阅读说明：本技术 一种集群化能源系统分布式协同控制方法 (Distributed cooperative control method for clustered energy system ) 是由 孙子路 吕冬翔 仇海波 钟豪 李钊 李钏 于 2021-08-19 设计创作，主要内容包括：一种集群化能源系统分布式协同控制方法,所述方法包括步骤：获取集群化能源系统；获取所述集群化能源系统的建模模型；获取所述集群化能源系统的能量需求情况；获取所述集群化能源系统的能量供应情况；根据所述能量需求情况和所述能量供应情况对所述集群化能源系统的模型进行分布式协同控制。本申请提供的一种集群化能源系统分布式协同控制方法,可以实现集群化能源系统的功率平衡和储能均衡目标,以便控制策略有效应用及系统状态的实时观察,可应用于太阳能飞行器集群化能源系统的能量管理系统设计制造。(A distributed cooperative control method for a clustered energy system, the method comprising the steps of: acquiring a clustered energy system; obtaining a modeling model of the clustered energy system; acquiring the energy demand condition of the clustered energy system; acquiring the energy supply condition of the clustered energy system; and carrying out distributed cooperative control on the model of the clustered energy system according to the energy demand condition and the energy supply condition. The distributed cooperative control method for the clustered energy system can achieve the power balance and energy storage balance targets of the clustered energy system, so that the control strategy can be effectively applied and the system state can be observed in real time, and the distributed cooperative control method can be applied to design and manufacture of an energy management system of the clustered energy system of a solar aircraft.)

1. A distributed cooperative control method for a clustered energy system is characterized by comprising the following steps:

acquiring a clustered energy system;

obtaining a modeling model of the clustered energy system;

acquiring the energy demand condition of the clustered energy system;

acquiring the energy supply condition of the clustered energy system;

and carrying out distributed cooperative control on the model of the clustered energy system according to the energy demand condition and the energy supply condition.

2. The distributed cooperative control method for the clustered energy system as recited in claim 1, wherein the clustered energy system comprises: and the intelligent power supply modules are sequentially connected in parallel.

3. The distributed cooperative control method for the clustered energy system as recited in claim 2, wherein the smart power module comprises: the power supply comprises a power input module, a power output module, a communication module and a battery unit, wherein the power input module, the power output module and the communication module are all connected with the battery unit in parallel.

4. The distributed cooperative control method for the clustered energy system as claimed in claim 1, wherein the expression of the modeling model is:

wherein x is_iRepresenting the state of charge, V, of the battery in the ith model_iRepresenting internal bus voltage, Q, in a modeling model_iRepresents the battery capacity, P_iRepresents the difference, τ, between the power produced by the photovoltaic cell and the PCU output power out of the modeling model_iRepresenting the battery drain power.

5. The distributed cooperative control method for the clustered energy system as claimed in claim 1, wherein the distributed cooperative control of the model of the clustered energy system according to the energy demand situation and the energy supply situation comprises the steps of:

constructing a distributed cooperative controller;

acquiring a first state parameter of a target intelligent power generation unit in the modeling model;

acquiring a second state parameter of the target intelligent power generation unit close to the intelligent power generation unit;

acquiring prior knowledge of the battery state in the modeling model;

acquiring a photovoltaic detection value in the modeling model;

inputting the first state parameter, the second state parameter, the prior knowledge of the battery state, and the detected photovoltaic value into the distributed cooperative controller;

acquiring output power output by the distributed cooperative controller;

and installing the output power control modeling model.

6. The distributed cooperative control method for the clustered energy system as claimed in claim 5, wherein the expression of the distributed cooperative controller is:

wherein the controller state x_ciBy a coincidence error sa_ij(x_i-x_j) Output power errorCo-regulation, when the i-th ISM module stores more energy than the other modules, x_ciIncrease, and then u_iThe output power is increased to consume the stored energy quickly. While the output to load power u of the other modules with relatively small SOC_jAnd reducing the SOC so as to reach the consistency of the energy storage of each module.

7. The distributed cooperative control method for the clustered energy system as claimed in claim 5, wherein the expression of the distributed cooperative controller is:

wherein the controller state x_ciBy a coincidence error sa_ij(x_i-x_j) Output power errorCo-regulation, when the i-th ISM module stores more energy than the other modules, x_ciIncrease, and then u_iThe output power is increased to consume the stored energy quickly. While the output to load power u of the other modules with relatively small SOC_jAnd reducing the SOC so as to reach the consistency of the energy storage of each module.

