CN113675834B - Distributed cooperative control method for clustered energy system - Google Patents
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Abstract
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 the design and manufacture of the energy management system of the clustered energy system of the solar aircraft.
Description
Technical Field
The invention belongs to the technical field of solar aircrafts, and particularly relates to a distributed cooperative control method of a clustered energy system.
Background
Solar aircraft are unmanned aircraft that use solar energy as a source of energy and can fly continuously at high altitudes for long periods of time. With the development needs of national defense industry in China, china starts to develop solar aircrafts, and due to the advantages of high flying height, long dead time, low cost and the like, the solar aircrafts have the 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 management, scientific research and the like, and are an important development direction of nearby space ultra-long-endurance aircrafts.
The energy system is the heart of the solar aircraft and is the basis for guaranteeing the high-efficiency operation and fully playing the functions of the solar aircraft. Thus, the performance of the energy system is a key factor in maintaining the long-term reliable functioning of solar aircraft. The solar aircraft utilizes solar energy to provide energy, secondary batteries such as lithium ion batteries and the like store electric energy, and a plurality of groups of motor propellers are used for propulsion, so that 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 power required by the system, and the energy storage balance means that the energy storage units in the system can be filled and emptied simultaneously. Practical aircraft energy system research needs to pay attention to factors such as complex and changeable environmental factors, various load characteristics and the like. Therefore, the design of the power supply system needs to take account of two targets of power balance and energy storage balance, and the charging and discharging strategies of the energy storage unit can be adjusted at any time according to environmental changes, so that the requirements of different power utilization units are met, and the discharging depth is improved to the greatest extent.
The typical structure of the existing solar energy aircraft energy system is a centralized energy system structure for moving a ground power grid, the technology is mature but lacks flexibility, the photovoltaic efficiency is greatly influenced by the characteristics of the aircraft, the condition of a battery is difficult to sense 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 the centralized energy system, the photovoltaic elements, the energy storage units can be controlled by a plurality of micro converters in groups, thereby evolving a clustered energy system. The clustered energy system divides the photovoltaic array and the battery array into a plurality of sub-modules respectively, forms an energy subsystem by the photovoltaic cell sub-modules and the energy storage unit sub-modules, and is connected with the bus through the power output control module. Because the subsystem bus voltage is lower, the energy system topology can be flexibly designed, a multi-bus structure with multiple voltage classes coexisting is formed, and the load access with various forms is convenient.
The modularization and clustering of the energy system structure bring about great improvement in energy efficiency, specific power design and the like, but the problem of cooperative control is introduced, and how to coordinate a large number of power supply submodules through an effective control means becomes a main problem at present.
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 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.
Preferably, the clustered energy system comprises: and the intelligent power supply modules are sequentially connected in parallel.
Preferably, the intelligent power module includes: the power input module, the power output module, the communication module and the battery unit are all connected in parallel with the battery unit.
Preferably, the expression of the modeling model is:
wherein x is i Representing the state of charge of the battery in the ith modeling model, V i Representing internal bus voltage in modeling model, Q i Representing battery capacity, P i Representing the difference between the power generated by the photovoltaic cell and the output power of the PCU in the modeling model, τ i Indicating battery power consumption.
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 an adjacent intelligent power generation unit of the target intelligent power generation unit;
acquiring prior knowledge of battery states in the modeling model;
obtaining a photovoltaic detection value in the modeling model;
inputting the first state parameter, the second state parameter, the battery state prior knowledge and the photovoltaic detection value into the distributed cooperative controller;
obtaining output power output by the distributed cooperative controller;
installing the output power control modeling model.
Preferably, the expression of the distributed cooperative controller is:
wherein the controller state x ci From the coherence error Sigma a ij (x i -x j ) Error in output powerCo-regulation, x when the ith ISM module stores more energy than the other modules ci Increase and then u i The output power is increased to quickly consume the stored energy. While the power u output to the load of the other modules with relatively small SOCs j And the SOC of the stored energy of each module is agreed upon by the reduction.
Preferably, the expression of the distributed cooperative controller is:
wherein the controller state x ci From the coherence error Sigma a ij (x i -x j ) Error in output powerCo-regulation, x when the ith ISM module stores more energy than the other modules ci Increase and then u i The output power is increased to quickly consume the stored energy. While the power u output to the load of the other modules with relatively small SOCs j And the SOC of the stored energy of each module is agreed upon by the reduction.
