the fluctuation of power angle, frequency and the like is still in the allowable range of the system; in contrast, the distributed control has the worst effect on stabilizing the power angle and frequency of the system, and under the current system parameters, the fluctuation of the system frequency is close to an allowable critical value in the regulation and control process, and steady-state errors occur in the regulation and control of the power angle. As can be seen from fig. 13, the output ratios of the aggregates are relatively consistent due to the adoption of a consistent control algorithm in the distributed control and the centralized control, and the output ratios of the aggregates are randomly changed under the distributed control due to the free response of the aggregates. Further observation is made by combining fig. 12, the power and the tracking error fluctuation of the distributed control and the centralized control are small in the steady state, and the static stability is good, while the power and the tracking error fluctuation of the distributed control are large in the steady state, and the static instability is presented to a certain degree. The reasons for the above control differences were analyzed as follows:
1. the reverse control is a control strategy based on the Lyapunov stability principle, and has stronger robustness on a control object; the consistency control coordinates that the output proportion of each polymer tends to be consistent, so that the computability of the system can be greatly improved;
2. in the centralized control, each polymer can directly receive system regulation and control information from the control center, and an intermediate transmission link is not needed, so that errors in the regulation and control process can be greatly reduced, and excellent tracking performance is shown. However, this is established on the high-speed effective communication between the control center and each aggregate, but if there is a communication line fault, the system will lose the regulation and control capacity of this aggregate, which puts forward high requirements on the communication reliability, communication transmission synchronism and communication quality of the system;
3. distributed control is within an allowable range, and a certain control precision is sacrificed to obtain great control robustness. Distributed control does not need each aggregator to get in touch with the control center, changes the upper and lower layer communication of most aggregators and control centers into local communication among aggregators, and greatly reduces the communication requirement of the control system. Even if the control effect under the high sparsity communication matrix is satisfactory as taken in the present example, and if the communication connection between polymer 5 and polymer 6 is interrupted, polymer 6 can still obtain control information through polymer 7 to participate in system regulation.
4. In the distributed control, because a uniform allocation center is not provided, the polymers are difficult to coordinate with each other, insufficient response or excessive response is easy to occur, and even the problems of mutual offset of the regulation and control effects occur, and the distributed control may have static fluctuation; it is more difficult to obtain control over the state of all the polymers in the process, since each polymer is free to respond. These factors are detrimental to the stability control of the system.
In summary, the homogeneous bilinear polymerization model of the TCL is expanded to an improved heterogeneous bilinear polymerization model, the accumulated error of the bilinear polymerization model is optimized, multiple generators in a regional power system are equivalent to one generator in an inertial center system, and a distributed hierarchical multi-target coordination control method based on consistency control and reverse-thrust control is provided to coordinate multiple temperature control load aggregates to fully participate in power system regulation and control. Research proves that the accuracy of model description is improved by improving the optimization of the heterogeneous bilinear polymerization model; the distributed hierarchical multi-target coordination control method can effectively regulate and control the system state under different control modes, so that a plurality of control targets such as power angles, frequencies, powers and the like can quickly track respective reference values, and the correctness and the better robustness of the coordination control strategy are shown; the distributed layered multi-target coordination control can greatly reduce the communication requirement of the control system and is superior to the traditional centralized and distributed control in the aspects of robustness and computability.
The above description is only a preferred embodiment of the present invention, and therefore should not be considered as limiting the scope of the invention, and any equivalent substitutions, modifications, improvements, etc. made within the spirit and scope of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. An improved heterogeneous temperature control load bilinear polymerization model and a distributed layered multi-target coordination control method thereof comprise an improved heterogeneous temperature control load bilinear polymerization model constructed based on an original homogeneous temperature control load bilinear polymerization model and a distributed layered multi-target coordination control method for coordinating a plurality of temperature control load polymers based on consistency control and reverse control design.