8. The distributed cooperative control method for the clustered energy system as claimed in claim 5, wherein the distributed cooperative control of the model of the clustered energy system according to the energy demand situation and the energy supply situation further comprises the steps of:

and carrying out saturation treatment on the output current of the target intelligent power generation unit in the modeling model.

9. The distributed cooperative control method for the clustered energy system as claimed in claim 8, wherein the saturation processing of the output current of the target intelligent power generation unit in the modeling model comprises:

adjusting the output current of the target intelligent power generation unit to be positive;

calculating corresponding upper and lower limits of output current according to the SOC state and charge-discharge constraints;

carrying out full-range saturation treatment on the output current of the target intelligent power generation unit;

calculating the constraint converted from the output index constraint to the output current dimension;

taking the intersection of the upper limit of the output index constraint and the upper limit of the constraint used for the full-range saturation treatment as a new constraint;

and (5) carrying out saturation processing on the fixed droop coefficients one by one in the module.

Technical Field

The invention belongs to the technical field of solar aircrafts, and particularly relates to a distributed cooperative control method for a clustered energy system.

Background

The solar aircraft is an unmanned aircraft which uses solar energy as an energy source and can continuously fly at high altitude for a long time. With the development requirement of national defense industry in China, China starts to vigorously develop solar aircrafts, and due to the advantages of high flying height, long dead time, low cost and the like, the solar aircrafts have irreplaceable advantages of conventional aircrafts, have wide application prospects in the fields of military reconnaissance, patrol early warning, high-altitude communication relay, environment monitoring, forest area management, scientific research and the like, and are an important development direction of the aircrafts in near space for ultra-long voyage.

The energy system is the heart of the solar aircraft and is the basis for guaranteeing the efficient operation of the solar aircraft and fully playing the functions of the solar aircraft. Therefore, the performance of the energy system is a key factor in maintaining the reliable effectiveness of the solar aircraft for a long period of time. The solar aircraft utilizes solar energy to provide energy, secondary batteries such as lithium ion batteries and the like store electric energy, and the propellers of the multiple groups of motors are propelled, so that the long-endurance high-altitude flight of the aircraft can be realized.

The aircraft energy system needs to meet the requirements of power balance and energy storage balance. The power balance means that the output power of the energy system is equal to the required power of the system, and the energy storage balance means that the energy storage units in the system can be filled and emptied simultaneously. The practical research of the aircraft energy system needs to pay attention to the factors of complex and variable environmental factors, various load characteristics and the like. Therefore, the design of the power supply system needs to take two goals of power balance and energy storage balance into consideration, the charging and discharging strategies of the energy storage unit can be adjusted at any time according to environmental changes in real time, the requirements of different power utilization units are met, and the discharging depth is improved to the maximum extent.

The typical structure of the existing solar aircraft energy system is used for moving the centralized energy system structure of the ground power grid, although the technology is mature, the flexibility is lacked, the system is influenced by the characteristics of the aircraft, the photovoltaic efficiency is greatly influenced, the condition of a battery is difficult to perceive and adjust, and the volume and the weight of the energy system are difficult to adapt to the installation and application conditions of the aircraft. In order to overcome the weakness of a centralized energy system, photovoltaic elements and energy storage units can be controlled by a plurality of micro-converters in groups, so that a clustered energy system is evolved. The clustered energy system divides the photovoltaic array and the battery array into a plurality of sub-modules respectively, and the photovoltaic battery sub-modules and the energy storage unit sub-modules form an energy subsystem which is connected with the bus through a power output control module. Because the bus voltage of the subsystem is lower, the topology of the energy system can be flexibly designed, a multi-bus structure with various voltage grades coexisting is formed, and the access of various loads is facilitated.

The modularization and the clustering of the energy system structure bring great improvement on the aspects of energy efficiency, specific power design and the like, but the problem of cooperative control is introduced along with the modularization and the clustering, and the main problem at present is how to coordinate a large number of power supply sub-modules by an effective control means.

Disclosure of Invention

In order to solve the above problems, the present invention provides a distributed cooperative control method for a clustered energy system, the method includes the steps of:

acquiring a clustered energy system;

obtaining a modeling model of the clustered energy system;

acquiring the energy demand condition of the clustered energy system;

acquiring the energy supply condition of the clustered energy system;

and carrying out distributed cooperative control on the model of the clustered energy system according to the energy demand condition and the energy supply condition.

