Distributed coordination control method for high-proportion new energy system

Abstract

The invention relates to the technical field of new energy, in particular to a distributed coordination control method for a high-proportion new energy system. Acquiring output voltage and output current data of the energy storage system, and performing quick response control on the energy storage system based on a droop control method of a virtual resistor; the state of charge (SOC) of the lithium battery and the super capacitor is dynamically adjusted and controlled to realize coordinated operation of the multi-energy storage system; and performing distributed control through a sparse communication network to compensate a voltage compensation item into droop control so as to realize adjustment of average voltage and proportional current of each energy storage system. Adjusting the droop coefficient of the virtual resistor according to the output current of the energy storage system, and generating a reference voltage value based on the droop coefficient; power distribution is realized through a low-pass filter, low-frequency components are distributed to the lithium batteries, and high-frequency components are distributed to the super-capacitors; and when the SOC exceeds a preset range, current compensation control is carried out. An adjacent matrix is established through a sparse communication network to represent a communication connection relation between nodes, and average voltage observation and proportional current adjustment are realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a distributed coordinated control method for a high-proportion new energy system. Background Art

[0002] For power supply in some remote low-load areas, considering the high transmission cost, the electricity problem of local residents can be solved by establishing an island-type DC microgrid. However, due to the lack of support from the large power grid, an energy storage system is needed to stabilize the DC bus voltage. Due to the high proportion of new energy generation and the randomness and volatility of loads in microgrids, the regulation capacity requirements of energy storage equipment are also relatively strict, and a single type of energy storage cannot meet various regulation needs. The energy storage system (ESS) composed of lithium batteries + supercapacitors has been widely studied in recent years. It can not only give play to the advantages of lithium batteries' high energy density in long-term regulation, but also use the high power density characteristics of supercapacitors to deal with the impact of instantaneous power fluctuations. It has advantages that are difficult to match with single-type energy storage in terms of regulation capacity and operating economy.

[0003] In view of the problem of power control within ESS, the common practice is to use a low-pass filter to divide the system's fluctuating power into low-frequency fluctuations and high-frequency fluctuations, and then distribute them to lithium batteries and supercapacitors. In practical applications, DC microgrids usually use multiple ESSs to smooth the fluctuating power. Different allocation strategies are adopted according to the size of the fluctuating power, which involves the control problem between ESSs. By using a centralized controller, the coordinated economic operation of all ESSs can be achieved by collecting information from all ESSs and optimizing their respective power requirements. However, centralized control requires a two-way communication link between the central controller and the signal source, and is greatly affected by single-point failures, and has poor flexibility and scalability. A decentralized ESS control strategy is proposed. The supercapacitor uses virtual capacitor droop control to adjust its own state of charge (SOC), and virtual resistor droop control is used for lithium batteries with slow dynamic response. Decentralized control can solve the problem of single-point failure, but cannot handle the problem of ESS power allocation accuracy caused by line resistance. Distributed control does not have the problem of centralized single-point failure, and has plug-and-play practical functions, with high reliability and robustness. Compared with decentralized control, sparse communication networks can achieve coordination between ESSs and reduce the impact of line resistance on power distribution, so they are more economical. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to maintain the proportional output of each DC power supply while ensuring the stability of the DC bus voltage. Considering the influence of line resistance difference on droop control, the present invention uses an improved consistency algorithm to shorten the control time, maintain the stability of the DC bus voltage, and reasonably adjust the output power of each power supply by formulating a distributed control strategy for the energy storage system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a distributed coordinated control method for a high-proportion new energy system, which includes obtaining output voltage and output current data of an energy storage system;

[0008] The droop control method based on virtual resistance is used to quickly respond to the energy storage system;

[0009] Dynamically adjust and control the state of charge (SOC) of lithium batteries and supercapacitors to achieve coordinated operation of multiple energy storage systems;

[0010] Distributed control is performed through a sparse communication network, and the voltage compensation term is added to the droop control to achieve average voltage and proportional current regulation of each energy storage system.

[0011] As a preferred solution of the distributed coordinated control method of a high-proportion new energy system described in the present invention, the droop control method based on virtual resistance includes: adjusting the droop coefficient of the virtual resistance according to the output current of the energy storage system;

[0012] A reference voltage value is generated according to the droop coefficient of the virtual resistor; and the output of the energy storage system is controlled according to the reference voltage value.

[0013] As a preferred scheme of the distributed coordinated control method of a high-proportion new energy system described in the present invention, the virtual resistance droop control also includes: presetting the upper limit and lower limit of the lithium battery SOC value; when the lithium battery SOC value is within the preset range, dynamically adjusting the virtual resistance value according to the current SOC value; when the lithium battery SOC value exceeds the preset range, setting the virtual resistance value to a preset maximum value or minimum value according to the situation of exceeding the upper and lower limits.

[0014] As a preferred solution of the distributed coordinated control method of a high-proportion new energy system described in the present invention, the dynamic adjustment control includes: dividing the output power of the energy storage system into a high-frequency component and a low-frequency component through a low-pass filter; allocating the low-frequency component to the lithium battery for response; and allocating the high-frequency component to the supercapacitor for response.

