CN111725798A - Distributed economic dispatching prediction control method for direct-current micro-grid cluster - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/10—The network having a local or delimited stationary reach
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Abstract
The invention discloses a distributed economic dispatching prediction control method for a direct current micro-grid cluster in the technical field of power systems, which comprises the following steps: introducing an economic scheduling problem; modeling the direct current micro-grid cluster and the control circuit; the method comprises the steps that under the control structure, a global tertiary controller is compensated through the prediction function controller based on a distributed dynamic consistency algorithm to generate global optimal incremental cost so as to realize economic operation of the whole cluster; meanwhile, the method is different from the economic dispatching problem under the action of the traditional PI controller, the dynamic performance of the system is mainly improved by changing the bus voltage within a certain range, and the contradiction between algorithm optimization and large calculation amount is solved.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a distributed economic dispatching prediction control method for a direct-current micro-grid cluster.
Background
In recent years, a direct-current micro-grid becomes an inevitable component of a modern power distribution network, various distributed power sources (such as photovoltaic power, a fan, a storage battery and the like) can be integrated, the utilization rate of renewable energy sources is improved, and adverse effects brought by the fact that the renewable energy sources are connected to the power distribution network are reduced. The direct-current micro-grid is increasingly deeply researched in China, and a direct-current micro-grid laboratory and a demonstration project are established in the university of fertilizer industry, Zhejiang electric power company and the like, so that technical support is provided for direct-current micro-grid engineering.
In order to improve the utilization rate of renewable energy to the maximum extent, accurately schedule transmission power between networks and enhance the overall reliability and availability, the direct-current micro-grid is interconnected to form a cluster, which becomes the future development trend of the power grid. With the gradual and deep research of a single direct current microgrid and the gradual improvement of key technologies thereof, researchers propose that a plurality of direct current Microgrids are interconnected to form a cluster to improve the reliability and stability of a system, for example, in the published document "Distributed Global environmental active mapping for a cluster of DC Microgrids" IEEE Transaction on Power Systems (DOI:10.1109, Feb.2020), the economic scheduling problem of the direct current microgrid cluster is introduced, so that the bus voltage reference value is changed as a cost, and the output Power distribution is optimized to reduce the Global Power generation cost. However, the economic dispatching strategy adopts a linear PI controller to compensate the incremental cost deviation, the control effect is poor, the bus voltage tracking speed is low, the power distribution among direct current micro-grids is seriously influenced, and the power supply reliability and stability of the system are extremely unfavorable. In order to obtain a better voltage regulation rate, the invention designs a distributed economic dispatching prediction control method for a direct current micro-grid cluster to solve the problems.
Disclosure of Invention
The present invention aims to provide a distributed economic dispatching predictive control method for a dc micro-grid cluster, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a distributed economic dispatching prediction control method for a direct current micro-grid cluster comprises the following steps:
s1: introducing an economic scheduling problem;
s2: modeling the direct current micro-grid cluster and the control circuit;
s3: building a prediction function controller;
and optimizing the stable operation of the cluster from the perspective of reducing the overall power generation cost aiming at the conditions that distributed power supplies with different properties are arranged in the network and the power generation cost is different.
Further, the step S1 is specifically: distributed power supplies in the microgrid can be divided into schedulable power supplies and non-schedulable power supplies, and the schedulable power supplies can be storage batteries for example; non-dispatchable power sources, such as photovoltaic power sources and wind turbines, which can be controlled locally to operate at the maximum power point when the system is operating, the objective of the economic dispatch problem is to minimize the global power generation costs, given that there are n dispatchable distributed power sources operating in the microgrid, namely:
PD＝PLoad-PRES(2)
where n is the number of distributed power sources in the microgrid i, Pi,mIs the power generated by the mth distributed power supply in the microgrid i, Ci,m(Pi,m) Is the operating cost of power supply i, PDIs the required load power, P, in the microgrid ilossRepresenting losses, P, incurred by transmission lines in the networkLoadAnd PRESRepresenting requirements within the microgrid;
the cost function of power generation for a schedulable distributed power supply is typically approximated by a quadratic function:
α thereinm,i，βm,iAnd gammam,iA cost function coefficient representing a respective distributed power source;
calculating the minimum value of the power generation cost by using a Lagrange operator:
λ represents the power-dependent lagrangian multiplier, the lagrangian operator is minimized by solving the equation;
the step of matching the incremental cost values of all local distributed power supplies, namely the derivative of the cost function, is crucial to economic operation in the microgrid, and in order to optimize global load balancing, the interconnected microgrid must converge to the same incremental cost to realize a global optimal operating point, so as to realize economic dispatching at a system level.
