Aircraft energy system night distributed dispatching control method

Abstract

The application discloses a kind of aircraft energy system night distributed scheduling control method, comprising the following steps: S1, the demand of actual near space aircraft night cruising to energy system is established Energy system single bus DC microgrid system model and establishes near space aircraft energy system operation loss minimization problem;S2, based on multiple update recursive least square algorithm DC / DC converter loss model parameter identification;S3, the optimal working point of system under different working conditions is obtained by finite time distributed scheduling algorithm;S4, according to the optimal working point of system, based on multi-agent PI consistency algorithm Design distributed control algorithm, realize current distribution and bus voltage regulation.The application can realize the distributed optimization control for near space aircraft energy system, while taking into account the operation loss of aircraft energy system, the bus voltage regulation and current distribution of aircraft energy system are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optimal control of aircraft energy systems, and particularly relates to a night distributed scheduling control method for an aircraft energy system. BACKGROUND

[0002] In recent years, with the increasing strategic value of near space, research on near space aircraft has rapidly developed. As high-altitude pseudo-satellites, near space aircraft can play an important role in communication relay, surveillance and monitoring, mapping, and weather prediction. In order to maximize its unique advantages, such aircraft need to maintain a cruising altitude while having long endurance. Using the abundant photovoltaic power generation resources in near space can effectively alleviate the limitations of fuel cells in terms of endurance. With the rapid progress of solar photovoltaic technology, the development of long-endurance solar-powered unmanned aerial vehicles has attracted worldwide attention. Against this background, the energy management system of a near space aircraft can be regarded as a direct current micro-grid composed of multiple photovoltaic power generation nodes, which ensures the continuous flight of the aircraft in the high-altitude environment through efficient, low-loss energy optimization scheduling and distributed collaborative control.

[0003] In the existing scheduling control technology of aircraft energy systems, the optimization control problem of minimizing the loss of direct current micro-grid is usually implemented by a hierarchical framework. This framework usually first solves the problem of minimizing the loss by optimization method, and then the upper system outputs the power output ratio of each node using the scheduling algorithm, and the lower system distributes the current according to the ratio. However, the loss model of the direct current micro-grid lacks effective online identification means in terms of electrical parameter identification. In addition, the current system also lacks a fully distributed scheduling control algorithm, and the lower system has not realized the regulation of the bus voltage. Given the advantages of fully distributed technology in terms of flexible expansion, fault tolerance, high availability, decentralization, load balancing, and data redundancy, it has become an inevitable trend for the future development of near space aircraft energy systems composed of multiple power generation nodes. Therefore, there is an urgent need for a distributed optimization control method for aircraft energy systems that takes into account the loss to meet the needs of practical engineering. SUMMARY

[0004] The purpose of the present application is to provide a night distributed scheduling control method for an aircraft energy system, which can realize distributed optimization control for near space aircraft energy systems, take into account the operation loss of the aircraft energy system, and realize bus voltage regulation and current distribution of the aircraft energy system.

[0005] To achieve the above purpose, the present application provides a night distributed scheduling control method for an aircraft energy system, comprising the following steps:

[0006] S1, based on the power supply demand of the energy system put forward by the night cruise of the near space vehicle, a single bus DC microgrid system model of the near space vehicle energy system is established and a problem of minimizing the operation loss of the near space vehicle energy system is constructed;

[0007] S2, each power generation node independently adopts a multiple update recursive least square algorithm to identify the parameters of the DC / DC converter loss model;

[0008] S3, based on the finite time distributed scheduling algorithm, the optimal working point of the system under different working conditions is obtained;

[0009] S4, according to the optimal working point of the system, a distributed control algorithm is designed based on the multi-agent PI consistency algorithm to realize current distribution and bus voltage regulation.