2. The improved heterogeneous temperature control load aggregation model and the distributed hierarchical multi-objective coordination control method thereof according to claim 1, wherein:
expanding an original homogeneous temperature control load bilinear polymerization model obtained by deducing the first-order thermodynamic parameter model by using a second-order differential equation equivalent thermodynamic parameter model of the temperature control load, and approximating the load transfer rate of each temperature interval to an expected temperature set value
And starting material temperature Tm0Obtaining a heterogeneous temperature control load bilinear polymerization model by the following average transfer rate:
in the formula: x (t) ═ x1(t)，x2(t)，…，xQ(t)]TThe state variable matrix is a Qx 1 order state variable matrix and represents the quantity of temperature control loads in each temperature interval after finite difference discretization, and Q is the total quantity of the temperature intervals;
for control input, Tset(t) is a temperature set value of the temperature control load; pr(t) the total output power of the aggregate temperature control load;
is a 1 XQ-order output matrix, and P is single temperature control load operation electric power; a is a Q × Q system matrix, and is described as:
wherein,
respectively representing the average load transfer rate of the temperature-controlled load in the "on/off" state; caIs the heat capacity of indoor air, CmIs the heat capacity of indoor material, RaIs the heat resistance of indoor air, RmIs the thermal resistance of indoor material, T∞The outdoor environment temperature, delta T is discrete step length;
the matrix B is a Q multiplied by Q order bilinear matrix which has the same structure as the matrix A, and alpha in the matrix A ison/offAll are set to-1, namely, B ═ A (-1, -1);
aiming at pairs in the derivation process of the bilinear polymerization model
Optimizing the accumulated error problem caused by the approximate acquisition by using the real-time temperature set value Tset(t) substitution
In (1)
By real-time mass temperature Tm(t) substitution
T in (1)m0Finally, the improved heterogeneous temperature control load bilinear polymerization model is obtained by derivation:
in the formula,
control input u1(t)、u2(t)、u3(t) respectively reflecting the real-time influence of the temperature set value change on the temperature control load, the cumulative influence of the temperature set value change on the temperature control load and the influence of heterogeneity on the temperature control load.
3. The improved heterogeneous temperature-controlled load bilinear polymerization model and the distributed hierarchical multi-objective coordination control method thereof as claimed in claim 1, wherein:
according to the consistency control and reverse control principle, a distributed layered multi-target coordination control method for temperature control load multi-polymer control is designed; the consistency control is applied to the aggregator level and used for balancing the output of each temperature control load aggregate, and the communication topological matrix of the consistency control is represented as follows:
in the formula, hijIndicating the communication link between the ith and jth aggregates, if communication is possible between the ith and jth aggregates, then hij1, otherwise hij0; furthermore, if the ith aggregator can communicate with the control center, then h0i1, otherwise h0i＝0；hii1 is suitable for any aggregate, and indicates that all aggregates can obtain self information;
in order to make the output power of each temperature control load polymer consistent in proportion, the following settings are set:
in the formula, PiIs the output power, P, of the ith temperature-controlled load aggregatei，maxIs the maximum power of the ith temperature control load polymer, and lambda is the output power coefficient of the temperature control load polymer;
control signal lambda of the control center0(t) is constructed based on PI control conceptThe following:
in the formula, Δ P is the system power shortage;
the consistency control algorithm for the N temperature controlled load aggregates can be described as:
in the formula, λi(t) is the time-varying output power coefficient, lk，iThe control gain of the load polymer is more than 0, and is taken as:
as long as Δ P ≠ 0, λ0(t)、λi(t) iterates until the system power is balanced, and each λiThe values of (t) tend to be consistent in the iterative calculation process, and the output power tracking value corresponding to the self maximum power ratio can be distributed to each aggregate;
the reverse-thrust control is applied to a temperature control load aggregate layer and is used for controlling the output power quota distributed by the temperature control load output power tracking load aggregator, and a mathematical model of the power system and the temperature control load is constructed as follows:
in the formula, deltasIs the equivalent power angle, omega, of the inertia center systemsIs the equivalent angular velocity, omega, of the inertial center systems，0Is the standard angular velocity of the inertial center system, Tj，sIs the equivalent time constant of the inertial center system, DsEquivalent damping coefficients of an inertia center system are obtained; pm，sIs the total mechanical work of the systemRate, Pe，sIs the total electromagnetic power of the system, PTCLFor temperature-controlled loading of the total output power, PLFor a rigid load of the system, PnewGenerating power for a distributed power supply of the system, PAGCThe generated power is increased for the AGC unit; xi(t)、Ai、Bi、Ui(t) respectively representing a state variable matrix, a system matrix, a bilinear matrix and a control input of the temperature control load polymer i;
according to the reverse-thrust control principle, the reference value of the given power angle is deltarefSequentially deriving the virtual angular velocity control quantity omegarefAnd a temperature-controlled load output power virtual control quantity PTCL，refThen, each temperature controlled load aggregate control input U can be obtainedi(t) is:
in the formula, eP，iSystem power error variable, k, for the ith TCLP，iIs an adjustable power control parameter greater than 0.
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