Further, the step S2 is specifically: adopting a distributed three-level control strategy based on a leader-follower protocol, wherein a communication layer comprises a two-level communication network, and performing local information interaction in the network to realize economic dispatching to converge a local increment cost value; each direct current microgrid is provided with a special proxy node, each direct current microgrid in the cluster carries out information sharing in the global state through the proxy node, and the global optimal incremental cost is diffused to all distributed power supplies of the whole system in a fully distributed mode by means of the proxy node, so that the complexity of a communication link is simplified, the communication cost of the system is saved, and the communication pressure of the cluster is relieved;
setting that a direct-current micro-grid cluster comprises two direct-current micro-grids, wherein each direct-current micro-grid consists of two distributed power supplies and a buck circuit, the direct-current micro-grids are interconnected through a pi-shaped connecting line, the nominal value of the voltage of each bus is 48V, each generating unit is converted into a linear time-invariant circuit through averaging, and then the linear time-invariant circuit is subjected to linearization processing, so that a prediction function controller can be designed by using a linear control method.
Furthermore, the prediction function controller realizes the control of the nonlinear DC/DC converter, and the combination of the prediction function controller and the DC/DC converter in the direct current microgrid can optimize the controlled quantity in a limited time domain, effectively eliminate the influence of model errors and interference in a rolling mode, and has more advantages than a linear PI controller.
Furthermore, compared with other model predictive control algorithms, the predictive function controller has the advantages of simple algorithm and small calculated amount, adopts a distributed control framework, reduces the online calculation time of the controller, solves the problem of contradiction between improvement of dynamic performance and large calculated amount of predictive control, and has generally small capacity of a direct-current microgrid and no good disturbance resistance; after the micro-grids are interconnected to form a cluster, the operation conditions in the operation process of the micro-grids are various, such as load switching, plug and play of internal units and communication link faults, strict requirements are imposed on the steady-state precision and the dynamic performance of the DC/DC converter, the controller can obviously improve the dynamic response of the system, and negative effects caused by large signal changes when the converter operates at high frequency are effectively avoided.
Further, the step S3 includes the following steps:
s30: establishing basis functions
Starting from the control rule of the control quantity by adopting a prediction function control algorithm, paying attention to the structural property of the control input quantity, and regarding the structural property as a main factor for ensuring the good and bad effect of the tertiary control; the voltage deviation amount of the cubic control generated by the prediction function control algorithm is formed by linearly superposing the selected basis functions, and the basis functions are respectively established for each direct current micro-grid by adopting a completely distributed control structure, namely:
from the above formula, it can be seen that the voltage deviation generated by the control input quantity of the prediction function control algorithm is cubic controlThe parameter is closely related to the selection of the basis function, and the basis function is usually selected in a compromise mode according to the characteristics, the control precision and the complexity of the control process of the adjacent controlled model; by selecting as step functions as basis functions, for each selected basis function gkj(i) The output value corresponding to the basis function can be calculated off line, and the voltage deviation amount generated by three-time control is obtained by linearly superposing different basis function output values, so that the voltage value of each direct-current micro-grid bus generates deviation, and economic dispatching is realized;
s31: establishing a reference trajectory
In order to avoid the phenomena of drastic change, overshoot or oscillation of the voltage deviation amount during the control process of the prediction function control algorithm, the controlled system is usually made to follow a set reference trajectory which gradually approaches to the set value, and the reference trajectory can be represented by the following formula:
ICr(k+i|k)＝f(IC(k),ICsp(k+i)) (7)
wherein: IC (integrated circuit)r(k + i | k) is the reference output IC value of each DC microgrid at the k + i moment predicted at the k moment, f (IC (k), ICsp(k + i)) is a linear or branched alkyl group with IC (k) and ICsp(k + i) correlation function, IC (k) is the actual output current value at time k, ICsp(k + i) is a set output current value at the time of k + i;
the reference trajectory is set to the form of a first order exponent, i.e.:
ICr(k+i|k)＝ICsp(k+i)-βi(ICsp(k)-IC(k)) (8)
wherein β is a coefficient of the frequency,
Tris a reference track time constant, TsSampling time for the system;
for a given set value ICsp(k + i), which can be generally expressed in terms of a polynomial sum, i.e.:
wherein: n is a radical ofcIs the order of the polynomial; c. Cj(k) Is a coefficient of a polynomial; c. C0(k) Is a number, c, since the output current set value is not changed0(k) The value of (1) is the constant value;
the reference trajectory expression of the prediction function control algorithm obtained from the above equation is:
s32: prediction model