[0010] Preferably, the specific steps of establishing the single bus DC microgrid system model of the near space vehicle energy system in S1 are as follows: first, the near space vehicle energy system is simplified as a single bus DC microgrid with N power generation nodes supplying power to M loads, considering the resistive impedance of the transmission line and ignoring the inductance, each power generation node includes an energy storage unit and a DC / DC converter; then, according to Kirchhoff's law, a single bus DC microgrid system model of the near space vehicle energy system is established

[0011] V b =V i -R i I i ,i=1,2,…,N;

[0012]

[0013] Wherein, V b is the bus voltage, V i and I i are the output voltage and output current of the DC / DC converter of the distributed power generation unit respectively; is the load current of the remote load, R i is the line resistance of the power line where the power generation unit is located;

[0014] The operation loss of the DC microgrid includes the operation loss of the DC / DC converter and the line loss of the power transmission line, the loss of the DC / DC converter is quantitatively expressed as a quadratic function of the output current, that is Where a i ,b i ,c i are the loss coefficients of the converter; the power transmission line loss of the DC / DC converter connected to the bus is R i is the line resistance; the overall operation loss of the microgrid is expressed as follows:

[0015]

[0016] Considering the actual electrical constraints and voltage and power constraint requirements, the loss optimization problem of the DC microgrid is expressed as:

[0017]

[0018] wherein, is the load current of the remote load, V i , is the upper and lower bounds of the output voltage V i of the DC / DC converter, P i , is the upper and lower bounds of the output power P i of the power generation node i under the power generation capacity constraint.

[0019] According to the KKT optimality condition, the optimal solution of the optimization problem P2 is:

[0020]

[0021] wherein Ω s is the set of nodes with saturated output current, λ * is called the global optimal incremental cost, and when the incremental cost of each power generation node converges to λ * , the operation loss of the entire microgrid is minimized.

[0022] Preferably, in S2, the process of loss model parameter identification is expressed by the following formula:

[0023]

[0024] wherein, is the identified value of the loss power, and the regression vector is the parameter μ to be identified μ = [a i ,b i ,c i ] T ; the parameter identification of μ is realized by solving the optimization problem with the minimum objective function;

[0025]

[0026] wherein, P(k) is the actual loss power, and λ is called the forgetting factor; the multiple update recursive least square algorithm is used to identify the value of μ.

[0027] Preferably, in S3, the finite-time distributed scheduling algorithm is designed as:

[0028]

[0029] ω i k d sig(D i (t))],i=1,2,…,N;

[0030]

[0031] where k c ,k d ,γ>0,sig(x)=|x| α sign(x),for the generation nodes that can obtain the load information, ω i =1, to observe the global power mismatch in a distributed manner by means of the D i (t) term, and ω i =0 for other nodes; the input of the finite-time distributed scheduling algorithm is the load electrical data sampling of the near space vehicle energy system, and the output is the output coefficient of each node of the near space vehicle energy system; the designed control algorithm is distributed in the near space, and distributed design and distributed implementation are adopted to ensure stability.

[0032] Preferably, in S4, the output voltage of each generation node is controlled by a local distributed controller, and the following distributed secondary PI controller is adopted:

[0033]

[0034] where The constructed error term e i is composed of two parts, the voltage regulation error e V and the current distribution error k vi ,k ui >0 are weight coefficients of the voltage regulation error and the current distribution error, respectively; the distributed controller controls the exchange of output voltage and output current data of the near space vehicle energy system with neighbors through a communication network, and adjusts the output voltage of the generation node of the near space vehicle energy system according to the distributed control algorithm; the input of the distributed control algorithm is the data sampling of the input voltage and current and the output voltage and current of the near space vehicle energy system, and the output is the output voltage of the near space vehicle energy system.

[0035] Therefore, the aircraft energy system night distributed scheduling control method can realize distributed optimization control for the near space vehicle energy system, take into account the running loss of the aircraft energy system, and realize bus voltage regulation and current distribution of the aircraft energy system.

[0036] The technical solutions of the present application are described in further detail below with reference to the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a flow chart of an embodiment of a night distributed scheduling control method of an aircraft energy system of the present application;

[0038] Figure 2 is an example diagram of an energy micro-grid during night cruising of a near space aircraft in the present application;

[0039] Figure 3 is an algorithm block diagram of a night distributed scheduling control method of an aircraft energy system of the present application;

[0040] Figure 4 is an overall block diagram of a night distributed scheduling control method of an aircraft energy system of the present application;

[0041] Figure 5 is a load current curve graph for identification in the embodiment;

[0042] Figure 6 is a loss power and loss coefficient identification curve graph for identification in the embodiment;

[0043] Figure 7 is a load working condition curve graph for scheduling in the embodiment;