[0015] As a preferred solution of the distributed coordinated control method of a high-proportion new energy system described in the present invention, it also includes compensation control of the supercapacitor: presetting the safe operating range of the supercapacitor SOC value; when the SOC value of the supercapacitor is within the preset range, no compensation is performed; when the SOC value of the supercapacitor exceeds the preset range, the compensation current is calculated based on the deviation between the supercapacitor and the reference SOC value, and compensation control is performed on the supercapacitor.

[0016] As a preferred solution of the distributed coordinated control method of a high-proportion new energy system described in the present invention, the construction of the sparse communication network includes: setting each energy storage system as a communication node; establishing an adjacency matrix to represent the communication connection relationship between each node; when there is a direct communication connection between the nodes, the corresponding adjacency matrix element is set to a non-zero value, otherwise it is set to zero.

[0017] As a preferred solution of the distributed coordinated control method of a high-proportion new energy system described in the present invention, the step of average voltage observation includes: obtaining the actual voltage value of the current energy storage system; obtaining the average voltage observation value of the adjacent energy storage system; and calculating the average voltage observation value of the current energy storage system based on the connection relationship of the adjacency matrix.

[0018] As a preferred solution of the distributed coordinated control method of a high-proportion new energy system described in the present invention, the method also includes proportional current regulation: setting a reference value of the output current of the energy storage system; calculating the deviation between the actual output current and the reference value; generating a voltage compensation amount according to the deviation value to achieve current equalization control.

[0019] As a preferred solution of the distributed coordinated control method of a high-proportion new energy system described in the present invention, the proportional current regulation also includes: obtaining current information of adjacent energy storage systems only through a sparse communication network; calculating the proportional current difference between each energy storage system based on the current information; multiplying the proportional current difference by the control gain and obtaining a voltage compensation term through an integrator.

[0020] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a distributed coordinated control method for a high-proportion new energy system as described in the first aspect of the present invention are implemented.

[0021] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a distributed coordinated control method for a high-proportion new energy system as described in the first aspect of the present invention are implemented.

[0022] The beneficial effects of the present invention are as follows: the present invention conducts research on the control problem of multiple ESSs in an island-type DC microgrid, and proposes a distributed coordinated control method for multiple ESSs to solve the power distribution problem caused by frequent communication and high proportion of new energy access during the current distributed control of microgrids. The droop control layer uses a low-pass filter to realize the power distribution of supercapacitors and lithium batteries, and realizes the power distribution between each ESS by adjusting the virtual resistor by the lithium battery SOC of each ESS, and proposes a current compensation strategy based on the supercapacitor SOC. Distributed control uses a sparse communication network to adjust the average voltage and proportional current of the ESS, which can reduce the communication between ESSs and solve the problems of large bus voltage deviation and poor power distribution accuracy caused by the existence of droop control and line resistance. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flow chart of a distributed coordinated control method for a high-proportion new energy system;

[0025] Figure 2 A computer device diagram for a distributed coordinated control method for a high-proportion new energy system;

[0026] Figure 3 A schematic diagram of a DC microgrid with multiple energy storage systems for a distributed coordinated control method of a high-proportion new energy system;

[0027] Figure 4 The block diagram of the energy storage system droop control for the distributed coordinated control method of the high-proportion new energy system;

[0028] Figure 5 ESSi distributed control diagram for the distributed coordinated control method of high-proportion new energy system;

[0029] Figure 6 It is the droop control line graph of the distributed coordinated control method for high-proportion new energy systems;

[0030] Figure 7 A distributed coordinated control line diagram of a distributed coordinated control method for a high-proportion new energy system;

[0031] Figure 8 It is the line graph of ESS internal power distribution and supercapacitor SOC for the distributed coordinated control method of high-proportion new energy system;

[0032] Fig. 9 This is the ESS current line graph when the lithium battery SOC exceeds the limit for the distributed coordinated control method of high-proportion new energy system. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0036] Example 1

[0037] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a distributed coordinated control method for a high-proportion new energy system, comprising:

[0038] S100: Acquire output voltage and output current data of the energy storage system;

[0039] In the embodiment of the present application, the output voltage and output current data of the energy storage system are collected by high-precision sampling equipment. Specifically, the voltage sampling adopts a voltage sensor with an accuracy of 0.1% and a sampling frequency of 10kHz; the current sampling adopts a Hall current sensor with an accuracy of 0.2% and a sampling frequency of 10kHz.

[0040] In an optional embodiment, the system can also collect auxiliary data such as temperature and SOC. The temperature data is collected by a PT100 temperature sensor with an accuracy of ±0.1°C; the SOC data is calculated by the ampere-hour integration method, taking into account the influence of coulomb efficiency.

[0041] In a preferred embodiment, the data acquisition system adopts a layered architecture design: the bottom layer uses the STM32F407 single-chip microcomputer for data acquisition and preprocessing; the middle layer uses the DSP TMS320F28335 for data calculation; the upper layer uses an industrial computer for data storage and display. Each layer communicates through the CAN bus with a communication rate of 1Mbps.

[0042] It should be noted that high-precision data acquisition is crucial to the stable operation of the system. The voltage data is collected by differential sampling, which can effectively suppress common-mode interference; the current is collected by closed-loop Hall sensor, which has good linearity and temperature characteristics. The system uses floating-point operations to ensure calculation accuracy. At the same time, photoelectric isolation technology is used to achieve signal isolation and improve the system's anti-interference ability.