The prediction model can predict a future bus voltage value according to the current bus voltage state quantity and the voltage deviation control quantity, a topology and an inner ring controller structure of a single microgrid are considered, a small signal model is established by adopting a state space average method, an obtained transfer function is used as an internal prediction model of a prediction function control algorithm, and the expression of the transfer function is as follows:
wherein: v. oftThe control variable is a control variable of a prediction model, namely a voltage deviation amount generated by three times of control; i is an output variable of the prediction model, namely the output current of the converter; v. ofinIs the input voltage of the converter; kpv，KpiPI proportion systems of the voltage loop and the current loop respectively; l is the inductance of the converter in the network; c is the output capacitance of the converter in the network; r is a load resistor of the direct-current microgrid;
s33: feedback correction
Because the control levels of the hierarchical control are more, and factors of errors, interference, parameter change and model mismatch exist during model establishment, the prediction model cannot be completely matched with an actual system, feedback correction is introduced, and the error quantity is compensated into bus voltage generated by an adjacent direct current micro-grid so as to correct the model prediction value;
the error amount is generally defined as:
e(k)＝IC(k)-ICm(k|k-1) (12)
the amount of error for time k + i may be selected as:
e(k+i)＝ai·e(k)，i＝0,1,…P-1 (13)
wherein: a isiError compensation coefficient for the ith time, where a is used for subsequent calculation convenienceiTaking a constant 1;
the error amount is fed back and compensated to the predicted output current of the adjacent direct current micro-grid, so that the output value of the model is corrected, and the feedback is more accurate; the corrected model output values are as follows:
ICc(k+i|k)＝ICm(k+i|k)+e(k+i) (14)
s34: roll optimization
Solving the optimal solution of the control variable in a finite time domain by using a prediction function control algorithm, namely solving the voltage deviation amount generated by the tertiary control, and updating each moment in real time in a rolling mode in the solving process, thereby providing a control condition for the operation of the tertiary control;
the objective function for establishing cubic control is:
wherein: IC (integrated circuit)c(k+hiI k) is corrected k + hiA bus voltage value at a time; IC (integrated circuit)r(k+hiI k) is k + h predicted at k timeiA reference trajectory value of a time; s is the number of fitting points; h isiIs the specific time value of the fitting point;
to solve for the optimal control quantity, the partial derivatives of J (k) are calculated such that
Then there are:
μ(k)＝(Gk TGk)-1Gk TL(k) (16)
order (G)kTGk)-1GkWhen T is equal to M, then
μ(k)＝M·L(k) (17)
In order to ensure the accuracy of the voltage deviation of the three-time control, generally, only the current-time control quantity is applied to two adjacent direct-current micro-grids, and the following steps are included:
wherein: gk(0)＝[gk1(0) gk2(0) … gkN(0)]T；
From the above calculation, k0，k1，k2All values of (a) can be obtained by off-line solving, and c0(k) The values of i (k) are known, so that only the state variables [ i (k) v (k) ] of the DC microgrid group need to be solved online]The specific expression of the control input quantity u (k) of the three-time control can be deduced; in addition, the number of the fitting points has influence on the stability and robustness of the closed loop, and the four fitting points are adopted to achieve better control effect after debugging.
Compared with the prior art, the invention has the beneficial effects that: the method is mainly used for optimizing power distribution between the micro-grid and the power generation unit when the micro-grid supplies a plurality of loads so as to reduce the overall power generation cost to the maximum extent, and comprises a local third-level controller and an overall third-level controller; meanwhile, the method is different from the economic dispatching problem under the action of the traditional PI controller, the dynamic performance of the system is mainly improved by changing the bus voltage within a certain range, and the contradiction between algorithm optimization and large calculation amount is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a DC microgrid cluster configuration according to the present invention;
FIG. 2 is a block diagram of the distributed economic dispatch predictive control proposed in the present invention;
FIG. 3 is a flow chart of the distributed economic dispatch predictive control of the present invention;
FIG. 4 is a bus voltage waveform when the cluster performs load switching in the present invention;
FIG. 5 is a waveform of incremental cost when the cluster performs load switching in the present invention;
FIG. 6 is a bus voltage waveform when the power generation unit 2 of the DC micro-power i in the cluster is switched;
FIG. 7 is an incremental cost waveform when the power generation unit 2 of the DC micro-electricity i in the cluster is switched;
FIG. 8 is a bus voltage waveform when a global communication link of a direct current micro-grid i in a cluster fails according to the invention;
fig. 9 is an incremental cost waveform when a global communication link of a dc microgrid i in a cluster fails according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1-9, the present invention provides a technical solution: a distributed economic dispatching prediction control method for a direct current micro-grid cluster comprises the following steps:
s1: introducing an economic scheduling problem;
s2: modeling the direct current micro-grid cluster and the control circuit;
s3: building a prediction function controller;
and optimizing the stable operation of the cluster from the perspective of reducing the overall power generation cost aiming at the conditions that distributed power supplies with different properties are arranged in the network and the power generation cost is different.