[0044] Figure 8 is an output current curve graph of the system under the proposed algorithm and a comparative algorithm, wherein (a) is an output current curve graph under the proposed algorithm of the present application, and (b) is an output current curve graph under the comparative algorithm;

[0045] Figure 9 is a bus voltage curve graph of the system under the proposed algorithm and the comparative algorithm, wherein (a) is a bus voltage curve graph under the proposed algorithm of the present application, and (b) is a bus voltage curve graph under the comparative algorithm;

[0046] Figure 10 is a loss power curve graph of the system under the proposed algorithm and the comparative algorithm. DETAILED DESCRIPTION

[0047] The technical solutions of the present application are described in further detail below with reference to the accompanying drawings and examples.

[0048] Unless otherwise defined, technical terms or scientific terms used in the present application shall have the ordinary meaning as understood by a person having ordinary skill in the art to which the present application pertains. The terms "first", "second", and similar terms are used herein merely to distinguish one element from another, and are not intended to imply any order or importance. The terms "include", "comprise", and similar terms are intended to be inclusive, and not to exclude other elements or items. The terms "connected", "coupled", and similar terms are not limited to direct connections or physical or mechanical connections, but can include indirect connections or electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", and similar terms are used merely to indicate relative positions, and can change accordingly when the absolute positions of the described objects change.

[0049] Embodiment one

[0050] As Figure 1 shown, the present application provides a method for night distributed scheduling control of an aircraft energy system, comprising the following steps:

[0051] S1, based on the power supply demand of the near space aircraft night cruising to the energy system, a single bus DC microgrid system model of the near space aircraft energy system is established and a near space aircraft energy system operation loss minimization problem is constructed. First, the near space aircraft energy system is simplified as a single bus DC microgrid system with N power generation nodes supplying power to M loads, considering the resistive impedance of the transmission line and ignoring the inductance, each power generation node contains an energy storage unit and a DC / DC converter, and the system structure is as shown in Figure 2 According to Kirchhoff's law, a single bus DC microgrid system model of the near space aircraft energy system is established as follows:

[0052] V b =V i -R i I i ,i=1,2,…,N

[0053]

[0054] wherein V b is the bus voltage, V i and I i are the output voltage and output current of the DC / DC converter of the distributed power generation unit, respectively; is the load current of the load, R i is the line resistance of the power line where the power generation unit is located.

[0055] The operation loss of the DC microgrid mainly includes two aspects, one is the operation loss of the DC / DC converter, and the other is the line loss of the power transmission line. The loss of the DC / DC converter The quantification can be expressed as a quadratic function of the output current, i.e. where a i ,b i ,c i are the loss coefficients of the converter. The power transmission line loss of the DC / DC converter connected to the bus is R i is the line resistance. Obviously, the overall operation loss of the microgrid can be expressed as follows:

[0056]

[0057] Considering the actual electrical constraints and voltage and power constraint requirements, the loss optimization problem of the DC microgrid can be expressed as:

[0058] P1:min P Loss

[0059] s.t.

[0060]

[0061] where, is the load current of the load, V i , is the upper and lower bound of the output voltage V i of the DC / DC converter, P i , is the upper and lower bound of the output power P i of the generation node i under the generation capacity constraint.

[0062] It is noted that, where is the power injected into the bus by the generation node. The voltage and power inequality constraints are converted into current inequality constraints

[0063]

[0064] Therefore, the optimization problem can be simplified as a classic economic dispatch problem:

[0065]

[0066] s.t.

[0067]

[0068] where, is the total load current. According to the KKT optimality condition, the optimal solution of the optimization problem P2 is

[0069]

[0070] where Ω s is the set of nodes whose output current saturate, λ * is called the global optimal incremental cost, and the incremental cost of each generation node converges to λ * , the operation loss of the entire microgrid is minimum.

[0071] S2, each generation node independently adopts a multiple update recursive least square algorithm to identify the parameters of the DC / DC converter loss model.

[0072] Before performing the scheduling and control of the direct current microgrid, the loss coefficient of the DC / DC converter needs to be obtained. Therefore, a multiple update least square method is proposed in the embodiment to identify the loss parameters.

[0073] The process of loss model parameter identification is represented by the following formula:

[0074]

[0075] wherein, is the identified value of the loss power, and the regression vector is the parameter to be identified μ=[a i ,b i ,c i ] T ; the parameter identification of μ is realized by solving an optimization problem with a minimized objective function.