[0043] S200: A droop control method based on virtual resistance is used to quickly respond to the energy storage system;

[0044] In the embodiment of the present application, the droop control adopts a dual-loop control structure, the outer loop is a voltage control link, and the inner loop is a current control link. The voltage loop adopts a PI controller, and the proportional coefficient K p =0.5, integral coefficient K i =20; the current loop also uses a PI controller, with a proportional coefficient K p =0.8, integral coefficient K i =50.

[0045] The PWM modulation adopts a phase-shifted full-bridge scheme, the carrier frequency is 10kHz, and the dead time is set to 2μs. The output filter of the system adopts an LCL structure, where L1=2mH, C=100μF, and L2=1mH.

[0046] In an optional embodiment, in order to improve the dynamic response performance of the system, a feedforward compensation link can be added to the PI controller. Voltage feedforward coefficient K fv =0.2, current feedforward coefficient K fi =0.4. At the same time, in order to prevent integral saturation, integral separation and anti-saturation design are adopted.

[0047] In a preferred embodiment, the value of the virtual resistance can be dynamically adjusted according to the system operation status. During normal operation, the virtual resistance value ranges from 0.1Ω to 1Ω; during the on-grid / off-grid switching process, the virtual resistance can be temporarily increased to 2Ω to limit transient current.

[0048] It should be noted that the controller parameter setting has an important influence on the stability and dynamic performance of the system. Parameter setting needs to consider system bandwidth, phase margin and other indicators. Through simulation and experimental verification, under the above parameter settings, the system bandwidth can reach 1kHz and the phase margin is greater than 45°, which can meet the requirements of fast response.

[0049] S201: The droop control method based on virtual resistance includes: adjusting the droop coefficient of the virtual resistance according to the output current of the energy storage system;

[0050] A reference voltage value is generated according to the droop coefficient of the virtual resistor; and the output of the energy storage system is controlled according to the reference voltage value.

[0051] S202: Virtual resistance droop control also includes: presetting the upper limit and lower limit of the lithium battery SOC value; when the lithium battery SOC value is within the preset range, dynamically adjusting the virtual resistance value according to the current SOC value; when the lithium battery SOC value exceeds the preset range, setting the virtual resistance value to a preset maximum value or minimum value according to the situation of exceeding the upper and lower limits.

[0052] S300: Dynamically adjust and control the state of charge (SOC) of lithium batteries and supercapacitors to achieve coordinated operation of multiple energy storage systems;

[0053] In the embodiment of the present application, the dynamic regulation control is based on the SOC state of the energy storage unit. The SOC of the lithium battery is calculated using the improved ampere-hour integration method, taking into account the influence of factors such as temperature and aging; the SOC of the supercapacitor is calculated by the voltage ratio:

[0054]

[0055] To ensure the safe operation of the energy storage unit, the SOC operating range of the lithium battery is set to 20% to 90%, and the SOC operating range of the supercapacitor is set to 10% to 95%. When the SOC exceeds this range, the system will automatically adjust the power allocation strategy.

[0056] In an optional embodiment, the temperature information of the energy storage unit can also be introduced for joint control. When the temperature exceeds the set threshold (60°C for lithium battery and 65°C for supercapacitor), the output power of the corresponding energy storage unit will be limited. The closer the temperature is to the threshold, the greater the degree of limitation. The specific limitation coefficient can be expressed as:

[0057]

[0058] In a preferred embodiment, power allocation also needs to consider the life characteristics of the energy storage unit. The number of charge and discharge times of lithium batteries is limited to 3,000 times, and the number of charge and discharge times of supercapacitors can reach 1,000,000 times. The system can dynamically adjust the power allocation ratio according to the number of completed charge and discharge times to extend the service life of the energy storage system.

[0059] It should be noted that dynamic regulation and control is the key to achieving coordinated operation of the energy storage system. The system needs to monitor the status of each energy storage unit in real time, including parameters such as SOC, temperature, voltage, and current. The sampling period of the control system is 100ms, which is sufficient to ensure the real-time status monitoring. At the same time, the system has a fault diagnosis function, and when an abnormality is detected in the energy storage unit, it can automatically switch to the backup operation mode.

[0060] S301: Dynamic regulation control includes: dividing the output power of the energy storage system into a high-frequency component and a low-frequency component through a low-pass filter; allocating the low-frequency component to the lithium battery for response; and allocating the high-frequency component to the supercapacitor for response.

[0061] S302: Also includes compensation control of the supercapacitor: presetting a safe operating range of the supercapacitor SOC value; when the supercapacitor SOC value is within the preset range, no compensation is performed; when the supercapacitor SOC value exceeds the preset range, the compensation current is calculated based on the deviation between the supercapacitor SOC value and the reference SOC value, and compensation control is performed on the supercapacitor.

[0062] S400: Distributed control is performed through a sparse communication network, and the voltage compensation term is compensated into the droop control to achieve average voltage and proportional current regulation of each energy storage system.

[0063] In the embodiment of the present application, the sparse communication network adopts a star topology based on the CAN bus. The communication rate is 500kbps, a periodic communication mode is adopted, and the communication cycle is 10ms. Each communication frame contains information such as node ID, voltage value, current value, SOC, etc., with a total length of 8 bytes.