Wherein, step S1 specifically includes: distributed power supplies in the microgrid can be divided into schedulable power supplies and non-schedulable power supplies, and the schedulable power supplies can be storage batteries for example; non-dispatchable power sources, such as photovoltaic power sources and wind turbines, which can be controlled locally to operate at the maximum power point when the system is operating, the objective of the economic dispatch problem is to minimize the global power generation costs, given that there are n dispatchable distributed power sources operating in the microgrid, namely:
PD＝PLoad-PRES(2)
where n is the number of distributed power sources in the microgrid i, Pi,mIs the power generated by the mth distributed power supply in the microgrid i, Ci,m(Pi,m) Is the operating cost of power supply i, PDIs the required load power, P, in the microgrid ilossRepresenting losses, P, incurred by transmission lines in the networkLoadAnd PRESRepresenting requirements within the microgrid;
the cost function of power generation for a schedulable distributed power supply is typically approximated by a quadratic function:
α thereinm,i，βm,iAnd gammam,iA cost function coefficient representing a respective distributed power source;
calculating the minimum value of the power generation cost by using a Lagrange operator:
λ represents the power-dependent lagrangian multiplier, the lagrangian operator is minimized by solving the equation;
the step of matching the incremental cost values of all local distributed power supplies, namely the derivative of the cost function, is crucial to economic operation in the microgrid, and in order to optimize global load balancing, the interconnected microgrid must converge to the same incremental cost to realize a global optimal operating point, so as to realize economic dispatching at a system level.
Step S2 specifically includes: adopting a distributed three-level control strategy based on a leader-follower protocol, wherein a communication layer comprises a two-level communication network, and performing local information interaction in the network to realize economic dispatching to converge a local increment cost value; each direct current microgrid is provided with a special proxy node, each direct current microgrid in the cluster carries out information sharing in the global state through the proxy node, and the global optimal incremental cost is diffused to all distributed power supplies of the whole system in a fully distributed mode by means of the proxy node, so that the complexity of a communication link is simplified, the communication cost of the system is saved, and the communication pressure of the cluster is relieved;
setting that a direct-current micro-grid cluster comprises two direct-current micro-grids, wherein each direct-current micro-grid consists of two distributed power supplies and a buck circuit, the direct-current micro-grids are interconnected through a pi-shaped connecting line, the nominal value of the voltage of each bus is 48V, each generating unit is converted into a linear time-invariant circuit through averaging, and then the linear time-invariant circuit is subjected to linearization processing, so that a prediction function controller can be designed by using a linear control method.
The prediction function controller realizes the control of the nonlinear DC/DC converter, and is combined with the DC/DC converter in the direct current microgrid, so that the controlled quantity can be optimized in a limited time domain, the influence of model errors and interference is effectively eliminated in a rolling mode, and the prediction function controller has more advantages than a linear PI controller.
Compared with other model predictive control algorithms, the predictive function controller has the advantages of simple algorithm and small calculated amount, adopts a distributed control framework, reduces the online calculation time of the controller, solves the problem of the contradiction between the improvement of dynamic performance and the large calculated amount of predictive control, and has small capacity of a direct-current micro-grid and no good disturbance resistance; after the micro-grids are interconnected to form a cluster, the operation conditions in the operation process of the micro-grids are various, such as load switching, plug and play of internal units and communication link faults, strict requirements are imposed on the steady-state precision and the dynamic performance of the DC/DC converter, the controller can obviously improve the dynamic response of the system, and negative effects caused by large signal changes when the converter operates at high frequency are effectively avoided.