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 comprises 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 upper and lower bounds corresponding to the output current according to the SOC state and the charge-discharge constraint;
carrying out full-range saturation treatment on the output current of the target intelligent power generation unit;
calculating the constraint of the output index, which is converted into the constraint under the dimension of the output current;
the intersection set of the constraint upper limit of the output index and the constraint upper limit used in the full-range saturation treatment is taken as a new constraint;
and fixing the sagging coefficient and carrying out intra-module saturation treatment one by one.
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 the design and manufacture of the energy management system of the clustered energy system of the solar aircraft.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of a distributed cooperative control method for a clustered energy system;
FIG. 2 is a diagram of the whole structure of a clustered energy system in the distributed cooperative control method of the clustered energy system provided by the invention;
FIG. 3 is a block diagram of a distributed controller;
FIG. 4 is a simplified block diagram of a distributed controller;
FIG. 5 is a schematic diagram showing peak clipping during saturation treatment;
FIG. 6 is a schematic diagram of a valley fill process;
FIG. 7 is a schematic diagram of a saturation process flow;
FIG. 8 is a schematic diagram of a clustered energy system experimental platform structure in a clustered energy system distributed cooperative control method provided by the invention;
fig. 9 is a diagram showing the SOC variation trend in the distributed cooperative control method of the clustered energy system provided by the present invention.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent by the following detailed description of the present invention with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
In an embodiment of the present application, as shown in fig. 1, 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 intelligent power module includes: the power input module, the power output module, the communication module and the battery unit are all connected in parallel with the battery unit.
In the embodiment of the present application, the expression of the modeling model is:
wherein x is i Representing the state of charge of the battery in the ith modeling model, V i Representing internal bus voltage in modeling model, Q i Representing battery capacity, P i Representing the difference between the power generated by the photovoltaic cell and the output power of the PCU in the modeling model, τ i Indicating battery power consumption.
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 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 an adjacent intelligent power generation unit of the target intelligent power generation unit;
acquiring prior knowledge of battery states in the modeling model;
obtaining a photovoltaic detection value in the modeling model;
inputting the first state parameter, the second state parameter, the battery state prior knowledge and the photovoltaic detection value into the distributed cooperative controller;
obtaining output power output by the distributed cooperative controller;
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 ci From the coherence error Sigma a ij (x i -x j ) Error in output powerCo-regulation, x when the ith ISM module stores more energy than the other modules ci Increase and then u i The output power is increased to quickly consume the stored energy. While the power u output to the load of the other modules with relatively small SOCs j And the SOC of the stored energy of each module is agreed upon by the reduction.
Preferably, the expression of the distributed cooperative controller is:
wherein the controller state x ci From the coherence error Sigma a ij (x i -x j ) Error in output powerCo-regulation, x when the ith ISM module stores more energy than the other modules ci Increase and then u i The output power is increased to quickly consume the stored energy. While the power u output to the load of the other modules with relatively small SOCs j And the SOC of the stored energy of each module is agreed upon by the reduction.
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 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.
In an embodiment of the present application, the saturation processing for 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 upper and lower bounds corresponding to the output current according to the SOC state and the charge-discharge constraint;
carrying out full-range saturation treatment on the output current of the target intelligent power generation unit;
calculating the constraint of the output index, which is converted into the constraint under the dimension of the output current;
the intersection set of the constraint upper limit of the output index and the constraint upper limit used in the full-range saturation treatment is taken as a new constraint;
and fixing the sagging coefficient and carrying out intra-module saturation treatment one by one.
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 power generation and the power utilization matching are met in layers, and the balance among all the energy storage units is realized.
System model for each ISM module
The distributed cooperative controller is designed as follows:
the controller is a dynamic linear distributed controller, and the controller is in a state x ci From the coherence error Sigma a ij (x i -x j ) Error in output powerCo-regulation, x when the ith ISM module stores more energy than the other modules ci Increase and then u i The output power is increased to quickly consume the stored energy. While the power u output to the load of the other modules with relatively small SOCs j And the SOC of the stored energy of each module is agreed upon by the reduction.