[0064] The reliability of the network is ensured by the following mechanisms: CRC check: using 15-bit CRC check code; message timeout detection: the timeout threshold is set to 20ms; network redundancy design: each node is equipped with dual CAN channels.

[0065] In an optional embodiment, the communication network can use more advanced Ethernet technology. Using industrial Ethernet switches to build the network, the communication rate can be increased to 100Mbps. The TCP / IP protocol is used to ensure the reliability of data transmission. At the same time, network segmentation is achieved to improve communication efficiency.

[0066] In a preferred embodiment, the system can also be equipped with a remote monitoring function. Through the 4G / 5G wireless communication module, remote monitoring of the system status and parameter configuration can be achieved. The monitoring center can view the operating status of each energy storage unit in real time and perform remote intervention when necessary.

[0067] It should be noted that the performance of distributed control depends largely on the quality of the communication network. The communication scheme adopted by this system has the following characteristics: low latency: end-to-end communication latency <5ms; high reliability: packet loss rate <0.1%; scalability: supports up to 64 nodes; fault tolerance: single point failure does not affect the operation of the entire system.

[0068] Furthermore, the distributed control strategy is implemented based on modular design. The main controller uses TI's DSPTMS320F28379D, which has the following configurations: main frequency: 200MHz; RAM: 204KB; Flash: 1MB; peripherals: 16-channel ADC, 8-channel PWM, 4-channel CAN, etc.

[0069] The development of the control code adopts a modular concept and mainly includes the following functional modules: data acquisition module: responsible for the collection of voltage, current and other data; communication processing module: processing data exchange between nodes; control algorithm module: implementing core algorithms such as voltage compensation; protection function module: implementing overvoltage, overcurrent and other protection functions; human-machine interface module: providing parameter configuration and status display functions.

[0070] S401: Construction of a sparse communication network includes: setting each energy storage system as a communication node; establishing an adjacency matrix to represent the communication connection relationship between each node; when there is a direct communication connection between nodes, the corresponding adjacency matrix element is set to a non-zero value, otherwise it is set to zero.

[0071] S402: Distributed control includes: observing the average voltage of the energy storage system; calculating a voltage compensation value based on the observation result; and compensating the voltage compensation value into the droop control.

[0072] S403: The step of average voltage observation includes: obtaining the actual voltage value of the current energy storage system; obtaining the average voltage observation value of the adjacent energy storage system; and calculating the average voltage observation value of the current energy storage system according to the connection relationship of the adjacency matrix.

[0073] S403: The method also includes proportional current regulation: setting a reference value of the output current of the energy storage system; calculating the deviation between the actual output current and the reference value; generating a voltage compensation amount according to the deviation value to achieve current balancing control.

[0074] S404: Proportional current regulation also includes: obtaining current information of adjacent energy storage systems only through a sparse communication network; calculating proportional current differences between the energy storage systems based on the current information; and obtaining a voltage compensation term through an integrator after multiplying the proportional current difference by a control gain.

[0075] Specific implementation scheme of voltage compensation.

[0076] In the embodiment of the present application, voltage compensation adopts a two-stage compensation structure: primary compensation: fast compensation based on local measurement values; secondary compensation: precise compensation based on communication data.

[0077] The calculation of the compensation amount takes into account the following factors: voltage drop caused by line impedance; voltage fluctuation caused by load change; SOC state of the energy storage unit.

[0078] In an optional embodiment, the compensation strategy can be further optimized by introducing an adaptive control algorithm to dynamically adjust the compensation parameters according to the system operation status. The controller parameters are obtained through online identification and can adapt to changes in system parameters.

[0079] In a preferred embodiment, the system can also add a predictive control function. A system model is established based on historical data to predict state changes in the future, and compensation adjustments are made in advance to improve the dynamic performance of the system. The predictive control uses the following parameter settings: prediction step: 10 sampling cycles; control step: 5 sampling cycles; weight matrix: Q = diag (1, 0.5), R = 0.1; constraint condition: |△u| ≤ 0.1.

[0080] It should be noted that the effect of voltage compensation directly affects the stability of the system. The calculation of the compensation value needs to take into account the dynamic characteristics of the system to avoid introducing new oscillations. Through simulation verification, when the load of this system changes, the voltage fluctuation can be controlled within ±2%, and the recovery time is less than 100ms.

[0081] Specific implementation scheme of proportional current regulation.

[0082] In the embodiment of the present application, the proportional current regulation adopts a hierarchical control structure: inner layer: fast current loop, response time <1ms; middle layer: power sharing control, cycle is 10ms; outer layer: SOC balance control, cycle is 100ms.

[0083] The controller parameters of each layer are set as follows: Inner layer PI parameters: K p =0.8,K i =50; Middle layer PI parameter: K p =0.5,K i =20; Outer PI parameter: K p =0.2,K i =5.

[0084] In an optional embodiment, a fuzzy control algorithm can be introduced to dynamically adjust the current distribution ratio according to the SOC state of the energy storage unit and the load demand. The input variables of the fuzzy controller are the SOC deviation and the load change rate, and the output variable is the power adjustment coefficient.

[0085] In a preferred embodiment, the efficiency characteristics of the energy storage unit can also be considered. The efficiency of the energy storage unit is different at different working points. The system can select the optimal power allocation scheme according to the efficiency curve to improve the overall efficiency of the system.