Step S3 includes the following steps:
s30: establishing basis functions
Starting from the control rule of the control quantity by adopting a prediction function control algorithm, paying attention to the structural property of the control input quantity, and regarding the structural property as a main factor for ensuring the good and bad effect of the tertiary control; the voltage deviation amount of the cubic control generated by the prediction function control algorithm is formed by linearly superposing the selected basis functions, and the basis functions are respectively established for each direct current micro-grid by adopting a completely distributed control structure, namely:
from the above formula, it can be seen that the control input amount in the prediction function control algorithm is the voltage deviation generated by the cubic control, the parameter is closely related to the selection of the basis function, and the basis function is usually selected in a compromise manner according to the characteristics, the control accuracy and the complexity of the control process of the adjacent controlled model; by selecting as step functions as basis functions, for each selected basis function gkj(i) The output value corresponding to the basis function can be calculated off line, and the voltage deviation amount generated by three-time control is obtained by linearly superposing different basis function output values, so that the voltage value of each direct-current micro-grid bus generates deviation, and economic dispatching is realized;
s31: establishing a reference trajectory
In order to avoid the phenomena of drastic change, overshoot or oscillation of the voltage deviation amount during the control process of the prediction function control algorithm, the controlled system is usually made to follow a set reference trajectory which gradually approaches to the set value, and the reference trajectory can be represented by the following formula:
ICr(k+i|k)＝f(IC(k),ICsp(k+i)) (7)
wherein: IC (integrated circuit)r(k + i | k) is the reference output IC value of each DC microgrid at the k + i moment predicted at the k moment, f (IC (k), ICsp(k + i)) is a linear or branched alkyl group with IC (k) and ICsp(k + i) correlation function, IC (k) is the actual output current value at time k, ICsp(k + i) is a set output current value at the time of k + i;
the reference trajectory is set to the form of a first order exponent, i.e.:
ICr(k+i|k)＝ICsp(k+i)-βi(ICsp(k)-IC(k)) (8)
wherein β is a coefficient of the frequency,
Tris a reference track time constant, TsSampling time for the system;
for a given set value ICsp(k + i), which can be generally expressed in terms of a polynomial sum, i.e.:
wherein: n is a radical ofcIs the order of the polynomial; c. Cj(k) Is a coefficient of a polynomial; c. C0(k) Is a number, c, since the output current set value is not changed0(k) The value of (1) is the constant value;
the reference trajectory expression of the prediction function control algorithm obtained from the above equation is:
s32: prediction model
The prediction model can predict a future bus voltage value according to the current bus voltage state quantity and the voltage deviation control quantity, a topology and an inner ring controller structure of a single microgrid are considered, a small signal model is established by adopting a state space average method, an obtained transfer function is used as an internal prediction model of a prediction function control algorithm, and the expression of the transfer function is as follows:
wherein: v. oftThe control variable is a control variable of a prediction model, namely a voltage deviation amount generated by three times of control; i is an output variable of the prediction model, namely the output current of the converter; v. ofinIs the input voltage of the converter; kpv，KpiPI proportion systems of the voltage loop and the current loop respectively; l is the inductance of the converter in the network; c is the output capacitance of the converter in the network; r is a load resistor of the direct-current microgrid;
s33: feedback correction
Because the control levels of the hierarchical control are more, and factors of errors, interference, parameter change and model mismatch exist during model establishment, the prediction model cannot be completely matched with an actual system, feedback correction is introduced, and the error quantity is compensated into bus voltage generated by an adjacent direct current micro-grid so as to correct the model prediction value;
the error amount is generally defined as:
e(k)＝IC(k)-ICm(k|k-1) (12)
the amount of error for time k + i may be selected as:
e(k+i)＝ai·e(k)，i＝0,1,…P-1 (13)
wherein: a isiError compensation coefficient for the ith time, where a is used for subsequent calculation convenienceiTaking a constant 1;
the error amount is fed back and compensated to the predicted output current of the adjacent direct current micro-grid, so that the output value of the model is corrected, and the feedback is more accurate; the corrected model output values are as follows:
ICc(k+i|k)＝ICm(k+i|k)+e(k+i) (14)
s34: roll optimization
Solving the optimal solution of the control variable in a finite time domain by using a prediction function control algorithm, namely solving the voltage deviation amount generated by the tertiary control, and updating each moment in real time in a rolling mode in the solving process, thereby providing a control condition for the operation of the tertiary control;
the objective function for establishing cubic control is:
wherein: IC (integrated circuit)c(k+hiI k) is corrected k + hiA bus voltage value at a time; IC (integrated circuit)r(k+hiI k) is k + h predicted at k timeiA reference trajectory value of a time; s is the number of fitting points; h isiIs the specific time value of the fitting point;
to solve for the optimal control quantity, the partial derivatives of J (k) are calculated such that
Then there are:
μ(k)＝(Gk TGk)-1Gk TL(k) (16)
order (G)k TGk)-1Gk TWhen M is equal to
μ(k)＝M·L(k) (17)
In order to ensure the accuracy of the voltage deviation of the three-time control, generally, only the current-time control quantity is applied to two adjacent direct-current micro-grids, and the following steps are included:
wherein: gk(0)＝[gk1(0) gk2(0) … gkN(0)]T；
From the above calculation, k0，k1，k2All values of (a) can be obtained by off-line solving, and c0(k) The values of i (k) are known, so that only the state variables [ i (k) v (k) ] of the DC microgrid group need to be solved online]The specific expression of the control input quantity u (k) of the three-time control can be deduced; in addition, the number of the fitting points has influence on the stability and robustness of the closed loop, and the four fitting points are adopted to achieve better control effect after debugging.