[0076]

[0077] wherein, P(k) is the actual loss power at time k, and λ is a forgetting factor.

[0078] In order to balance the accuracy, fast convergence and robustness of parameter identification, a multiple update recursive least square algorithm is used to identify the value of μ in the embodiment, and the process of the algorithm is as follows:

[0079] (1) initialization: initialize the parameter vector estimate Covariance matrix C(0)=g·I, wherein g is a positive scalar large enough, I is an n×n unit matrix, set t=1, the number of inner loops M tol , and the forgetting factor λ∈(0,1).

[0080] (2) calculate the Kalman gain:

[0081]

[0082] (3) Perform inner loop multiple iteration update:

[0083] a: Set m = 1 and

[0084] b: Update the identified value at the mth cycle of (t, m):

[0085]

[0086] c: Update the mth identified parameter of μ:

[0087]

[0088] d: If the maximum number of inner loop iterations is reached, set and exit the inner loop; otherwise, set m = m + 1, and repeat steps b-c.

[0089] e: End.

[0090] (4) Update the covariance matrix C(t):

[0091]

[0092] (5) Set t = t + 1, and repeat steps (2)-(4).

[0093] It should be noted that, compared with the classic RLS, the introduction of the inner loop of multiple updates makes the convergence speed of the identification algorithm faster. The greater the number of inner loop iterations M, the faster the convergence speed of the algorithm. However, too large M may lead to divergence of identification.

[0094] S3, based on a finite time distributed scheduling algorithm, obtains optimal working points of the system under different working conditions, and realizes the minimization of the loss of the entire near space energy microgrid under the premise of meeting the power generation and electrical constraints and the balance of power supply and demand.

[0095] The input of the finite time distributed scheduling algorithm is the load electrical data sampling of the near space vehicle energy system, and the output is the output coefficient of each node of the near space vehicle energy system. In order to ensure the stability of the execution in the near space, the designed control algorithm adopts distributed design and distributed implementation.

[0096] The distributed scheduling algorithm block diagram is shown in Figure 3 , and the specific design is:

[0097]

[0098] Wherein, k c ,k d ,γ>0,sig(x)=|x|β sign(x), ω = 1 for the generation nodes that can obtain the load information, and ω = 0 for the other nodes. i i (t) term observes the global power generation-consumption mismatch amount, and ω = 0 for the other nodes. i

[0099] It is worth noting that, compared with the existing limited time increment cost consistency algorithm, the proposed algorithm is fully distributed, without global increment cost consistency judgment and consistency value in the execution process, and the number of iterations and operations is less, and the algorithm converges faster.

[0100] S4, the output voltage of each generation node is controlled by a local distributed controller, and the distributed controller controls the exchange of output voltage and output current data of the near-space vehicle energy system through the communication network with the neighbors, and adjusts the output voltage of the near-space vehicle energy system generation node according to the distributed control algorithm. According to the optimal working point of the system, the distributed control algorithm is designed based on the multi-agent PI consistency algorithm, and the current distribution and bus voltage regulation are realized.

[0101] Assuming that the output voltage regulation speed of the DC / DC converter is fast enough to track the reference voltage in real time That is The reference voltage is designed as

[0102]

[0103] wherein, denotes the nominal value of the bus voltage V b , u i is the quadratic control input, denotes the droop coefficient of the generation node i, and generally

[0104] In order to realize bus voltage regulation and current distribution at the same time, the following distributed secondary PI controller is adopted:

[0105]

[0106] wherein the error term e i is composed of two parts, the voltage regulation error e V and the current distribution error k vi , k ui >0 are the weight coefficients of the voltage regulation error and the current distribution error, respectively; is the set of generation nodes in direct communication with the generation node i, also known as the neighborhood set on the communication network. ​​​

[0107] In combination of S1-S4, the framework of the proposed distributed dispatching control method is shown in Figure 4 .

[0108] To demonstrate the feasibility of the proposed algorithm, simulation and experiment are conducted. In this embodiment, the parameters of the energy microgrid of the near space vehicle during night cruising are shown in Table 1, and the parameters of the proposed algorithm are shown in Table 2.