[0086] It should be noted that proportional current regulation needs to ensure the coordinated operation of each energy storage unit. The control strategy needs to consider both response speed and stability. Through reasonable parameter design, the current imbalance of the system in steady state is less than 2%, and the maximum imbalance in the dynamic process does not exceed 10%.

[0087] Specific implementation plan of the system protection strategy.

[0088] In the embodiment of the present application, the system adopts a multi-level protection mechanism: software protection: monitoring parameters such as voltage and current, and triggering protection when exceeding the limit; hardware protection: overcurrent protection, short circuit protection, over-temperature protection, etc.; system-level protection: protection against communication abnormalities, coordination control failure, etc.

[0089] Protection threshold settings: Overvoltage protection: 1.1 times the rated voltage; Undervoltage protection: 0.9 times the rated voltage; Overcurrent protection: 1.2 times the rated current; Overtemperature protection: Energy storage unit temperature > 65°C.

[0090] In an optional embodiment, a predictive protection function can be added. By analyzing the trend of the system status, preventive measures can be taken before a fault occurs. For example, when an abnormal current rise rate is detected, current limiting protection is started in advance.

[0091] In a preferred embodiment, the system may also have a self-recovery function. For transient faults, the system may automatically try to recover. The recovery process uses a soft start method to gradually increase the output power to avoid shocks.

[0092] It should be noted that a perfect protection mechanism is the basis for ensuring the safe operation of the system. The response time of the protection function needs to be fast enough: software protection <100μs, hardware protection <10μs. At the same time, the protection strategy should take into account the impact of false operation and adopt a "secondary confirmation" mechanism to improve reliability.

[0093] In summary, the present invention adopts a droop control method based on virtual resistance to perform rapid response control on the energy storage system, thereby solving the communication delay problem in the traditional control method, improving the dynamic response capability of the system, and enhancing the anti-interference ability and stability of the system.

[0094] The present invention dynamically adjusts and controls the state of charge (SOC) of lithium batteries and supercapacitors, so that the lithium battery takes on the low-frequency part of the power demand and the supercapacitor takes on the high-frequency part, giving full play to the characteristic advantages of different energy storage units, realizing the coordinated operation of multiple energy storage systems, effectively extending the service life of energy storage components, and improving the overall energy utilization efficiency of the system.

[0095] The present invention is based on a compensation control strategy for the supercapacitor SOC. By real-time monitoring of the SOC state of the supercapacitor and performing current compensation, the supercapacitor is ensured to operate within a safe range, overcharging and over-discharging are avoided, and the reliability and service life of the system are further improved.

[0096] The present invention adopts a sparse communication network for distributed control, and effectively solves the voltage deviation problem caused by line resistance by compensating the voltage compensation term into the droop control. This solution not only reduces the communication burden of the system and the control complexity, but also realizes power sharing among energy storage units and avoids excessive charging and discharging of energy storage units.

[0097] The present invention realizes the "more work for more capable" allocation strategy of energy storage units through the linkage control of virtual resistance and SOC, and realizes the balanced use of energy storage units while ensuring system stability. This adaptive capability cannot be achieved by traditional control methods. At the same time, the combination of distributed control and sparse communication not only ensures control performance but also reduces communication overhead, achieving the optimal balance between system performance and communication overhead, and significantly improving the cost performance of the system.

[0098] The present invention adopts a multi-level protection mechanism to form all-round protection from unit to system, which ensures safety without affecting normal operation and provides reliability guarantee superior to the existing technology. Overall, the invention not only solves the problems of power distribution accuracy and communication burden, but also provides a comprehensive solution through various innovations, realizing efficient, reliable and economical operation of energy storage system.

[0099] Example 2

[0100] Reference Figure 3 - Fig. 9 , which is the second embodiment of the present invention.

[0101] The present invention takes an island-type DC microgrid containing multiple ESSs as the research object. The microgrid consists of a high-proportion renewable energy generation system, a DC load, and an ESS connected to a DC bus through a power electronic converter. The line resistance between different distributed power sources and the DC bus is calculated. The basic structure is as follows: Figure 3 In order to realize the distributed coordinated control among multiple ESSs, the corresponding communication layer and control layer are equipped in the microgrid.

[0102] In order to maximize the use of high proportion of new energy, Figure 3 The distributed power sources work in MPPT mode and do not participate in system voltage control. Therefore, the unbalanced power of the entire DC microgrid system is borne by all ESSs. Figure 3The ESS in the system is a modular energy storage structure. Each ESS consists of lithium batteries and supercapacitors and is connected to the DC microgrid as a whole. Each ESS implements primary control of the bus voltage based on VI droop control, and dynamically adjusts its virtual resistance according to the SOC of each ESS. The power of lithium batteries and supercapacitors is divided and distributed within the ESS through a low-pass filter, that is, the lithium battery responds to the low-frequency part of the power regulation demand, while the supercapacitor responds to the high-frequency part.

[0103] The communication layer uses a sparse communication network to achieve direct exchange of information between adjacent ESSs and indirect exchange of information between non-adjacent ESSs. The communication layer is abstracted using graph theory. The communication network involved in this paper is described by an undirected graph G = (V, E), where V is a set of nodes, the number of nodes is equal to the number of connected ESSs, and E is an edge set, indicating whether there is a direct communication link between ESSs. If node i can exchange information with node j, then node i and node j are neighbor nodes.