Preferably, the clustered energy system comprises: and the intelligent power supply modules are sequentially connected in parallel.

Preferably, the smart power module includes: the power supply comprises a power input module, a power output module, a communication module and a battery unit, wherein the power input module, the power output module and the communication module are all connected with the battery unit in parallel.

Preferably, the expression of the modeling model is:

wherein x is_iRepresenting the state of charge, V, of the battery in the ith model_iRepresenting internal bus voltage, Q, in a modeling model_iRepresents the battery capacity, P_iRepresents the difference, τ, between the power produced by the photovoltaic cell and the PCU output power out of the modeling model_iRepresenting the battery drain power.

Preferably, the distributed cooperative control of the model of the clustered energy system according to the energy demand situation and the energy supply situation includes the steps of:

constructing a distributed cooperative controller;

acquiring a first state parameter of a target intelligent power generation unit in the modeling model;

acquiring a second state parameter of the target intelligent power generation unit close to the intelligent power generation unit;

acquiring prior knowledge of the battery state in the modeling model;

acquiring a photovoltaic detection value in the modeling model;

inputting the first state parameter, the second state parameter, the prior knowledge of the battery state, and the detected photovoltaic value into the distributed cooperative controller;

acquiring output power output by the distributed cooperative controller;

and installing the output power control modeling model.

Preferably, the expression of the distributed cooperative controller is as follows:

wherein the controller state x_ciBy a coincidence error sa_ij(x_i-x_j) Output power errorCo-regulation, when the i-th ISM module stores more energy than the other modules, x_ciIncrease, and then u_iThe output power is increased to consume the stored energy quickly. While the output to load power u of the other modules with relatively small SOC_jAnd reducing the SOC so as to reach the consistency of the energy storage of each module.

Preferably, the expression of the distributed cooperative controller is as follows:

wherein the controller state x_ciBy a coincidence error sa_ij(x_i-x_j) Output power errorCo-regulation, when the i-th ISM module stores more energy than the other modules, x_ciIncrease, and then u_iThe output power is increased to consume the stored energy quickly. While the output to load power u of the other modules with relatively small SOC_jReduced to make the SOC of each module energy storage reach the same。

Preferably, the distributed cooperative control of the model of the clustered energy system according to the energy demand situation and the energy supply situation further includes the steps of:

and carrying out saturation treatment on the output current of the target intelligent power generation unit in the modeling model.

Preferably, the saturation processing of the output current of the target intelligent power generation unit in the modeling model includes the steps of:

adjusting the output current of the target intelligent power generation unit to be positive;

calculating corresponding upper and lower limits of output current according to the SOC state and charge-discharge constraints;

carrying out full-range saturation treatment on the output current of the target intelligent power generation unit;

calculating the constraint converted from the output index constraint to the output current dimension;

taking the intersection of the upper limit of the output index constraint and the upper limit of the constraint used for the full-range saturation treatment as a new constraint;

and (5) carrying out saturation processing on the fixed droop coefficients one by one in the module.

The distributed cooperative control method for the clustered energy system can achieve the power balance and energy storage balance targets of the clustered energy system, so that the control strategy can be effectively applied and the system state can be observed in real time, and the distributed cooperative control method can be applied to design and manufacture of an energy management system of the clustered energy system of a solar aircraft.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a schematic flow chart of a distributed cooperative control method for a clustered energy system according to the present invention;

fig. 2 is an overall structural diagram of a clustered energy system in the distributed cooperative control method of the clustered energy system according to the present invention;

FIG. 3 is a diagram of a distributed controller architecture;

FIG. 4 is a simplified block diagram of a distributed controller;

FIG. 5 is a schematic illustration of peak reduction during saturation treatment;

FIG. 6 is a schematic diagram of the saturation process;

FIG. 7 is a schematic flow diagram of a saturation process;

fig. 8 is a schematic structural diagram of an experimental platform of a clustered energy system in a distributed cooperative control method of the clustered energy system according to the present invention;

fig. 9 is a variation trend of the SOC in the distributed cooperative control method for the clustered energy system provided by the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.