When each ISM outputs workWhen the rate is lower than the required power, ρ > 1 and x are then increased ci Increase, u i Increasing and gradually regulating the output power to reach P * . Note that although the output power u i The sum of (2) is 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 relation to be regulated and transmitted into a primary controller, the primary controller ensures that the actual mountain-conveying power is equal to the load, and the actual consumption power is determined by the load characteristic of the primary controller, namely,scaling factor p in the system model i Output power calculated by the controller +.>And the required power P * Continuously changing; on the other hand, when the system converges, the consistency error Σ j a ij (x i -x j ) =0, i.e.)>At the same time ρ converges to 1, output power error +.>
To provide a system with a faster convergence speed and better noise immunity, the controller (1) is modified to be a non-smooth dynamic controller as follows
Wherein sign (·) is a sign function satisfying
The cooperative control strategy proposed by the subsection is a distributed control strategy, namely, each intelligent power supply module independently operates the control strategy, the control strategy comprises a control amount calculation method for each intelligent power supply module, and each intelligent power supply module only needs to feed back according to the state of the intelligent power supply module and transmits the acquired information state 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 to
(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 cj And the ratio ρ of the actual output power to the required power;
(4) Acquiring a current voltage value v of the energy storage unit through a detection element i ；
(5) Calculating x in a microcontroller ci And u i The method comprises the steps of carrying out a first treatment on the surface of the Due to the presence of the pair x in the controller ci By discretizing it, a differential equation solving method is adopted, i.e. at each sampling time, the value x of the previous time is stored in the memory unit ci (K-1) and calculates an increment dx based on 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 the sampling interval;
(6) To interface with existing hardware platforms, it is necessary to configure the processing coefficients of the hardware platform according to the ratio of the output power.
Since the controller is a distributed control strategy, the controller has certain fault tolerance capacity, namely, when certain nodes are out of operation due to faults, the built-in microcontroller automatically cuts off the nodes. The excised node is not broadcasting its own data, so the node's original neighbors will not receive the data it sent. For each intelligent power module, the calculation of the control quantity only depends on the received information state data of the neighbors, and the disconnection of a small number of nodes does not affect the system to realize balance, so long as the number of disconnected nodes is small and the rest of the network still comprises a spanning tree.
To make the description of the energy system imbalance more understandable, a system imbalance metric Δ (t) =max is defined i (x i (t))-min i (x i (t)) defining a system imbalance index delta ∞ ＝lim t→∞ Delta (t). When (when)When the system is consistent, the SOC of the energy storage units are close to each other, and finally the system is as follows>The system state enters the invariant set, i.e. the final system imbalance index +.>Wherein (1)>ε 2 Is an arbitrary positive number.
From the above analysis, it can be seen that while the system does not necessarily converge completely to a consistent manifold, it is notable that P w Reflecting the change condition of the photovoltaic power, the first term is 0 when the photovoltaic power generation power is stable and unchanged, and the system can be completely agreed. On the other hand, the size of the final invariant set may be reduced by appropriate adjustment of the controller parameters, i.e. increasing k 1 System imbalance may be minimized.
A numerical description of the system imbalance indicator may be obtained by system stability analysis, i.e.,note that when the energy storage unit parameters are the same and the photovoltaic power remains unchanged, delta=0, i.e. the system can fully agree that all battery SOCs are the same. In reality, however, the output power of the photovoltaic cells varies continuously over time, so that in practice the SOCs are not exactly the same, but are continually shifted around each other in the same state. However, the theoretical analysis above has given the maximum range of toggle, and therefore this data can be used as a reference for battery capacity configuration.
The energy storage unit SOC gradually converges to a consistent state, the envelope of the system energy storage unit SOC state curve can be obtained through the estimation of the convergence speed, and from the safety point of view, in order to ensure that the lowest battery of the SOC cannot completely empty electric energy, the actual energy usage amount is required to be as much as the following envelope, so that the safe operation boundary of the system can be obtained.
The saturation 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 affected if the saturation phenomenon is light, and the irreversible error of the system is caused if the saturation phenomenon is heavy, so that the final paralysis of the whole system is caused. The saturation processing process is a solution to the phenomenon, and in the process of executing the controller algorithm, the secondary adjustment of the control quantity is directly performed according to the specific running state of the system and the constraints received by each unit, so that the stable running of each ISM channel and the whole energy system interlayer unit in the direction of the equilibrium and consistency of the energy storage element can be finally ensured on the basis that each unit of the system can not generate the saturation problem.
In the design of the saturation process, four constraints on the system are mainly considered: constraints on battery charge and discharge current, constraints on battery state of charge variation range, output power constraints that take into account safety of the power output module, and constraints on sag factors. The constraints of the battery charge and discharge current and the constraints of the battery state of charge change range can be solved by directly constraining the output current, and the constraints of the sagging coefficient need to be considered by calculating the control input quantity to the dimension of the output index. In the present invention, the saturation treatment process is as follows:
(1) And adjusting the control input quantity into output currents, wherein the output currents are respectively positive values, the lowest output current is 0A, scaling, and adjusting the adjusted output currents into output currents corresponding to the voltage and the load power of the energy storage unit battery at the current moment.
(2) The upper and lower bounds (u 2) and (u 1) of each channel under the current condition are calculated by considering the upper and lower bounds of the battery charging and discharging current, 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 lower charge state is limited below the photovoltaic input current, and the lower limit of the channel output current with higher charge state is limited above the photovoltaic input current.