[0104] Droop Control:

[0105] The droop control of any i-th energy storage system in the DC microgrid is:

[0106] V i * =V ref -R i I i (1)

[0107] V i * is the reference voltage output by the ESSi droop controller, V ref is the rated voltage of the DC bus, R i is the virtual resistance, I i is the output current of ESSi.

[0108] In a distributed control system, different virtual resistors can be set to achieve power distribution of different distributed power sources in a DC microgrid. Considering the line resistance R line The relationship between different ESS output currents is:

[0109]

[0110] When R i >>R linei When , the output current of ESSi is inversely proportional to the virtual resistance, and the power can be proportionally distributed among multiple ESSs by adjusting the virtual resistance.

[0111] Power allocation control strategy between ESSs

[0112] In order to ensure a high power distribution accuracy, the virtual resistance is usually set larger, but a larger virtual resistance will lead to a larger steady-state voltage deviation. In order to make the voltage deviation meet the grid operation requirements, R i The setting value must meet the following requirements:

[0113]

[0114] is the upper limit of the virtual resistance, ΔV max The maximum allowable voltage deviation is ±5% of the rated bus voltage in this paper. is the rated current of the ESSi inverter.

[0115] For systems with multiple adjustable energy storage modules, long-term operation according to a fixed virtual resistance will increase the state differences between the modules. In extreme cases, some units may lose their ability to adjust due to prematurely crossing the state safety range, thereby reducing the overall regulation ability of the energy storage system. Therefore, in order to improve the coordinated control effect between the hybrid energy storages, the virtual resistance can be adjusted according to the SOC of each ESS. Due to the relatively high cost of supercapacitors, the capacity of lithium batteries (bat) is usually much larger than that of supercapacitors (sc) when configuring the capacity of ESS. The state difference between ESSs is mainly reflected in the lithium battery SOC. Therefore, this paper uses the lithium battery SOC value to adjust R i The value of is shown in formula (4):

[0116]

[0117] In the formula, and They represent the upper and lower limits of the lithium battery SOC respectively. Operation Symbols Respectively represent the rounding operation downward and upward; SOC b,i (t) is the SOC of the lithium battery in ESSi at time t.

[0118] Considering that the SOC of lithium batteries may exceed the upper and lower limits when they are initially put into use and during the charging and discharging process, when the SOC value of the lithium battery does not meet the constraints When R i Make adjustments.

[0119]

[0120] ESS internal power allocation control strategy

[0121] In order to alleviate the decline of lithium battery life and give full play to the fast response ability of supercapacitors, this paper adopts a low-pass filter to Figure 4 In It is divided into high and low frequency parts. The high frequency reference current command is sent to the supercapacitor current inner loop, and the low frequency reference current command is sent to the lithium battery current inner loop. The lithium battery and supercapacitor current inner loop collect their own current output current and compare it with their respective reference currents. Then the difference is controlled by PI and then the PWM waveform is obtained, thereby controlling the output power of the DC / DC converter and realizing the hybrid energy storage power frequency response and bus voltage stability.

[0122] Since the capacity of supercapacitor is small, its SOC is too high or too low when it is initially put into use, which may easily lead to exceeding the upper and lower limits during long-term operation. This paper designs a current compensation strategy based on supercapacitor SOC. In order to make full use of the capacity of supercapacitor, its SOC meets When the SOC exceeds the upper and lower limits, the supercapacitor is compensated according to formula (6).

[0123]

[0124] In the formula, c i is the current compensation term control gain coefficient, and its value is positively correlated with the capacity of the supercapacitor.

[0125] Therefore, the droop control structure of the energy storage system is as follows: Figure 4 As shown:

[0126] By adjusting the virtual resistance, the current sharing of the load by the parallel ESS can be achieved, but the existence of the virtual resistance will cause DC voltage deviation. At the same time, due to the existence and unknown of the line resistance, the accuracy of power distribution will be reduced, and it may even accelerate the uneven aging of the battery pack. A distributed control scheme is needed to overcome these shortcomings of the traditional droop control method. This paper introduces a sparse communication network to realize the information exchange between adjacent ESSs, so that the decentralized control system becomes a distributed control system, and a distributed control method is used to perform secondary regulation of the bus voltage and power distribution. Distributed control consists of two parts: average voltage regulation and proportional current regulation.

[0127] 1) Average voltage adjustment

[0128] The terminal voltage of ESS output will be affected by the virtual resistance R i The voltage drop of the droop control layer deviates from the rated voltage and is also affected by the uncertainty of the line impedance. In order to reduce this effect, an average voltage regulation compensation term is added to the droop control layer. Compensation is performed. The average voltage observer is defined as

[16] :

[0129]

[0130] In the formula, and are the average voltage observation values ​​of ESSi and ESSj, V i is the actual sampled value of ESSi output voltage; N i is the set of adjacent units of ESSi; a ij is an element of the adjacency matrix A. a ij Indicates the communication status between ESSi and ESSj. When ESSi and ESSj are in communication, a ij =1, 0 when there is no communication.