[0109] Table 1 Simulation electrical parameters of the energy system of the near space vehicle

[0110]

[0111] Table 2 Simulation algorithm parameters

[0112]

[0113]

[0114] For the four heterogeneous Boost units, Boost simulation identification is respectively conducted, and the load working conditions are shown in Figure 5 . The identification results are shown in Figure 6 , in which from top to bottom are the identification results of Boost nodes 1 to 4, the left column is the coefficient curve of identification, and the right column is the actual and estimated loss power curve. It can be seen that the loss power identified by the proposed Boost identification algorithm is almost completely consistent with the actual loss, and the online fitting effect of the algorithm is good. In addition, the loss coefficient also converges within 5s, indicating that the loss model itself can accurately describe the loss mechanism. The steady-state loss coefficient identification results of the DC converter identification simulation are shown in Table 3, and the subsequent dispatching and control are conducted by using the identification results.

[0115] Table 3 Steady-state loss coefficient of DC converter identification simulation

[0116] Parameters Node 1 Node 2 Node 3 Node 4 a i ]]> -0.4793 -0.3946 -0.4673 -0.3038 b i ]]> 5.249 5.876 7.101 8.919 c i ]]> 2.386 2.551 3.195 3.885

[0117] To demonstrate the feasibility and superiority of the proposed dispatching control algorithm, the proposed distributed dispatching control algorithm and the traditional algorithm are simulated and compared, the load working condition setting is shown in Figure 7 , the load current and working condition design of each time interval are shown in Table 4. In addition, the output constraints are shown in Table 4:

[0118] Table 4 Load current value

[0119] Time interval Load current (A) Operating condition design 0-2s 8.82 Dispatch control algorithm not running 2-4s 8.82 Dispatch control algorithm running 4-6s 10.33 Dispatch control algorithm running 6-8s 10.33 Relaxing generation constraints 8-10s 2.87 Relaxing generation constraints

[0120] Table 5 Power generation constraints

[0121]

[0122] For the above working conditions, the simulation comparison of the scheduling control algorithm is carried out. Figure 8 (a) and Figure 8 (b) respectively shows the output current curves of the DC converter under the distributed scheduling control algorithm and the traditional scheduling control algorithm proposed in this embodiment. In the steady state, the steady-state output currents of the proposed algorithm and the comparative algorithm are shown in Table 6:

[0123] Table 6 Simulation steady-state output currents of the proposed algorithm and the comparative algorithm

[0124]

[0125]

[0126] According to the above table, it can be seen that the output current of the proposed algorithm is within the requirements of the power generation constraint at each time, while the comparative algorithm has the phenomenon of exceeding the power generation constraint at node 4 at 4-6s and 8-10s.

[0127] Figure 9 The bus voltage curves of the two scheduling control algorithms are given. Obviously, both algorithms can keep the bus voltage around the reference value of 48V. The loss power curves of the aircraft energy system under the distributed scheduling control algorithm and the traditional scheduling control algorithm proposed in this embodiment are shown in Figure 10 At 0-2s, the scheduling control algorithm has not been run, and each node flows output. In 2-4s, both the proposed algorithm and the comparative algorithm can complete loss minimization within the power generation constraint. In 4-6s, both the proposed algorithm and the comparative algorithm can complete loss minimization. The output current of the proposed scheduling control algorithm is within the requirements of the power generation constraint, but the output current of the comparative algorithm has the phenomenon of exceeding the power generation constraint at this time. At this time, the load condition is kept unchanged, and the power generation constraint is relaxed, as shown in Table 4. Thereafter, in 6-8s, both the proposed algorithm and the comparative algorithm can complete loss minimization within the power generation constraint. However, when the load is drastically reduced, the power generation constraint becomes more stringent. In 8-10s, the proposed algorithm can complete loss minimization within the power generation constraint, while the traditional algorithm has higher loss and the phenomenon of exceeding the power generation constraint.

[0128] Compared with the traditional algorithm, the distributed scheduling control algorithm proposed in this embodiment can make the entire aircraft energy system continuously operate in the lowest loss state, and no matter what the situation is, the distributed algorithm proposed in this embodiment can meet a series of electrical constraint requirements.

[0129] Therefore, the aircraft energy system night distributed scheduling control method can realize the distributed optimization control of the near space aircraft energy system, and can realize the bus voltage regulation and current distribution of the aircraft energy system while considering the operation loss of the aircraft energy system.