One specific application of this embodiment is: referring to fig. 1, the method can be used for controlling a plurality of direct current microgrid clusters, a simulation platform for interconnecting two direct current microgrid clusters is built, and the feasibility of the proposed strategy is verified; each direct-current micro-grid is composed of a photovoltaic unit, two energy storage units and a corresponding load, the photovoltaic unit, the two energy storage units and the corresponding load are connected with a bus through a buck converter, the grid is connected with the grid through a tie line, the rated value of the voltage of the bus is selected to be 48V, as shown in figure 1, 1 and 3 are all direct-current micro-grids in a cluster, and 2 is a pi-shaped tie line existing when the direct-current micro-grids are interconnected;
fig. 2 is a block diagram of a distributed economic dispatch prediction control for dispatching incremental costs (i.e., ICs) of each microgrid unit to achieve power balance of each dc microgrid, and 10 is a communication network for collecting current signals of adjacent units for communication; 20 is a cubic control block diagram, wherein ekIs an IC deviation term, ai,12As a neighbor weight matrix of a local cubic controller, bi,1Is a contiguous weighting matrix, λ, of a global cubic controlleri,1、λi,2IC values of two power generation units of the direct-current micro-grid i are respectively; 30 is a secondary control block diagram, the average voltage of each power generation unit is updated in real time by a consistency algorithm, then the average voltage is stabilized at 48V by a voltage loop,
the voltage deviation item generated by the level control is finally sent to the reference value of the voltage of the bottom layer control as the bus through low bandwidth communication to realize the control target;
fig. 3 is a flow chart of distributed economic dispatch based on PFC, which is to initialize parameters, collect state variables and output variables of corresponding time according to a time sequence, realize continuous online rolling in a limited time domain, and finally obtain input quantities to be transmitted to primary control, so as to realize economic dispatch of a cluster at the cost of changing bus voltage;
fig. 4 and 5 show waveforms of bus voltage and output current under the control of the present invention when a load jumps, and it can be seen that only three local control actions are performed when the load jumps, each microgrid in a cluster operates independently above or below a nominal voltage, and IC values of each power generation unit in the network converge to be consistent; when t is greater than 0.3s, the global tertiary controller is activated, voltage deviation is rapidly generated in the cluster, so that the voltages of the buses of the three direct-current micro-grids are changed, the IC values of all micro-grids in the cluster tend to be consistent, and the power balance distribution of the whole cluster is realized; when t is 0.4s, when the load 2 of the microgrid 1 changes from 16 Ω to 8 Ω, the bus voltage and the output current of each direct-current microgrid are kept dynamically consistent, and the current change on the transmission lines enables each direct-current microgrid IC to be rebalanced; when the load jumps from 8 omega to 16 omega, the ICs in the cluster are scheduled again to balance the power of each direct current micro-grid;
fig. 6 and 7 show the operation conditions of the bus voltage and the incremental cost after the cluster is cut out from the 2 nd power generation unit in the microgrid i, and it can be seen that when t is less than 0.2s, the direct-current microgrid cluster stably operates under the secondary control; when t is greater than 0.4s, the power generation unit 2 is switched out of the cluster to operate independently, the bus voltage and the output current of the power generation unit are recovered to the rated level for operation, the rest units are recombined in the cluster, the power distribution is realized by self-adaptive adjustment and three-time control, the bus voltage and the output current in the cluster are kept dynamic and consistent, and the plug-and-play function of the invention is verified;
fig. 8 and 9 show waveforms of bus voltage and incremental cost when a communication fault occurs in the global tertiary controller of the dc microgrid i, which can reduce communication redundancy to the maximum extent, and when t is less than 0.4s, the system stably operates under the global tertiary control and t is 0.4s, the global controller has a communication fault and the waveform is restored to the local tertiary control mode, it can be seen that the control provided by the present invention has high flexibility.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A distributed economic dispatching prediction control method for a direct current micro-grid cluster is characterized by comprising the following steps:
s1: introducing an economic scheduling problem;
s2: modeling the direct current micro-grid cluster and the control circuit;
s3: building a prediction function controller;
and optimizing the stable operation of the cluster from the perspective of reducing the overall power generation cost aiming at the conditions that distributed power supplies with different properties are arranged in the network and the power generation cost is different.