[0131] The difference between the average voltage observer output and the rated voltage is multiplied by the control gain k V The input integrator is then adjusted to obtain the average voltage regulation compensation term Among them, k V >0.

[0132] 2)) Current Regulation

[0133] From formula (2), we can see that the expected current in this paper can achieve the control effect of formula (8).

[0134] R1I1=R2I2=…=R n I n (8)

[0135] Due to the existence of line resistance and the difficulty in obtaining it, droop control alone cannot achieve the distribution of output current of each ESS according to a given virtual resistance. Therefore, this paper introduces proportional current regulation to compensate for droop control. The proportional current difference is:

[0136]

[0137] in, is the reference value of the proportional current regulation link, b i is the reference signal reception coefficient. If ESSi can receive The value of b i =1, otherwise 0.

[0138] Since the communication network studied in this paper is a sparse communication network, ESSi can only obtain the information of neighboring units. Therefore, the reference value of the proportional current is set as:

[0139]

[0140] Will Multiply by the control gain k I The voltage compensation term can be obtained by adjusting the input integrator Where,,. The total voltage compensation u i for:

[0141]

[0142] The total voltage compensation u i Compensated to the voltage reference value given by the distributed control layer, the reference voltage output by the droop controller is:

[0143] V i * =V ref -R i I i +u i (12)

[0144] From equations (7), (9) and (10), it can be seen that the distributed control designed in this section only requires ESS to communicate with adjacent ESS through a sparse communication network, which is a completely distributed structure. From equation (12), it can be concluded that when the distributed control fails, the total voltage compensation term is 0, and the ESS can still operate normally according to the droop control. The distributed control block diagram is as follows Figure 5 As shown:

[0145] In order to verify the feasibility and effectiveness of the proposed control method, this paper uses the MATLAB / Simulink platform to build a Figure 1 The simulation model of an island DC microgrid system with a high proportion of renewable energy generation, DC loads and four hybrid energy storages corresponding to the structure is used to verify the feasibility and effectiveness of the proposed hierarchical distributed control and event-triggered control. The system and control parameter design of this paper are shown in Table 1.

[0146] Table 1 DC microgrid and its controller parameters

[0147]

[0148]

[0149] The sparse communication network of the four ESSs is a ring, and the subsequent simulations are distinguished by 1-simulation rows. The output power of the distributed power source of each bus is constant. The communication topology and initial adjacency matrix of the battery unit are shown in Appendix A. The initial SOCs of the four ESSs are ESS1 (SOCb1 = 0.65, SOCsc1 = 0.6); ESS2 (SOCb2 = 0.45, SOCsc2 = 0.89); ESS3 (SOCb3 = 0.45, SOCsc3 = 0.90); ESS4 (SOCb4 = 0.65, SOCsc4 = 0.6); From the virtual (electric) adjustment method shown in formula (4), it can be seen that the virtual resistance of ESS1 and ESS4 is the same, and the virtual resistance of ESS2 and ESS3 is the same. The entire simulation process is: at t = 0s, the microgrid is disconnected from the main grid, 50% of the initial load is cut off at t = 1s, 75% of the initial load is increased at t = 4s, and 75% of the initial load is cut off at t = 7s.

[0150] Distributed coordinated control effect

[0151] The DC bus voltage and output current of each ESS obtained by using traditional droop control and distributed coordinated control are as follows: Figure 6 shown.

[0152] Depend on Figure 6 (a) Figure 7 (a) It can be seen that reasonable It ensures that the voltage deviation of each busbar under the two-site control mode is within the maximum allowable deviation range. The traditional droop control is affected by the voltage drop caused by the virtual resistance and line resistance, and the average busbar voltage will deviate from the reference voltage when it returns to steady state. The average busbar voltage using distributed coordinated control is finally stabilized at around 220V, and the overall voltage control effect is better, which is mainly due to the The effect of virtual impedance is reduced, so that the average voltage is stabilized at 220V. Figure 6 (b) Figure 7 (b) It can be seen that under the traditional droop control, ESS cannot be controlled according to the given R i After adopting distributed coordinated control, the output currents of ESSs 1 and 4 and 2 and 3 converge, respectively, indicating that the proportional current compensation link effectively eliminates the adverse effects of uneven system energy distribution and line resistance on current sharing control, and shortens the current distribution time.

[0153] Internal power distribution of energy storage unit

[0154] Taking ESS2 and ESS3 as examples, the total reference current is allocated to lithium batteries and supercapacitors according to the proposed strategy. Figure 8 The internal power distribution of ESS and the supercapacitor SOC change curve are given.

[0155] from Figure 8 (a) and (c) show that when the load changes suddenly, the supercapacitor first provides instantaneous output power support under the frequency division of the low-pass filter, and the lithium battery bears the relatively gentle low-frequency power. As the system gradually enters a steady state, the output of the lithium battery increases and gradually becomes the main bearer of the output power. The output power of the supercapacitor gradually returns to 0 in a steady state. Figure 8 In (d), the supercapacitor SOC of ESS3 exceeds the upper limit in regions A and B. At this time, the system performs current compensation on the supercapacitor to gradually restore it to 90%. Figure 8 (c) Due to the role of current compensation, the power distribution of lithium batteries and supercapacitors is significantly accelerated. Figure 8 In (b), the supercapacitor SOC of ESS2 does not exceed the upper limit, so no current compensation occurs.