As shown in fig. 1, in an embodiment of the present application, the present invention provides a distributed cooperative control method for a clustered energy system, where the method includes the steps of:

s1: acquiring a clustered energy system;

s2: obtaining a modeling model of the clustered energy system;

s3: acquiring the energy demand condition of the clustered energy system;

s4: acquiring the energy supply condition of the clustered energy system;

s5: and carrying out distributed cooperative control on the model of the clustered energy system according to the energy demand condition and the energy supply condition.

In an embodiment of the present application, the clustered energy system includes: and the intelligent power supply modules are sequentially connected in parallel.

In an embodiment of the present application, the smart power module includes: the power supply comprises a power input module, a power output module, a communication module and a battery unit, wherein the power input module, the power output module and the communication module are all connected with the battery unit in parallel.

In the embodiment of the present application, the expression of the modeling model is:

wherein x is_iRepresenting the state of charge, V, of the battery in the ith model_iRepresenting internal bus voltage, Q, in a modeling model_iRepresents the battery capacity, P_iRepresents the difference, τ, between the power produced by the photovoltaic cell and the PCU output power out of the modeling model_iRepresenting the battery drain power.

In an embodiment of the present application, the performing distributed cooperative control on the model of the clustered energy system according to the energy demand condition and the energy supply condition includes:

constructing a distributed cooperative controller;

acquiring a first state parameter of a target intelligent power generation unit in the modeling model;

acquiring a second state parameter of the target intelligent power generation unit close to the intelligent power generation unit;

acquiring prior knowledge of the battery state in the modeling model;

acquiring a photovoltaic detection value in the modeling model;

inputting the first state parameter, the second state parameter, the prior knowledge of the battery state, and the detected photovoltaic value into the distributed cooperative controller;

acquiring output power output by the distributed cooperative controller;

and installing the output power control modeling model.

In this embodiment of the present application, the expression of the distributed cooperative controller is:

wherein the controller state x_ciBy a coincidence error sa_ij(x_i-x_j) Output power errorCo-regulation, when the i-th ISM module stores more energy than the other modules, x_ciIncrease, and then u_iThe output power is increased to consume the stored energy quickly. While the output to load power u of the other modules with relatively small SOC_jAnd reducing the SOC so as to reach the consistency of the energy storage of each module.

Preferably, the expression of the distributed cooperative controller is as follows:

wherein the controller state x_ciBy a coincidence error sa_ij(x_i-x_j) Output power errorCo-regulation, when the i-th ISM module stores more energy than the other modules, x_ciIncrease, and then u_iThe output power is increased to consume the stored energy quickly. While other SOCs are relatively smallOutput to load power u of small modules_jAnd reducing the SOC so as to reach the consistency of the energy storage of each module.

In an embodiment of the present application, the performing distributed cooperative control on the model of the clustered energy system according to the energy demand condition and the energy supply condition further includes:

and carrying out saturation treatment on the output current of the target intelligent power generation unit in the modeling model.

In an embodiment of the present application, the saturation processing on the output current of the target intelligent power generation unit in the modeling model includes:

adjusting the output current of the target intelligent power generation unit to be positive;

calculating corresponding upper and lower limits of output current according to the SOC state and charge-discharge constraints;

carrying out full-range saturation treatment on the output current of the target intelligent power generation unit;

calculating the constraint converted from the output index constraint to the output current dimension;

taking the intersection of the upper limit of the output index constraint and the upper limit of the constraint used for the full-range saturation treatment as a new constraint;

and (5) carrying out saturation processing on the fixed droop coefficients one by one in the module.

In the hardware structure of the power supply system, each ISM module is regarded as a node unit, and all the node units cooperate with each other, so that the matching of power generation and power utilization is met in a layer, and the balance among all the energy storage units is realized.

For each ISM module system model

The distributed cooperative controller is designed as follows:

the controller is a dynamic linear distributed controller, the controller state x_ciBy a coincidence error sa_ij(x_i-x_j) Output power errorCo-regulation, when the i-th ISM module stores more energy than the other modules, x_ciIncrease, and then u_iThe output power is increased to consume the stored energy quickly. While the output to load power u of the other modules with relatively small SOC_jAnd reducing the SOC so as to reach the consistency of the energy storage of each module.