(3) The first saturation process is performed in the full range, namely peak clipping and valley filling output is adjusted current (converted to a battery end). In this process, a judgment adjustment is made for each output channel in the system. First, a "peak clipping" operation is performed.
Fig. 5 shows an example of processing an output current exceeding an upper bound.
The figure shows a case where the output current exceeds the upper limit. The original output currents of ISM1 and ISM4 in the system exceed the real-time output current upper bound calculated by the channel, and 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, and the converted power of the channels capable of increasing the output current in the upper limit range is calculated, each channel (such as ISM2 and ISM3 in the figure) with adjustable output current in the upper limit range is processed, so that the output current of each channel is increased according to the ratio of the reduced power to the adjustable power, and the output current of each channel is ensured to be in the upper limit range.
(4) The "filling the valley" process is performed.
Fig. 6 shows an example of a valley filling process.
First, the channels marked out of the individual channels beyond the output current lower bound, ISM2 and ISM3, are checked and the power to be reduced and the adjustable output power range are calculated. If the adjustable output power range can meet the requirement of reduced power, namely, the output current of each channel below the lower limit is adjusted to be increased to be lower bound, and the output currents of other adjustable channels (ISM 1, ISM 4) are reduced proportionally. If the adjustable output power range does not meet the requirement of reduced power, namely 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 a constant power mode to reduce the output power.
Fig. 7 is a flow chart of the saturation process.
In the process, the full-range saturation processing is carried out on the system under the constraint condition of the energy storage unit on the charge state and the charge-discharge current, so that the output current of each channel is ensured to be within a reasonable range.
And finally checking whether the output current of each channel meets the constraint condition, whether the output coefficient of each channel is within the upper limit, and if the current of each channel still does not meet the constraint condition, which means that the system cannot be balanced, reporting a specific error type to the host computer, and stopping the system.
After saturation processing, the power can still reach equilibrium. When the system is in a solution condition (the input power is greater than or equal to the load power), the basic variable for adjustment in the saturated output process is the output current of each channel, and the whole system does not consider the energy consumption of a switching device and a controller in the working process, so that the power at two ends of the voltage boosting and reducing circuit is balanced. Under the condition that the input and output voltages of all channels in the system can be obtained in real time, the constraint on the power can be converted into the constraint on the current, and then the output current of all channels can be directly regulated in the saturation processing process, so that the whole system can be directly ensured to be in a proper constraint range. In the saturation process, the adjustment of the output current of each channel mainly comprises the 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 improved, so that the power is 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 saturation is caused by hard and soft constraints imposed by the hardware and software of the system, the controller is mainly tuned based on the three constraints imposed by the system. When the control quantity output by the controller can strictly ensure that the states of all channels do not exceed the constraint, the natural system does not generate saturation phenomenon.
The constraint of the SOC and the maximum charge-discharge current is processed in the full-range saturation processing process. The full-range adjustment is adopted because the controller is a distributed controller, the basic unit can be regarded as each channel, the energy which each channel should output to the load is correspondingly output according to the system state of each channel, the sagging control among the modules is not involved, and the system is saturated under the condition that the states of all the single channels and corresponding constraint conditions are simultaneously considered, so that the output current of each channel is directly ensured to be within a reasonable range. In addition, the control quantity is adjusted in the whole range, so that the control quantity scheduling among the modules can be performed, and the solving range of the control quantity is enlarged to a certain extent.
In the full-range saturation processing process, the input current of the photovoltaic and the output current of the battery end are simultaneously considered to be converted into the output current of each channel, the photovoltaic input current of each channel is converted into the maximum and minimum output current of each channel, under the constraint of the state of the SOC (when the charge state of an energy storage unit of a certain channel is too low or too high, the output force of the channel needs to be reduced or improved, and meanwhile, the maximum charge or discharge current is used for charging or discharging as much as possible), the output input current converted into the battery end of each channel is regulated, the upper and lower limits of each output current are obviously not exceeded, and the energy storage units which are on the boundary of the SOC are ensured to be charged or discharged reasonably.
The invention designs a step-by-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 in the system by 1 POM circuit board, a controller is provided for each POM circuit board in the experiment. However, the control algorithm uses each ISM channel as a control unit, so that 3 independent controllers are simultaneously operated in each controller, and a foundation is laid for a later single ISM node network.
Fig. 8 shows a structure diagram of the experimental platform of the clustered energy system.