[0156] Lithium battery SOC over-limit adjustment test

[0157] In order to verify that the algorithm in this paper can still maintain the output power of each ESS at a given ratio when the SOC of some energy storage units exceeds the upper and lower limits, the initial lithium battery SOCs of the four ESSs are set to: SOCb1 = 0.95, SOCb2 = 0.15, SOCb3 = 0.85, SOCb4 = 0.05. Fig. 9 It shows the charging / discharging process of each energy storage system when the differences between lithium batteries are too large and the SOC of some lithium batteries exceeds the upper and lower limits.

[0158] According to formula (4) and formula (5), it is calculated that when charging, R1 = R3 = 0.2, R2 = 0.025, R4 = 0.02; when discharging, R1 = 0.02, R3 = 0.025, R2 = R4 = 0.2. Fig. 9 The small figure in the upper left corner shows the evolution of the virtual resistance from 3 to 8 seconds, which is consistent with the theoretical analysis. During charging operation, the charging current is mainly borne by ESS2 and ESS4 with lower SOCb. ESS1 and ESS3 have lower charging current due to their higher SOCb. During discharging operation, ESS1 and ESS3 with higher SOCb bear the main discharge current. ESS2 and ESS4 have lower discharge current due to their lower SOCb. The allocation result meets the principle of "more work for those who are capable", avoiding the situation where ESS exits operation due to too high or too low SOCb. It should be noted that in order to prevent R i Rapid changes lead to large system disturbances. i In the calculation equations (4) and (5), SOCb is rounded up / down, and R is updated only when the change in SOCb exceeds 10%. i , due to the short running time, the SOC change is small, so Fig. 9Only the virtual resistance change process caused by the charge / discharge state transition is shown.

[0159] Example 3

[0160] This embodiment also provides a computer device, which is suitable for a distributed coordinated control method for a high-proportion new energy system, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a forced oscillation detection and positioning method for a distribution network as proposed in the above embodiment.

[0161] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, a forced oscillation detection and positioning method for a distribution network is implemented as proposed in the above embodiment.

[0162] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0163] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0164] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0165] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0166] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0167] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed coordinated control method for a high-proportion new energy system, characterized in that: Including obtaining output voltage and output current data of the energy storage system; The droop control method based on virtual resistance is used to quickly respond to the energy storage system; Dynamically adjust and control the state of charge (SOC) of lithium batteries and supercapacitors to achieve coordinated operation of multiple energy storage systems; Distributed control is performed through a sparse communication network, and the voltage compensation term is added to the droop control to achieve average voltage and proportional current regulation of each energy storage system.

2. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 1, characterized in that: The droop control method based on virtual resistance includes: adjusting the droop coefficient of the virtual resistance according to the output current of the energy storage system; A reference voltage value is generated according to the droop coefficient of the virtual resistor; and the output of the energy storage system is controlled according to the reference voltage value.

3. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 2, characterized in that: The virtual resistance droop control also includes: presetting the upper limit and lower limit of the lithium battery SOC value; when the lithium battery SOC value is within the preset range, dynamically adjusting the virtual resistance value according to the current SOC value; when the lithium battery SOC value exceeds the preset range, setting the virtual resistance value to a preset maximum value or minimum value according to the situation of exceeding the upper and lower limits.

4. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 3, characterized in that: The dynamic regulation control includes: dividing the output power of the energy storage system into a high-frequency component and a low-frequency component through a low-pass filter; allocating the low-frequency component to the lithium battery for response; and allocating the high-frequency component to the supercapacitor for response.

5. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 4, characterized in that: It also includes compensation control for the supercapacitor: a safe operating range of the supercapacitor SOC value is preset; when the supercapacitor SOC value is within the preset range, no compensation is performed; when the supercapacitor SOC value exceeds the preset range, the compensation current is calculated based on the deviation from the reference SOC value, and compensation control is performed on the supercapacitor.

6. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 5, characterized in that: The construction of the sparse communication network includes: setting each energy storage system as a communication node; establishing an adjacency matrix to represent the communication connection relationship between each node; when there is a direct communication connection between the nodes, the corresponding adjacency matrix element is set to a non-zero value, otherwise it is set to zero.

7. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 6, characterized in that: The distributed control includes: observing the average voltage of the energy storage system; calculating the voltage compensation value according to the observation result; and compensating the voltage compensation value into the droop control.

8. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 7, characterized in that: The step of average voltage observation includes: obtaining the actual voltage value of the current energy storage system; obtaining the average voltage observation value of the adjacent energy storage system; and calculating the average voltage observation value of the current energy storage system according to the connection relationship of the adjacency matrix.

9. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 8, characterized in that: The method also includes proportional current regulation: setting a reference value of the output current of the energy storage system; calculating the deviation between the actual output current and the reference value; generating a voltage compensation amount according to the deviation value to achieve current balancing control.

10. A distributed coordinated control method for a high-proportion new energy system as claimed in claim 9, characterized in that: The proportional current regulation also includes: obtaining current information of adjacent energy storage systems only through a sparse communication network; calculating the proportional current difference between the energy storage systems based on the current information; and obtaining a voltage compensation term through an integrator after multiplying the proportional current difference by a control gain.

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