2. The distributed economic dispatch predictive control method for a dc microgrid cluster of claim 1, characterized in that: the step S1 specifically includes: distributed power supplies in the microgrid can be divided into schedulable power supplies and non-schedulable power supplies, and the schedulable power supplies can be storage batteries for example; non-dispatchable power sources, such as photovoltaic power sources and wind turbines, which can be controlled locally to operate at the maximum power point when the system is operating, the objective of the economic dispatch problem is to minimize the global power generation costs, given that there are n dispatchable distributed power sources operating in the microgrid, namely:
PD＝PLoad-PRES(2)
where n is the number of distributed power sources in the microgrid i, Pi,mIs the power generated by the mth distributed power supply in the microgrid i, Ci,m(Pi,m) Is the operating cost of power supply i, PDIs the required load power, P, in the microgrid ilossRepresenting losses, P, incurred by transmission lines in the networkLoadAnd PRESRepresenting requirements within the microgrid;
the cost function of power generation for a schedulable distributed power supply is typically approximated by a quadratic function:
α thereinm,i，βm,iAnd gammam,iA cost function coefficient representing a respective distributed power source;
calculating the minimum value of the power generation cost by using a Lagrange operator:
λ represents the power-dependent lagrangian multiplier, the lagrangian operator is minimized by solving the equation;
the step of matching the incremental cost values of all local distributed power supplies, namely the derivative of the cost function, is crucial to economic operation in the microgrid, and in order to optimize global load balancing, the interconnected microgrid must converge to the same incremental cost to realize a global optimal operating point, so as to realize economic dispatching at a system level.
3. The distributed economic dispatch predictive control method for a dc microgrid cluster of claim 1, characterized in that: the step S2 specifically includes: adopting a distributed three-level control strategy based on a leader-follower protocol, wherein a communication layer comprises a two-level communication network, and performing local information interaction in the network to realize economic dispatching to converge a local increment cost value; each direct current microgrid is provided with a special proxy node, each direct current microgrid in the cluster carries out information sharing in the global state through the proxy node, and the global optimal incremental cost is diffused to all distributed power supplies of the whole system in a fully distributed mode by means of the proxy node, so that the complexity of a communication link is simplified, the communication cost of the system is saved, and the communication pressure of the cluster is relieved;
setting that a direct-current micro-grid cluster comprises two direct-current micro-grids, wherein each direct-current micro-grid consists of two distributed power supplies and a buck circuit, the direct-current micro-grids are interconnected through a pi-shaped connecting line, the nominal value of the voltage of each bus is 48V, each generating unit is converted into a linear time-invariant circuit through averaging, and then the linear time-invariant circuit is subjected to linearization processing, so that a prediction function controller can be designed by using a linear control method.
4. The distributed economic dispatch predictive control method for a direct current microgrid cluster of claim 3, characterized in that: the prediction function controller realizes the control of the nonlinear DC/DC converter, combines the nonlinear DC/DC converter with the DC/DC converter in the direct current microgrid, can optimize the controlled quantity in a limited time domain, effectively eliminates the influence of model errors and interference in a rolling mode, and has more advantages than a linear PI controller.
5. The distributed economic dispatch predictive control method for a direct current microgrid cluster of claim 4, characterized in that: compared with other model predictive control algorithms, the predictive function controller has the advantages of simple algorithm and small calculated amount, adopts a distributed control framework, reduces the online calculation time of the controller, solves the problem of high dynamic performance and large calculated amount of predictive control, and has small capacity of a direct-current micro-grid and no good disturbance resistance; after the micro-grids are interconnected to form a cluster, the operation conditions in the operation process of the micro-grids are various, such as load switching, plug and play of internal units and communication link faults, strict requirements are imposed on the steady-state precision and the dynamic performance of the DC/DC converter, the controller can obviously improve the dynamic response of the system, and negative effects caused by large signal changes when the converter operates at high frequency are effectively avoided.