Wherein, C1, C2 are two control cores of the distributed controller, built-in distributed control algorithm, the apparatus exchanges data with PC through CAN bus, communicate with existing platform PVCM and POM through 485 bus, in order to realize the 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 in the system by 1 POM circuit board, a controller is provided for each POM circuit board in the experiment. However, the control algorithm takes each ISM channel as a control unit, so that 3 independent controllers are simultaneously operated in each control, and a foundation is laid for a later single ISM node network.
In order to monitor the system state of each part in real time, the experimental data is convenient to record and analyze, and experimental parameters and control methods are timely adjusted. An upper computer software comprising functions of communication, display, serialization and the like is designed. The design has the following functions:
a data interaction function; the CAN bus CAN be used for carrying out data interaction with the embedded controller, so that the real-time uploading and the issuing of data are realized.
A data display function; the data uploaded by the controller can be displayed in a variety of ways. Such as a tabular display and a waveform display.
A serialization function; the online data can be serialized to the local in real time, so that the realization summary and analysis can be conveniently carried out in the later stage.
At present, the upper computer software design has realized the data interaction function and the waveform display function of partial data. The CAN network of the embedded controller is connected with the computer through the USBCAN, CAN upload and send CAN messages in real time and display the SOC of each group of batteries in real time by using a waveform interface.
Fig. 9 shows an upper computer software interface.
The cooperative controller after parameter resetting can enable 6 ISMs to have obvious convergence trend under the specified working condition, and to be converged uniformly at about 5000s, and keep balance to continue charging, and when the experiment is finished, the maximum SOC and the minimum SOC are different by 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 the design and manufacture of the energy management system of the clustered energy system of the solar aircraft.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explanation of the principles of the present invention and are in no way limiting of the invention. Accordingly, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present invention should be included in the scope of the present invention. Furthermore, the appended claims are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or equivalents of such scope and boundary.
Claims (6)
1. A distributed cooperative control method for a clustered energy system, the method comprising the steps of:
obtaining a clustered energy system, the clustered energy system comprising: the intelligent power module, wherein, all the intelligent power module connects in parallel in proper order, the intelligent power module includes: the power input module, the power output module, the communication module and the battery unit are all connected in parallel with the battery unit;
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;
performing distributed cooperative control on the model of the clustered energy system according to the energy demand condition and the energy supply condition, wherein
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 an adjacent intelligent power generation unit of the target intelligent power generation unit;
acquiring prior knowledge of battery states in the modeling model;
obtaining a photovoltaic detection value in the modeling model;
inputting the first state parameter, the second state parameter, the battery state prior knowledge and the photovoltaic detection value into the distributed cooperative controller;
obtaining output power output by the distributed cooperative controller;
installing the output power control modeling model.
2. The clustered energy system distributed cooperative control method of claim 1, wherein the modeling model has an expression:
wherein x is i Representing the state of charge of the battery in the ith modeling model, V i Representing internal bus voltage in modeling model, Q i Representing battery capacity, P i Representing the difference between the power generated by the photovoltaic cell and the output power of the PCU in the modeling model, τ i Indicating battery power consumption.
3. The clustered energy system distributed cooperative control method of claim 1, wherein the expression of the distributed cooperative controller is:
wherein the controller state x ci From the coherence error Sigma a ij (x i -x j ) Error in output powerCo-regulation, x when the ith ISM module stores more energy than the other modules ci Increase and then u i The output power is increased to quickly consume energy storage, and the power u output to the load of other modules with relatively smaller SOCs is increased j And the SOC of the stored energy of each module is agreed upon by the reduction.
4. The clustered energy system distributed cooperative control method of claim 1, wherein the expression of the distributed cooperative controller is:
wherein the controller state x ci From the coherence error Σa ij (x i -x j ) Error in output powerCo-regulation, x when the ith ISM module stores more energy than the other modules ci Increase and then u i The output power is increased to quickly consume energy storage, and the power u output to the load of other modules with relatively smaller SOCs is increased j And the SOC of the stored energy of each module is agreed upon by the reduction.
5. The clustered energy system distributed cooperative control method of claim 1, wherein the distributed cooperative control of the clustered energy system model according to the energy demand condition and the energy supply condition 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.
6. The distributed cooperative control method of a clustered energy system according to claim 5, wherein 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 upper and lower bounds corresponding to the output current according to the SOC state and the charge-discharge constraint;
carrying out full-range saturation treatment on the output current of the target intelligent power generation unit;
calculating the constraint of the output index, which is converted into the constraint under the dimension of the output current;
the intersection set of the constraint upper limit of the output index and the constraint upper limit used in the full-range saturation treatment is taken as a new constraint;
and fixing the sagging coefficient and carrying out intra-module saturation treatment one by one.
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