When each ISM output power is insufficient for the required power, ρ > 1 and x_ciIncrease, u_iIncreasing, gradually regulating output power to P^*. Note that although the output power u is_iIs not always exactly equal to P^*On the one hand, however, the output power u calculated in the application process is converted into a proportional relationship and transmitted to the primary controller for regulation, and the primary controller ensures that the actual output power is equal to the load, and the actual consumed power is determined by the load characteristic, that is,therefore the proportionality coefficient p in the system model_iWill follow the output power calculated by the controllerAnd the required power P^*Continuously changing; on the other hand, when the system converges, the coherency error Σ_ja_ij(x_i-x_j) 0, i.e. x_i＝x_j，At the same time, ρ converges to 1, the output power error

In order to make the system have faster convergence speed and better anti-interference performance, the controller (1) is improved into a non-smooth dynamic controller

Wherein sign (·) is a sign function satisfying

The cooperative control strategy proposed in this subsection is a distributed control strategy, that is, each intelligent power module independently operates the control strategy, the control strategy includes a control amount calculation method for each intelligent power module, and each intelligent power module only needs to feed back according to its own state and transmits the acquired information states of other control nodes to the corresponding input end of the controller through a communication network. Specifically, for the ith intelligent power module, it is necessary

(1) Acquiring capacity data of the energy storage unit through priori knowledge;

(2) setting controller parameters according to theoretical analysis results;

(3) by obtaining the state x of other nodes on the network_j，x_cjAnd the ratio ρ of the actual output power to the required power;

(4) acquiring current voltage value v of energy storage unit by detecting original element_i；

(5) Calculating x in a microcontroller_ciAnd u_i(ii) a Due to the presence of the pair x in the controller_ciThe integration of (c) can be solved by discretizing it, using a differential equation, i.e. at each sampling instant,storing the last moment value x in the memory unit_ci(k-1) and calculating an increment dx according to the current state_ci(k-1), the value at the current time is x_ci(k)＝x_ci(k-1)+Tdx_ci(k-1), wherein T is a sampling interval;

(6) in order to connect with the existing hardware platform, the processing coefficient of the hardware platform needs to be configured according to the proportion of the output power.

Because the controller is a distributed control strategy, the controller has certain fault tolerance capability, namely, when some nodes exit operation due to faults, the built-in microcontroller automatically cuts off the nodes. The removed node is not broadcasting its own data, so the original neighbor of the node will not receive the data it sent. For each intelligent power supply module, the calculation of the control quantity only depends on the received information state data of the neighbors, the balance of the system is not influenced when a small number of nodes are disconnected, and the intelligent power supply module only needs to be provided with a small number of disconnected nodes and the residual network still comprises the directed spanning tree.

To make the description of the imbalance of the energy system more understandable, a system imbalance metric Δ (t) max is defined_i(x_i(t))min_i(x_i(t)), defining a system imbalance indicator Δ_∞＝lim_t→∞Δ (t). When in useWhen the system is consistent, the SOC of the energy storage units are close to each other, and finally, the system is consistentThe time system state enters an invariant set, i.e. the final system imbalance indicatorWherein the content of the first and second substances,ε₂is any positive number.

From the above analysis, it can be seen that although the system does not necessarily converge completely to a consistent manifoldBut notably P_wReflecting the change condition of the photovoltaic power, when the photovoltaic power generation power is stable and unchanged, the first term is 0, and the system can completely reach the agreement. On the other hand, the size of the final invariant set can be reduced, i.e. k can be increased, by adjusting the controller parameters appropriately₁The system imbalance can be minimized.

A numerical description of the system imbalance indicator may be obtained by a system stability analysis, i.e.,note that when the energy storage unit parameters are the same and the photovoltaic power remains the same, Δ is 0, i.e., the system can achieve full agreement with all cells having the same SOC. In fact, however, the output power of the photovoltaic cell varies constantly over time, so that in practice the SOC is not exactly the same but is continuously shifted around the same state as each other. The theoretical analysis above has given the maximum range of dialing, and therefore this data can be used as a reference for battery capacity allocation.

The energy storage unit SOC gradually converges to a consistent state, the envelope of the SOC state curve of the system energy storage unit can be obtained through the estimation of the convergence speed, and the electric energy can not be completely emptied for ensuring the lowest SOC battery from the aspect of safety, so that the actual energy use amount needs to be based on the envelope, and therefore, the safe operation boundary of the system can be obtained.