[0130] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application, but not to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can still be modified or equivalently replaced, and these modifications or equivalent replacements should not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for night distributed dispatch control of an aircraft energy system, characterized in that: The method comprises the following steps: S1, based on the power supply demand of the near space vehicle energy system during night cruising, a single bus DC micro-grid system model of the near space vehicle energy system is established and a loss minimization problem of the near space vehicle energy system is constructed; The specific steps of establishing the single bus DC micro-grid system model of the near space vehicle energy system in S1 are as follows: first, the near space vehicle energy system is simplified as a single bus DC micro-grid system with N power generation nodes supplying power to M loads, the resistive impedance of the transmission line is considered, and the inductance is ignored, each power generation node comprises an energy storage unit and a DC / DC converter; then, according to Kirchhoff's law, a single bus DC micro-grid system model of the near space vehicle energy system is established ； ； wherein, is the bus voltage, and are the output voltage and output current of the DC / DC converter of the distributed generation unit, respectively; is the load current of the remote load, is the line resistance of the power line where the generation unit is located. The operation loss of the direct current micro-grid includes the operation loss of the DC / DC converter and the line loss of the power transmission line, the loss of the DC / DC converter is quantitatively expressed as a quadratic function of the output current, i.e. , is the loss coefficient of the converter; the power transmission line loss of the DC / DC converter connected to the bus is , is the line resistance; the overall operation loss of the micro-grid is expressed as follows: ； Considering the actual electrical constraints and voltage and power constraints, the loss optimization problem of the DC micro-grid system is expressed as: ； wherein is a load current of the remote load, , is an upper and lower bound of an output voltage of the DC / DC converter , , is an upper and lower bound of an output power of the power generation node under the power generation capability constraint, . According to the KKT optimality conditions, the optimal solution of the optimization problem is: ； wherein , is a set of nodes whose output current saturates, referred to as the global optimal incremental cost, the incremental cost of each generation node converges to the operation loss of the entire microgrid is minimized; S2, each power generation node independently adopts a multiple update recursive least square algorithm to identify the parameters of the DC / DC converter loss model; S3, based on a finite time distributed scheduling algorithm, the optimal working point of the system under different working conditions is obtained; S4, according to the optimal working point of the system, a distributed control algorithm is designed based on a multi-agent PI consistency algorithm to realize current distribution and bus voltage regulation.

2. The method of claim 1, wherein: In S2, the process of loss model parameter identification is expressed by the following formula: ； wherein is a recognized value of the loss power, the regression vector , the parameter to be recognized ; The parameter recognition of the parameter recognition is realized by solving an optimization problem with a minimized objective function; ； wherein, is the actual power dissipated, λ is called the forgetting factor; a multiple-update recursive least squares algorithm is employed to identify the value of.

3. The method of claim 1, wherein: In S3, the finite time distributed scheduling algorithm is designed as follows: ； ； ； ； wherein, , , for the generation nodes capable of acquiring load information, = 1, in a distributed manner by means of the term observes the global power generation-consumption mismatch, for the other nodes = 0; the input of the finite-time distributed scheduling algorithm is the load electrical data sampling of the near-space vehicle energy system, and the output is the output coefficient of each node of the near-space vehicle energy system; the designed control algorithm is distributed in the near-space, and distributed design and distributed implementation are adopted to ensure the stability of execution.

4. The method of claim 1, wherein: In S4, the output voltage of each power generation node is controlled by a local distributed controller, and the following distributed secondary PI controller is adopted: ； ； ； ； ； wherein, represents a reference voltage, represents a bus voltage of a nominal value, represents a generation node of a droop coefficient, , the constructed error term is composed of two parts, voltage regulation error and current distribution error , are weight coefficients of voltage regulation error and current distribution error, respectively; is a set of generation nodes directly communicating with the generation node ; the distributed controller controls the output voltage, output current data of the near space vehicle energy system exchanged with the neighbors through the communication network, and adjusts the output voltage of the generation node of the near space vehicle energy system according to the distributed control algorithm; the input of the distributed control algorithm is the data sampling of the input voltage, current and output voltage, current of the near space vehicle energy system, and the output is the output voltage of the near space vehicle energy system.

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