6. The distributed economic dispatch predictive control method for a dc microgrid cluster of claim 1, characterized in that: the step S3 includes the following steps:
s30: establishing basis functions
Starting from the control rule of the control quantity by adopting a prediction function control algorithm, paying attention to the structural property of the control input quantity, and regarding the structural property as a main factor for ensuring the good and bad effect of the tertiary control; the voltage deviation amount of the cubic control generated by the prediction function control algorithm is formed by linearly superposing the selected basis functions, and the basis functions are respectively established for each direct current micro-grid by adopting a completely distributed control structure, namely:
from the above formula, it can be seen that the control input amount in the prediction function control algorithm is the voltage deviation generated by the cubic control, the parameter is closely related to the selection of the basis function, and the basis function is usually selected in a compromise manner according to the characteristics, the control accuracy and the complexity of the control process of the adjacent controlled model; by selecting as step functions as basis functions, for each selected basis function gkj(i) The output value corresponding to the basis function can be calculated off-line, and the voltage deviation amount generated by three-time control is obtained by linearly superposing different basis function output values, so that the voltage value of each direct current micro-grid bus generates deviation, and the realization of the deviation of each direct current micro-grid bus voltage valueCarrying out economic dispatching;
s31: establishing a reference trajectory
In order to avoid the phenomena of drastic change, overshoot or oscillation of the voltage deviation amount during the control process of the prediction function control algorithm, the controlled system is usually made to follow a set reference trajectory which gradually approaches to the set value, and the reference trajectory can be represented by the following formula:
ICr(k+i|k)＝f(IC(k),ICsp(k+i)) (7)
wherein: IC (integrated circuit)r(k + i | k) is the reference output IC value of each DC microgrid at the k + i moment predicted at the k moment, f (IC (k), ICsp(k + i)) is a linear or branched alkyl group with IC (k) and ICsp(k + i) correlation function, IC (k) is the actual output current value at time k, ICsp(k + i) is a set output current value at the time of k + i;
the reference trajectory is set to the form of a first order exponent, i.e.:
ICr(k+i|k)＝ICsp(k+i)-βi(ICsp(k)-IC(k)) (8)
wherein β is a coefficient of the frequency,
Tris a reference track time constant, TsSampling time for the system;
for a given set value ICsp(k + i), which can be generally expressed in terms of a polynomial sum, i.e.:
wherein: n is a radical ofcIs the order of the polynomial; c. Cj(k) Is a coefficient of a polynomial; c. C0(k) Is a number, c, since the output current set value is not changed0(k) The value of (1) is the constant value;
the reference trajectory expression of the prediction function control algorithm obtained from the above equation is:
s32: prediction model
The prediction model can predict a future bus voltage value according to the current bus voltage state quantity and the voltage deviation control quantity, a topology and an inner ring controller structure of a single microgrid are considered, a small signal model is established by adopting a state space average method, an obtained transfer function is used as an internal prediction model of a prediction function control algorithm, and the expression of the transfer function is as follows:
wherein: v. oftThe control variable is a control variable of a prediction model, namely a voltage deviation amount generated by three times of control; i is an output variable of the prediction model, namely the output current of the converter; v. ofinIs the input voltage of the converter; kpv，KpiPI proportion systems of the voltage loop and the current loop respectively; l is the inductance of the converter in the network; c is the output capacitance of the converter in the network; r is a load resistor of the direct-current microgrid;
s33: feedback correction
Because the control levels of the hierarchical control are more, and factors of errors, interference, parameter change and model mismatch exist during model establishment, the prediction model cannot be completely matched with an actual system, feedback correction is introduced, and the error quantity is compensated into bus voltage generated by an adjacent direct current micro-grid so as to correct the model prediction value;
the error amount is generally defined as:
e(k)＝IC(k)-ICm(k|k-1) (12)
the amount of error for time k + i may be selected as:
e(k+i)＝ai·e(k)，i＝0,1,…P-1 (13)
wherein: a isiError compensation coefficient for the ith time, where a is used for subsequent calculation convenienceiTaking a constant 1;
the error amount is fed back and compensated to the predicted output current of the adjacent direct current micro-grid, so that the output value of the model is corrected, and the feedback is more accurate; the corrected model output values are as follows:
ICc(k+i|k)＝ICm(k+i|k)+e(k+i) (14)
s34: roll optimization
Solving the optimal solution of the control variable in a finite time domain by using a prediction function control algorithm, namely solving the voltage deviation amount generated by the tertiary control, and updating each moment in real time in a rolling mode in the solving process, thereby providing a control condition for the operation of the tertiary control;
the objective function for establishing cubic control is:
wherein: IC (integrated circuit)c(k+hiI k) is corrected k + hiA bus voltage value at a time; IC (integrated circuit)r(k+hiI k) is k + h predicted at k timeiA reference trajectory value of a time; s is the number of fitting points; h isiIs the specific time value of the fitting point;
to solve for the optimal control quantity, the partial derivatives of J (k) are calculated such that
Then there are:
μ(k)＝(Gk TGk)-1Gk TL(k) (16)
order (G)kTGk)-1GkWhen T is equal to M, then
μ(k)＝M·L(k) (17)
In order to ensure the accuracy of the voltage deviation of the three-time control, generally, only the current-time control quantity is applied to two adjacent direct-current micro-grids, and the following steps are included:
wherein: gk(0)＝[gk1(0) gk2(0)…gkN(0)]T；
From the above calculation, k0，k1，k2All values of (a) can be obtained by off-line solving, and c0(k) The values of i (k) are known, so that only the state variables [ i (k) v (k) ] of the DC microgrid group need to be solved online]The specific expression of the control input quantity u (k) of the three-time control can be deduced; in addition, the number of the fitting points has influence on the stability and robustness of the closed loop, and the four fitting points are adopted to achieve better control effect after debugging.
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