The saturation treatment process is indispensable in the design process of the controller of the system and plays a very important role. In the experimental process, if the saturation problem of the system is not considered, the saturation phenomenon of the system frequently occurs due to the constraint brought by hardware and software, the final control effect of the controller is influenced if the saturation phenomenon of the system is not considered, and the irreversible error of the system is caused if the saturation phenomenon of the system is not considered, so that the final paralysis of the whole system is caused. In the process of executing the controller algorithm, the secondary adjustment of the control quantity is directly carried out according to the specific running state of the system and the constraints received by each unit, and finally, on the basis that each unit of the system cannot generate the saturation problem, each ISM channel and the interlayer units of the whole energy system can stably run towards the direction of the balance and consistency of the energy storage elements.

In the design of the saturation process, four constraints on the system are mainly considered: the constraint of battery charging and discharging current, the constraint of battery state of charge variation range, the constraint of output power considering the safety of a power output module and the constraint of droop coefficient. The constraint of the battery charging and discharging current and the constraint of the battery state of charge variation range can be solved by directly constraining the output current, and for the constraint of the droop coefficient, the control input quantity needs to be converted into the dimension of the output index for consideration. In the present invention, the saturation treatment process is as follows:

(1) and adjusting the control input quantity to output current which is a positive value correspondingly, and the lowest output current is 0A, scaling, and adjusting the adjusted output current to the output current required by the voltage of the energy storage unit battery and the load power correspondingly at the current moment.

(2) And calculating the upper bound (u2) and the lower bound (u1) of each channel under the current condition by considering the upper bound and the lower bound of the charging and discharging current of the battery, the charge state of the energy storage unit and the output current of the photovoltaic energy source. The upper limit of the channel output current with the lower state of charge is limited below the photovoltaic input current, and the lower limit of the channel output current with the higher state of charge is limited above the photovoltaic input current.

(3) The first saturation process is carried out in the full range, namely peak clipping and valley filling are carried out to output the adjusted current (converted to the battery end). In this process, a decision adjustment is made for each output channel in the system. First, a "peak clipping" operation is performed.

Fig. 4 shows an example of handling an output current exceeding an upper bound.

The diagram is shown to handle situations where the output current exceeds an upper bound. When the original output currents of the ISM1 and ISM4 in the system exceed the upper bound of the real-time output current calculated by the channel, the output current of the corresponding channel corresponding to the part exceeding the upper bound is cut off, and the output current of the channel is equal to the upper bound. Meanwhile, the reduced power is calculated according to the battery voltage, the power converted by the channel capable of increasing the output current in the upper bound range is processed, and each channel (such as ISM2 and ISM3 in the figure) capable of adjusting the output current in the upper bound range is processed, so that the output current is increased according to the ratio of the reduced power to the adjustable power, and the output current of each channel is ensured in the upper bound range.

(4) The 'filling in the valley' process is carried out.

Fig. 5 shows an example of a valley filling process.

First, the check marks the channels in each channel that exceed the lower bound of the output current, ISM2 and ISM3, and calculates the required power reduction and the adjustable output power range. If the adjustable output power range can meet the reduced power requirement, the output current of each channel below the lower limit is adjusted to increase to the lower limit, and the output currents of other adjustable channels (ISM1, ISM4) are proportionally reduced. If the adjustable output power range does not meet the requirement of reduced power, which means that the photovoltaic input power is far greater than the load power at the moment, the problem is solved by adjusting the operation mode of the photovoltaic power supply to be the constant power mode so as to reduce the output power.

FIG. 6 is a flow chart of the saturation process.

In the process, the system is subjected to full-range saturation treatment only under the constraint conditions of the energy storage unit on the charge state and the charge and discharge current, so that the output current of each channel is ensured within a reasonable range.

And (4) carrying out final inspection, checking whether the output current of each channel meets constraint conditions or not, whether the output coefficient of each channel is within an upper bound or not, if the current of each channel does not meet the conditions yet, representing that the system cannot be balanced at the moment, reporting a specific error type to the upper computer, and stopping the system.

After saturation, the power may still be balanced. When the system is in a solution state (the input power is more than or equal to the load power), the adjustment basic variable aimed at in the saturated output process is the output current of each channel, and the power at two ends of the voltage boosting and reducing circuit is balanced in the working process of the whole system without considering the energy consumption of a switching device and a controller. Under the condition that the input and output voltages of each channel in the system can be obtained in real time, the power constraint can be converted into the current constraint, and then in the saturation treatment process, the output current of each channel is directly adjusted to directly ensure that the whole system is in the proper constraint range. In the saturation process, the adjustment of the output current of each channel mainly comprises two processes of peak clipping and valley filling, namely when the output current of one channel is reduced, the output current of other channels is increased, so that the power can be ensured to be strictly balanced in the saturation process.

The saturation treatment process aims at the system saturation phenomenon in the experiment and solves the system saturation problem at the source. Since the saturation phenomenon is caused by hard and soft constraints imposed by the hardware and software to which the system is subjected, the adjustment is made in the controller based mainly on the three constraints imposed on the system. When the control quantity output by the controller can strictly ensure that the state of each channel does not exceed the constraint, the natural system does not generate the saturation phenomenon.

And (5) for the constraint of SOC and the maximum charging and discharging current, processing in the full-range saturation processing process. The full-range adjustment is adopted because the controller is a distributed controller, and the basic unit can be regarded as each channel, the energy which is output to the load by each channel is correspondingly output according to the system state of each channel, droop control among modules is not involved, the system is subjected to saturation processing under the condition that the states of all single channels and corresponding constraint conditions are considered at the same time, and the output current of each channel is directly ensured to be in a reasonable range. In addition, the control quantity is adjusted in the whole range, so that the control quantity among modules can be scheduled, and the solving range of the control quantity is expanded to a certain extent.

In the full-range saturation processing process, the photovoltaic input current and the output current at the battery end are converted into the output current of each channel, the photovoltaic input current of each channel is converted into the maximum and minimum output currents of each channel, and the output input current converted to the battery end of each channel is adjusted under the constraint generated by the SOC state (when the charge state of an energy storage unit of a certain channel is too low or too high, the output of the channel needs to be reduced or improved, and the maximum charging or discharging current is used for charging or discharging as much as possible), so that the upper and lower limits of each output current can not be exceeded obviously, and the energy storage units on the SOC boundary can be ensured to be charged and discharged reasonably.

The invention designs a step controller based on the existing PVCM and POM circuits to realize the management and adjustment of the power supply system of the solar aircraft.

Since 3 ISM channels can be monitored simultaneously by 1 POM circuit board in the system, a controller is set for each POM circuit board in the experiment. However, the control algorithm uses each ISM channel as a control unit, so 3 independent controllers are simultaneously operated in each controller, and a foundation is laid for a later single ISM node network.

Fig. 7 is a structural diagram of an experimental platform of the clustered energy system.

The device performs data interaction with a PC through a CAN bus and communicates with the existing platforms PVCM and POM through a 485 bus so as to realize distributed control.

The distributed controller takes STM32 as a processing core and comprises 1 CAN bus communication channel. Since 3 ISM channels can be monitored simultaneously by 1 POM circuit board in the system, a controller is set for each POM circuit board in the experiment. However, the control algorithm is based on each ISM channel as a control unit, so 3 independent controllers are simultaneously operated in each control, and a foundation is laid for the later single ISM node network.

The system state of each part is monitored in real time, the experimental data is convenient to record and analyze, and the experimental parameters and the control method are adjusted in time. The software of an upper computer with the functions of communication, display, serialization and the like is designed. The design has the following functions:

a data interaction function; data interaction CAN be carried out with the embedded controller through the CAN bus, and real-time uploading and issuing of data are achieved.

A data display function; the data uploaded by the controller can be displayed in various ways. Such as a table display and a waveform display.

A serialization function; the online data can be serialized to the local in real time, and the later period can be conveniently summarized and analyzed.

At present, the design of upper computer software has realized the data interaction function and the waveform display function of partial data. The USBCAN is connected with the computer and the CAN network of the embedded controller, so that CAN messages CAN be uploaded and issued in real time, and the SOC of each battery set CAN be displayed in real time by using a waveform interface.

FIG. 8 shows the software interface of the upper computer.

The cooperative controller after parameter re-tuning can enable 6 ISMs to have obvious convergence trend under specified working conditions, converge consistently around 5000s, keep balance and continue charging, and till the experiment is finished, the difference between the maximum SOC and the minimum SOC is 0.0054.

Fig. 9 shows the SOC variation trend recorded in the experiment.

The distributed cooperative control method for the clustered energy system can achieve the power balance and energy storage balance targets of the clustered energy system, so that the control strategy can be effectively applied and the system state can be observed in real time, and the distributed cooperative control method can be applied to design and manufacture of an energy management system of the clustered energy system of a solar aircraft.

It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.